Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4) 9819914582, 9789819914586

Table of contents :
Contents
About the Editors
Probabilistic Arias Intensity Maps of Uttarakhand State (India)
1 Introduction
2 The Geological and Seismo-tectonic Framework of the Study Area
3 Seismic Source Zone Delineation
4 Development of Earthquake Catalogue
5 Estimation of Recurrence Parameters
5.1 Maximum Magnitude ( mmax )
6 Ground Motion Prediction Models for Arias Intensity Assessment
7 Logic Tree Structure
8 Seismic Hazard Assessment
9 Results and Discussions
10 Conclusion
References
Statistical Analysis of Seismicity Parameters and Completeness Period of Earthquake Catalog for Sree Padmanabhaswamy Temple, Thiruvananthapuram District, Kerala
1 Introduction
2 General Analysis of Earthquake Catalog
3 Declustering Process
4 Completeness of Catalog
4.1 Visual Cumulative Method
5 Gutenberg–Richter Recurrence Relationship
6 Maximum Magnitude
7 Conclusions
References
Effect of Open Stories on Expected Seismic Losses in Hilly Buildings
1 Introduction
2 Numerical Modeling and Ground Motion Selection
2.1 Numerical Modeling of Hilly Buildings
2.2 Ground Motion Selection and Scaling
2.3 Numerical Study
3 Seismic Performance of Hilly Buildings
4 Seismic Loss Estimation
5 Conclusions
References
Influence of Source Offset on the Resolution of Dispersion Image
1 Introduction
2 Methodology
2.1 Details of Test Setup
3 Results and Discussions
3.1 Offset Distance Influence
3.2 Selection and Extraction of Dispersion Curve
4 Conclusions
References
Co-seismic Deformation of Iran, 2021 Earthquake Using DInSAR Technique
1 Introduction
2 Study Area
3 Methodology and Data Set
4 Results and Discussion
5 Conclusion
References
Three-dimensional Crustal Velocity Structure of Tehri, Garhwal Himalaya
1 Introduction
2 Study Area
3 Seismic Observation Network and Dataset
4 Methodology
5 Results
5.1 Three Dimensional Velocity Structure
5.2 Checkerboard Test
6 Conclusion
References
Correlation Between Cone Tip Resistance and Shear Wave Velocity for Quaternary Alluvium
1 Introduction
2 Some Existing CPT-VS Correlations
3 Methodology
4 Results and Discussion
4.1 Regression Analysis
4.2 Validation of Results
5 Conclusions
References
Ground Motion Predictive Equations and Its Applicability in North-Eastern Indian Region: A Critical Appraisal
1 Introduction
2 Seismotectonics of the Study Area
3 GMPEs Used in the Study
4 Methodology
5 Results and Discussion
6 Conclusion
References
Influence of Site-City Interaction on the Response of Buildings on Trapezoidal Basin
1 Introduction
2 Model Parameters
3 S-Wave Response of 3D Trapezoidal Basin
3.1 Seismic Response of Basin
3.2 Spectral Amplification in Basin
4 Site-City Interaction Effects in Basin
4.1 Response of Standalone Building
4.2 SCI Effects in Cluster 1 (9 Buildings)
4.3 SCI Effects in Cluster 2 (25 Buildings)
5 Discussion
6 Conclusions
References
Assessment of Double Resonance from Microtremor Observations for Jammu Region in India
1 Introduction
2 Single Station Microtremor Testing
3 Field Survey and Structural Integrity of Building Scenario
4 Evaluation of Empirical Relationship Between Vibration Period (Tf) and Building Height (H)
5 Double Resonance Effect (DRE) in Jammu Region (JR)
6 Conclusion
References
Influence of Epistemic Uncertainty on the Seismic Vulnerability of Indian Code-Compliant RC Frame Building
1 Introduction
2 Description of the Study RC Frame
2.1 Material and Geometry Nonlinearity
2.2 SVA of Selected 4-Storey RC Frame
3 Results and Discussion
4 Conclusion
References
Spatial Distribution of the Gutenberg-Richter Parameters and Fractal Dimension and Their Correlations in Northeast India and Its Vicinity
1 Introduction
2 Seismotectonics of Study Region
3 Earthquake Data Used and Seismic Zonation
4 Methods Applied
4.1 Frequency-Magnitude Recurrence Relation
4.2 Fractal Dimension
5 Results and Discussion
5.1 a- and b-values Variation
5.2 Fraction Dimension Variation
5.3 Correlation Between G-R Parameters and Dc Values
6 Conclusions
References
Seismic Hazard Analysis Considering the Effect of the Shape, Size, and EQ Distribution of Seismic Sources for Different Locations in Sikkim, NE India
1 Introduction
2 Study Area and Its Past Earthquake Scenario
3 Methodology
3.1 Seismic Source Models Used for the Analyses
3.2 Deterministic Seismic Hazard Analysis of the Locations
3.3 Probabilistic Seismic Hazard Analysis of the Locations
4 Results and Discussion
4.1 DSHA Results
4.2 PSHA Results
5 Conclusion
References
A Comparative Study on Application of Machine Learning Algorithms in Ground Motion Prediction Equations
1 Introduction
2 Dataset
3 Machine Learning Algorithms
3.1 Support Vector Regression (SVR)
3.2 Decision Trees (DT)
3.3 Random Forest (RF)
4 Performance Analysis
4.1 Evaluation Metrics
4.2 Physical Observed Trends
4.3 Weighted Average Ensemble Technique
5 Comparison with Existing Models
6 Summary and Conclusions
References
Study of an Anomalous Behavior of Atmospheric Parameters—As an Earthquake Precursors for Himalayan Region Earthquakes
1 Introduction
2 Methodology
2.1 Atmospheric Parameters
3 Results
4 Discussion
5 Conclusion
6 Funding and Acknowledgments
References
Probabilistic Seismic Hazard Assessment of North East India
1 Introduction
2 Seismicity and Seismotectonics
2.1 Tectonic Provinces
2.2 Earthquake Database and Treatment of Catalog
3 Seismic Source Models
3.1 Areal Sources
3.2 Linear Sources
3.3 Gridded Seismicity Sources
4 Seismicity Parameters and Maximum Magnitude
5 Ground Motion Prediction Equation
6 Logic Tree Structure
7 Probabilistic Seismic Hazard Analysis and Results
8 Conclusions
References
Experimental Investigation of Seismic Response of Hybrid Shear Wall with External Energy Dissipating Reinforcement
1 Introduction
2 Description of the Experimental Model
2.1 Material Properties
2.2 Instrumentation
2.3 Post-tensioning
2.4 Loading
3 Experimental Results and Discussions
3.1 Comparison of Backbone Curves
3.2 Comparison of Stiffness Degradation
3.3 Comparison of Energy Dissipation
4 Summary and Conclusions
References
1D Velocity Model for NW India in and Around Delhi
1 Introduction
2 Data
3 Methodology
3.1 Inversion of the Velocity Model
4 Results and Discussion
5 Conclusion
References
Anomalous Deviations in Atmospheric Parameters as Pre-earthquake Signals-A Case Study on Sumatra Region Earthquakes (M ≥ 6.0)
1 Introduction
1.1 Study Area
2 Methodology
3 Results and Discussion
3.1 01–09–1993: M6.3 Simeulue, Indonesia Earthquake
3.2 02–11–2002: M7.4 Simeulue, Indonesia Earthquake
3.3 26–12–2004: M9.1 Sumatra–Andaman Islands Earthquake
3.4 07–04–2007: M6.1 Simeulue, Indonesia Earthquake
3.5 20–02–2008: M7.4 Simeulue, Indonesia
3.6 09–12–2009: M6.0 Simeulue, Indonesia
3.7 09–05–2010: M7.2 Northern Sumatra, Indonesia
3.8 25–07–2012:M6.4 Simeulue, Indonesia Earthquake
3.9 Discussion
4 Conclusion
References
Spatial Distribution of Stress Orientation by Inversion of Focal Mechanism Solutions Using MSATSI: A Case Study Across Japan Trench
1 Introduction
2 Stress Inversion
3 Methodology
4 Estimated Stress Patterns and Their Comparison with Tectonic Motions
5 Conclusion
References
Seismic Landslide Hazard Assessment of Mandi Town, Himachal Pradesh
1 Introduction
2 Methodology
2.1 Estimation of Critical Acceleration (ky)
3 Results and Discussion
4 Conclusions
References
Infill Wall Effect on Seismic Analysis of Reinforced Concrete Buildings
1 Introduction
2 Methodology
2.1 Theoretical Background
2.2 Details of the Building Models
2.3 Modelling of Infill Wall
3 Results and Discussion
3.1 Experimental Results
3.2 Results from Numerical Analysis
4 Conclusion
References
Geotechnical Seismic Base Isolation Using Rubber Sand Mixtures—Review
1 Introduction
2 Historical Background
3 Mechanical and Engineering Properties of Sand-Tire Chip Mixtures
4 Static and Dynamic Properties of Rubber–Sand Mixtures
5 Numerical Studies on Soil-Scrap Tire Mixtures
6 Limitations of Rubber–Sand Mixtures
7 Conclusions
References
Development of Soil Amplification Factors Using 1D and 2D Ground Response Analysis
1 Introduction
2 Selected Sites and Earthquake Ground Motions
2.1 Sites Selected for the Study
2.2 Ground Motion Selection
3 Methodology
3.1 Ground Response Analysis
3.2 GRA Using DEEPSOIL (Discrete Soil Modelling)
3.3 GRA Using ABAQUS (Continuum Soil Modelling)
4 Results and Discussion
5 Conclusion
References
Probabilistic Seismic Hazard Assessment for Assam, North-East India
1 Introduction
2 Geological and Tectonic Setting
2.1 Seismic Source Models
3 Earthquake Catalogue
3.1 Declustering of Catalogue
3.2 Evaluation of Seismicity Parameters
3.3 Maximum Magnitude (Mmax)
4 Ground Motion Prediction Equation (GMPE)
5 Computation Model for PSHA
5.1 Results and Discussion
6 Conclusions
References
Effect of Randomness of Slip and Source Time Function on Pseudo-Dynamically Simulated Ground Motion Characteristics
1 Introduction
2 Methodology Adopted
3 Ground Motion Simulations
3.1 Validation of Pseudo-Dynamic Rupture Implementation
3.2 Effect of Randomization of Slip Pattern
4 Conclusions
References
Quantification of Ridge-Weathering Effects on the Simulated Ground Motion Characteristics Across 2D and 3D Topography Models
1 Introduction
2 Methodology Adopted
3 Geometry of Models
4 Seismic Response of 2D Cross-Section of 3D Topography
4.1 SH-Wave Response
4.2 SV-Wave Response
5 Effect of 3D Topography
6 Comparison of 2D and 3D Weathering Effect
7 Conclusions
References
Developing a Comprehensive Historical Tsunami Database for the Indian Ocean
1 Introduction
2 Magnitude Homogenisation
3 Calculation of Tsunami Magnitude (Mt)
4 Tsunami Intensity-Magnitude Relationship
5 Conclusions
References
Seismically Induced Landslide Hazard Analyses for a Road Corridor in the Lower Himalayas
1 Introduction
2 Study Area
3 Methodology
3.1 Probabilistic Seismic Hazard Assessment
3.2 Generation of Landslide Potential Index (LPI)
4 Preparation of Thematic Layers of Landslide Controlling Parameters
5 Seismically Induced Landslide Hazard Zonation: Results and Discussion
5.1 Assignment of Ranks (Ri) to Landslide Thematic Layers
5.2 Assignment of Weights (Wi) to Subclasses of Landslide Thematic Layers
5.3 Generation of the Seismically Induced Landslide Hazard Zonation (LHZ) Map of the Study Area
5.4 Validation of the Seismically Induced Landslide Hazard Map
6 Conclusion
References
Assessment of Proxy-Based Vs30 Estimation in Roorkee, Uttarakhand
1 Introduction
2 Study Area
3 Methodology
3.1 Geotechnical Method—Multichannel Analysis of Surface Waves (MASW)
3.2 Proxy-Based Vs30 Estimation and Site Classification
4 Results
5 Conclusions
References
Ground Motion Prediction Equation for NW Himalaya Region
1 Introduction
2 Geology and Tectonics
3 Data
4 Development of Ground Motion Relationships
5 Results and Conclusions
References
Identification of Strong Motion Generation Area of the 2019 Hualien Earthquake
1 Introduction
2 Geology and Seismotectonics of Hualien Region
3 Data
4 Methodology
5 Analysis of Strong Motion Generation Areas
6 Discussions and Conclusions
References
Role of SH Wave in the Mapping of Shallow Subsurface
1 Introduction
2 Study Area
3 Data
4 Methodology
5 2D Shear-Wave Velocity Model
6 Comparison of Experimental and Numerical Study
7 Conclusions
References
A Variational Mode Decomposition Approach for Modal Identification of Structures
1 Introduction
2 Modal Identification Algorithm
2.1 Extracting Modal Responses and Frequencies
2.2 Identification of Modal Damping Ratios
2.3 Identification of Mode Shapes
2.4 Modal Assurance Criteria (MAC)
2.5 Effect of Measurement Noise
3 Numerical Study and Validation
3.1 Identification of 3-DOF Shear-Beam Type Building
3.2 Identification of Multistory Building
4 Conclusions
References
Geo-Factor Inference Modelling with Empirical Susceptibility Weights Approach for GIS-Based Seismic Hazard Mapping of Thiruvananthapuram City
1 Introduction
2 Study Area and Geo-Factor Data
2.1 Regional Geographical and Geological Settings
2.2 Regional Geomorphological, Geotechnical Aspects and Seismicity
3 Methodology and Data Analysis for GFI Model
3.1 Empirical Susceptibility Weights Approach (ESWA) in GIS Layers
3.2 Testing and Calibration of ESWA Weights with ROC Weighting
3.3 Comparison of ESWA Weights with AHP
4 GIS Data for Spatial Analysis
5 Results and Discussion
5.1 Reliability of ESWA Weights for the GFI Model
5.2 Weighted Overlay Analysis
5.3 Estimation of PGA for Susceptible Hazard Spot of Study Area
5.4 Comparison with Other Studies
6 Conclusions
References
Octave Tool for Probabilistic Seismic Hazard Assessment
1 Introduction
2 Seismic Hazard Analysis
2.1 Earthquake Catalogue and Treatment
2.2 Gutenberg–Richter (G-R) Recurrence Law
2.3 Identify Earthquake Distances
2.4 Predictive Relationships
2.5 Seismic Hazard Curves and Mean Annual Rate of Occurrences
3 Regional Seismicity and Seismic Hazard Curves
4 Conclusions
References
Site Amplification Study Using Strong Motion Data Recorded at Various Stations in India from Far-Field Earthquakes
1 Introduction
2 Data Processing and Methodology
3 Results and Discussions
4 Conclusions
References
ROSERS—A Deep Learning Framework for Earthquake Early Warning and Its Interpretation
1 Introduction
2 ROSERS Framework
3 Implementation on NGA-West2 Database
3.1 Computation of Latent Variables Using VAE
3.2 Estimation of Latent Variables Using DNNs
4 Interpretation of Neural Networks of ROSERS
4.1 Interpretation of the VAE Decoder
4.2 Interpretation of the DNN
5 Conclusions
References
Early Warning System: An Efficient Earthquake Disaster Mitigation Tool
1 Introduction
2 EEWS Set up Around the Globe
3 Scenario Earthquake Warnings, Mock Drills, and Awareness
4 Benefit-Cost Study of Earthquake Mitigation Approaches
5 Losses Due to Earthquakes
6 Maps
7 Tentative Lead Time
8 Conclusion
References
Application of Regression Techniques for Preparing a Homogeneous Earthquake Catalog—An Overview
1 Introduction
2 Regression Methodologies
2.1 SLR
2.2 GOR
2.3 A Graphical Representation of GOR
2.4 Chi-Squared Regression
3 Homogenization of Earthquake Catalogs in Seismic Literature
4 Conclusions
References
A Cluster-Based Seismic Risk Assessment: Economic Loss Using GIS for Jaipur Sub-Urban Area
1 Introduction
2 Study Area
3 Methodology
3.1 Study of City Urbanization Process Through Identifying the Homogenous Built-Form Regions Using Satellite Image
3.2 Field Survey
3.3 Creation of GIS-Based Dataset
3.4 Hazard
3.5 Vulnerability Estimation
3.6 Economic Loss Estimation
4 Result and Discussion
5 Conclusion
References
Modelling of Empirical Accelerograms of 1999 Chamoli Earthquake (Himalaya) Using a Modified Hybrid Approach
1 Introduction
2 Seismotectonics
3 Methodology
4 Results and Discussion
5 Conclusion
References
Development and Implementation of a Regional Earthquake Early Warning System in Northern India
1 Introduction
2 Regional Eew System in Uttarakhand
3 Sensor Array
4 Warning Dissemination System
5 EEW Warning Dissemination Devices
6 Warning Dissemination App: Uttarakhand Bhookamp Alert
7 Mock Drills and Success Stories
8 Conclusion
9 Future Scope
References
A Critical Review of Existing Building Regulations and Bye-Laws in Hilly Regions of India
1 Introduction
2 Overview
3 Building Regulations for Residential Buildings
4 Summary and Conclusions
References
Microseismic Analysis Using Event Count and Potency Displacement for Stability Evaluation of an Underground Cavern
1 Introduction
2 Microseismic Monitoring System at the Underground Powerhouse of Tala Hydropower Plant
3 Four Major Wall Stability Status Using Microseismic Data Processing and Analysis
3.1 Upstream Wall of the Machine Hall
3.2 Downstream Wall of the Machine Hall
3.3 Upstream Wall of Transformer Hall
3.4 Downstream Wall of Transformer Hall
4 Statistical Hazard Assessment of THP Underground Powerhouse Cavern
5 Conclusions
References
Characteristics of Strong Ground Motions for Delhi National Capital Region (NCR) Using Small to Moderate Size Earthquakes
1 Introduction
2 Geology and Seismotectonic
3 Strong Ground Motion Data and Analysis
4 Results and Discussion
5 Conclusion
References
Detection of Liquefaction Phenomenon from the 2015 Nuweiba Earthquake Using Remote Sensing Data
1 Introduction
2 Study Area
3 Dataset and Methodology
4 Results and Discussion
5 Conclusion
References
Estimation of Site Amplification Factor and Predominant Frequency in and Around Panchkula City, Haryana, India
1 Introduction
2 Geology and Seismotetonics of the Study Area
3 Methodology
4 Results and Discussion
5 Conclusions
References
Use of GIS for Hypsometric Analysis for Determining Erosion Proneness of Mandakini Watershed, Lesser Himalaya, Uttarakhand, North India
1 Introduction
2 Study Area
3 Materials and Methods
3.1 Hypsometric Curve (HC) Estimation
3.2 Hypsometric Integral (Hsi) Estimation
4 Results and Discussion
4.1 Hypsometric Curve
4.2 Hypsometric Integral
4.3 Relationship with Past Earthquakes
4.4 Prioritization, Conservation and Management of Watershed
5 Conclusions
References
Understanding the Structure and Tectonic Configuration of Bengal Basin for Earthquake Magnitude Prediction
1 Introduction
1.1 Data and Methods
2 Geology of Bangladesh
3 Tectonics of Bengal Basin
4 Crustal Configuration and Sediment Thickness
5 The Geometry of Tectonic Motion Vector in and Around Bengal Basin
6 Exploring the State of Moho and Deep Crustal Configuration of the Bengal Basin
7 Seismic Zonation Maps
8 Discussion
References
Exploring an Alternate Perspective of the Importance Factor for Seismic Design of Structures
1 Introduction
2 Probabilistic Seismic Hazard Assessment
2.1 Hazard Computation with GMPE Rule and Logic Tree
3 Results and Discussion
4 Summary and Conclusion
References
Simplified Damping Modification Factor for Vertical Response Spectra
1 Introduction
2 Seismic Events and Ground Motions Considered
3 Median Vertical Spectrum for Scenario-Based Assessment
3.1 Damping Modification Factor Using a Set of Recorded Events
4 Dependence of Damping Modification Factor on Seismological Parameters
5 Simplified Damping Modification Factor for V/H
6 Assessment of Proposed Damping Modification Factor
7 Demonstration of Utility of Damping Modification Factor
7.1 Site-Specific Spectrum
7.2 Code Specific Spectrum
8 Summary and Conclusion
References
December 01, 2020, Haridwar, Earthquake: Fault Plane Solution and Tectonic Implications
1 Introduction
2 Geology and Tectonic of the Area
3 Methodology and Qualitative Analysis
3.1 Moment Tensor Inversion
4 Data Set and Analysis
5 Results
6 Discussion
References
Local Seismicity Around Tehri Dam, Garhwal Himalaya
1 Introduction
2 Seismological Network
3 Data Set
4 Data Analysis
4.1 Spatial Variation of Local Seismicity
4.2 Depth Distribution and Depth Sections of Seismicity
4.3 Focal Mechanism
5 Seismic Hazard Parameter—b-Value
6 Reservoir-Induced Seismicity (RIS)
7 Conclusions
References

Citation preview

Lecture Notes in Civil Engineering

Manish Shrikhande Pankaj Agarwal P. C. Ashwin Kumar   Editors

Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4)

Lecture Notes in Civil Engineering Volume 332

Series Editors Marco di Prisco, Politecnico di Milano, Milano, Italy Sheng-Hong Chen, School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan, China Ioannis Vayas, Institute of Steel Structures, National Technical University of Athens, Athens, Greece Sanjay Kumar Shukla, School of Engineering, Edith Cowan University, Joondalup, WA, Australia Anuj Sharma, Iowa State University, Ames, IA, USA Nagesh Kumar, Department of Civil Engineering, Indian Institute of Science Bangalore, Bengaluru, Karnataka, India Chien Ming Wang, School of Civil Engineering, The University of Queensland, Brisbane, QLD, Australia

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Manish Shrikhande · Pankaj Agarwal · P. C. Ashwin Kumar Editors

Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4)

Editors Manish Shrikhande Department of Earthquake Engineering Indian Institute of Technology Roorkee Roorkee, Uttarakhand, India

Pankaj Agarwal Department of Earthquake Engineering Indian Institute of Technology Roorkee Roorkee, Uttarakhand, India

P. C. Ashwin Kumar Department of Earthquake Engineering Indian Institute of Technology Roorkee Roorkee, Uttarakhand, India

ISSN 2366-2557 ISSN 2366-2565 (electronic) Lecture Notes in Civil Engineering ISBN 978-981-99-1458-6 ISBN 978-981-99-1459-3 (eBook) https://doi.org/10.1007/978-981-99-1459-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Contents

Probabilistic Arias Intensity Maps of Uttarakhand State (India) . . . . . . . Kunal Gupta and Neelima Satyam Statistical Analysis of Seismicity Parameters and Completeness Period of Earthquake Catalog for Sree Padmanabhaswamy Temple, Thiruvananthapuram District, Kerala . . . . . . . . . . . . . . . . . . . . . . . M. P. Hari Padmanabhan, Siddhardha R, Sreevalsa Kolathayar, and Ramakrishna Hegde

1

17

Effect of Open Stories on Expected Seismic Losses in Hilly Buildings . . . Yati Aggarwal and Sandip Kumar Saha

27

Influence of Source Offset on the Resolution of Dispersion Image . . . . . . Ashhad Imam, Virendra Kumar, Keshav Kumar Sharma, and Alvin Harison

41

Co-seismic Deformation of Iran, 2021 Earthquake Using DInSAR Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hardeep, A. Bahuguna, K. Arun Saraf, and J. Das

53

Three-dimensional Crustal Velocity Structure of Tehri, Garhwal Himalaya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Modi, S. Mukhopadhyay, and M. L. Sharma

65

Correlation Between Cone Tip Resistance and Shear Wave Velocity for Quaternary Alluvium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Mishra, A. Paul, and P. Chakrabortty

75

Ground Motion Predictive Equations and Its Applicability in North-Eastern Indian Region: A Critical Appraisal . . . . . . . . . . . . . . . . P. Kumar and S. S. Kumar

87

Influence of Site-City Interaction on the Response of Buildings on Trapezoidal Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Neeraj Kumar, J. P. Narayan, Pooja Lohchab, and Sanjay Kumar v

vi

Contents

Assessment of Double Resonance from Microtremor Observations for Jammu Region in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Abdullah Ansari, Falak Zahoor, K. S. Rao, A. K. Jain, Aashi Pal, Neeraj Kumar, Sakib Majid Hajam, Pallavi Shukla, Krishna Sharma, Faizan Fayaz, Mir Akhtar Yousuf, Shakir Riyaz, and Umer Altaf Khan Influence of Epistemic Uncertainty on the Seismic Vulnerability of Indian Code-Compliant RC Frame Building . . . . . . . . . . . . . . . . . . . . . . . 127 Kaushik Gondaliya, Vishisht Bhaiya, Sandip Vasanwala, Atul Desai, and Jignesh Amin Spatial Distribution of the Gutenberg-Richter Parameters and Fractal Dimension and Their Correlations in Northeast India and Its Vicinity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 R. B. S. Yadav, P. Chauhan, M. Sandhu, R. Kumar, and V. Singh Seismic Hazard Analysis Considering the Effect of the Shape, Size, and EQ Distribution of Seismic Sources for Different Locations in Sikkim, NE India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Borah Niranjan, Mondal Joy Kumar, and Kumar Abhishek A Comparative Study on Application of Machine Learning Algorithms in Ground Motion Prediction Equations . . . . . . . . . . . . . . . . . . 163 A. Ahmed and M. Gade Study of an Anomalous Behavior of Atmospheric Parameters—As an Earthquake Precursors for Himalayan Region Earthquakes . . . . . . . . 175 M. Senthil Kumar and Natarajan Venkatanathan Probabilistic Seismic Hazard Assessment of North East India . . . . . . . . . . 187 C. Lallawmawma, M. L. Sharma, and J. Das Experimental Investigation of Seismic Response of Hybrid Shear Wall with External Energy Dissipating Reinforcement . . . . . . . . . . . . . . . . 205 Ankhiparna Guha, S. R. Dash, and Goutam Mondal 1D Velocity Model for NW India in and Around Delhi . . . . . . . . . . . . . . . . 217 Deepak Kumar, G. Suresh, S. C. Gupta, M. L. Sharma, and Hasbi Ash Shiddiqi Anomalous Deviations in Atmospheric Parameters as Pre-earthquake Signals-A Case Study on Sumatra Region Earthquakes (M ≥ 6.0) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 Ramya Jeyaraman and N. Venkatanathan Spatial Distribution of Stress Orientation by Inversion of Focal Mechanism Solutions Using MSATSI: A Case Study Across Japan Trench . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 Sucheta Das, Sandeep, Sonia Devi, Himanshu Mittal, Praveen Kumar, and Monika

Contents

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Seismic Landslide Hazard Assessment of Mandi Town, Himachal Pradesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 A. Kothiala, P. S. Nayek, Maheshreddy Gade, and U. V. Kala Infill Wall Effect on Seismic Analysis of Reinforced Concrete Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 C. H. Sirajudheen and Behera Dibyadarshi Geotechnical Seismic Base Isolation Using Rubber Sand Mixtures—Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 S. L. Divyasree, K. M. Jithin, and Renjitha Mary Varghese Development of Soil Amplification Factors Using 1D and 2D Ground Response Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 A. Sharma and S. Adhikary Probabilistic Seismic Hazard Assessment for Assam, North-East India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 M. Borah, M. L. Sharma, and R. N. Dubey Effect of Randomness of Slip and Source Time Function on Pseudo-Dynamically Simulated Ground Motion Characteristics . . . . . 325 Vishal, J. P. Narayan, and L. Joshi Quantification of Ridge-Weathering Effects on the Simulated Ground Motion Characteristics Across 2D and 3D Topography Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Vishal and J. P. Narayan Developing a Comprehensive Historical Tsunami Database for the Indian Ocean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 Nazeel Sabah and Daya Shanker Seismically Induced Landslide Hazard Analyses for a Road Corridor in the Lower Himalayas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 A. Tyagi, R. R. Nath, M. L. Sharma, and J. Das Assessment of Proxy-Based V s30 Estimation in Roorkee, Uttarakhand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 M. Srivastava and M. L. Sharma Ground Motion Prediction Equation for NW Himalaya Region . . . . . . . . 389 Vandana, Harendra K. Dadhich, Himanshu Mittal, and O. P. Mishra Identification of Strong Motion Generation Area of the 2019 Hualien Earthquake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 Saurabh Sharma, A. Joshi, S. Singh, C. M. Lin, C. H. Kuo, and K. L. Wen Role of SH Wave in the Mapping of Shallow Subsurface . . . . . . . . . . . . . . . 411 Jyoti Singh, A. Joshi, Saurabh Sharma, and Mohit Pandey

viii

Contents

A Variational Mode Decomposition Approach for Modal Identification of Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 S. Gupta and S. Kaloni Geo-Factor Inference Modelling with Empirical Susceptibility Weights Approach for GIS-Based Seismic Hazard Mapping of Thiruvananthapuram City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433 Madhu Mohan Velapgy and E. S. M. Suresh Octave Tool for Probabilistic Seismic Hazard Assessment . . . . . . . . . . . . . 451 D. R. Majhi and M. Shrikhande Site Amplification Study Using Strong Motion Data Recorded at Various Stations in India from Far-Field Earthquakes . . . . . . . . . . . . . . 461 Sireesha Jaladi, Babita Sharma, Himanshu Mittal, and O. P. Mishra ROSERS—A Deep Learning Framework for Earthquake Early Warning and Its Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473 Jawad Fayaz Early Warning System: An Efficient Earthquake Disaster Mitigation Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487 P. Kumar, Govind Rathore, Kamal, M. L. Sharma, R. S. Jakka, Pratibha, and A. Kumar Application of Regression Techniques for Preparing a Homogeneous Earthquake Catalog—An Overview . . . . . . . . . . . . . . . . . 501 Ranjit Das, H. R. Wason, and Claudio Meneses A Cluster-Based Seismic Risk Assessment: Economic Loss Using GIS for Jaipur Sub-Urban Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 V. Anand, M. Mahatab, A. Sharma, D. Raj, M. K. Jat, R. Sarkar, and S. Pal Modelling of Empirical Accelerograms of 1999 Chamoli Earthquake (Himalaya) Using a Modified Hybrid Approach . . . . . . . . . . . 523 A. Sharma, D. Kumar, and A. Paul Development and Implementation of a Regional Earthquake Early Warning System in Northern India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 537 Govind Rathore, Pankaj Kumar, Mukat Lal Sharma, Kamal, Ravi Sankar Jakka, and Ashok Kumar A Critical Review of Existing Building Regulations and Bye-Laws in Hilly Regions of India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545 P. Das Choudhury and D. Raj Microseismic Analysis Using Event Count and Potency Displacement for Stability Evaluation of an Underground Cavern . . . . . . 563 Vikalp Kumar, V. R. Balasubramaniam, and K. S. Divyalakshmi

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Characteristics of Strong Ground Motions for Delhi National Capital Region (NCR) Using Small to Moderate Size Earthquakes . . . . . 577 Abhishek, Manisha Sandhu, and Babita Sharma Detection of Liquefaction Phenomenon from the 2015 Nuweiba Earthquake Using Remote Sensing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587 Hrik Chaudhury, Abhishek Kumar, and Rishikesh Bharti Estimation of Site Amplification Factor and Predominant Frequency in and Around Panchkula City, Haryana, India . . . . . . . . . . . . 599 M. Sandhu, R. B. S. Yadav, D. Kumar, and Abhishek Use of GIS for Hypsometric Analysis for Determining Erosion Proneness of Mandakini Watershed, Lesser Himalaya, Uttarakhand, North India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613 James Xavier Paul and Daya Shanker Understanding the Structure and Tectonic Configuration of Bengal Basin for Earthquake Magnitude Prediction . . . . . . . . . . . . . . . . . . . . . . . . . 625 Mir Fazlul Karim and Daya Shanker Exploring an Alternate Perspective of the Importance Factor for Seismic Design of Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 639 Narsiram Gurjar and Dhiman Basu Simplified Damping Modification Factor for Vertical Response Spectra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655 Ravi Kanth Sriwastav and Dhiman Basu December 01, 2020, Haridwar, Earthquake: Fault Plane Solution and Tectonic Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 669 Pooja Mahto and S. C. Gupta Local Seismicity Around Tehri Dam, Garhwal Himalaya . . . . . . . . . . . . . . 683 M. L. Sharma, S. C. Gupta, J. P. Narayan, J. Das, A. Sen, S. K. Jain, A. K. Jindal, Subhash Patel, Prajawal Tandekar, Avichal Rastogi, Rajeev Vishnoi, Atul Jain, Virendra Singh, and S. K. Saxena

About the Editors

Dr. Manish Shrikhande is Professor and Head of the Department of Earthquake Engineering at Indian Institute of Technology Roorkee. His current research interests are vibration monitoring and control, seismic risk and mitigation, and computational mechanics. Dr. Pankaj Agarwal is Professor at the Department of Earthquake Engineering, Indian Institute of Technology Roorkee. His research interests are earthquakeresistant design of masonry and RC structures, post-damage assessment survey of earthquake-affected areas, risk assessment, cyclic testing of structures, seismic instrumentation in multi-storied buildings, health monitoring, and damage detection in buildings. He is continuously engaged in research on structurally sound and seismically efficient construction and has published several research papers in national and international journals and conferences/seminars/symposia. Dr. P. C. Ashwin Kumar is Assistant Professor at the Department of Earthquake Engineering, Indian Institute of Technology Roorkee. He has been engaged in teaching and research in earthquake-resistant design of steel structures, development of passive devices for seismic protection, vulnerability assessment, and retrofitting of structures.

xi

Probabilistic Arias Intensity Maps of Uttarakhand State (India) Kunal Gupta

and Neelima Satyam

Abstract Uttarakhand, an Indian state in the Western Himalayan region, is highly seismically active and falls under zones IV (severe) and V (very severe) as per the seismic code of India. The region is home to some highly significant hydropower and infrastructure projects which are either operational or in the planning phase in the region. Therefore, it is vital to determine the area to be impacted by the high seismicity of this region. As Arias intensity is a very efficient measure to quantify the strength of ground motion, none of the prior studies tried to map the Arias intensity distribution throughout Uttarakhand. Therefore, in the present study, Arias intensity is mapped for the Uttarakhand state by the Cornell–McGuire approach. A thorough earthquake inventory was created, considering all earthquake occurrences within a 300 km radius of the research area, incorporating events of magnitude (M w ) > 4 from 1900 to 2020. The inventory was declustered and homogenized into a commonly used moment magnitude scale. Ten distinct seismogenic area source zones were established in the study area. Seismic recurrence parameters for all sources were computed using the earthquake inventory and tectonic framework. The hazard evaluation at the bedrock level was conducted using a logic tree framework that included regional and global attenuation models. The findings were presented in terms of Arias intensity hazard maps for 475 and 2475 years return period. It was observed that Arias intensity values vary from 0.15 to 2.13 m/s for 475-year return period and 0.21 to 6.23 m/s for the 2475-year return period. Approximately 80% of the study area was found to be vulnerable to co-seismic hazards. The ground motion intensity estimated in this study will assist in the effective planning of major infrastructure projects and earthquake-induced landslide hazard assessment. Keywords Arias intensity · PGA · Uttarakhand · PSHA · Logic tree · Attenuation model K. Gupta (B) · N. Satyam Department of Civil Engineering, IIT Indore, Indore, Madhya Pradesh, India e-mail: [email protected] N. Satyam e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_1

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K. Gupta and N. Satyam

1 Introduction The Indian subcontinent’s northern state, Uttarakhand (Western Himalayas), has a high seismic activity level. It is a section of the Alpine-Himalayan seismic belt, which was created when the Indian and Eurasian Plates collided [1]. The overlapping motion of these plates caused strain energy to build up. In the past, the region has experienced earthquakes of various sizes brought on by releasing this energy. Significantly large earthquake occurrences with magnitudes greater than 6 include the Kangra earthquake of 1905, the Uttarkashi earthquake of 1991, and the Chamoli earthquake of 1999. The region has also experienced several low- to high-magnitude earthquakes over the past century, demonstrating the strong seismic vulnerability of this region. Due to its significant seismic activity, the state of Uttarakhand is classified by the Indian seismic code as being in zones IV (severe) and V (extremely severe) [2]. To identify the locations with a likelihood of having high ground motion parameter values, seismic hazard assessment is regarded as a crucial instrument. Either the deterministic seismic hazard assessment approach (DSHA) or the probabilistic seismic hazard assessment approach (PSHA) can be used to determine the seismic risk in each location. The same datasets, which include earthquake origins, occurrence frequencies, and connections between ground motion attenuation, are used in both methods. A semi-empirical method of seismic zonation developed by Joshi et al. [3] is based on the deterministic modelling of finite ruptures along identified faults in a region. Seismic hazard maps for the Indian regions of the Doon Valley [4], Assam Valley [3], and the Uttarakhand Himalaya [5] have been created using this seismic zonation technique based on semi-empirical modelling of the rupture plane. Khattri et al. [6] prepared PSHA maps for the entire India and adjoining regions. Shankar [7] and Nayak et al. [8] produced the seismic hazard maps of the Uttarakhand state using the PSHA approach. The PSHA for Uttarakhand and the neighbouring Himalayan region has previously been conducted by a small number of researchers. The sole goal of these studies was to map the seismic risk to help design earthquake-resistant structures [9]. The distribution of the Arias intensity (Ia ) [10] over the complete state of Uttarakhand was not attempted in any study, but the spectral acceleration (SA) and peak ground acceleration (PGA) were. A matrix called the Ia is used to quantify the force of a ground motion [11]. To determine how much shaking is occurring, the Ia matrix detects the acceleration of transient seismic waves [10]. Ia provides greater details on overall shaking energy in contrast to other scalar measurements such as SA and PGA. The significance of Ia has been emphasized by a variety of ground vibration simulation techniques, including spectral element methods [12], empirical function methods [13], and source model convolution Green’s functions-based methods [14]. The Ia has a better link with deformations caused by the ground shaking [15] and is a more trustworthy measure for assessing the risk of landslides [16–18]. In terms of Ia , this study performs the PSHA of Uttarakhand state in detail. Seismotectonic sources within and around the research area were examined for seismic

Probabilistic Arias Intensity Maps of Uttarakhand State (India)

3

hazard estimates. The spatial distribution of hazards due to earthquake shaking in the study region was examined using earthquake catalogue data from 1900 to 2020. The recently established global and regional ground motion prediction models were considered to evaluate the ground motion characteristics. Hazard maps and curves were used to depict the results for return periods of 475 and 2475 years. The obtained hazard maps are essential for determining how vulnerable a place is to earthquakes. The study will offer a fresh perspective on the assessment of earthquake-induced hazards in the study area.

2 The Geological and Seismo-tectonic Framework of the Study Area The research region is in the Southern Himalayan range between 77–81° E longitude and 28–32° N latitude (Fig. 1). With China to the north and Nepal to the east, this region has an area of 53,483 km2 . It is classified as zones IV and V under the Indian seismic code [2]. A total of 86% of the state’s land area is covered by the Himalayas [19]. As the Himalayan Mountain ranges are one of the world’s youngest mountain ranges, the region shows high geological instability and is particularly susceptible to earthquakes. Numerous intricate tectonic systems were formed due to the ongoing collision of the Indian and Eurasian tectonic plates. The Indian tectonic plate is currently advancing in the north direction at 50 mm per year rate. Himalayan seismic activity is confined to the region along the plate boundary and is associated with the underthrusting and collision of the Indian plate beneath the Eurasian plate. Along with the

Fig. 1 Geographical location of the study area: a India b Uttarakhand state

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K. Gupta and N. Satyam

Alpine-Himalayan seismic belt, this underthrusting has resulted in continual deformation, faulting, and folding [20]. The Main Boundary Thrust (MBT), Main Frontal Thrust (MFT), Main Central Thrust (MCT), and Indus Suture Zone (ISZ) are the significant tectonic features of the area. The ISZ is located at the region’s northern border. The MBT is presently an active thrust system, whereas the MCT is passive and the oldest. The MFT is thought to be the newest thrust system. The Tethys Himalaya, the Lesser Himalaya, the Outer Himalaya, and the Greater Himalaya, which pass through the state of Uttarakhand, are the four primary thrust systems that divide the Himalayas. The Greater Himalayas are situated between the ISZ and the MCT to the north of the MCT. It contains an intrusive igneous rock with a coarse texture. South of MBT, the outer Himalaya comprises Miocene molasse sediments that have been bent and faulted. Sedimentary strata that have undergone modest metamorphism and have undergone severe folding make up the 100 km wide Tethys Himalaya [21–23]. Due to the high likelihood of future earthquakes, the major area of the state of Uttarakhand falls inside the Central Seismic Gap, which crosses both Uttarakhand and Western Nepal. Additionally, there are a number of additional Himalayan thrusts, faults, and lineaments which affect the seismic activity of the research area and are shown in Fig. 2.

3 Seismic Source Zone Delineation Delineating seismic source zones is the first step in assessing the seismic risk for a given area. Areal sources were taken into account for seismicity modelling because there is not enough information on specific faults, and there may be undiscovered faults in the vicinity. This led to the investigation of various area source zone arrangements. Several factors, such as seismic activity, fault plane distribution, tectonic regime, and source depth, were considered throughout the delineation process. In total, 10 source zones were drawn around the study area (Fig. 3). The Himalayan structural belt exhibits a number of the region’s tectonic features, as shown in Fig. 2. The studied region is largely within earthquake zones 1, 2, 3, 4, and 5. The MCT, Alakananda Fault (AF), North Almora Thrust (NAT), and Martoli Thrust (MT) are important tectonic features in these zones. High tectonic activity was seen in the region between these zones, where there was a substantial concentration of earthquakes. Normal faulting is present in zones 6 and 7, which are a component of the western part of the Himalayas. The principal tectonic characteristics of this area include the Indus Suture Zone (ISZ), the Kaurik Fault System (KFS), and the Karakoram Fault (KF). In the Western and Central Himalayas, there is a strong correlation between the active faults and regional tectonic features. Multiple moderateto high-magnitude earthquakes have hit this area over the past 200 years. The lesser Himalayas are covered by zones 8, 9, and 10, and this area has historically seen the modest earthquake activity. These zones did not experience earthquakes of a high magnitude.

Probabilistic Arias Intensity Maps of Uttarakhand State (India)

5

Fig. 2 Major seismo-tectonic features and earthquake occurrences within and around the state of Uttarakhand; Mahendragarh Dehradun Fault (MDF), Moradabad Fault (MF); Great Boundary Thrust (GBT), Ramgarh Thrust (RT), South Almora Thrust (SAT), Alakananda Fault (AF), North Almora Thrust (NAT), Martoli Thrust (MT), Indus Suture Zone (ISZ), the Kaurik Fault System (KFS), and the Karakoram Fault (KF)

4 Development of Earthquake Catalogue The first stage in hazard assessment by the probabilistic method is the identification of the seismic sources, which requires collecting historical seismicity data and compiling an updated homogenous earthquake occurrence database. The data for the earthquake catalogue used in this study was gathered from a number of sources, including the India Meteorological Department (IMD), the United States Geological Survey (USGS), the International Seismological Centre (ISC), and previously published studies. This analysis considers the earthquakes that happened in Uttarakhand and the surrounding areas between 1900 and 2020. The spatial distribution of the instrument-collected earthquake data for the given time period is depicted in Fig. 2. Because information was gathered from a variety of agencies and sources of literature, resulting in a diverse catalogue, the earthquake occurrences were reported using multiple magnitude scales. Different magnitudes were transformed into the

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K. Gupta and N. Satyam

Fig. 3 Areal seismic source zones demarcated in and around the Uttarakhand state for the present study

moment magnitude (Mw ) scale to provide a homogeneous record. The study used the moment magnitude scale since the surface-wave magnitude (Ms ), local magnitude (Ml ), and body-wave magnitude (Mb ) were only accurate across a limited range of distance and frequency. For large earthquakes, Mw provides the most precise earthquake size estimation. It is necessary to establish actual relationships between them specific to each place to convert other scales to Mw scale. Global linkages created by [24] allowed Ms to be transformed into Mw . The region-level relationships for the Indian Himalayan region by [25] were used for the conversion of Mb to Mw . Ml to Mw conversions were done using [26] correlations. Equation (1) presents the empirical relationship provided by [25] used to convert the seismic records in terms of intensity measure to Mw . Mw = 0.762M M I + 0.865

(1)

where MMI is Modified Mercalli Intensity. The aforementioned relationships were used to create a homogenous earthquake catalogue and inferred that certain earthquake data in the final database overlapped and that the same event was likely recorded more than once. Based on the reliability and accuracy of the source, all duplicate data entries were eliminated in the first elimination stage. The primary event induced dynamic and static stress changes, resulting in several dependent events, including foreshocks, mainshocks, and aftershocks. To

Probabilistic Arias Intensity Maps of Uttarakhand State (India)

7

create an independent dataset, these dependent events must be removed, and the process is known as declustering. In the present work, declustering was performed using the [27] technique. The aftershock progression’s duration and spatial extent were computed in terms of the mainshock. Following the declustering process, it was discovered that the catalogue was lacking for several time periods and magnitude ranges. Since the catalogue’s completeness is crucial for determining seismicity characteristics, the catalogue’s completeness was evaluated using Stepp’s method [28]. As magnitudes smaller than 4.0 could not pose significant risks [8], therefore it was taken as the lowest value of magnitude (Mmin ).

5 Estimation of Recurrence Parameters PSHA requires the use of the recurrence parameters (a, b). The Gutenberg-Richter recurrence law was used to determine these parameters. According to the law, earthquakes occur randomly and independently in terms of both location and time in any given region according to the Poisson distribution. The representation of Gutenberg-Richter’s law is presented in Eq. (2). log λm = a − bm

(2)

where a and b are recurrence parameters describing the area’s seismicity and λm represents the average rate exceedance of magnitude m. A given region’s seismicity is related to the parameters a and b, and the parameter b describes the relative size distribution of earthquake events. A lower b value indicates that a higher magnitude predominates over a low-magnitude earthquake. A larger b value, in contrast, indicates that a smaller magnitude predominates over a higher magnitude. In Fig. 4, Guttenberg-Richter’s relationship for the entire research region is depicted. Using the ZMAP software, the recurrence parameter values and the magnitude of completeness (Mc ) were calculated [29].

5.1 Maximum Magnitude (m max ) For a known seismic setting, the earthquake with the maximum magnitude (m max ) is the one responsible for the most extreme ground shaking. According to this research, m max refers to the upper limit of Mw for a fault under consideration that is specified for a seismic zone. The presently used m max estimation techniques can be either deterministic or probabilistic, and these techniques frequently use seismicity data to make their estimates [30]. Some methods also incorporate fault features [31] and geodetic measurements [32]. Approaches based on earthquake occurrence data are probabilistic; in contrast, deterministic methods are based on geodetic measurements

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K. Gupta and N. Satyam

Fig. 4 Guttenberg-Richter’s curve for the Uttarakhand state

and fault characteristics. The non-parametric Gaussian estimator proposed by [30] was used in this investigation to calculate m max .

6 Ground Motion Prediction Models for Arias Intensity Assessment Many empirical connections have been found in recent years to link the ground movements caused by earthquakes to the Ia . Ia is a numerical indicator of the intensity of an earthquake’s shaking. It is described as the sum of the energies per unit weight stored by a collection of simple undamped oscillators with resonance frequencies ranging from zero to infinity at the conclusion of an earthquake [10]. The equation of Ia is given in Eq. (3). Ia =

π Td ∫ a(t)2 dt 2g 0

(3)

where T d is the ground motion duration, g is the gravitational acceleration, and a(t) is the recorded temporal acceleration history. The first correlation between the intensity measure and the distribution of seismically generated landslides was made by [16], who also gave the first global attenuation relationship for Ia , which is represented by Eq. (4). log Ia = −4.1 + M − 2 log R ± 0.44

(4)

where R is the distance from the source measured from the slip surface, M is the earthquake magnitude, and 0.44 is the standard deviation derived from the regression analysis. Regression analysis was used to generate Eq. (4) from a dataset of 30 recordings of eight Californian events, one from Hawaii and one from Japan. Jibson

Probabilistic Arias Intensity Maps of Uttarakhand State (India)

9

[33] amended the same dataset using information from the Tabas (Iran) earthquake that occurred in 1987 and provided Eq. (5) for calculating Ia . log Ia = −4.9 + 0.98M − 1.35 log R

(5)

Travasarou et al. [34] discussed about the usefulness of employing Ia to evaluate the behaviour of structures subjected to seismic loading which is influenced by the high-frequency part of ground motion. He gave Eq. (6) for the computation of Ia . 

M log Ia = 2.8 − 1.981(M − 6) + 20.72 ln 6 +(0.454 + 0.101((M − 6))SC

 − 1.703 ln

√

R 2 + 77.09

 (6)

+(0.479 + 0.334((M − 6))S D − 0.166FN + 0.512FR where S D and SC are dummy variables dependent on the soil types (both 0 for site category B, 1 and 0 for site category C, and 0 and 1 for site category D) and FR and FN are dummy variables based on the fault types (1 and 0 for normal faults, both 0 for strike-slip faults, and 0 and 1 for reverse or reverse-oblique faults). Although [34] model is reliable and has global applicability, it has some shortcomings, most notably in the shallow depth site response modelling. A new empirical model for Ia computation, which has global applicability was suggested by [35] using the Pacific Earthquake Engineering Research Center, Next Generation of Attenuation (PEER NGA) database. The Foulser-Piggott Attenuation (FPA) model is given by Eq. (7): log Ia = c1 − c2 (8.5 − M) + (c3 − c4 M) ln 2



/ R2

+

c52

+ c6 FRV

(7)

where FRV is an indicator variable having a value of 1 for reverse-oblique and reverse ruptures and 0 otherwise. Parameters for this model found through the regression analysis are presented in Table 1. For shallow crustal earthquakes with moment magnitudes of 5 to 8, distances of under 100 km, and VS30 values between 200 and 1000 m/s, the FPA model can be used. The FPA and Travasarou models were thought to be appropriate for the Table 1 FPA model coefficients for Ia (modified from [35])

Coefficient

Value

Standard deviation

c1

4.9862

0.4716

c2

− 0.1939

0.0453

c3

− 4.0332

0.3351

c4

0.2887

0.0524

c5

6.3049

0.9341

c6

0.3507

0.1615

10

K. Gupta and N. Satyam

Uttarakhand state to evaluate the Ia because shallow crustal earthquakes are frequent in this region.

7 Logic Tree Structure The choice of a seismic hazard model is challenging because of the uncertainty associated with developing and implementing an attenuation model used to evaluate seismic hazards. These uncertainties fall into two categories: epistemic and aleatory. Epistemic uncertainty refers to the lack of information in simulating and understanding complicated earthquake phenomena. Combining various ground motion models helps defeat it. The inherent unpredictability of earthquake occurrences is represented by aleatory uncertainty. It cannot be reduced, but more information can help it generate accurate estimates. A reliable method to reduce epistemic uncertainty is the logic tree, which may be used with various ground motion prediction models [36]. A logic tree comprises a series of branches and nodes, where each branch and node depict a particular model. Based on engineering judgement, each of these branches might be assigned a subjective weightage. It was ensured that these equations are collectively exhaustive and mutually exclusive when deciding how much weight to give each of these branches, with the weights from an individual node added together to a value equalling 1 [37]. The logic tree used in this research is depicted in Fig. 5. An area source model was  considered for the study area, and by taking into account the observed magnitude Mmax(obs) and m max obtained using [30] estimator, the uncertainty in the calculation of m max was reduced. By merging two attenuation equations and assigning various weightages to each, Ia was mapped. Both the FPA and Travasarou models, which are two widely utilized models for calculating Ia , were taken into consideration for the present research. The Travasarou model obtained a weightage of 0.3, whereas the FPA model was given a weightage of 0.7 due to its usage of the most recent database.

8 Seismic Hazard Assessment The PSHA method, which [38] proposed, was used in this work to calculate seismic hazards. By computing the probability of exceedance of a specific value, x, of a ground movement parameter, X , and multiplying it by the likelihood that the earthquake of a specified magnitude would take place at a specific location, this approach can determine the likelihood of an earthquake at one probable location. The following equation was used to compute the exceedance probability: m min rmax

P(X > x) = ∫ ∫ P(X > x|m, r ) f M (m) f R (r )dr dm m min 0

(8)

Probabilistic Arias Intensity Maps of Uttarakhand State (India)

11

Fig. 5 Logic tree framework adopted in the study

where P(X > x|m, r ) is derived from the attenuation model and f R (r ) and f M (m) are the probability density functions (PDFs) for distance and magnitude, respectively. The process was repeated for each combination of locations and magnitudes. Equation (8) can be generalized for potential earthquake sources (N s), each of which has a mean rate of threshold ground motion parameter exceedance, λi = e∝i −βi m 0 , then the total rate of exceedance is given by Eq. (9). λ(X > x) =

Ns Σ i=1

m min rmax

λi ∫ ∫ P(X > x|m, r ) f Mi (m) f Ri (r )dr dm

(9)

m min 0

Since the parameters in the aforementioned equation are complex, integrals cannot be evaluated analytically to estimate accurate PSHA. As a result, for analysis, magnitude is divided into ranges and distance is split into a number of discrete segments. The probabilistic approach hazard assessment of the study area based on Poisson’s model was carried out using the R-CRISIS [39] programme and a collection of MATLAB algorithms. Each source zone received a set of highest and lowest magnitude values and recurrence parameters. The triangulation approach used by the programme to discretize the area sources was carried out repeatedly until one of the criteria was met. These criteria are the minimum triangle size (S) and minimum source-to-site distance ratio (R). The sensitivity study included evaluating different S and R combinations. Each source with M vertices was preliminarily subdivided into M − 2 triangles, and the further subdivision was performed until the specified R or Svalue was attained. To carry out this subdivision, a recursive function was employed. A calculation was made to determine the distance between the triangle’s centroid and the computation site. The seismicity of the local source was placed in each triangle’s centre. R-CRISIS uses the aforementioned approach to sample seismic source models

12

K. Gupta and N. Satyam

and forecast hazards while considering all likely earthquake sites inside the source. The outcomes of the risk analysis were displayed using hazard maps. With different weights assigned to each for Ia computations, the logic tree technique merged the two distinct attenuation models. The ground motion parameters were calculated at each grid point’s centre for 2 and 10% likelihood of exceedance in 50 years.

9 Results and Discussions The PSHA technique was used to determine the state of Uttarakhand’s seismic hazard zones, and the results are shown as hazard maps. The regional distribution of Ia for 475 years return period is shown in Fig. 6. It was observed that the Ia values range from 0.15 to 2.13 m/s. Significant portions of the area are quite susceptible to seismic shaking. Values of Ia greater than 1.1 m/s were found in the Greater Himalayan districts such as Rudraprayag, Tehri Garhwal, Chamoli, Uttarkashi, Pithoragarh, and Bageshwar and are extremely susceptible to earthquake-induced landslide hazards. Dehradun, Nainital, Pauri Garhwal, Champawat, and Almora are all within a moderate to high seismic activity zone, with intensities ranging from 0.21 to 1.21 m/s. Low values of Ia were found in the lower altitude regions, such as Udham Singh Nagar and Haridwar, making them less susceptible to co-seismic dangers. Ia values range from 0.21 to 6.23 m/s for 2475 years return period, as shown in Fig. 7. While the obtained mapping of seismic hazard is comparable to that seen for 475 years of return periods, a sizable portion of the territory has Ia values greater than 0.32 m/s for the 2475-year return period, thus making it extremely vulnerable to earthquake-induced hazards.

10 Conclusion The Cornell–McGuire probabilistic approach was used to assess the hazard due to seismic activity in the state of Uttarakhand at the bedrock level. The hazard was evaluated in terms of Ia for 2 and 10% probability of exceedance in 50 years using various global and regional attenuation models. For a 10% likelihood of exceeding in 50 years, the values of Ia ranged from 0.15 to 2.13 m/s, and for a 2% likelihood of exceeding in 50 years, Ia ranged from 0.21 to 6.23 m/s. The acquired results suggested that the Greater Himalayan region of Uttarakhand has considerable seismic activity. Except for Udham Singh Nagar and Haridwar, all the districts of Uttarakhand state are at risk from earthquake-induced hazards, according to an analysis of Ia maps. Of all the districts, the Chamoli district has the highest vulnerability to seismic hazards. This seismic hazard zonation of the Uttarakhand state in terms of Ia at bedrock will serve as a foundation for the evaluation of the earthquake-induced landslide hazard in the area. This estimated ground motion intensity will help with the careful planning of significant infrastructure projects and the safe building of structures.

Probabilistic Arias Intensity Maps of Uttarakhand State (India)

Fig. 6 Arias intensity map for 475 years return period

Fig. 7 Arias intensity map for 2475 years return period

13

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References 1. Rajendran, K., Parameswaran, R.M., Rajendran, C.P.: Seismotectonic perspectives on the Himalayan arc and contiguous areas: inferences from past and recent earthquakes. Earth-Sci Rev. 173, 1–30 (2017). https://doi.org/10.1016/j.earscirev.2017.08.003 2. IS 1893 (Part 1): Criteria for earthquake resistant design of structures: general provisions and buildings. Bureau of Indian Standards, New Delhi, pp. 1–39 (2016) 3. Joshi, A., Mohan, K., Patel, R.C.: A deterministic approach for preparation of seismic hazard maps in North East India. Nat. Hazards 43, 129–146 (2007). https://doi.org/10.1007/s11069007-9112-7 4. Singh, P., Patel, R.C., Lal, N.: Plio-Plistocene in-sequence thrust propagation along the main central thrust zone (Kumaon-Garhwal Himalaya, India): new thermochronological data. Tectonophysics 574–575, 193–203 (2012). https://doi.org/10.1016/j.tecto.2012.08.015 5. Joshi, A., Kumar, A., Castanos, H., Lomnitz, C.: Seismic hazard of the Uttarakhand Himalaya, India, from deterministic modeling of possible rupture planes in the area. Int. J. Geophys. 2013(825276), 1–12 (2013). https://doi.org/10.1155/2013/825276 6. Khattri, K.N., Rogers, A.M., Perkins, D.M., Algermissen, S.T.: A seismic hazard map of India and adjacent areas. Tectonophysics 108(1–2), 111–134 (1984). https://doi.org/10.1016/00401951(84)90156-2 7. Shanker, D., Shubhami: Seismic hazard scenario in Western Himalaya, India. In: SnT2019 T1.2-P80 (2019) 8. Nayak, M., Sitharam, T.G., Kolathayar, S.: A revisit to seismic hazard at Uttarakhand. Int. J. Geotech. Earthq. Eng. 6(2), 56–73 (2015). https://doi.org/10.4018/IJGEE.2015070104 9. Gopiram, A., Satyam, D.N., Ramancharla, P.K.: Dynamic analysis of transmission towers under strong ground motion. In: Proceedings, 3rd International Earthquake Symposium, Bangladesh, Dhaka, pp. 1–7 (2010) 10. Arias, A.: Measure of earthquake intensity. In: Hansen, R.J. (ed) Seismic Design for Nuclear Power Plants, Mass. Massachusetts Inst. of Tech. Press, Cambridge, pp. 438–483 (1970) 11. Satyam, N.: Seismic microzonation studies for Delhi region. Dissertation, Indian Institute of Technology Delhi (2006) 12. Dhanya, J., Gade, M., Raghukanth, S.T.G.: Ground motion estimation during 25th April 2015 Nepal earthquake. Acta Geod. Geophys. 52, 69–93 (2017). https://doi.org/10.1007/s40328016-0170-8 13. Kaveh, A., Bakhshpoori, T., Hamze-Ziabari, S.M.: Derivation of new equations for prediction of principal ground-motion parameters using M5, algorithm. J. Earthq. Eng. 20(6), 910–930 (2016). https://doi.org/10.1080/13632469.2015.1104758 14. Ma, J., Dong, L., Zhao, G., Li, X.: Qualitative method and case study for ground vibration of tunnels induced by fault-slip in underground mine. Rock Mech. Rock Eng. 52, 1887–1901 (2019). https://doi.org/10.1007/s00603-018-1631-x 15. Harp, E.L., Wilson, R.C.: Shaking intensity thresholds for rock falls and slides: evidence from 1987 Whittier Narrows and Superstition Hills earthquake strong motion record. Bull. Seismol. Soc. Am. 85(6), 1739–1757 (1995). https://doi.org/10.1785/BSSA0850061739 16. Wilson, R.C., Keefer, D.K.: Predicting areal limits of earthquake-induced landsliding. In: Ziony, J.I. (ed) Evaluating Earthquake Hazards in the Los Angeles Region: An Earth-science Perspective, vol. 1360, pp. 317–345. U.S. Geological Survey Professional Paper (1985) 17. Gupta, K., Satyam, N.: Estimation of Arias intensity and peak ground acceleration (PGA) using probabilistic seismic hazard assessment of Uttarakhand state (India). Arab. J. Geosci. 15, 437 (2022). https://doi.org/10.1007/s12517-022-09733-9 18. Satyam, N.D., Towhata, I.: Site-specific ground response analysis and liquefaction assessment of Vijayawada city (India). Nat. Hazards 81, 705–724 (2016). https://doi.org/10.1007/s11069016-2166-7 19. Gupta, K., Satyam, N.: Co-seismic landslide hazard assessment of Uttarakhand state (India) based on the modified Newmark model. J. Asian Earth Sci. X 8, 100120 (2022). https://doi. org/10.1016/j.jaesx.2022.100120

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20. Valdiya, K.S.: Tectonics and evolution of the central sector of the Himalaya. Philos. Trans. R. Soc. London Ser. A Math. Phys. Sci. 326(1589), 151–175 (1988). https://doi.org/10.1098/rsta. 1988.0083 21. Abraham, M.T., Satyam, N., Pradhan, B., Segoni, S., Alamri, A.: Developing a prototype landslide early warning system for Darjeeling Himalayas using SIGMA model and real-time field monitoring. Geosci. J. 26, 289–301 (2022). https://doi.org/10.1007/s12303-021-0026-2 22. Abraham, M.T., Satyam, N., Pradhan, B., Alamri, A.M.: Forecasting of landslides using rainfall severity and soil wetness: a probabilistic approach for Darjeeling Himalayas. Water 12(3), 804 (2020). https://doi.org/10.3390/w12030804 23. Sharma, M., Satyam, N., Reddy, K.R.: Effect of freeze-thaw cycles on engineering properties of biocemented sand under different treatment conditions. Eng. Geol. 284, 106022 (2021). https://doi.org/10.1016/j.enggeo.2021.106022 24. Wason, H.R., Das, R., Sharma, M.L.: Magnitude conversion problem using general orthogonal regression. Geophys. J. Int. 190(2), 1091–1096 (2012). https://doi.org/10.1111/j.1365-246X. 2012.05520.x 25. Das, R., Wason, H.R., Sharma, M.L.: Temporal and spatial variations in the magnitude of completeness for homogenized moment magnitude catalogue for northeast India. J. Earth Syst. Sci. 121, 19–28 (2012). https://doi.org/10.1007/s12040-012-0144-3 26. Ristau, J.: Moment magnitude-local magnitude calibration for earthquakes in Western Canada. Bull. Seismol. Soc. Am. 95(5), 1994–2000 (2005). https://doi.org/10.1785/0120050028 27. Gardner, J.K., Knopoff, L.: Is the sequence of earthquakes in Southern California, with aftershocks removed, Poissonian? Bull. Seismol. Soc. Am. 64, 1363–1367 (1974) 28. Stepp, J. C.: “Analysis of completeness of the earthquake sample in the Puget Sound area,” In contributions to seismic zoning, NOAA Tech. Report, ERL 267-ESL 30, Washington, D.C. pp. 16–28 (1973) 29. Wiemer, S.: A software package to analyze seismicity: ZMAP. Seismol. Res. Lett. 72, 373–382 (2001). https://doi.org/10.1785/gssrl.72.3.373 30. Kijko, A.: Estimation of the maximum earthquake magnitude, mmax . Pure Appl. Geophys. 161, 1655–1681 (2004). https://doi.org/10.1007/s00024-004-2531-4 31. Wells, D.L., Coppersmith, K.J.: New empirical relationships among magnitude, rupture length, rupture width, rupture area, and surface displacement. Bull. Seismol. Soc. Am. 84(4), 974–1002 (1994). https://doi.org/10.1785/BSSA0840040974 32. Schiffman, C., Bali, B.S., Szeliga, W., Bilham, R.: Seismic slip deficit in the Kashmir Himalaya from GPS observations. Geophys. Res. Lett. 40(21), 5642–5645 (2013). https://doi.org/10. 1002/2013GL057700 33. Jibson, R.W.: Summary of research on the effects of topographic amplification of earthquake shaking on slope stability. In: US Geological Survey Open-file Report, pp. 87–268 (1987). https://doi.org/10.3133/ofr87268 34. Travasarou, T., Bray, J.D., Abrahamson, N.A.: Empirical attenuation relationship for Arias Intensity. Earthq. Eng. Struct. Dyn. 32(7), 1133–1155 (2003). https://doi.org/10.1002/eqe.270 35. Foulser-Piggott, R., Stafford, P.J.: A predictive model for Arias intensity at multiple sites and consideration of spatial correlations. Earthq. Eng. Struct. Dyn. 41(3), 431–451 (2012). https:// doi.org/10.1002/eqe.1137 36. Stepp, J.C., Wong, I., Whitney, J., Quittmeyer, R., Abrahamson, N., Toro, G., et al.: Probabilistic seismic hazard analyses for ground motions and fault displacement at Yucca Mountain, Nevada. Earthq. Spectra 17(1), 113–151 (2001). https://doi.org/10.1193/1.1586169 37. Bommer, J.J., et al.: On the use of logic trees for ground-motion prediction equations in seismichazard analysis. Bull. Seismol. Soc. Am. 95(2), 377–389 (2005). https://doi.org/10.1785/012 0040073 38. Cornell, C.A.: Engineering seismic risk analysis. Bull. Seismol. Soc. Am. 58(5), 1583–1606 (1968) 39. Ordaz, M., Salgado-Gálvez, M.A.: R-CRISIS v20 validation and verification document. In: ERN Technical Report; Mexico City, Mexico (2020)

Statistical Analysis of Seismicity Parameters and Completeness Period of Earthquake Catalog for Sree Padmanabhaswamy Temple, Thiruvananthapuram District, Kerala M. P. Hari Padmanabhan, Siddhardha R, Sreevalsa Kolathayar, and Ramakrishna Hegde Abstract In the present study, seismicity data analysis of Sree Padmanabhaswamy Temple, Thiruvananthapuram District, Kerala, India, has been performed. The area is having a radius of 500 km with Sree Padmanabhaswamy Temple (8.48°N Latitude and 76.94°E Longitude) as the center. Earthquake catalog from 1822 to 2021 has been compiled and homogenized into equivalent moment magnitude (Mw) using regional empirical relationships. Data on earthquakes were declustered using Urhammer method to exclude foreshocks and aftershocks within a time and space window, and then statistical analysis was done to ensure data completeness. Using the GuntenbergRitchers recurrence relationship, the seismic parameters obtained are 1.234 and 0.532, respectively. The M max value calculated using Kijko’s MATLAB algorithm found to be 6.08 ± 0.26. Keywords Gutenberg · Richter · Statistical analysis · Foreshocks · Seismicity

M. P. H. Padmanabhan Department of Earthquake Engineering, Srinivas University, Mangaluru, Karnataka, India R. Siddhardha (B) Department of Civil Engineering, NIT Warangal, Warangal, Telangana, India e-mail: [email protected] S. Kolathayar Department of Civil Engineering, NIT Surathkal, Mangaluru, Karnataka, India e-mail: [email protected] R. Hegde Department of Civil Engineering, Srinivas University, Mangaluru, Karnataka, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_2

17

18

M. P. H. Padmanabhan et al.

1 Introduction Sree Padmanabhaswamy Temple is located in the center of the circle-shaped research region, which has a radius of 500 km, as shown in Fig. 1. The temple’s locational coordinates are 8.48°N Latitude and 76.94°E Longitude. Making a single working catalog for a region under consideration is a crucial undertaking since earthquake catalogs provide the first crucial input for defining seismic source zones and characterizing them. In various parts of the research region, the seismological and geological data identify several lineaments and active faults. From the Bhukosh website of Geological Survey of India (www.bhukosh.gsi.gov.in), the location and direction of the lineaments and faults that are the linear seismic sources were determined. To create a seismotectonic map, these lineaments and faults were digitally recorded using QGIS software. The instrumental data have also been sourced from internationally renowned earthquake databases on the internet, including the International Seismological Center (ISC) and the USGS 2008 online bulletin. The catalogue has been homogenized using the regression equations developed by [1] into an equivalent moment magnitude (Mw). Thiruvananthapuram’s current earthquake database has 66 earthquakes with M w > 3.0 that occurred between 1822 and 2021. There have been six documented earthquake events with a magnitude greater than 5.0, with a maximum of M w 6.0.

Fig. 1 Geographical extent of the study region

Statistical Analysis of Seismicity Parameters and Completeness Period …

19

2 General Analysis of Earthquake Catalog The number of earthquakes every decade was separated into six magnitude ranges, as indicated in Table 1: 3.0 ≤ M w ≤ 3.49, 3.5 ≤ M w ≤ 3.99, 4.0 ≤ M w ≤ 4.49, 4.5 ≤ M w ≤ 4.99, 5.0 ≤ M w ≤ 5.49, and M w ≥ 5.49. The histogram representation of the earthquake data is shown in Fig. 2. The histogram indicates that a significant number of earthquakes were reported between 1982 and 1991. The earthquake reporting was subpar and inaccurate before to 1982. Unreliable findings are obtained when the seismicity parameter is calculated using incomplete data [2]. To solve this issue, the data’s completeness was verified using the CUVI approach.

3 Declustering Process Foreshocks and aftershocks are removed from the catalog by declustering. The earthquake inventory for Thiruvananthapuram, as previously indicated, has 66 earthquake events with M w > 3 from 1822 to 2021 A.D. The primary earthquake shocks are discrete, Poisson-distributed events [3]. The dynamic window approach by Uhrhammer in 1986 has been employed in this study [4]. It was found that there were no dependent events in the catalog. All events are main shocks only. Figure 3 depicts the entire seismotectonic map of the study area, which includes the faults, lineaments, and declustered earthquake events. In the figure, all the magnitudes are M w . Table 1 lists a few of the significant faults found in the research area.

4 Completeness of Catalog Making sure a recurrence connection is complete; it is essential to confirm that all earthquakes for each magnitude range of interest have been documented for the time period under consideration. Utilizing the Visual Cumulative (CUVI) approach by [5], completeness assessments have been conducted [6]. To compute the period of catalog completion, various magnitude classes are used in the current study.

4.1 Visual Cumulative Method Figure 4 displays plots of the total number of events over time from the start of the catalog for four different classes of magnitudes for the whole study region as a single source zone (a–e). The era of completeness is thought to start for a particular magnitudes class at the earliest point at which the slope of the fitting curve may be reasonably represented by a straight line.

20 Table 1 List of major faults in research area

M. P. H. Padmanabhan et al. S.No

Fault

Length (km)

1

Arkavati fault

120.3

2

Bhavali fault

87 130

3

Cauveri fault

4

Kottagudi–Kokkal–Palani fault

58

5

Malayattur–Vadakkancheri fault

36

6

Ottappalam–Kuttampuzha fault

98

7

Pattikkad–Kollengol fault

40

8

Periyar fault

84

9

Sakleshpur–Bettadpur fault

83

10

Tekkadi–Kodaivannalur fault

48

11

Tenmalai fault

70

12

Tiruppur fault

85

13

Valparai–Anamudi fault

45

14

Amaradakki fault

51

15

Amirdi fault

16

Attur fault

17

Ayakkudi–Vinupaksha fault

18

Bhavani–Kanumudi fault

19

Cauveri fault

20

Chitradurga boundary fault

96 160 29 61 196 81

21

Crystalline-sedimentary contact fault

26

22

Javadi hills fault

87

23

Main fault N 45 Degrees E

66 120

24

Main fault N 45 Degrees E

25

Manamelkudi–Tonti fault

35

26

Mettur east fault

82

27

Mettur east fault

28

Mouth of Coleroon–point Calimere

36 126

5 Gutenberg–Richter Recurrence Relationship The source model, expressed by the Gutenberg–Richter (G–R) activity parameters ‘a’ and ‘b’ for each of the seismic zones, is the fundamental input for the seismic hazard analysis [7]. By calculating the slope of a straight line connecting the distribution of the logarithm of the number of earthquakes to their magnitude, log(λ M ) = a−bM

Statistical Analysis of Seismicity Parameters and Completeness Period … 14 Number of Earthquakes in a given magnitude range 3-3.49

12

Number of Earthquakes in a given magnitude range 3.5-3.99

Number of Earthquakes

10 8

Number of Earthquakes in a given magnitude range 4-4.49

6 Number of Earthquakes in a given magnitude range 4.5-4.99

4

Number of Earthquakes in a given magnitude range 5-5.49

2 0

Number of Earthquakes in a given magnitude range 5.5-6

Years

Fig. 2 A Histogram of seismic events in the research area

Fig. 3 Map showing the research region’s seismicity

21

22

M. P. H. Padmanabhan et al. (a)

Mw = 3 to3.49

Cumulative number of Earthquakes

2.5 2 1.5 1 0.5 0 2006

2007

2008

2009

2010

2011

2012

2013

T ime (years)

Cumulative number of Earthquakes

(b)

Mw = 3.5 to 3.99

30 25 20 15 10 5 0 1985

1990

1995

(c)

2000

2005 2010 T ime (years)

2015

2020

2025

Mw = 4 to 4.49

Cumulative number of Earthquakes

20 18 16 14 12 10 8 6 4 2 0 1985

1990

1995

2000 2005 T ime (years)

Fig. 4 (a–e) CUVI method for catalog completeness

2010

2015

2020

Statistical Analysis of Seismicity Parameters and Completeness Period … (d)

23

Mw = 4.5 to 4.99

Cumuative number of Earthquakes

10 9 8 7 6 5 4 3 2 1 0 1980

1985

1990

(e)

1995

2000 2005 T ime (years)

2010

2015

2020

2025

Cumulative number of Earthquakes

Mw ≥ 5 10 9 8 7 6 5 4 3 2 1 0 1800

1850

1900 1950 T ime (years)

2000

2050

Fig. 4 (continued)

where ‘a’ and ‘b’ are the characteristic constants of the seismic zone; λM = the mean annual rate of exceedance of magnitude M. The constants ‘a’ and ‘b’ can be evaluated using the least square regression analysis. The ‘b’ value is sometimes thought of as a measure of the brittle–ductile transition of the crust [8]. The regression analysis estimates the ‘a’ and ‘b’ values from the cumulative annual rate of earthquake occurrence and the mean of the magnitude range. The analysis makes the assumption that the seismic activity in the area will follow the earthquake law proposed by Gutenberg and Richter, which implies an exponential distribution of earthquake magnitudes. These have undergone comprehensive evaluation by regression analyses using the existing catalog and is furnished in Fig. 5.

24

M. P. H. Padmanabhan et al. Gutenberg Richter plot

1 0.5

log (λM)

0

0

1

2

3

4

5

6

-0.5 -1

log λM = -0.5321Mw + 1.2349

-1.5 -2

Moment magnitude (Mw)

Fig. 5 G–R relationship for single source zone using CUVI method

6 Maximum Magnitude The maximum magnitude (M max ) is a crucial parameter for seismologists, insurance companies, and disaster management organizations. The term ‘M max ’ refers to the maximum earthquake magnitude that can occur in the area under consideration. The selection of maximum magnitude (M max ) in Peninsular India is seriously questionable due to the short time period covered by the earthquake catalog in comparison to the frequency of high-magnitude earthquakes [9]. In order to estimate the value of M max , the Kijko–Sellevoll–Bayes (K-S-B) method proposed by Kijko [10] takes into account both the complete and the partial portions of the earthquake database. The M max value was calculated using Kijko’s MATLAB algorithm (M max ). The obtained M max value is 6.08 ± 0.26.

7 Conclusions The current study examines the occurrence rate for a number of magnitude criteria in order to verify the catalog’s completeness. Completeness threshold has been determined using CUVI method. The regional recurrence relations are obtained based on nearly 199 years (1822–2021 A.D.) of past data. A crucial seismicity parameter is the magnitude–frequency relationship’s b value. The estimated values of a and b, which are crucial input parameters in the Gutenberg–Richter recurrence relationship, are 1.234 and 0.532, respectively, for the research region.

Statistical Analysis of Seismicity Parameters and Completeness Period …

25

References 1. Kolathayar, S., Sitharam, T.G., Vipin, K.S.: Spatial variation of seismicity parameters across India and adjoining areas. Nat. Hazards (Springer Publications) 60(3), 1365–1379 (2012) 2. Schorlemmer, D., Zechar, J.D., Werner, M.J., Field, E.H., Jackson, D.D., Jordan, T.H., & RELM Working Group: First results of the regional earthquake likelihood models experiment. In Seismogenesis and Earthquake Forecasting: The Frank Evison Volume II (pp. 5–22) (2010). Springer, Basel 3. Gardner, J.K., & Knopoff, L.: Is the sequence of earthquakes in Southern California, with aftershocks removed, Poissonian?. Bull. Seismol. Soc. Am. 64(5), 1363–1367 (1974) 4. Uhrhammer, R.: Characteristics of Northern and Central California seismicity. Earthq. Notes 57(1), 21 (1986) 5. Tinti, S., Mulargia, F.: Effects of magnitude uncertainties on estimating the parameters in the Gutenberg-Richter frequency-magnitude law. Bull. Seismol. Soc. Am. 75(6), 1681–1697 (1985) 6. Nath, S.K., Mandal, S., Adhikari, M.D., Maiti, S.K.: A unified earthquake catalogue for South Asia covering the period 1900–2014. Nat. Hazards 85(3), 1787–1810 (2017) 7. Gutenberg, B., Richter, C.F.: Frequency of earthquakes in California. Bull. Seismol. Soc. Am. 34(4), 185–188 (1944) 8. Amitrano, D.: Brittle-ductile transition and associated seismicity: experimental and numerical studies and relationship with the b-value. J. Geophys. Res. Solid Earth 108(B1), 1–8 (2003) 9. Khan, M.M., Munaga, T., Kiran, D.N., & Kumar, G.K. Seismic hazard curves for Warangal city in Peninsular India. Asian J. Civ. Eng. 21, 543–554 (2020) 10. Kijko, A.: Estimation of the maximum earthquake magnitude, Mmax . Pure Appl. Geophys. 161(8), 1655–1681 (2004)

Effect of Open Stories on Expected Seismic Losses in Hilly Buildings Yati Aggarwal

and Sandip Kumar Saha

Abstract Due to the lack of adequate flat land in the hilly region, it is often opted to construct buildings on the slope. Further, to accommodate the need for large covered open spaces (e.g., parking and shops), open stories are provided in the hilly buildings at levels based on the location of the approach road. In this study, an attempt is made to determine the variation in the expected seismic losses due to the presence of open story in reinforced concrete (RC) hilly buildings. For this, two basic types of hilly buildings with different story ratios are considered. Bi-directional nonlinear dynamic analyses are performed under 22 ground motions. The expected repair cost is estimated considering the repair cost of four drift-sensitive building components. The component-wise and floor-wise deaggregation of the expected repair costs due to the considered components are studied. It is observed that the unreinforced masonry wall contributes to the majority of the expected seismic repair cost in all the considered hilly buildings. Moreover, the presence of open stories just above the uppermost foundation level is observed to be the most critical in terms of estimated seismic losses to structural components. Keywords Hilly buildings · Open story · Seismic loss estimation

1 Introduction An increase in population causes indiscriminate growth in the activities of building infrastructure in the Indian Himalayan region. Due to scarcity of flat land in hilly regions, buildings are usually constructed over slopes. It is evident from the literature [1, 2] as well as from post-earthquake reconnaissance studies [3, 4] that reinforced Y. Aggarwal (B) · S. K. Saha School of Civil and Environmental Engineering, Indian Institute of Technology Mandi, Kamand, Himachal Pradesh, India e-mail: [email protected] S. K. Saha e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_3

27

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Y. Aggarwal and S. K. Saha

concrete (RC) hilly building configurations perform poorly in comparison to the buildings with regular configuration. The poor performance of hilly buildings is due to the presence of short columns at intermediate floors. Moreover, to cater to the need of open spaces for vehicle parking or other utility purposes, open stories are provided in hilly buildings. Contribution of unreinforced masonry walls is often ignored during the analysis and design phases of the building. When open stories are provided in hilly buildings to serve any specific purpose, consequential difference in the performance of hilly buildings is observed as reported by Aggarwal and Saha [5]. Until now, we are aware of the seismic performance of hilly buildings without and with open stories [5]. Nevertheless, their seismic loss assessment is also important as it helps to gauge the performance of these buildings in terms of monetary losses and count of vulnerable occupants, therefore enabling the decision-makers to plan for mitigation policies prior to an impending disaster. Herein, an attempt has been made to quantify the seismic loss in terms of financial losses due to repair or reconstruction of hilly building components without and with open stories. Thus, the objectives of present study are (i) to estimate the componentwise repair cost, taking into account the contribution of unreinforced masonry wall, RC beam, RC column, RC beam-column joint, in hilly buildings without and with open story, and (ii) to study the repercussions of having an open story on seismic loss estimation in hilly buildings.

2 Numerical Modeling and Ground Motion Selection In this section, nonlinear numerical modeling of RC hilly building configurations is explained and then followed by the selection of scenario-based ground motion records. These ground motion records are scaled to make them compatible with the code-defined response spectra. Considered hilly buildings are then subjected to these scaled ground motions and their seismic responses are noted.

2.1 Numerical Modeling of Hilly Buildings For studying the effect of open story, two RC hilly building configurations, i.e., stepback (SB) and split-foundation (SF), are selected. The elevation and plan views of the considered buildings are depicted in Fig. 1. Three story ratios, (a) 0.5, (b) 1, and (c) 2, are selected, keeping the number of floors below the uppermost foundation level (UFL) uniform as four. Column height below the plinth level in SB varies from 1.1 to 2.75 m to match the ground slope. In split-foundation building, column height below the plinth level is 1.1 m. Table 1 enlists the dead and live loads assigned to the buildings as prescribed by the Indian standards [6, 7]. The behavior of slab is modeled as a rigid diaphragm. The grade of concrete and steel is considered as M30 and Fe500,

Effect of Open Stories on Expected Seismic Losses in Hilly Buildings

29

respectively. The second moment of area of RC beam and RC column is reduced to 35 and 70% of the gross second moment of area as per the recommendations of IS 1893 [8] to account for cracked section properties. The cross section details and dynamic characteristics of the considered buildings are mentioned in Table 2. The lumped plasticity model is used to simulate the material nonlinearity in RC beams and RC columns. The force–deformation (backbone curve) behavior of RC beam and column is defined based on the ASCE 41 [9] recommendations. To account for the cyclic degradation, energy-based degrading model is used. The parameters used in this model are used from the study performed by Surana et al. [2]. Unidirectional bending (M 3 ) plastic hinges are allocated to RC beams, whereas axialbi-directional bending (P-M 2 -M 3 ) plastic hinges are allocated to RC columns. The length of flexural plastic hinge is calculated using the expression proposed by Paulay and Priestley [10] and is assigned at a distance of half of the flexural plastic hinge length from both ends of the RC beams as well as RC columns. Shear plastic hinges 4-story SB

2-story SB

2-story SF

8-story SB

4-story SF

8-story SF

8 stories @ 3.3m

8 stories @ 3.3m

Across slope direction Along slope direction

4 stories @ 3.3m

LFL

4 stories @ 3.3m

UFL

UFL

LFL

1.1m

(b)

1.1m

Downhill side

Uphill side 6m 3m 6m

(a)

8 bays @3.2m = 25.6m

(c)

Fig. 1 Hilly building configurations a elevation view of stepback buildings, b elevation view of split-foundation buildings, and c building plan

Table 1 Details of dead and live loads considered in this study

Component

Magnitude

Slab finishing load

1 kN/m2

Masonry load

20 kN/m3

Reinforced concrete load

25 kN/m3

Roof treatment

1.5 kN/m2

Roof live

1.5 kN/m2

Live load

3 kN/m2

Height of parapet wall

1.5 m

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Y. Aggarwal and S. K. Saha

Table 2 Cross section details and fundamental periods of hilly buildings Building type

Column (mm)

Beam (mm)

Short column (mm)

Slab thickness (mm)

Fundamental period in across-slope direction (s)

Fundamental period in along-slope direction (s)

2-story SB 350 × 350

300 × 300

450 × 450, 750 × 750

150

0.29

0.2

4-story SB 350 × 350

400 × 300

450 × 450, 800 × 800

0.50

0.36

8-story SB 300 × 300, 350 × 350

400 × 300

500 × 500, 800 × 800

1.05

0.72

2-story SF 350 × 350

300 × 300

450 × 450

0.33

0.23

4-story SF 350 × 350

400 × 300

450 × 450

0.52

0.38

8-story SF 350 × 350, 450 × 450

400 × 300

500 × 500

1.00

0.71

(force-controlled) are assigned to short columns to simulate the shear failure in short columns. The shear hinge length is considered as 1.5 times the depth (d) of the short column and is assigned at a distance of d from the end of the short column. Geometric nonlinearity in the form of P-delta effect is also considered in this study. To simulate the effect of unreinforced masonry wall, equivalent diagonal struts are modeled as per the recommendations of IS 1893 [8]. The strength of unreinforced masonry wall is considered as 4.1 MPa (fair category as per ASCE 41[9]). The thickness (t) of equivalent strut is same as the thickness of unreinforced masonry wall. The width (wds ) of equivalent strut is estimated on the basis of relative stiffness of wall and frame: wds = 0.175αh−0.4 L ds

(1)

where L ds is the length of equivalent diagonal strut, and αh is estimated as: / αh = h

4

E m t sin 2θ 4E f Ic h

 (2)

where h is the height of unreinforced masonry wall; E m and E f are the elastic moduli of masonry and RC frame, respectively; I c is second moment of area of columns; θ is the angle between equivalent strut and beam. The compression-only axial behavior

Effect of Open Stories on Expected Seismic Losses in Hilly Buildings

31

SB-IA

SB-IRB

SB-IRT

SB-IRBoth

SF-IA

SF-IRB

SF-IRT

SF-IRBoth

Fig. 2 Hilly buildings with prospective locations of open story

of equivalent strut is considered. The thickness of exterior and interior unreinforced masonry walls is considered as 230 mm and 150 mm, respectively. The presence of masonry infill is considered during both analysis and design phases of the hilly buildings. Further, to assess the consequences of having an open story in hilly buildings, masonry walls (infills) are removed from the floors which are close to the approach road. Four prospective cases are selected based on the field survey and assessed in this study. These are (a) infills considered throughout the building represented as IA, (b) open story at lowermost foundation level (LFL) represented as IRB, (c) open story at UFL represented as IRT, and (d) open story at both LFL and UFL represented as IRBoth, as shown in Fig. 2.

2.2 Ground Motion Selection and Scaling A set of 22 ground motion records is selected to conduct this study with the limitation on moment magnitude (M w ) and site to source distance (Rjb ). The magnitude of selected strong far-field ground motion records is in between 6.0 and 8.0, and site to source distance is between 15 and 30 km to avoid near-field effects as per the guidelines of FEMA P695 [11]. The ground motion records are taken from PEER strong ground motion database [12], and their details are mentioned in the authors’ previous work [5]. Both the horizontal components of the records are used for performing bi-directional nonlinear time history analysis. Across-slope direction of building is subjected to ground motion component with higher magnitude of peak ground acceleration (PGA), and along-slope direction is subjected to another component of ground motion.

32

Y. Aggarwal and S. K. Saha

The selected ground motion records are scaled with respect to spectral acceleration at fundamental period of the building corresponding to 5% damping (S a (T 1 , 5%)). The spectral acceleration is scaled to match the target spectrum [13] for rocky soil in seismic zone V specified by Indian seismic design code [8]. The fundamental periods of the considered buildings are different in their two translational directions; therefore, scaling of two ground motion components is done separately to match the target spectrum.

2.3 Numerical Study Nonlinear time history analyses are performed. These analyses employ Newmark-β integration method, to assess the seismic performance of 24 hilly buildings. These 24 building models include two different building configurations, three different number of stories, and four different prospective locations of open story. Ground motions are assigned in two translational directions of the buildings. The 5% proportional damping is assigned to the first period of vibration and the period at which at least 90% of the cumulative modal mass is participating. For performing numerical modeling of buildings and dynamic analyses, FEM-based structural analysis software, SAP2000v21 [14], is used. Seismic response in terms of peak interstory drift ratio (IDR) is noted in all the considered buildings. Median of 22 responses is calculated to estimate the expected seismic losses due to one non-structural and three structural building components.

3 Seismic Performance of Hilly Buildings Seismic response of buildings in terms of peak IDR is an important engineering demand parameter because seismic losses are estimated using IDR, especially in structural components. IDR in across-slope direction of the buildings is reported in this study because the stiffness of considered buildings is less in lateral direction (across-slope direction) as compared to their longitudinal direction. Thus, higher interstory drift is obtained in lateral direction of the building. In each considered building, the median of 22 peak IDR is calculated at every floor level. Figure 3 shows the variation in peak IDR corresponding to relative story height in considered the hilly buildings. The shaded portion in Fig. 3 represents the building portion below the uppermost foundation level. It is observed from Fig. 3 that in two-story buildings when all masonry infills are present, peak IDR remains within the permissible limit of 0.4% as recommended by [8]. However, when two-story hilly buildings have open story, peak IDR increases drastically and exceeds the permissible limit. In four-story and eight-story hilly buildings without and with open story, peak IDR exceeds the permissible limit of 0.4%. Presence of an open story decreases stiffness of story, thereby increase in peak IDR is observed.

Effect of Open Stories on Expected Seismic Losses in Hilly Buildings

33

1.00

IA IRB IRT IRBoth

0.75

Relative Story Height

0.50 0.25 2-story SB

4-story SB

8-story SB

4-story SF

8-story SF

0.00 1.00 0.75 0.50 0.25 2-story SF 0.00 0.0

0.5

1.0

1.5 0.0

0.5

1.0

1.5

2.00.0

1.0

2.0

3.0

4.0

IDR (%) - Across Slope Direction

Fig. 3 Variation in peak interstory ratio in lateral direction of hilly building for different prospective locations of open story

Moreover, it is observed that the increase in peak IDR is higher when open story is at UFL in comparison to the LFL of the building. About 3–4 times increase in peak interstory ratio is observed when open story is at UFL. This is because the quantity of masonry infills is lesser at LFL; thus, removal of these masonry infills imparts lesser flexibility to the building in comparison to removal of masonry infills from UFL. Interestingly, when open story is present at both UFL and LFL, two spikes are observed in split-foundation buildings, whereas only one spike of increased peak IDR is observed in stepback buildings. Again, the reason is same that the quantity of masonry infills at LFL is less in SB building in comparison to SF building. Thus, dynamic analysis of hilly buildings has revealed that the maximum peak IDR (maximum among all floor levels) increases significantly due to the presence of open story.

4 Seismic Loss Estimation The expected seismic loss is estimated considering four building components, i.e., unreinforced masonry wall, RC beam, RC column, and RC beam–column joint. For this, response (peak IDR) of hilly buildings, without and with open story, is utilized to estimate the probability of exceeding a damage state (P(DSi |I D R)) using the expressions mentioned in FEMA P58[15]. The fragility functions for the considered building components are referred from relevant literatures as shown in Fig. 4. For unreinforced masonry wall, fragility functions are taken from the experimental study performed by Cardone and Perrone [16]. For RC beam and RC column, fragility functions are referred from the library of FEMA P58[15]. Fragility functions for

34

Y. Aggarwal and S. K. Saha Reinforced concrete beam [13]

Unreinforced masonry wall [14]

Probability of exceedance

1.0

0.5

DS-1 DS-2 DS-3 DS-4

0.0

MedianDS-1 = 0.0015 DispersionDS-1 = 0.50 MedianDS-2 = 0.0040 DispersionDS-2 = 0.50 MedianDS-3 = 0.0100 DispersionDS-3 = 0.40 MedianDS-4 = 0.3500 DispersionDS-4 = 0.35

MedianDS-1 = 0.0137 DispersionDS-1 = 0.21 MedianDS-2 = 0.0264 DispersionDS-2 = 0.33 MedianDS-3 = 0.0428 DispersionDS-3 = 0.74

Reinforced concrete beam-column joint [15]

Reinforced concrete column [13] 1.0

0.5

0.0 0.00

MedianDS-1 = 0.0068 DispersionDS-1 = 0.89 MedianDS-2 = 0.0176 DispersionDS-2 = 0.526 MedianDS-3 = 0.0392 DispersionDS-3 = 0.297 MedianDS-4 = 0.0513 DispersionDS-4 = 0.089

MedianDS-1 = 0.0200 DispersionDS-1 = 0.40 MedianDS-2 = 0.0275 DispersionDS-2 = 0.30 MedianDS-3 = 0.0500 DispersionDS-3 = 0.30

0.05

0.10

0.00

0.05

0.10

Peak interstory drift ratio

Fig. 4 Fragility functions for the considered building components

reinforced beam–column joint are adopted from the work of Brown and Lowes [17]. The probability of exceeding a damage state is calculated considering the median of responses in which no simulated collapse instances are observed. For the instances, in which simulated collapse is observed, probability of collapse (Pc ) is estimated as the ratio of number of cases for which simulated collapse is observed and total number of cases (i.e., 22 ground motion records). Total probability of exceeding a damage state (Pt (DSi |I D R)) is calculated as [18]: Pt (DSi |I D R ) = Pc + (1 − Pc ) × P(DSi |I D R )

(3)

After calculating the probability of exceeding a damage state for considered building components, their repair cost under different damage states is also required to be estimated for seismic loss estimation. Repair cost functions for considered building components under different damage states are adopted from the work of Aggarwal and Saha [19]. They proposed region-specific repair cost functions for estimating the repair cost (RE ) (in Indian rupees) of building components under different damage states. The general form of repair cost functions is as follows: (4)

where m, n, p, k are the upper limits of the controlling parameters i, j, q, l and their values depend on the type of building component; x, y, z, and w are the independent variables that account for the cross-sectional dimensions of the building components.

Effect of Open Stories on Expected Seismic Losses in Hilly Buildings

35

  The values of coefficients ai, j,q,l are taken, from appendices of Aggarwal and Saha [19], corresponding to Himachal Pradesh. Using estimated total probability, quantity of considered building components present in hilly buildings and repair cost of each building component for each damage state expected repair cost (L e ) of the buildings are estimated as per the following expression [15]: Le =

n1 Σ n2 Σ n3 Σ

Nc × R E × Pt (DSi |I D R )

(5)

Fk=1 C j=1 DSi=1

where N c is the quantity of each component at a floor; n1 , n2 , n3 are the number of floors, type of components per floor and considered damage states in a building and also the upper limits of F k , C j , and DS i , respectively. Component-wise (separately for each considered building component) repair cost for considered hilly buildings is estimated using the expression discussed above. The obtained component-wise repair cost is then normalized with the reconstruction cost of its respective hilly building and denoted as component-wise repair cost ratio. The reconstruction cost for all the considered hilly buildings is mentioned in Table 3. Figure 5 shows the variation in component-wise repair cost ratio in all the considered hilly buildings without and with open story. It is observed that the component-wise repair cost ratio is lesser in SB than SF building. This is probably due to the fact that in SF building, there is comparatively larger livable space available below the UFL in comparison to SB building that adds to the greater number of components in SF building. Also, the peak IDR of SB building below the UFL is lower in comparison to SF building due to the presence of short columns at intermediate story in SB building that increase its story stiffness; therefore, increase in component-wise repair cost ratio is observed in SF building. It is also observed that component-wise repair cost ratio increases as the number of stories increases. It is important to note here that repair cost ratio of unreinforced masonry wall is lesser for open story as compared to without any open story. However, the repair cost ratio of other structural components, especially RC beam and RC column, increases significantly. This concludes that due to the presence of an open story IDR increases and thus higher damages are expected which further increases the repair cost ratio of the structural components. Table 3 Reconstruction cost of the considered buildings (in 103 |) Building Configuration

SB IA

SF IRB

IRT

IRBoth

IA

IRB

IRT

IRBoth

2-story

17,936

17,370

16,140

15,575

19,776

18,801

17,980

17,005

4-story

27,163

26,616

25,427

24,880

29,034

28,091

27,298

26,354

8-story

44,330

43,783

42,594

42,047

46,817

45,873

45,081

44,137

36

Y. Aggarwal and S. K. Saha Unreinforced masonry wall

RC beam

RC column 0.15

0.09

0.06

RC beam-column joint 8-story SB

4-story SB

2-story SB

Component-wise repair cost ratio

0.12 0.04

0.06

0.02

0.03

0.09 0.06 0.03

0.00

IA

IRB

0.06

IRT

IRBoth

2-story SF

0.00

0.04

0.06

0.02

0.03

0.00

IA

IRB

IRT

IRBoth

IA

IRB

0.09

0.00

IRT

IRBoth

4-story SF

0.00

IA

IRB

IRT

IRBoth

8-story SF

0.12

0.08

0.04

IA

IRB

IRT

IRBoth

0.00

IA

IRB

IRT

IRBoth

Fig. 5 Deaggregation of component-wise repair cost ratio

Further, floor-wise repair cost by considering the repair cost of all building components in a floor under all damage states is estimated. Then, floor-wise repair cost is normalized with the reconstruction cost of that floor and represented as floor-wise repair cost ratio as shown in Fig. 6. Figure 6 shows the variation in floor-wise repair cost ratio in all the considered buildings. It is noticed that the location of experiencing maximum floor-wise repair cost ratio is typically at uppermost foundation level irrespective of the presence or absence of open story. However, marginal increase in floor-wise repair cost ratio at uppermost foundation level is observed in most cases when open story is considered. Figure 7 shows the floor-wise repair cost ratio in hilly buildings after ignoring the contribution of unreinforced masonry walls. Significant increase in the floor-wise repair cost ratio is observed in hilly building with an open story. It is known that due to open story, peak IDR increases (Fig. 3). This increase in response causes higher damages specifically to the location of open story and higher damages cause higher repair costs. Figure 5 shows that maximum seismic loss is contributed by unreinforced masonry walls. Therefore, at the location of open story, where no unreinforced masonry walls are present, marginal increase in floor-wise repair cost ratio is observed when the contribution of unreinforced masonry walls is considered in Fig. 6. Nevertheless, it is interesting to observe from Fig. 7 how repair cost of other structural members increases due to the presence of open story. The repair cost of all floor levels (including all four considered building components) is aggregated to represent the overall repair cost of the building. This repair cost is normalized with the reconstruction cost of the corresponding building and

Effect of Open Stories on Expected Seismic Losses in Hilly Buildings 1.0

37

2-story SB

4-story SB

8-story SB

2-story SF

4-story SF

8-story SF

Relative story height

0.5

0.0 1.0

0.5

0.0 0.00 0.02 0.04 0.06 0.08 0.10

0.00

0.05

0.10

0.15

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Floor-wise repair cost ratio

Fig. 6 Floor-wise variation in repair cost ratio 1.0

2-story SB

4-story SB

8-story SB

2-story SF

4-story SF

8-story SF

Relative story height

0.5

0.0 1.0

0.5

0.0 0.00 0.02 0.04 0.06 0.08 0.10

0.00

0.05

0.10

0.15

0.20 0.0

0.1

0.2

0.3

0.4

0.5

0.6

Floor-wise repair cost ratio

Fig. 7 Floor-wise variation in repair cost ratio without considering the repair cost of unreinforced masonry infill

38

Y. Aggarwal and S. K. Saha 0.09

2-story SB

0.06

Repair cost ratio

0.050

0.046

0.3 0.080

0.041

8-story SB

0.4

0.06

0.04

0.02

4-story SB

0.2

0.080

0.040 0.03

0.053

0.054

0.367 0.131

4-story SF

0.09

2-story SF

0.06

0.3 0.091

0.060

0.051

0.02

0.2

0.082

0.045 0.03

0.060

0.029 0.00

IA

IRB

IRT

IRBoth

0.00

8-story SF

0.4

0.06

0.04

0.128

0.0

0.00

0.00

0.367

0.1

IA

IRB

IRT

0.054

IRBoth

0.1 0.0

0.122

0.110

0.126

IA

IRB

IRT

0.124 IRBoth

Fig. 8 Variation in repair cost ratio for hilly buildings

denoted as repair cost ratio. Figure 8 shows the repair cost ratio of considered buildings. It is observed that stepback buildings without open story have lower repair cost ratio in comparison to split-foundation buildings without open story. Also, when open story is present, a decrease in repair cost ratio is observed (in most cases). Thus, it is concluded that hilly buildings with open story have lower overall repair cost ratio; however, repair cost of their structural components is significantly higher.

5 Conclusions In this study, open stories are considered at different locations based on the connectivity with approach road, i.e., no open story, open story at LFL, open story at UFL, and open stories at both LFL and UFL. Seismic loss estimation, in terms of monetary losses, is performed for 24 hilly buildings without and with open story. The following conclusions are drawn from this study: • Unreinforced masonry wall contributes maximum to the seismic losses. Therefore, contribution of non-structural elements should not be ignored while performing seismic loss assessment. • The floor at uppermost foundation level experiences maximum monetary loss irrespective of the presence of open story. • Presence of an open story in hilly buildings increases the damage to structural components thereby increasing the repair cost of structural components. • In two-story and four-story hilly buildings, the repair cost ratio of building without open story building is highest. Whereas in eight-story buildings, the repair cost ratio is highest with open story at UFL.

Effect of Open Stories on Expected Seismic Losses in Hilly Buildings

39

References 1. Singh, Y., Gade, P., Lang, D.H., Erduran, E.: Seismic behavior of buildings located on slopes – an analytical study and some observations from Sikkim earthquake of September 18, 2011. In: 15th World Conference on Earthquake Engineering, Lisboa, Portugal (2012) 2. Surana, M., Singh, Y., Lang, D.H.: Seismic characterization and vulnerability of building stock in hilly regions. Nat. Hazard. Rev. 19(1), 04017024 (2018) 3. Varum, H., Damaru, R., Furtado, A., Barbosa, A.R., Gautam, D., Rodrigues, H.: Seismic performance of buildings in Nepal after the Gorkha earthquake. Impacts Insights Gorkha Earthq., Elsevier, pp 47–63 (2018) 4. Sharma, M.L., Maheshwari, B.K., Singh, Y., Sinvhal, A.: Damage pattern during Sikkim, India earthquake of September 18, 2011. In: 15th World Conference on Earthquake Engineering, Lisboa, Portugal (2012) 5. Aggarwal, Y., Saha, S.K.: Seismic performance assessment of reinforced concrete hilly buildings with open story. Structures 34, 224–238 (2021) 6. IS 875 Part-1.: Indian standard code of practice for design loads (other than earthquake) for buildings and structures. Dead loads- unit weights of building materials and stored materials. Bureau of Indian Standards,New Delhi, India (1987) 7. IS 875 Part-2.: Indian standard code of practice for design loads (other than earthquake) for buildings and structures. Imposed loads. Bureau of Indian Standards,New Delhi, India (1987) 8. IS 1893.: Criteria for earthquake resistant design of structures. Bureau of Indian Standards,New Delhi, India (2016) 9. ASCE 41.: Seismic evaluation and retrofit of existing buildings. American Society of Civil Engineers, Reston, Virginia, USA (2013) 10. Paulay, T., Priestley, M.J.N.: Seismic design of reinforced concrete and masonry buildings.Wiley, New York, p. 744 (1992) 11. FEMA P695. Quantification of buildings seismic performance factors, Washington (2009) 12. PEER.: Pacific earthquake engineering research center next generation of ground motion attenuation models west 2 database (2012). www.peerberkeley.edu/ngawest2 13. PEER.: User manual for the PEER ground motion database web application, Beta Version (2010) 14. CSI.: Integrated software for structural analysis & design SAP2000, version 20.0.1. Analysis reference manual, Computers and Structures Inc., Berkeley, USA (2021) 15. FEMA P58.: Seismic performance assessment of buildings, volume 1- methodology, Federal Emergency Management Agency, Washington, DC, USA (2018) 16. Cardone, D., Perrone, G.: Developing fragility curves and loss functions for masonry infill walls. Earthq. Struct. 9(1), 257–279 (2015) 17. Brown, P.C., Lowes, L.N.: Fragility functions for modern reinforced-concrete beam-column joints. Earthq. Spectra 23(2), 263–289 (2007) 18. Benjamin, J.R., Cornell, C.A.: Probability, statistics, and decision for civil engineers. Courier Corporation (2014) 19. Aggarwal, Y., Saha, S.K.: Component repair cost functions in indian context for seismic loss estimation of reinforced concrete buildings. Structures 44, 1974–1994 (2022)

Influence of Source Offset on the Resolution of Dispersion Image Ashhad Imam , Virendra Kumar, Keshav Kumar Sharma, and Alvin Harison

Abstract A non-invasive method called multichannel analysis of surface waves (MASW), which has been used for geotechnical site characterization since the late 1990s, has become very prevalent. In field surveys, things like the quality of the input source and the geophone parameters (resolution, spacing, orientation layout) can have a big impact on the dispersion curve. This paper will look at how the distance between the source and the first geophone receiver (offset) affects the resolution of dispersion images in the MASW method. An experimentation site was chosen within the NIT Jamshedpur campus. According to borehole data, the location (22°46, 37.2,, N, 86°08, 38.5,, E) has a stiff silty clay soil (up to a depth of 5 m) followed by a dense to very dense weathered mica schist. A 10 kg sledgehammer was used to strike the striker plate, which generated surface waves that were recorded by 24 geophones (of 4.5 Hz frequency) arranged in a linear array pattern. Wave fields were recorded using a sampling frequency of 1000 Hz and a varying offset distance (1, 2, 4, 6, 8, 10, and 12 m) with 1 m geophone spacing and five stacking. Based on the results obtained, an optimum offset was determined as 8 m for 1000 Hz sampling frequency and 1 m geophone spacing, producing a high signal-to-noise ratio of 92%, resulting in an appropriate resolution of the dispersion image. Keywords Offset distance · Dispersion images · Active MASW · Geophones

A. Imam (B) Sam Higginbottom University of Agriculture Technology and Sciences, Prayagraj, Uttar Pradesh 211007, India e-mail: [email protected] V. Kumar · K. K. Sharma · A. Harison National Institute of Technology Jamshedpur, Jamshedpur, Jharkhand 831014, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_4

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1 Introduction Geophysical investigations concerning the creation and propagation of seismic waves are frequently employed in earthquake geotechnical engineering. Source mechanisms, seismic wave transmission in the field, and site characteristics all have an impact on earthquake ground motions [1]. The use of geophysical investigation, utilizing seismic waves, has made its mark in the recognizance and characterization of the subsurface. Since the development of techniques of seismic wave for identifying the subsurface characteristics of soil, geophysical exploration has significantly advanced. It is one of the most popular non-destructive seismic techniques. MASW uses a multireceiver module to identify subsurface segmentation of layers, i.e., shearwave velocity profiling down the depth from the ground surface [2–4]. To determine the shear-wave velocity (V s ) changes below the identified area, which are primarily liable for the surface waves’ investigated propagation velocity pattern, MASW initially monitors seismic surface waves originated by various seismic causes, such as sledgehammers. The dynamic soil parameter known as Vs has been linked to the bulk modulus, seismic amplification, fundamental vibration frequency, shear modulus, and Poisson’s ratio [5]. V s has been successfully used in several important geotechnical and earthquake engineering domains, including the evaluation of seismic site effects, the estimation of soil liquefaction potential, the characterization of seismic sites, and seismic microzonation studies [6–8]. The strongest energy trend must be extracted, which makes the accuracy of the V s profile directly dependent on the dispersion images resolution [9]. Multichannel analysis of surface waves (MASW) is the most popular surface wave technique worldwide. The approach has peculiar data collecting, analysis, and interpretation procedures. There is a potential for significant uncertainty to be introduced into the outcomes at each stage of the MASW. Even a single mistake can lead to erroneous findings that are hard to spot by a third party. Researchers have previously reported a large number of such occurrences. Since the MASW data are eventually helpful for the generation of the design response spectrum, such techniques may result in significant losses [10, 11]. Moreover, the assumption of homogeneous or horizontally layered soil models is made in order to perform the inversion of the surface wave dispersion curve; nevertheless, this hypothesis could represent a limitation on the practical use of the MASW technique [12]. Additionally, an effective validation of results using an invasive technique, such as a borehole survey or crosshole/downhole method, must be performed for MASW data in order to support the accuracy of the information it provides regarding subsurface anomalies. Because of all of these factors, there is an urgent requirement for the spread of information regarding the recommendations for the reliable practise of MASW testing. In a similar line, a report by Jakka et al. [10] includes a full overview of the MASW approach, its three steps of data acquisition, processing, and inversion, as well as methods for increasing confidence in the results. Several data acquisition parameters, particularly offset distance, have an impact on the image resolution [2, 13–15]. MASW frequently experiences near-field effects,

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which can lead to either an underestimation or an overestimation of the Rayleigh wave phase velocity due to the presence of body wave’s contamination close to the source [16]. Despite the fact that there has been a lot of study on active MASW surveys, the majority of them employ different parametric configurations to meet the requirements of the investigation, according to a critical analysis of the literature. There is hardly any literature that makes an effort to offer recommendations for the use of different factors in the ongoing MASW survey to improve the precision of the estimations. However, it is also found that there is a lack of consensus among the studies on the most effective variables to make precise subsurface profiles. The trustworthiness of the result is significantly influenced by the resolution of the image of the dispersion. The best acceptable factors (offset distance) can be chosen, but it is important to opt an appropriate resolution of the dispersion images from the acquired field data. This article’s goal is to emphasize the data acquisition parameter (offset distance) that governs each testing and evaluation stage. Owing to the above discussions, a study was planned and the experimental field data was gathered through an active MASW survey, and a commercial data interpretation software i.e. Parkseis was used that processes and interprets the data. It is found that varying field topography and combinations result in different dispersion images with different resolutions. The best dispersion images with the highest resolution are to be identified with the help of this study. The suitability is assessed using visual inspections and the signal to noise ratio obtained for different cases of the study. The present paper demonstrates the effect of varying offset distance along with the soft site condition on the quality of the dispersion images (a detailed report is presented by Imam et al. [17] on data acquisition parameters and site characterization for the proposed site). As part of the field experimental program, an Active MASW survey was conducted on the NIT Jamshedpur campus in Jamshedpur, India (Fig. 1). The area (22°46, 37.2,, N, 86°08, 38.5,, E) is characterized by a stiff silty clay soil (depth up to 5 m) followed by a dense and very dense weathered mica schist. Due to the existence of rock-type strata, the borehole investigation was limited to a depth of 15 m. Non-invasive procedures, such as MASW, come into play when invasive techniques (boreholes) cannot be employed to accomplish a deeper depth exploration. As per the SPT-N standards measured at the location, the average shear-wave velocity determined was to be around 350 m/s.

2 Methodology The three primary phases of an active MASW survey are collection of data, dispersion analysis, and inversion analysis [2]. A network of on-the-ground geophone receivers is used to capture the vibration caused by waves that are propagating from an active impulsive source [18–20]. The dispersion image is produced by converting the time signatures to the frequency domain and then identifying the dispersion curve using combinations of phase velocities and frequencies that have the local highest energies

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Dense silty clay with Surface Materials

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0m (G.L)

Medium dense weathered mica schist

a) Jamshedpur Map

Depth

5m

Very dense weathered mica schist

10 m

15 m c) Lithology from borehole data b) Study Area (NIT Jamshedpur) Fig. 1 Location details of the site chosen for field investigation using active MASW within NIT Jamshedpur campus and the corresponding lithology obtained from borehole data

[18, 21]. The flowchart in Fig. 2 shows steps of the whole process of a MASW survey (active or passive).

2.1 Details of Test Setup An active MASW investigation that was carried out in the field is schematically depicted in Fig. 3. An impulsive sledgehammer strike that passes through the soil generates seismic waves, which can be detected by a linear arrangement of geophone receivers. The receivers are connected to a portable seismograph and a computer system which will act as an interface to present the recorded field data. A 10 kg sledgehammer was used to make an impact on the striker plate which further generated

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DATA ACQUISITION

DISPERSION ANALYSIS

STEP 1: DISPERSION IMAGING

STEP 2: DISPERSION CURVE EXTRACTION

INVERSION 'GRADIENT BASED ITERATIVE APPROACH' ANALYSIS INVERSION

SHEAR WAVE VELOCITY PROFILE 1D & 2D

Fig. 2 Flowchart of MASW method for data acquisition and processing

surface waves propagating on the ground recorded by 24 numbers of geophones (of 4.5 Hz frequency) which were arranged in a linear array pattern. With 1 m geophone spacing, wave fields were controlled using a sampling frequency of 1000 Hz and different offset distances (1, 2, 4, 6, 8, and 12 m). Following the collection of the raw wave fields during field experiments, they are subjected to preprocessing, dispersion, and inversion analyses, among other stages of analysis. Parkseis 3.0 as processing software was utilized in the current study to analyze and interpret the field recorded data and thereby resulting in the generation of an appropriate shear-wave velocity profile along the depth.

Fig. 3 Schematic diagram for data acquisition setup of MASW

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3 Results and Discussions To obtain accurate shear-wave velocity profile from dispersion curve, maximum signal-to-noise ratio (S/N) is required. Also it affects the extraction process from obtained dispersion image if the resolution is inadequate. After being transmitted from the inversion process to the next phase, dispersion analysis helps to evaluate dispersion curves. To produce an accurate shear-wave velocity profile, data acquisition parameters’ impact should be taken into account for quality of the dispersion images. With the high signal-to-noise (S/N) ratio, presumably surface wave is one of the important parameter which is mostly observed in a strong quality data set; however, the noise is common factor to weak quality data set. Occurrence of all body wave and higher modes surface wave is counted as noise. This part documents the offset distance influence for generated dispersion images on visually identifiable characteristics.

3.1 Offset Distance Influence From the source to first geophone receiver the offset distance, producing highresolution dispersion images and reliable wavefield records is critical [2]. To capture the planer wave field, receiver array is designed, generally surface waves, which travelled a defined distance after generation of wave field from its source. Waves which are longer in wavelength travel more prior to becoming planer, and vice versa. The far-offset effect is generally observed in geophone array, the distance beyond which impact generated wave field is not properly recorded in geophone array. There are near-offset effects and far-offset effects which are discussed in the literature [2, 13, 16]. The wave field’s wavelength composition determines the properties of both the near-offset and far-offset effects. The ideal offset distance in a surface wave survey must be adjusted to the range of distance from the source where surface waves are most effectively generated in order to determine subsurface velocity (V s ) for a planned depth range (e.g., 0–30 m). Source offset (X1 ) and receiver spread length (L) are two field geometry variables that are closely related. The ideal configuration of these two factors effectively suggests that there is a requirement of ideal positioning of offset that it should not to be too close or too far away from seismic source. In order to assess the impact of the offset distance on the dispersion image, a linear type of spread was recorded at the site location at a length of 23 m with geophones spaced at 1 m. In this manner, with a varied offset distances (1, 2, 4, 6, 8, 10, and 12 m), seven MASW data were collected. For spread in 23 m length, the produced dispersion images are shown in Fig. 5a–g and correspond to the collected wavefield recordings shown in Fig. 4a–g. Figures 5f and g show the findings for offset distance of 10 and 12 m, respectively. Frequently, it is also observed that all the dominant waves are entirely not recorded by the geophone array, which is responsible for missing data in the collected record [17].

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a) Offset Distance = 1 m

b) Offset Distance = 2 m

c) Offset Distance = 4 m

d) Offset Distance = 6 m

e) Offset Distance = 8 m

f) Offset Distance = 10 m

g) Offset Distance = 12 m Fig. 4 Effect of offset distance on seismic wave records obtained from the study region for 512 samples at 1, 2, 4, 6, 8, 10, and 12 m (sampling frequency: 1000 Hz, geophone spacing: 1 m @ five stacking)

A near-offset effect was identified when tests conducted with 1 and 2 m offset distances. A weak M0 dispersion band decoded from the dispersion images. M0 dispersion band also termed as weak fundamental mode, also having energy content mainly gathered at the lower-frequency zone (Refer Fig. 5a–b). When offset distance was set to 4 m, fundamental mode is found to be less dominant than the higher mode. For an 8 m offset distance, Fig. 5e) generated fundamental dispersion band is showing more distinct outcome, but the higher modes are not evident enough when offset distances are greater than 8 m. Due to far offset effect at 10 and 12 m offset

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a) Offset Distance = 1 m

b) Offset Distance = 2 m

c) Offset Distance = 4 m

d) Offset Distance = 6 m

e) Offset Distance = 8 m

f) Offset Distance = 10 m

g) Offset Distance = 12 m Fig. 5 Impact of offset distance on dispersion images at 1, 2, 4, 6, 8, 10, and 12 m (sampling frequency: 1000 Hz, geophone spacing: 1 m @ five stacking)

distances (Fig. 5f–g), there is dominating adulteration throughout for analysis of significant frequency range of the generated dispersion images. Due to the low S/N ratio, adulteration of the obtained dispersion curve occurs, which has an adverse effect on the shear-wave velocity profile for a considerably reduced depth of study. The results for the offset distance are consistent with and agree well with the suggestions made by earlier studies. The recommended values, according to some sources, can have a tolerance of 20% and need to be updated regularly depending on testing done in different places with different types of soil [13, 17, 21]. The main purpose of this research is to discover a fair offset distance to obtain high-resolution dispersion image to match with fundamental curve or M0 dispersion

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curve. M0 dispersion curve opens the doors to present the substrata characteristics efficiently via inverted profiles with a wide range of frequency. For the presented site, an 8-m offset with 1-m geophone spacing was found to be the best solution with highest signal-to-noise ratio as 92%. Based on the research with some realistic uncertainties, to obtain a high-resolution dispersion image for firmer strata, 6–8 m offset distance can be considered optimum. Based on this optimum offset distance decides M0 dispersion curve is extracted, and through this, inversion analysis could be done and shear-wave velocity profile could be developed.

3.2 Selection and Extraction of Dispersion Curve The outcome of the inversion analysis is substantially impacted by the magnitude of the dispersion curve that is found in the frequency domain. According to the available research, the depth of investigation decreases as the amplitude of the frequencies in a band increases. It is a well-known fact that higher frequencies are related to lower wavelengths, which can penetrate lower depths in the subsurface in order to disclose its information. This is one of the reasons why higher frequencies are associated with lower wavelengths. If a lower-frequency band is chosen, there is a possibility that an inaccurate reading in shallow depths is obtained. The dispersion image in Fig. 6a represents the highest concentration of propagating energy over various combinations of frequency and phase velocities, with the darkest shade being the maximum concentration of propagating energy. The color represents the amplitude of the signal, while the white dots correspond to the fundamental mode (M0 dispersion curve), based on which the signal-to-noise ratio was maintained as 92% for all the data points. Figure 6b depicts the 10-layer 1-D shear-wave velocity (V s ) profile obtained from the inversion analysis of the extracted dispersion curve automatically selected across a wide range of frequency band. The approach of automatic selection may lead to a consistent shear-wave velocity profiles leading to a high reliability of the post-processing analysis. The soil profile (lithology) from the borehole data acquired from the study location was compared to the 1-D V s profile obtained (Fig. 1c). The V s profile generated from the MASW survey with a 8 m offset offered information on deeper strata (up to 30 m) that could not be studied by borehole survey due to existence of hard strata in the site of investigation. The information obtained from MASW approach indicated sound agreement with the lithological information gathered from borehole survey.

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(a)

(b)

Fig. 6 a Extraction of dispersion curve from the dispersion image (offset 8 m); b 1D shear wave velocity profile using inversion analysis

4 Conclusions This study provides a description of the active MASW survey that was carried out in Jamshedpur, the potential location for NIT Jamshedpur. The suggested site’s underlying geology has been researched as part of a larger study for Jamshedpur in preparation for future foundation excavation and design development [17]. Suggested observations are submitted below as per the experimental findings: • The investigations highlighted that suitable sampling frequency is site dependent [22–24]. This can also be supported by the report published by Penumadu and Park [24], which states that the acquisition time for rigid/stiff-type medium should be 500 ms and the sampling frequency should be 1000 Hz. Based on which an appropriate sampling frequency for the proposed study was adopted as 1000 Hz, it allows for the suitable completion of phase propagation and wavefield recordings.

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• To obtain the best possible record of propagating wave fields, offset ranges were suitably adopted for the site, such that the requirement of completing phase propagation through the receiver array without causing noise adulteration in the wavefield record is met, and the planar wave propagation is satisfied, resulting in the best resolution dispersion image indicating a high range of signal-to-noise ratio. Based on the results obtained, an optimum offset was determined as 8 m, producing a high signal-to-noise ratio of 92%, resulting in an appropriate resolution of the dispersion image. • Depending on the power of the seismic source used, near and far source effects become more noticeable as the spread length grows, making the choice of dispersion curve for detecting the fundamental mode difficult. Hence, use of large receiver spacing is avoided since it causes significant attenuation of vibrant energy as it passes through the array, resulting in incomplete wave propagation. Hence, it is recommended to adopt a receiver (geophone) spacing of 1 m if a 10 kg sledgehammer is used for making an impact on the ground [17, 22, 23].

References 1. Joshi, A., Bhardwaj, P.: Site characterisation using multi-channel analysis of surface waves at various locations in Kumaon Himalayas, India. J. Ind. Geophys. Union 22(3), 265–278 (2018) 2. Park, C.B., Miller, R.D., Xia, J.: Multichannel analysis of surface waves. Geophysics 64(3), 800–808 (1999) 3. Park, C.B., Miller, R.D., Xia, J., Ivanov, J.: Multichannel analysis of surface waves (MASW)active and passive methods. Lead. Edge 26(1), 60–64 (2007) 4. Desai, A., Jakka, R.S.: Role of A-priori information in minimizing uncertainties in MASW testing. Indian Geotech. J. 52, 1182–1196 (2022) 5. Dikmen, U., Arisoy, M.O., Akkaya, I.: Offset and linear spread geometry in the MASW method. J. Geophys. Eng. 7(2), 211–222 (2010) 6. Eker, A.M., Akgün, H., Kockar, M.K.: Local site characterization and seismic zonation study by utilizing active and passive surface wave methods: a case study for the northern side of Ankara, Turkey. Eng. Geol. 151, 64–81 (2012) 7. Picozzi, M., Strollo, A., Parolai, S., Durukal, E., Ozel, O., Karabulut, S., Zschau, J., Erdik, M.: Site characterization by seismic noise in Istanbul, Turkey. Soil Dyn. Earthq. Eng. 29(3), 469–482 (2009) 8. Trupti, S., Srinivas, K.N.S.S.S., Kishore, P.P., Seshunarayana, T.: Site characterization studies along coastal Andhra Pradesh-India using multichannel analysis of surface waves. J. Appl. Geophys. 79, 82–89 (2012) 9. Taipodia, J., Baglari, D., Dey, A.: Recommendations for generating dispersion images of optimal resolution from active MASW survey. Innov. Infrastruct. Solut. 3, 14 (2018) 10. Jakka, R.S., Desai, A., Foti, S.: Guidelines for minimization of uncertainties and estimation of a reliable shear wave velocity profile using MASW testing: a state-of-the-art review. In: Sitharam, T.G., Jakka, R.S., Kolathayar, S. (eds.) Advances in Earthquake Geotechnics. Springer Tracts in Civil Engineering, pp. 211–253. Springer, Singapore (2023) 11. Jakka, R.S., Roy, N.: Uncertainties in site characterization using surface wave techniques and their effects on seismic ground response. In: Geotechnics for Natural and Engineered Sustainable Technologies. Developments in Geotechnical Engineering, pp 371–383. Springer, Singapore (2018)

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12. Evangelista, L., Magistris, F.S.: Some limits in the use of the MASW technique in soils with inclined layers. Geotech. Geol. Eng. 33, 701–711 (2015) 13. Park, C.B.: Imaging dispersion of MASW data-full vs. selective offset scheme. J. Environ. Eng. Geophys. 16(1), 13–23 (2011) 14. Socco, L.V., Boiero, D., Foti, S., Wisen, R.: Laterally constrained inversion of ground roll from seismic reflection records. Geophysics 74(6), 35–45 (2009) 15. Kanli, A.I., Tildy, P., Pronay, Z., Pinar, A., Hermann, L.: Vs 30 mapping and soil classification for seismic site effect evaluation in Dinar region, SW Turkey. Geophys. J. Int. 165(1), 223–235 (2006) 16. Roy, N., Jakka, R.S.: Near-field effects on site characterization using MASW technique. Soil Dyn. Earthq. Eng. 97, 289–303 (2017) 17. Imam, A., Sharma, K.K., Kumar, V., Singh, N.: Subsurface profiling of a region in Jamshedpur city using active MASW: a case study. Acta Geophys. 70(4), 1601–1617 (2022) 18. Park, C.B., Miller, R.D., Xia, J.: Imaging dispersion curves of surface waves on multichannel record. In: Proceedings of the 68th Annual International Meeting of Society of Exploration Geophysics. SEG Technical Program Expanded Abstracts, pp. 1377–1380. Society of Exploration Geophysicists (1998) 19. Xia, J., Miller, R.D., Park, C.B., Ivanov, J., et al.: Utilization of high-frequency Rayleigh waves in near-surface geophysics. Lead. Edge 23(8), 753–759 (2004) 20. Xia, J., Miller, R.D., Xu, Y., Luo, Y., Chen, C., Liu, J., Ivanov, J., Zeng, C.: High-frequency Rayleigh-wave method. J. Earth Sci. 20, 563–579 (2009) 21. Park, C.B., Miller, R.D., Xia, J.: Offset and resolution of dispersion curve in multichannel analysis of surface waves (MASW). In: 14th EEGS Symposium on the Application of Geophysics to Engineering and Environmental Problems, pp. 1–6. European Association of Geoscientists & Engineers (2001) 22. Park, C.B., Miller, R.D., Miura, H.: Optimum field parameters of an MASW survey. In: Japanese Society of Exploration Geophysics (SEG-J), Extended Abstracts, pp. 1–6 (2002) 23. Foti, S., Hollender, F., Garofalo, F., et al.: Guidelines for the good practice of surface wave analysis: a product of the InterPACIFIC project. Bull. Earthq. Eng. 16, 2367–2420 (2018) 24. Penumadu, D., Park, C.B.: Multichannel analysis of surface wave (MASW) method for geotechnical site characterization. Geo-Frontiers Congress, Texas, United States (2005)

Co-seismic Deformation of Iran, 2021 Earthquake Using DInSAR Technique Hardeep, A. Bahuguna, K. Arun Saraf, and J. Das

Abstract In the present study, the co-seismic deformation of an earthquake of M w 6.4 magnitude (maximum intensity between VII-IX) occurred NNW side of the Bandar Abbas in Iran on 14th November 2021 at 12:08:38 (UTC) was estimated. The epicentre of the event was 27.73° N, 56.068° E with a focal depth of 10 km. The epicentral region was near the southern margin of the collision zone of the Eurasian plate and the Arabian plate. Co-seismic deformation of the earthquake was estimated using the differential InSAR (DInSAR) technique, which is carried out using microwave SAR data pairs from both ascending and descending pass of the Sentinel-1A satellite. LOS ground deformation for the Iran earthquake that occurred on 14th November 2021 is estimated using GMTSAR open software. SRTM DEM of 30 m is used in the processing of the SAR data. The preliminary results indicate that the earthquake deformation field has thrust fault characteristics. In ascending pass, subsidence of about − 200 mm SSE side of the fault, i.e. away from the line of sight (LOS) of satellite and uplift of about 300 mm NNW side of the fault, i.e. towards the line of sight of the satellite. In descending pass, subsidence of about − 135 mm SSE side of the fault and uplift of about 348 mm NNW side of the fault. Keywords Sentinel-1A · DInSAR · Co-seismic deformation · Thrust fault · Microwave SAR data

Hardeep (B) · K. A. Saraf Department of Earth Sciences, IIT Roorkee, Roorkee, Uttarakhand, India e-mail: [email protected] K. A. Saraf e-mail: [email protected] A. Bahuguna · J. Das Department of Earthquake Engineering, IIT Roorkee, Roorkee, Uttarakhand, India e-mail: [email protected] J. Das e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_5

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1 Introduction Early use of remote sensing in earthquake studies are used to decipher or differentiate the active fault lines and lineaments. Later, remote sensing outspreads its applications towards detecting pre-seismic, co-seismic and post-seismic changes and deformations. Synthetic Aperture Radar Interferometry (InSAR) which comes in the microwave electromagnetic band, is rapidly growing remote sensing technology which is begin used extensively in the study of the seismology. Iran regions come under the active seismic zones and observed some destructive earthquake such as Bam (2003), Saravan (2013) and Iran-Iraq border (2017) earthquakes. Several works on these earthquakes have been done using DInSAR techniques such as [1–4]. Microwave SAR data images used since late 1980s to study the ground deformation due to earthquake using DInSAR technique [5–7]. SAR raw data images are generated after high data processing. With time, accuracy in spatial and temporal resolution is increased with improved instruments and increased number of satellites to provide data [8, 9]. This off-field DInSAR technique have some major advantages than on-field techniques (GPS, Triangulation surveys), like less time consuming, low cost of money, estimation of ground deformation of inaccessible areas, high accuracy, etc. [10–12]. Post seismic deformation is estimated by [13] of the 2001 Bhuj, India earthquake. Fault plane solutions are also derived from the estimated ground deformation using DInSAR technique [14–18]. In this work, Sentinel-1A, 1B SAR data images are used to estimate the ground deformation of Iran earthquake occurred NNW side of the Bandar Abbas. Sentinel1A, 1B data are available at European Space Agency (ESA) Copernicus Open Access Hub. Generic Mapping Tool SAR (GMTSAR) is used for the ground deformation estimation. Shuttle Radar Topography Mission (SRTM) DEM of 30 m spatial resolution is used in the processing. Ground deformation for both ascending and descending pass of sentinel-1A are estimated.

2 Study Area An earthquake of M w 6.4 magnitude NNW side of the Bandar Abbas in Iran on 14th November 2021 at 12:08:38 (UTC) occurred. The epicentre of the event is 27.73° N, 56.068° E with a focal depth of 10 km. The preliminary fault plane solution indicates that the earthquake occurs due to shallow dipping north-eastward thrust fault or steeply dipping south-westward thrust fault. Iran plateau is seismically active region where some major and devastating earthquake occurred in history. Based on the geological features, tectonic and seismic activity, Iran plateau are divided into many seismotectonic zones [19–22] but mainly in 6 zones as Alborz in north side, Azerbaijan in NW side, Zagros Mountain chains in SW side, Cenrtal Iran, Kopet Dagh in NE side and Makran Thrust in SE side [23].

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Fig. 1 Seismo-tectonic map of the study area

Iran (2021) earthquake occurs in young Zagros Mountains zone shown in Fig. 1 which is active continental collision zone and experience high seismic activity in the regions. The Arabian plate is moving towards the north with respect to the Eurasia at a rate of about 23 mm/yr. [24]. Zagros Thrust and Fold Zone (ZTFZ) are the resulted from Arabian and Eurasian plate collisions. The Zagros Mountains have been upthrust due to the result of northwest converging of these two plates. In this zone, accumulated stress releases with small rupture deformation so that high numbers of small magnitude events in range of M w 3–6 occurs more than other zones and events with M w greater than 6 occurs with less return period as compare to other parts of the Iran active zones [23]. Fault plane solutions of most earthquake occurs in Zagros Mountains zone have thrust fault characteristics [25]. Iran-Iraq Border (2017) earthquake also occurred in the Zagros Mountain chain and had thrust fault characteristics.

3 Methodology and Data Set Differential SAR Interferometry (DInSAR) technique uses for the estimation of ground deformation due to an earthquake. In this technique, the Microwave SAR data images are used which contains reflection amplitude as well as the phase information of the received signal. Sentinel-1A, 1B raw data images are available at European

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Space \Agency (ESA) Copernicus Open Access Hub (https://scihub.copernicus.eu/ dhus/#/home). Sentinel-1A launched on 3rd April 2014 and provided SAR data under the Copernicus Programme by ESA and have 12 days of revisit time period. After the launch of Sentinel-1B, the revisit time period reduces to 6 days. This satellite recorded the SAR data in C-band of microwave portion of EM spectrum and contains three sub-swaths for the complete one image coverage of 250 km areas. Each subswath further includes nine continue bursts and spatial resolution of SAR image is 5 and 20 m along range and azimuth directions, respectively. Phase information of SAR data is used for the estimation of ground deformation. Two SAR images, one before the occurrence of the earthquake (Master image) and another after the occurrence of the earthquake (Slave image) is used in DInSAR technique. Co-registration is the process of registering the pixels of master and slave image so that they represent the same surface area and this process significantly affects the accuracy of the deformation results. Then interferogram is generated which is process of complex multiplication of master and slave image in which their amplitude gets multiplied and phase information gets subtracted. Since phase is lies in the range of 0 to 360 degree or from − π to π, the phase information lies in wrapped form (its value repeat after 2π). With interferogram, coherence map also generated which provides reliability or accuracy of estimated ground deformation. The wrapped phase have many unwanted source along with ground deformation source and errors which are removed by some correction and filtering on the wrapped phase (Φ). Wrapped phase of different sources are as: Φint = Φ f + Φtopo + Φde f + Φatm + Φerr

(1)

where Φf is flat earth phase; Φtopo is topographic phase; Φdef is deformation phase; Φatm is atmospheric phase; Φerr is error component. After removing all the unwanted wrapped phase from the total wrapped phase map, only deformation phase values obtained. Then, this wrapped phase are unwrapped with using statistical-cost network-flow algorithm for phase unwrapping (SNAPHU). Then, unwrapped phase is converted into LOS ground deformation of the satellite. Since the wavelength of Cband is in the range of cm, LOS ground deformation values also lies in the same range. Flow chart of processing steps in GMTSAR are shown in Fig. 2. Sentinel1A SAR data images used for LOS ground deformation estimation of Iran (2021) earthquake are given in Table 1.

4 Results and Discussion An earthquake of M w 6.4 occurred 63 km NNW of Bandar abbas, Iran on 14th November, 2021 at 12:08:38 (UTC). At least death of 1 person is reported after the event. LOS ground deformation for both passes of Sentinel-1A, i.e. ascending and descending is estimated. Pair of SAR data for ascending pass are from 13th November, 2021 as master image and from 25th November, 2021 as slave image

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Fig. 2 Flow chart of processing in GMTSAR

Table 1 Sentinel-1A (IW) SAR data set parameters of Iran earthquake Sensor

Incident angle (θ i )

Sentinel-1A

40.5°–41.9°

Sentinel-1A

40.1°–41.5°

Data type

Pass

Path

Frames

Orbit

Acquisition date

Master

Descending

166

499

40488

09/11/2021

Slave

Descending

166

499

40663

21/11/2021

Master

Ascending

57

83

40554

13/11/2021

Slave

Ascending

57

83

40729

25/11/2021

is used. Similarly, for descending pass, pair of SAR data are from 9th November, 2021 as master and 21st November, 2021 as slave image is used. Wrapped phase or interferogram of Iran (2021) for both pass is shown in Fig. 3a and b. Two well developed fringe lobes are observed in both ascending and descending wrapped phase. In ascending pass (Fig. 3a), one large wrapped phase fringe lobe in the North and NW side and slightly smaller fringe lobe in South side can be observed. Near the fault line, fringes becomes relatively narrow indicating steeper change in the terrain area. In North side, more deformation occurs which forms narrow fringes in the lobe and in South side, relatively less deformation occurs which indicated by the wider fringes in the lobe. Similarly in the descending pass (Fig. 3b), two fully developed fringe lobes are observed. One large wrapped phase lobe in the North and NW side with relatively narrow fringe indicates the more deformation in this part. Another slightly smaller fringe lobe in South side with wider fringe indicates less deformation in this part.

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Fig. 3 Wrapped phase map of Iran (2021) earthquake a for ascending pass b for descending pass. Phase is wrapped in π to − π range. In ascending pass, one large, wrapped fringe lobe in North, NW side and one small fringe lobe in South side are observed. In descending pass, one slightly larger fringe lobe in North, NW and other smaller lobe in South are observed

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Due to the different incident angle of sensor (angle between LOS of sensor onboard satellite and nadir line of satellite), slight changes in the fringe lobe area in ascending and descending mode can be observed. Incident angle in ascending pass is from 40.1° to 41.5° whereas in descending pass, it is from 40.5° to 41.9° as given in Table 1. So, LOS ground deformation estimated by the DInSAR technique have imbedded effects of incident angle over the area. After removing the wrapped phase from topographic source, flat earth source, atmospheric source and applying filtering for removal of any errors, wrapped phase due to deformation source is obtained, which is further unwrapped using the SNAPHU. The unwrapped phase obtained using SNAPHU for both passes are shown in Fig. 4a and b. One unwrapped phase is equal to one complete wavelength of microwave radiation used. In ascending pass (Fig. 4a), large unwrapped phase lobe in North, NW side and slightly smaller lobe in South side can be observed. Range of unwrapped phase is from − 66.2 to 45.2. Similarly in descending pass (Fig. 4b), large unwrapped phase lobe in North, NW side and smaller lobe in South side can be observed with range of 77.2 to 29.39. Estimated LOS ground deformation due to Iran (2021) earthquake is shown in Fig. 5a and b. In ascending pass (Fig. 5a), upliftment of about 209 mm in North, NW side is estimated, i.e. towards the satellite. Similarly, subsidence of about − 199 mm in South side is estimated, i.e. away from the satellite. In descending pass (Fig. 5b), upliftment of about 340 mm in North, NW side is estimated and subsidence of about 129 mm in South side is estimated. Further, in ascending pass, incident angle vary from 40.1° to 41.5° (left to right). In ascending deformation map (Fig. 5a), it can be observed that in South side where subsidence occurs, i.e. movement away from LOS of satellite; however, have low incident angle value so it have more accurate result of subsidence. Similarly in North, NW side where upliftment occurs, i.e. movement towards the LOS of satellite, however it have more incident angle value so it have slightly less accurate results of upliftment. For the descending pass, range of incident angle are from 40.5° to 41.9° (right to left). In descending deformation map (Fig. 5b), in South side where subsidence occurs, i.e. movement away from LOS of satellite and also have large incident angle value so it has low values of subsidence and less accurate as compared to ascending deformation map (Fig. 5a). Further, in North, NW side where upliftment occurs, i.e. movement along LOS of satellite and have low incident angle value, it have more value of upliftment and more accurate results as compared to ascending deformation map (Fig. 5a).

5 Conclusion Sentinel-1A SAR data images are used to analyse the Iran (2021) earthquake of M w 6.4 magnitude (maximum intensity between VII-IX) with focal depth of 10 km. Ground deformation is estimated using DInSAR technique with GMTSAR. Estimated ground deformation have thrust fault characteristics like other earthquakes

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Fig. 4 Unwrapped phase map of Iran (2021) earthquake a for ascending pass b for descending pass. One unwrapped phase is equal to one complete wavelength of microwave radiation used. In ascending pass, one large, unwrapped lobe in North, NW side and small unwrapped phase lobe in South side are observed. In descending pass, one slightly larger fringe lobe in North, NW and other smaller lobe in South are observed

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Fig. 5 LOS ground deformation of Iran (2021) earthquake a for ascending pass b for descending pass. In ascending pass, large lobe in North, NW side indicates upliftment and relatively smaller lobe in South side indicates subsidence. In descending pass, large lobe along North, NW side indicate upliftment and along South side indicates subsidence

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have in Zargos mountains region of Iran. DInSAR-based ground deformation have accuracy in cm range which provides good results in less time period and also in the inaccessible areas compares to other field-based ground deformation estimation techniques. Using the high resolution DEM also improves the final results. Based on DInSAR technique, short term (earthquake) and long term (time series analysis) ground deformation can be estimated in seismically active regions.

References 1. Huang, Z., Zhang, G., Shan, X., Gong, W., Zhang, Y., Li, Y.: Co-seismic deformation and fault slip model of the 2017 Mw 7.3 Darbandikhan, Iran–Iraq earthquake inferred from D-InSAR measurements. Remote Sens. 11(21), 2521 (2019) 2. Liu, F., Elliott, J.R., Craig, T.J., Hooper, A., Wright, T.J.: Improving the resolving power of InSAR for earthquakes using time series: a case study in Iran. Geophys. Res. Lett. 48(14), e2021GL093043 (2021) 3. Peyret, M., Rolandone, F., Dominguez, S., Djamour, Y., Meyer, B. Source model for the Mw 6.1, 31 March 2006, Chalan-Chulan earthquake (Iran) from InSAR. Terra Nova 20(2), 126–133 (2008) 4. Vajedian, S., Motagh, M., Mousavi, Z., Motaghi, K., Fielding, E.J., Akbari, B., Wetzel, HU., Darabi, A.: Coseismic deformation field of the Mw 7.3 12 November 2017 Sarpol-e Zahab (Iran) earthquake: a decoupling horizon in the northern Zagros Mountains inferred from InSAR observations. Remote Sens. 10(10), 1589 (2018) 5. Gabriel, A.K., Goldstein, R.M., Zebker, H.A.: Mapping small elevation changes over large areas: differential radar interferometry. J. Geophys. Res. Solid Earth 94(B7), 9183–9191 (1989) 6. Massonnet, D., Rossi, M., Carmona, C., Adragna, F., Peltzer, G., Feigl, K., Rabaute, T.: The displacement field of the Landers earthquake mapped by radar interferometry. nature 364(6433), 138–142 (1993) 7. Scantland, S., Biegert, E.: Radar locates offshore oil slicks. Oceanogr. Lit. Rev. 3(44), 279–280 (1997) 8. Fielding, E.J., Lundgren, P.R., Burgmann, R., Funning, G.J.: Shallow fault-zone dilatancy recovery after the 2003 Bam earthquake in Iran. Nature 458, 64–68 (2009) 9. Stramondo, S., Moro, M., Doumaz, F., Cinti, F.R.: The 26 December 2003, Bam, Iran earthquake: surface displacement from Envisat ASAR interferometry. Int. J. Remote Sens. 26, 1027–1034 (2005) 10. Rosen, P.A., Hensley, S., Joughin, I.R., Li, F.K., Madsen, S.N., Rodriguez, E., Goldstein, R.M.: Synthetic aperture radar interferometry. Proc. IEEE 88(3), 333–382 (2000) 11. Sharma, K., Saraf, A.K., Das, J., Baral, S.S., Borgohain, S., Singh, G.: Mapping and change detection study of Nepal-2015 earthquake induced landslides. J. Indian Soc. Remote Sens. 46(4), 605–615 (2018) 12. Zia, M., Sharma, K., Saraf, A.K., Das, J., Baral, S., Das, M.: Ground deformational studies using ALOS-PALSAR data between 2007 and 2010 of the central Kutch area, Gujarat, India. Nat. Hazards 71(3), 1379–1388 (2014) 13. Saraf, A.K., Das, J., Biswas, A., Rawat, V., Sharma, K., Suzat, Y.: SAR interferometry in post-seismic ground deformation detection related to the 2001 Bhuj earthquake, India. Int. J. Remote Sens. 33(4), 1296–1308 (2012) 14. Ferretti, A., Savio, G., Barzaghi, R., Borghi, A., Musazzi, S., Novali, F., Prati, C., Rocca, F.: Submillimeter accuracy of InSAR time series: experimental validation. IEEE Trans. Geosci. Remote Sens. 45(5), 1142–1153 (2007) 15. Ganas, A., Elias, P., Briole, P., Cannavo, F., Valkaniotis, S., Tsironi, V., Partheniou, E.I.: Ground deformation and seismic fault model of the M6.4 Durres (Albania) Nov. 26, 2019 earthquake, based on GNSS/INSAR observations. Geosciences 10(6), 210 (2020)

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16. Li, Z., Cao, Y., Wei, J., Duan, M., Wu, L., Hou, J., Zhu, J.: Time-series InSAR ground deformation monitoring: atmospheric delay modeling and estimating. Earth-Science Rev. 192, 258–284 (2019) 17. Osmano˘glu, B., Sunar, F., Wdowinski, S., Cabral-Cano, E.: Time series analysis of InSAR data: methods and trends. ISPRS J. Photogramm. Remote. Sens. 115, 90–102 (2016) 18. Sakkas, V. Ground deformation modelling of the 2020 Mw 6. 9 Samos earthquake (Greece) based on INSAR and GNSS data. Remote Sens. 13(9), 1665 (2021) 19. Niazi, M., Basford, J.R.: Seismicity of Iranian plateau and Hindu Kush region. Bull Seismol Soc Am 58(1), 417–426 (1968) 20. Nowroozi, A.A.: Seismo-tectonics of the Persian plateau, eastern Turkey, Caucasus, and HinduKush regions. Bull Seismol Soc Am 61(2), 317–341 (1971) 21. Shoja-Taheri, J., Niazi, M.: Seismicity of the Iranian plateau and bordering regions. Bull. Seismol. Soc. Am. 71(2), 477–489 (1981) 22. Wilson, A.T.: Earthquakes in Persia. Bull. Sch. Orient. Afr. Stud. 6(01), 103–131 (1930) 23. Salamat, M., Zöller, G., Zare, M., Amini, M.: The maximum expected earthquake magnitudes in different future time intervals of six seismotectonic zones of Iran and its surroundings. J. Seismolog. 22(6), 1485–1498 (2018) 24. Barka, A., Reilinger, R. (1997). Active tectonics of the Eastern Mediterranean region: deduced from GPS, neotectonic and seismicity data. Ann. Geophys. 40(3), 587–610 (1997) 25. Jackson, J., McKenzie, D.: The relationship between plate motions and seismic moment tensor, and the rates of active deformation in Mediterranean and Middle East. Geophys. J. Int. 93(1), 45–73 (1988)

Three-dimensional Crustal Velocity Structure of Tehri, Garhwal Himalaya R. Modi, S. Mukhopadhyay, and M. L. Sharma

Abstract We estimated a three-dimensional (3D) structure of crustal velocity of Tehri, Garhwal Himalaya using seismic data of local earthquake events collected from January 2008 to December 2015 by a seismological network of 12 remote stations. The network is spread over an area of nearly 100 × 80 km around Tehri dam. The study area between 29°50´ N to 31°50´ N latitude and 77°50´ E to 80°E longitude, falls in the Garhwal Himalayan region of the central seismic gap between Main Central Thrust (MCT) and Main boundary Thrust (MBT). Inversions were performed simultaneously for P- and S-wave velocity anomalies and source coordinates. Dataset for tomographic inversions consists of 3543 local earthquake events with 29451 P- and 28692 S-wave picks. Results of inversion include P- and S-wave velocity anomalies and revised locations of earthquake sources. Tomograms of Pand S-wave velocity anomalies point to strong lateral heterogeneities in the region of investigation. Several zones of low velocity are depicted in the Lesser Himalayan region of the study area which indicates the presence of fluid filled structures in the region. Our velocity model shows that the Uttarkashi earthquake of 1991 is located at the transition zone of a high and a low velocity anomaly. Tomographic inversions could well resolve the investigated area up to a depth of 20 km. Keywords Tomography · Local earthquake data · Three-dimensional velocity model · Crustal velocity structure · Travel time inversion · Garhwal Himalaya

1 Introduction A realistic three-dimensional (3D) velocity model besides improving the location accuracy of earthquakes also sheds light on significant physical processes responsible R. Modi (B) · S. Mukhopadhyay Seismology Division, Bhabha Atomic Research Centre, Trombay, Mumbai 400085, India e-mail: [email protected] M. L. Sharma Department of Earthquake Engineering, IIT Roorkee, Roorkee, Uttarakhand, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_6

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for the occurrence of earthquakes and the material composition of the earth. Improved earthquake location accuracy facilitates improving our tectonic understanding of the earth, for example, by means of seismicity studies. Significant efforts have been made in this area and various techniques have been developed over the years to model the 3D velocity structures of the earth such as local earthquake tomography, surface wave tomography and ambient noise tomography. An appropriate technique can be chosen depending on the type of data available. Numerous seismic networks are operative the world over on various scales and the abundance of data available from these networks is being efficiently utilized to develop 1D and 3D realistic velocity models. In the present work, the focus is on the estimation of a realistic 3D structure of P- and S-wave velocities for the Tehri region of Garhwal Himalaya which is a highly seismically active region, also important from socio-economic point of view. The presence of a 260.5 m high earth and rock-fill dam, with a storage volume of 3.54 km3 in Tehri also calls to a study of the region to estimate the effect of reservoir-induced seismicity in the region, if any. As the realistic velocity model to be estimated depends on the unknown earthquake locations which in turn depend on the unknown velocity models, we have employed the method of simultaneous inversion where both velocity models and earthquake locations are inverted simultaneously. The estimated realistic velocity model and the relocated earthquake locations will help to improve the location of future earthquakes originating from this area thereby improving the tectonic understanding of the region.

2 Study Area Formed due to the continued convergence of the Indian and the Eurasian tectonic plates at approximately 4 cm/year, the Himalayas are one of the most seismically active regions of the world. Along this Himalayan arc of approximately 2400 km length on the northern border of the Indian subcontinent, several thrust zones have been postulated, the most prominent being the Main central thrust (MCT), Main boundary thrust (MBT) and Main frontal thrust (MFT) [1]. Khattri [2] proposed a seismic gap between the locations of the Kangra earthquake, 1905 (Mw 7.8) and the Bihar-Nepal earthquake, 1934 (Mw 8.0) called central gap, a 700 km long section on the plate boundary. Several moderate and large earthquakes have occurred in the Central seismic gap over the years but no great earthquake has occurred in the region in the past 200 years. The space geodetic measurements in the region suggest a substantial slip deficit [3]. The region is, thus, of great interest to seismologist. The study area bounded between 29°50´ N–31°50´ N latitude and 77°50´ E–80° E longitude falls in the Garhwal Himalayan region of the central seismic gap between MCT and MBT. Figure 1 shows major thrust faults of the study area, such as MCT, MBT, MFT and Srinagar thrust.

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Fig. 1 Distribution of seismic events (black outlined magenta colored circles) in the study area. Gray lines show the ray paths from sources to receivers, green triangles show recording stations, major fault lines are shown in black, prominent rivers in the region are shown in blue and Tehri reservoir is shown in cyan color. Yellow stars indicate the location of the Uttarkashi earthquake, 1991 and the Chamoli earthquake, 1999

3 Seismic Observation Network and Dataset The seismic observation system is a local seismic network comprising 12 recording stations operative in the environs of Tehri dam located in the region of Garhwal Himalaya. The network covers an approximate area of 100 × 80 km. Each recording station is equipped with a three-component short-period seismometer (Model: CMG 40T-1, M/s Guralp system, UK) to record the ground motion. The data acquisition system (Model: 130–01/03, M/s REFTEK, USA) digitizes the recorded signal using a 24-bit digitizer at a rate of 100 samples/sec/component. The geographical parameters of the network stations are listed in Table 1. The dataset for the study comprises of 3543 local seismic events recorded by the above-mentioned recording network from January 2008 to December 2015. Initial location analysis of seismic events was carried out by using SEISAN software package [4] with a three-layer 1D velocity model given by Kumar et al. [5]. The criteria for selecting the events for 3D tomographic inversions include greater than or equal to 10 (P + S) phase recordings and a maximum distance to the nearest station to be less than 100 km. The dataset, thus, is not restricted to events that originated from within the network only. Including out-of-the-network events provides a higher ray density and better ray coverage in the study area and thereby improves the results of 3D tomographic inversion. The use of out-of-the-network events in

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Table 1 Parameters of the recording stations of the local seismic network around Tehri region Station no.

Station code

Geographical coordinates Latitude (N)

Longitude (E)

Elevation (m)

1

AYR

30° 18.18’

78° 25.86’

2106

2

CHN

30° 18.31’

78° 37.14’

2244

3

CNT

30° 24.59’

78° 44.69’

1953

4

KHU

30° 34.81’

78° 29.68’

1730

5

NTT

30° 22.58’

78° 25.78’

1914

6

PRT

30° 27.48’

78° 28.54’

2128

7

SRL

30° 07.92’

78° 38.03’

1424

8

SRT

30° 36.76’

78° 17.93’

1617

9

SUR

30° 24.66’

78° 17.39’

2754

10

GYN

30° 45.34’

78° 25.26’

2113

11

RAJ

30° 50.64’

78° 14.29’

1908

12

VIN

30° 33.99’

78° 39.32’

1640

tomographic inversion studies is very well reasoned by Koulakov [6]. As shown in Fig. 1, the source-receiver pairs used in the study provide a good ray density and good ray coverage in the investigated area, especially in the region where network stations are located. As seismic activity occurs at shallow depth in the study region, in vertical direction, the ray path coverage is up to a depth of 20 km.

4 Methodology For 3D local earthquake tomography, an algorithm called Local Earthquake Tomography Software (LOTOS 12) was employed which works on the principle of simultaneous inversion of earthquake locations and velocity models [7]. The process starts with a rough estimation of source locations based on a grid search algorithm where reference travel-time tables constructed using simplified methods of ray tracing are used. The sources are then relocated in a 3D velocity model using a more sophisticated 3D ray bending tracing algorithm based on Fermat’s principle of traveltime minimization. For parameterization of velocity distribution, a grid of nodes is defined in the study volume depending on the density of rays between the sources and receivers. No nodes are defined in the region where there are no rays. To reduce any effect of parameterization of nodes, inversions are performed with four different grid orientations. Velocity anomalies from all the grids are combined for final results. The matrix inversions are performed simultaneously for P- and S-wave velocity anomalies, source parameters and station corrections using LSQR method [8, 9]. Input data to the code are P- and S-wave travel-time data of local earthquakes and station coordinates. One-dimensional (1D) P and S-wave velocity models are

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given as initial reference velocity models where velocity values vary vertically with depth. Velocities are linearly interpolated between different depth levels. A set of free parameters for source locations, grid construction and inversion, etc. are required to be defined. Further, damping parameters are needed to be defined to regulate the amplitude of anomalies and the smoothness of solution. Optimal values of damping parameters are estimated from the inversions performed with synthetic models which are given in the form of some anomalies. Damping parameters are set so as to obtain the best recovery of synthetic models after inversion. The same values of damping parameters are then used for real datasets.

5 Results 1D crustal velocity model to be used as initial velocity model for tomography studies is derived using VELEST algorithm [10] which solves the coupled hypocenter velocity model problem by simultaneous inversion of travel-time data of earthquake sources and velocity model. A dataset of 225 well-located earthquake events is used to obtain the 1D velocity model for P- and S-waves which is shown in Table 2. Because of the shallow nature of seismicity, inversion for the 1D velocity model resolved the velocity structure of the upper crust up to a depth of 20 km. Moho depth of ~ 44 km and the corresponding velocity information is obtained from the travel-time curves using cross-over distance technique. This model is used as initial reference velocity model for 3D tomography studies. Based on the results of synthetic model tests, parameters like 3D grid spacing, smoothness and amplitude damping are optimized. A number of test runs are performed with LOTOS to fix the most optimal values of these inversion parameters. In lateral directions, the grid nodes are placed a distance of 2 km each while in vertical direction node spacing is set at 1 km. Due to the topography of the study area and the altitude of recording stations, the first grid is defined at an altitude of 3 km above sea level. Inversions are carried out for the 3D velocity models and source Table 2 1D velocity model for Tehri, Garhwal Himalaya used as initial reference velocity model

Depth to the top of layer (km)

P-wave velocity (km/s)

S-wave velocity (km/s)

0

5.56

3.28

4

5.75

3.36

8

5.75

3.43

10

5.93

3.43

14

6.13

3.71

18

6.13

3.88

20

6.47

3.88

44

7.69

4.55

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Table 3 Average residuals of P- and S-waves before and after inversion and total variance reduction Initial average residual (s)

Average residual after fourth iteration (s)

Total variance reduction

P- wave

S- wave

P- wave

S- wave

P- wave

S- wave

0.23

0.32

0.16

0.22

29.58

28.44

locations with the optimized inversion parameters. Table 3 shows average traveltime residuals of P- and S-waves and total variance reduction after four iterations of tomographic inversions. The reduction in the average travel-time residual after 3D tomographic inversions is a clear indication of an improvement in the earthquake locations accuracy with 3D velocity model. The P- and S- wave velocity anomalies are computed at various horizontal depth levels such as 2, 4, 6, 8, 10, 12, 14, 16, 18 and 20 km. The tomograms corresponding to P- and S- wave velocity anomalies at depth sections 4, 10, 14 and 20 km are presented in Fig. 2.

5.1 Three Dimensional Velocity Structure The 3D velocity structure of the study area is given as a percentage deviation of Pand S-wave velocities from the reference model velocities at respective depth levels. To address the issue of uncertainty involved in S phase picking as compared to P phase picking, we used a 1.3 times larger weighting factor for P phases as compared to S phases for tomographic inversions. Further, based on the results of inversions performed with the checkerboard synthetic model, for the S-velocity model we used a smoothness parameter 2.3 times larger and a damping parameter 3 times larger than for the P-velocity model. Figure 2 shows P- and S-wave velocity anomalies in the horizontal sections at depths of 4, 10, 14 and 20 km with a backdrop of the most prominent tectonic features in the area of investigation. Strong lateral heterogeneities are clearly represented by the tomograms in the study area. A number of discontinuous high and low velocity zones can be seen scattered in the investigated area at various depth levels. High density of rays from sources to receivers in the central part of study area (Fig. 1) represents better resolution for this part as compared to the peripheral part of the study region. Up to a depth of 10 km, the anomalies are small-sized in the region where network stations are located. Low velocity anomalies for both P- and S-wave velocity models are stronger and more widespread below the depth of 10 km. Tomograms depict a slightly lower amplitude of S-wave anomalies than P-wave anomalies. A strong and shallow high velocity anomaly traverses the region between Srinagar thrust and MCT which fades and shifts to further northeast of MCT as we go deeper. A large low velocity anomaly is identified in the Garhwal lesser and higher Himalayan

Three-dimensional Crustal Velocity Structure of Tehri, Garhwal Himalaya

P anomalies

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S anomalies

Fig. 2 Results of tomographic inversions. Images in the first row show P-wave velocity anomalies at depth levels of 4, 10, 14 and 20 km. Images in the second row show S-wave velocity anomalies at depth levels of 4, 10, 14 and 20 km. Depth is mentioned at the top right corner of the tomogram with the corresponding reference velocity mentioned at the bottom left corner. With respect to these reference velocities are the velocity anomalies computed. Colors on color scale denote the percentage deviation of velocities from reference velocities. Locations of the recording stations are shown as black triangles. Yellow stars indicate the location of Uttarkashi earthquake, 1991 and Chamoli earthquake, 1999. Major fault lines are shown in black

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region along the strike direction of MCT from 14 to 20 km depth. Both P- and Svelocity tomograms depict several zones of low velocity in the lesser Himalayan region between MBT and MCT. These low velocity zones occur in the region where recording stations are located and run across the Srinagar thrust in lesser Himalaya which may be attributed to fluid-filled structures in the study region. A strong low velocity zone at a depth below 10 km is depicted around Uttarkashi earthquake of 1991. Above 10 km depth, the location of Uttarkashi earthquake is at the transition boundary of a positive and negative anomaly.

5.2 Checkerboard Test To test the resolution potential of source-receiver pairs as described in Fig. 1, we have performed checkerboard synthetic model tests. LOTOS algorithm permits various kinds of synthetic models such as vertical and horizontal anomalies in the form of polygons or periodic anomalies (checkerboard test). In the present work, we have defined the synthetic model as a 2D checkerboard pattern of size 20 × 20 km in the x and y directions, respectively. The pattern of anomalies does not change along the vertical direction. The amplitude of anomalies varies as ± 5% with respect to the reference 1D velocity model. The travel-times for synthetic tests are computed by 3D ray tracing between the real source-receiver pairs (as shown in Fig. 1). After computing the synthetic travel-time data, the program forgets about the source coordinates and velocity model. From the station coordinates, travel-time data and a starting 1D velocity model which is different from the true velocity model, the tomographic inversions are performed in the same way as for the real data, starting from the preliminary source location step. Figure 3 shows the results of the checkerboard tests for P- and S-wave velocity models after four iterations. The recovered velocity anomalies (both P-wave and S-wave) depict that the central part of the study area is well resolved (reasoned previously as well). Significant horizontal spreading is noticed in the peripheral regions in the study area. The amplitude of recovered Pwave anomalies matches the original checkerboard anomalies. In case of S-wave anomalies, however, the amplitude is slightly reduced. As evident from Fig. 3, the real source-receiver configuration is capable of resolving the velocity anomalies up to a depth of 20 km for both P- and S-wave velocity models.

6 Conclusion A 3D P- and S-wave velocity structure of Tehri, Garhwal Himalayan region is modeled by simultaneous inversion of the local earthquake data and velocity model. A significant reduction in the travel-time residual is obtained indicating improved earthquake locations after tomographic inversions. Resolution capability of the sourcereceiver pairs is demonstrated using Checkerboard synthetic model testing. The

Three-dimensional Crustal Velocity Structure of Tehri, Garhwal Himalaya

P anomalies

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S anomalies

Fig. 3 Results of checkerboard synthetic model tests. Images in the first row show the recovered P wave velocity anomalies at depth levels of 4, 10, 14 and 20 km. Images in the second row show the recovered S-wave velocity anomalies at depth levels of 4, 10, 14 and 20 km. The grid plotted over the recovered anomalies shows the synthetic checkerboard anomaly grid where alternate boxes are set as positive and negative velocity anomalies of amplitude ± 5%. Depth is mentioned at the top right corner of the tomogram with the corresponding reference velocity mentioned at the bottom left corner. With respect to these reference velocities are the velocity anomalies computed. Colors on color scale denote the percentage deviation of velocities from reference velocities. Locations of the recording stations are shown as black triangles. Yellow stars indicate the location of Uttarkashi earthquake, 1991 and Chamoli earthquake, 1999. Major fault lines are shown in black

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velocity structure of P- and S-waves in the study area is given as a percentage deviation from the reference velocity models. The tomograms of P- and S-wave velocity anomalies indicate strong lateral heterogeneities in the study area from the surface up to a depth of 20 km. Several zones of low velocity are delineated in the lesser Himalayan region bounded between MBT and MCT which suggests the presence of fluid-filled structures that may be contributing to the high seismicity of the region. Our model depicted that the location of Uttarkashi earthquake, 1991 is at the transition boundary of a positive and negative anomaly. The positive anomaly disappears below the depth of 10 km and a large negative anomaly surrounds this region.

References 1. Seeber, L., Armbruster, J.G.: Great detachment earthquakes along the Himalayan arc and longterm forecasting. In: Earthquake Prediction—An International Review, Maurice Ewing Series, AGU, vol. 4, pp. 259–277 (1981). Wiley Online Library, Washington, DC 2. Khattri, K.N.: Great earthquakes, seismicity gaps and potential for earthquake disaster along the Himalayan plate boundary. Tectonophysics 138, 79–92 (1987) 3. Bilham, R., Gaur, V.K., Molnar, P.: Earthquakes. Himalayan seismic hazard. Science 293(5534), 1442–1444 (2001) 4. Ottemoller, L., Voss, P., Havskov, J.: SEISAN earthquake analysis software for Windows, Solaris, Linux and MACOSX (Version 9.1), p. 368. Bergen University, Bergen (2011) 5. Kumar, A., Pandey, A.D., Sharma, M.L., Gupta, S.C., Verma, A.K., Gupta, B.K.:Processing and preliminary interpretation of digital data obtained from digital seismic array in Garhwal Himalaya. In: Proceedings of the 10th Symposium on Earthquake Engineering, University of Roorkee, Roorkee, India, vol. 1, pp. 141–152 (1994) 6. Koulakov, I.: Out-of-network events can be of great importance for improving results of local earthquake tomography. Bull. seism. Soc. Am. 99(4), 2556–2563 (2009a) 7. Koulakov, I.: LOTOS code for local earthquake tomographic inversion: benchmarks for testing tomographic algorithms. Bull. seism. Soc. Am. 99(1), 194–214 (2009b) 8. Paige, C.C., Saunders, M.A.: LSQR: an algorithm for sparse linear equations and sparse least squares. ACM Trans. Math. Softw. 8(1), 43–71 (1982) 9. Nolet, G.: Seismic tomography: with applications in global seismology and exploration geophysics. Springer Science & Business Media (1987) 10. Kissling, E., Ellsworth, W.L., Eberhart-Phillips, D., Kradolfer, U.: Initial reference models in local earthquake tomography. J. Geophys. Res. 99(B10), 19635–19646 (1994)

Correlation Between Cone Tip Resistance and Shear Wave Velocity for Quaternary Alluvium P. Mishra, A. Paul, and P. Chakrabortty

Abstract Several correlations were envisaged in the literature to find out shear wave velocity (V s ) using cone penetration test (CPT) data, but still, a considerable discrepancy is found when CPT correlations are compared for the studied site. A comparison study is conducted with the previously given correlations which also shows the requirement of developing a new correlation for the site. Empirical correlations have been derived in this study from measured field data at 5 sites in the IIT Patna Campus for the estimation of shear wave velocity using CPT data. The shear stress-related parameter, i.e., shear wave velocity is defined in terms of various parameters obtained from the CPT. Various parameters such as cone tip resistance ( ,) (qc ), soil behavior index (I C ), effective stress σ 0 , , and depth (z) are correlated with V s because some factors are interrelated, like the soil layer’s aging, effective , confining stress, etc. Regression analysis is carried out to correlate V s with qc , I C , σ 0 , and z. Empirical correlations are developed for a site and validated with the rest of the other locations in the campus area. This research will help to predict the subsurface material properties by reducing the overall time and cost of an investigation. By using this correlation, important soil parameters used in design can be obtained more accurately with only CPT data. Keywords Shear wave velocity · CPT · Correlation · Penetration tests · Quaternary alluvium

P. Mishra (B) · A. Paul · P. Chakrabortty Department of Civil and Environmental Engineering, IIT Patna, Bihta, Patna, Bihar, India e-mail: [email protected] A. Paul e-mail: [email protected] P. Chakrabortty e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_7

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1 Introduction Shear wave velocity (V S ) is one of the fundamental properties of the soil used in various geotechnical fields. The V S is directly related to the small strain shear modulus. The small strain shear modulus (G) is required in analytical procedures for estimating the dynamic soil response and soil-structure interaction analysis [1]. In soil dynamics, V s is used to evaluate the maximum shear modulus (Gmax ), as a soil stiffness parameter. In ground vibration problems, V s is used to calculate the actual amount of strain during the event. The V s is used to calculate the fundamental frequency of vibration and soil stiffness for the case of vibration under footings. For the measurement of V s in the laboratory, resonant column or bender element tests are used. Geophysical techniques are one of the best methods for determining shear wave velocity in the field. The primary field tests used to measure V S are the crossborehole test (CHT), downhole testing (DHT), and multichannel analysis of surface waves (MASW). All the geophysical tests are advantageous because they identify soil properties in their natural configuration with less disturbance compared to laboratory testing. It has been also observed that the degree of saturation significantly affects the G [2] and eventually V s . In practical problems, CHT and DHT require a borehole and it is not economically feasible as part of routine geotechnical testing. Moreover, MASW testing does not require any borehole and it can be performed quickly and economically [3]. But obtaining data from this test is quite complex because of the dispersive nature of the waves radiated. Another method of seismic cone penetration test (SCPT) gives excellent V S profiling, but it needed specialized equipment and sound technical expertise to collect data during the test. A velocity seismometer is equipped with cone penetration test (CPT) equipment to obtain the measurement of dynamic shear modulus. CPT is widely used to obtain vertical profiling and soil classification. The advantage of using CPT is that it does not require any sample collection as needed in SPT, moreover gives continuous data logging and is repeatable. The addition of a seismic velocity sensor that gives direct V s with CPT is advantageous. Separate testing for V s is not only time-consuming and expensive, but it also depends upon the importance of the project. The development of a multi-linear regression equation [4] between V S and cone penetration testing parameters is beneficial as it gives an estimate of V S without actual measurements. Several CPT-V S correlations have been developed for different soil types. Mayne and Rix [5] uses 31 different natural clays for correlating the cone tip resistance (qc ) and V S also improved the correlation by using the void ratio. Mayne and Rix [5] proposed correlations for specific soil types like sand, and clays using qc and sleeve friction (f s ). Hegazy and Mayne [6] reviewed the previous correlation of V s with qc and f s and found that V S majorly depends on qc and the soil type. Robertson and Wride [7] presented the soil behavior Index (I C ), which ( , )tells about the soil type and is the function of qc , f s , and vertical effective stress σ0 . Information regarding soil types and their behavior is best given by I C , so V S is correlated with qc and I C . Andrus et al. [8] have considered cone tip resistances,

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sleeve friction, depth, overburden pressure, soil behavior type index, and geologic age at the time of generating the correlation between CPT-V S . Here different geological aging parameter and increasing or decreasing geological age plays a significant role in increasing or decreasing qc . Robertson [9] performed the SCPT and generated the correlation between the net cone tip resistance, V s , and shear modulus by considering the normalized cone tip resistance (Qtn ) and I C value. McGann et al. [10] reviewed different available CPT-V S relations and calculated residual value from the difference between predicted and measured V S . Combination of different parameters from available models which are giving a minimum value of residual is used to find the functional form of correlation equation. Mola-Abasi et al. [11] have proposed a polynomial model with the conclusion that the cone tip is more crucial than the sleeve friction in their predicted model. More recently Mousa and Hussein [12] developed seven different correlation equations with different pairs of CPT parameters. Numerous researchers have attempted to correlate the CPT parameters with V S . Although researchers like Andrus et al. [8] have shown and incorporated some prime factors which can influence the change of V s profile. But still, some factors like geological factors, soil textural factors, etc. are left, which may be the cause of variation. Application of correlation to a site, which has a similar type of geological conditions may give reliable output. If those correlations do not give appropriate results, then the cause associated with it in terms of a local factor needs to be introduced in the existing correlation. Roy et al. [13] have considered different available V s –N correlations and tried to address the effect of uncertainty while selecting any V s –N correlation, especially in terms of site response analysis. Although these uncertainties need to be considered at the selection stage of any correlation, in this article factors considered which are solely responsible for causing the change in V s profile using cone penetration are studied. Emphasis is given to CPT-related parameters that should be present in correlation to predict V s . Mayne and Rix [5] showed that if any insitu void ratio (e0 ) can be incorporated, then correlation gets significantly improved. Traditionally, V S can be measured by the MASW test which required a huge open space area. MASW is a great geophysical testing tool for the estimation of V s . The V s has been estimated in three steps, i.e., (1) Data acquisition (2) Data processing by dispersion curve generation (3) Inversion and V s profile creation. In this article, shear wave velocity has been correlated with different parameters estimated using CPT. Jakka et al. [14] have discussed various uncertainties associated with MASW testing, such as (i) Model-based uncertainty (ii) Data measurement uncertainty (iii) Inversion uncertainty and proposed some guidelines to minimize that uncertainty. MASW testing has been performed, and results have been analyzed using proper guidelines like the one suggested by Jakka et al. [14] so that the minimum uncertainty can be induced. On the other side, CPT is a widely used test for assessing soil properties, subsurface stratigraphy associated with soil materials, and liquefaction potential. Different researchers have given correlations with CPT also, but it has been realized (shown in the next section) that the available correlations are not applicable to the studied soil. So, these correlation needs to be revised, and more significant factors need to be considered in correlations. Therefore, for all the above reasons, an attempt had been made to propose new correlations to compute V s from CPT data.

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Fig. 1 Location of CPT and MASW tests at IIT Patna campus used in this study

In this article, all the significant CPT-V S correlations available in the literature are investigated and plotted using the CPT data from the IIT Patna campus. The location of CPT testing results used from the IIT Patna campus in this study is shown in Fig. 1. Plot using a different available model of V S does not show good agreement with experimentally measured shear wave velocity using MASW tests. Therefore, a site-specific equation has been developed in this study for predicting the Vs value from CPT test data.

2 Some Existing CPT-V S Correlations In the first part of this study, different existing CPT-V S correlations (listed in Table 1) with their number of sample datasets along with the coefficient of determination (R2 ) are reviewed. Using these empirical equations, V S has been calculated and plotted along with the depth up to 30 m in Fig. 2. These plots of V S are varying with each other, with a huge difference in between them. Measured shear wave velocity (V SM ) is also plotted in a continuous line. The difference between actual and predicted V S is noticeable. Some of the models give huge differences while some show little gaps at varying depths. This lag between plots of predicted V S and measured V S tells us that none of the existing models can predict the V S correctly for this location. It also shows that existing correlations are site-specific and vary from region to region.

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Table 1 List of some well-known existing CPT-V s correlations Study

Geologic age

No of samples

R2

Vs

Hegazy and Mayne [15]

Quaternary

323

0.695

( / )0.3 (10.1 log(qc ) − 11.4)1.67 . f s qc ∗ 100

Piratheepan (2002)

Holocene

60

0.73

32.3qc0.089 f s0.121 D 0.215

Hegazy and Mayne [6]

–

558

0.854

0.0831 ∗ qc1n ∗

Mayne [16]

(

,

σ0 Pa

)0.25 ∗ e1.786IC

Quaternary

161

0.82

118.8 log( f s ) + 18.5

Andrus et al. Holocene (H) & [8] Pleistocene (P) age

185

H–0.73 P–0.43

2.62qt0.395 Ic0.912 D 0.124 S F

Robertson [9]

Quaternary

1035

–

10(0.55I c+1.68) (qt − σv )0.25

McGann et al. [10]

–

513

–

18.4qc0.144 f s0.0832 z 0.278

Mola-Abasi et al. [11]

–

37

–

Zhang and Tong [17]

–

–

0.798

100(1.40 + 1.59 f s + 0.09qc −1.33 f s2 − 0.002qc2 + 0.05 f s qc

)

1915qt0.317 Ic0.210 D 0.057 S F a

Vs (m/s) 0

200

400

600

800

0 5

Depth (m)

10 15 20 25 30 Andrus et al (2007) Hegazy and Mayne (1995) Mayne (2006) McGann et al (2015) Zhang et al

Hegazy and Mayne (2006) Molabassi et al Piratheepan (2002) Robertson (2009) Vsm

Fig. 2 Variation of shear wave velocity along depth estimated using different available CPT-V s models and experimentally measured for the studied site

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3 Methodology CPT and MASW tests have been conducted at different locations on the campus. Readings of qc and f s are taken at an interval of 20 cm each. So, CPT values are averaged up to 1 m. While V s values obtained from the MASW survey are obtained for 1 m intervals. CPT testing has been done up to a depth of 30 m while V s values obtained from the dispersion curve in MASW testing are also up to a depth of greater than 30 m. So, correlation developed using these values can be used up to 30 m depth. CPT testing gives qc and f s as output as shown in Fig. 3. Soil behavior Index (I C ) can be calculated by the following expressions given in the literature (Robertson and Wride [7]) using qc and f s : }0.5 { IC = (3.47 − log Q tn )2 + (log Fr + 1.22)2 Q tn =

(1)

( ) (qt − σv ) pa n , pa σ0

(2)

fs × 100(%) (qt − σ0 ) ( ,/ ) n = 0.381Ic + 0.05 σv pa − 0.15 ≤ 1.0 Fr =

(3) (4)

where Qtn = Normalized cone tip resistance; F r = Friction ratio; and Pa = atmospheric pressure (100 kPa) Cone tip resistance qc (kPa) 0 5

Depth (m)

10 15 20

200

0

400

0

CPT-1 CPT-3 CPT-8 CPT-12 CPT-15

5 10 Depth (m)

0

Sleeve friction fs (kPa) 5 10 CPT-1 CPT-3 CPT-8 CPT-12 CPT-15

15 20

25

25

30

30

Fig. 3 Experimentally determined CPT results a Cone tip resistance (qc ) and b Sleeve friction (f s ) values at different locations

Correlation Between Cone Tip Resistance and Shear Wave Velocity … Table 2 Soil classification according to the soil behavior type index (after Robertson [18])

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Soil behavior type index, IC

Zone

Soil behavior type

IC < 1.31

7

Gravelly sand to dense sand

1.31 < I C < 2.05

6

Sands: clean sand to silty sand

2.05 < I C < 2.60

5

Sand mixtures: silty sand to sandy silt

2.60 < I C < 2.95

4

Silt mixtures: clayey silt to silty clay

2.95 < I C < 3.60

3

Clays: silty clay to clay

I C > 3.60

2

Organic soils: peats

In the above equations, qt is the normalized value of the cone tip for pore water pressure acting just behind the tip of the cone with a value of area ratio and are related as qt = qc + u2 (1 − a), where qc = cone tip resistance; u2 = dynamic pore pressure, and a is the area ratio. Generally, the value of qc and qt are nearly equal except for some soils with greater fineness content. Using all the above parameters, the first normalized cone tip resistance (Qtn ) is calculated with an initial value of n as 1 for the first iteration. Then final values I C and n are calculated by iterations. Based on Qtn and friction ratio (F r ), Robertson [18] proposed a soil behavior chart in which 9 different zone of soil types (as mentioned in Table 2) has been described. Using the chart and values of CPT parameters (i.e., qc and f s ) soil type has been identified and shown in Fig. 4 and vertical soil profiling is done using I C . Here, I C is used because the soil sample is not taken in CPT testing, so information about soil type and its behavior can be obtained from the I C index (shown in Fig. 5). In Fig. 4, it can be seen that all the soils in the studied region lie from Zone–3 to Zone–6, which are classified as Clay to Sand. The majority of the soil found on the campus using the above chart is Sandy soil in nature Nilay et al. [19]. Soil variability with depth is also shown in Fig. 5. From the I C plot, it can be seen that at most CPT locations in IIT Patna soil is a sandy to silty mixture up to a depth of 30 m. While a layer of clayey soil is also observed between a depths of 18–24 m.

4 Results and Discussion 4.1 Regression Analysis The equation for predicting V S is obtained using non-linear regression with multiple variations. The concept behind using a non-linear model is based on the evaluation of previously available models like Mayne and Rix [5], and Andrus et al. [8]. From

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100.00

Qtn

CPT 15

10.00

1.00 0.100

Fr (%) 1.000

10.000

Fig. 4 Classification of soil using normalized cone tip resistance (Qtn ) and friction ratio (F r ) based on Robertson [18] Ic Value 2.50

1.50 0

3.50 CPT - 1 CPT - 3 CPT - 8 CPT - 12 CPT - 15

2

14 16

Sand

Depth (m)

12

18 20 22 24 26 28 30

Fig. 5 Variation of soil behavior index (I C ) along the depth

Clay

8 10

Silt Mixture Clayey silt to silty clay

6

Sand mixutre silty sand to sandy silt

4

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previous studies, it is found that qc is the most important parameter related to the undisturbed shear strength of the soil. Hegazy and Mayne [6] showed that cone tip resistance data represent better variability in V s than in f s . As discussed, and shown in the previous section, I C is an indicator of soil type, so our shear wave velocity (V s ) will depend upon this soil behavior ( , ) type (I C ). Other parameters in this correlation equation are effective stress σ 0 and depth (z). These parameters are those over which shear wave velocity (V s ) and depth are highly correlated. If we plot shear wave velocity against depth, it will be seen that with ( , ) an increase in depth, V s also increases, the same applied with effective stress σ 0 ; it also increases with depth. So, in CPT-V S correlation consideration of these two parameters will be useful. For this correlation equation, data collected from campus are shown in Fig. 6a. This pair of cone penetration and MASW testing data is used for regression analysis. It is listed as C15-M13, where C15 and M13 are the location of cone penetration and MASW tests, respectively. Using non-linear multiple variable statistical regression analysis, the following equation has been developed with an R2 value of 0.90. Vs = 828.693qc−0.013 Ic−0.203 σ0,−0.534 z 0.712

(5)

,

where V s is in m/s; qc is in kPa; σ 0 is in kPa, and z is depth in meters.

0

c) C3-M1

a) C15-M13

b) C1-M3

Shear wave (m/s)

Shear wave (m/s)

200

0

400

Shear wave (m/s) 0

500

Predicted Vs 5

10

10

10

20

25

Depth (m)

5

15

400 Measured Vs

5

Depth (m)

Depth (m)

Measured Vs Predicted Vs

15

200

0

0

0

15

20

20

25

25

30

30

Measured Vs Predicted Vs 30

Fig. 6 Plots of shear wave velocity (V S ) for different CPT sites using the regression equation to validate the model. a Site near C15 b Site near C1 c Site near C3

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4.2 Validation of Results To validate the above non-linear CPT-V S equation, CPT values from other locations in the site are plugged into the above equation. All other selected CPT locations and their predicted value of V S show a good agreement. The plots between predicted and measured values for other CPT locations are shown in Fig. 6. Using the regression Eq. (5) given in this study, predicted shear wave velocity (V SP ) is shown in the same figure. To check the efficiency of the proposed equation, the term coefficient of determination R2 is used. The value of R2 of two locations, namely C1-M3 and C3-M1 [shown in Fig. 6b and c], have been found as 0.90 and 0.91, respectively. Both CPT locations with their predicted and measured values of V s are shown in Fig. 7. In this figure, both the results are plotted over a 45° line. The closer the results with this line, the better the accuracy of predicted V S . Deviation of V s value from predicted and measured, has been also presented by a term called velocity ratio denoted by ‘K’, i.e., the ratio of measured to the predicted value. The value of K is plotted against depth and shown in Fig. 8. Scatter plot which is closest to K = 1, represents that predicted and measured V S are the same. CPT site location C3 shows a good relationship as the K value is nearly close to 1 up to 20 m depth, while C1 initially shows predicted value is less than measured up to a depth of 5 m, then it crosses and started over predicting the V S value. Predicted values are in the range of ± 30% approximately. Fig. 7 Variation between measured and predicted V S for different sites

290

Predicted VSP (m/s)

270 250 230 210 190 C1 170 C3 150 150

170

190

210

230

250

Measured VSM (m/s)

270

290

Correlation Between Cone Tip Resistance and Shear Wave Velocity … Fig. 8 Velocity ratio (K) compared along with the depth

0.5

85

Velocity ratio (K) 1

1.5

0 5

C1

Depth (m)

10 15 20 25 30

5 Conclusions In the first part of this study, soil classification is performed using I c values along the depth. The available CPT-V s correlations are used to predict the value of shear wave velocity. Existing empirical correlations may be region-specific or applicable to a particular soil type, but none of the relations gives reliable values for the site studied here. All models give different results, and there is a considerable difference (up to 280%) between the measured and predicted values for the selected site. So, for this reason, a non-linear multiple variable regression equation is developed (Eq. (5)). This equation is further validated with data obtained from other sites inside the campus. Validation results match the measured value of V S at a different location on the campus with sufficient accuracy. The proposed regression equation is not validated for the site other than the studied region. The proposed CPT-V S equations can be used for other similar sites with caution. Before using these correlation, site conditions, geologic similarity, etc. needs to be considered. If the direct measurement of V S is possible, then preference should be given to all those methods. Therefore, further study is needed to check the applicability of the proposed model globally.

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References 1. Youd, T.L., Idriss, I.M.: Liquefaction resistance of soils: summary report from the 1996 NCEER and 1998 NCEER/NSF workshops on evaluation of liquefaction resistance of soils. J. Geotech. Geoenvironmental Eng. 127(4), 297–313 (2001) 2. Chakrabortty, P., Roshan, A.R., Das, A.: Evaluation of dynamic properties of partially saturated sands using cyclic triaxial tests. Indian Geotech. J. 50(6), 948–962 (2020) 3. Chakrabortty, P., Kumar, U., Puri, V.: Seismic site classification and liquefaction hazard assessment of Jaipur city, India. Indian Geotech. J. 48(4), 768–779 (2018) 4. Das, A., Chakrabortty, P.: Simple models for predicting cyclic behaviour of sand in quaternary alluvium. Arab. J. Geosci. 15, 385 (2022) 5. Mayne, P.W., Rix, G.J.: Correlations between shear wave velocity and cone tip resistance in natural clays. Soils Found. 35(2), 107–110 (1995) 6. Hegazy, Y.A., Mayne, P.W.: A global statistical correlation between shear wave velocity and cone penetration data. In: Site and Geomaterial Characterization (American Society of Civil Engineers (ASCE), Geotechnical Special Publication (GSP) 149), Proc GeoShanghai, pp. 243– 248 (2006) 7. Robertson, P.K., Wride, C.E.: Evaluating cyclic liquefaction potential using the cone penetration test. Can. Geotech. J. 35(3), 442–459 (1998) 8. Andrus, R.D., Mohanan, N.P., Piratheepan, P., Ellis, B.S., Holzer, T.L.: Predicting shear-wave velocity from cone penetration resistance. In: Proceedings of the 4th International Conference on Earthquake Geotechnical Engineering, Thessaloniki, Greece, paper no. 1454, pp. 1–12 (2007) 9. Robertson, P.K.: Interpretation of cone penetration tests—a unified approach. Can. Geotech. J. 46(11), 1337–1355 (2009) 10. McGann, C.R., Bradley, B.A., Taylor, M.L., Wotherspoon, L.M., Cubrinovski, M.: Development of an empirical correlation for predicting shear wave velocity of Christchurch soils from cone penetration test data. Soil Dyn. Earthq. Eng. 75(5), 66–75 (2015) 11. Mola-Abasi, H., Dikmen, U., Shooshpasha, I.: Prediction of shear-wave velocity from CPT data at Eskisehir (Turkey), using a polynomial model. Near Surf. Geophys. 13(2), 155–168 (2015) 12. Mousa, A., Hussein, M.: Prediction of shear wave velocity in fine-grained soils from cone penetration test data: toward a global approach. Transp. Res. Rec. J. Transp. Res. Board 2676(6), 565–582 (2022) 13. Roy, N., Shiuly, A., Sahu, R.B., Jakka, R.S.: Effect of uncertainty in V S -N correlations on seismic site response analysis. J. Earth Syst. Sci. 127(7), 1–21 (2018) 14. Jakka, R.S., Desai, A., Foti, S.: Guidelines for minimization of uncertainties and estimation of a reliable shear wave velocity profile using MASW testing: a state-of-the-art review. In: Advances in Earthquake Geotechnics, pp. 211–253 (2022) 15. Hegazy, Y.A., Mayne, P.W.: Statistical correlations between V S and CPT data for different soil types. In: Proceedings of the 1st international symposium on cone penetration testing (CPT’95), vol. 2, pp. 173–178 (1995) 16. Mayne, P.W.: In-situ test calibrations for evaluating soil parameters. Characterisation Eng. Prop. Nat. Soils 3(4), 1601–1652 (2007) 17. Zhang, M., Tong, L.: New statistical and graphical assessment of CPT-based empirical correlations for the shear wave velocity of soils. Eng. Geol. 226, 184–191 (2017) 18. Robertson, P.K.: Soil classification using the cone penetration test. Can. Geotech. J. 27(1), 151–158 (1990) 19. Nilay, N. Chakrabortty, P., Popescu, R.: Liquefaction hazard mapping using various types of field test data. Indian Geotech. J. 52, 280–300 (2022)

Ground Motion Predictive Equations and Its Applicability in North-Eastern Indian Region: A Critical Appraisal P. Kumar and S. S. Kumar

Abstract The level of ground shaking, to which earthquake-resistant structures are subjected, is estimated using Ground Motion Prediction Equation (GMPE) in terms of ground motion parameters. More than 1000 GMPEs worldwide and more than 50 GMPEs for India are available to estimate ground motion parameters like PGA, PGV, PGD, spectral acceleration, etc. However, the selection of a GMPE for seismic hazard analysis, out of available ground motion models becomes a critical task for seismic hazard analysis. In the present study, the GMPEs were selected for the Northeast region of India according to qualitative analysis and then ranked to those GMPEs for their suitability using the quantitative information-theoretic approach, based on the estimation of the log-likelihood value. Further, the ranking of models for discrete ranges of distance was estimated considering respective weights for the Northeast region. Based on the variable performance of models, for the different range of data, it has been observed that (NDMA in Govt. of India New Delhi, 2010) and (Bajaj and Anbazhagan in Soil Dyn. Earthq. Eng. 126, 2019) are the best-suited model for the study area. However, the weighted ground motion models have shown the efficiency and robustness in the process of selecting and ranking GMPEs for diverse applications, particularly for the Northeast Indian region, which has witnessed some devastating earthquakes in past and is vulnerable as per seismic hazard analysis. Keywords Ground motion · GMPE · PGA · LLH

1 Introduction Northeast India is considered as one of the most seismically active regions in the world due to frequent occurrence of the earthquakes of moderate to large magnitude. The continuous subsidence of Indian plate under the Eurasian plate and the Indo-Burma subduction zone is the major region to trigger such events. The devastating effect of P. Kumar (B) · S. S. Kumar Department of Civil Engineering, NIT Patna, Bihar 800005, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_8

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these earthquakes can be minimized by identifying the seismic risk and the strong ground motion, induced by the earthquakes, prior to the design and construction of the structures. The strong ground motion parameters can be evaluated using a ground motion prediction equation (GMPE) for a respective area and seismic risk can be assessed by conducting the region-specific seismic hazard analysis. Also, the assessment of the conditional chance that, in the event of the earthquake, a specific acceleration limit can be surpassed at the site of interest, requires a GMPE. GMPEs are widely used relationships for calculating the seismic ground motion’s amplitude parameters such as the response spectrum, peak ground acceleration, and peak ground velocity, take into consideration the impacts of the source, path, and location. For the estimation of ground motion parameters, incorporating an appropriate attenuation model, at a specific site for each earthquake scenario is the fundamental part of Probabilistic Seismic Hazard Analysis (PSHA). Seismic hazard study for any region must start with an appropriate GMPE to calculate the ground motion in terms of peak ground acceleration (PGA) or spectral acceleration (SA). The availability of number of GMPE pertaining to a single region requires the selection of an appropriate one, mere random selection may carry forward practical problems associated with the model [1]. There are various methods available for the selection of GMPE which can be brought under the category of qualitative and quantitative approaches. Cotton et al. [2] offered a list of qualitative suggestions for disliking ground motion equations whereas Scherbaum et al. [1] have used exceeding probability for the selection and ranking of the GMPEs. Further, Scherbaum et al. [3] have also suggested an information-theoretic strategy for the ranking of GMPEs based on the consideration of log-likelihood (LLH) for the refinement of earlier work. Early-stage GMPEs have demonstrated a reduction in epistemic uncertainty with an increase in model complexity, such as source and site properties. However, the recent research revealed that the uncertainty associated with the earthquake, source, path, and location, necessitates the use of numerous GMPEs for seismic hazard assessments using logic tree architecture following their selection and ranking. The purpose of this study is to find appropriate GMPEs for various regions of Northeast India and to rank and weight the same for seismic hazard analyses by taking into the account of data of region-specific earthquakes and GMPEs that are already accessible. Eight of the regionally applicable GMPEs are considered in the proposed work (Table 1). GMPEs considered in the study vary from each other in the context of applicable distance range. Hence, the analysis is bifurcated into three distance bins (I) up to 100 km, (II) 100–300 km, and (III) 300–500 km. All the applicable GMPEs are used to estimate the PGA value for each distance bin. Further, these PGA values were utilized to calculate the rank and weights for GMPEs based on LLH values and Data Support Index (DSI), taking account of all the earthquakes considered in this study. In an effort to avoid overestimating and underestimating the PGA at shorter and larger distances, a segmented-based ranking of GMPEs is explored.

Ground Motion Predictive Equations and Its Applicability … Table 1 Abbreviations for GMPEs

S.N

Attenuation relationship

89 Abbreviation for equation

1

Nath et al. (2009)

NA09 [4]

2

NDMA (2010)

ND10 [5]

3

Nath et al. (2012)

NA12 [6]

4

Anbazhagan et al. (2013)

AN13 [7]

5

Raghukanth and Kavitha (2014)

RK14 [8]

6

Singh et al. (2016)

SI16 [9]

7

Kumar et al. (2017)

KU17 [10]

8

Bajaj and Anbazhagan (2019)

BA19 [11]

2 Seismotectonics of the Study Area India’s Northeastern region and its environs can be classified into six tectonic blocks [12]. The Arakan-Yoma belt region is part of the Indo-Burma subduction zone and other zones namely Eastern Himalaya, Surma Valley, Shillong Plateau, Assam Valley, and Naga Hill region, which are not in the subduction zone, correspond to the active region. Due to the fact that earthquakes are occurring in the crust, intraslab, and interface subduction zones, the NEI is regarded as a seismically unique region in the world. The thrust planes, Main Central Thrust (MCT), Main Boundary Thrust (MBT), Main Frontal Thrust (MFT), and their subsidiary thrusts, make up the Himalayan structures in the Northeastern region. A notable tectonic complexity is formed in the Mishmi region by the Mishmi thrust, Lohit thrust, Po Chu fault, and Tidding suture. The Tsangpo suture, Tuting, Kopili, Oldham, Dubri, Dauki, and Bame faults are further significant deformities [14]. A no. of thrusts such as Disang thrust, Lohit thrust, Naga thrust, and eastern boundary thrust is also present in the zone, making Northeast one of the most seismically active regions in the world that demands a regional seismic hazard analysis. A reliable hazard analysis requires selecting a GMPE which predicts the amplitude parameter closely to actual values. Various tectonic regions along with the faults and recorded events used in the study are depicted in Fig. 1.

3 GMPEs Used in the Study Eight suitable GMPE models were selected to analyze their relative closeness to actual PGA values. NA09 used the Finite fault stochastic model (EXSIM) of Motazedian and Atkinson [15] which models the spectral amplitude based on the Convolution theorem [16]. NA09 utilized relations of Motazedian and Atkinson [15] for source

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Fig. 1 Seismotectonic map of Northeastern India along with Locations of earthquakes used in the study (Modified after Rahman [13])

modeling and adopted the GMPE form proposed by Campbell and Bozorgnia [17] and reported a similar trend of Amplitude-site distance plot for distances < 30 km. Nath’s relation applies to northeast India over a wide range of Magnitudes. GMPE developed by ND10 is applicable for the whole of India and, it was developed considering India has various seismotectonic zones as per the works of Sabeer et al. [18] and determining potential maximum magnitude for each of 32 sources assigned to tectonic zones using prehistoric data by Kijko and Graham [19] method. NDMA’s GMPE relationship was derived from 80,000 samples originating from 38,860 events which makes it applicable for a large distance and Magnitude range (Table 2). The concept of Apparent Station was first utilized in the region by AN13 to get an even plot of ground motion and corresponding magnitude for the development of a GMPE relation based on FINSIM model of Beresnev and Atkinson [20]. Anbazhagan et al. [7] model was derived from 420 ground motions from 14 EQ recorded at 30 stations placed at an interval of 10 km, and includes the past seismicity data obtained from a correlation between MMI and surface PGA values. NA12 model has been developed for the Shillong region considering the earthquake to be originated from two separate crust zones based on the depth criteria. This model is based on the works

Ground Motion Predictive Equations and Its Applicability …

91

Table 2 Functional form and applicability range of GMPEs used in the study S. GMPE Functional form of GMPE No.

Magnitude Distance range range

loge y = 9.143 + 0.247MW 1

NA09

4.8–8.1 −0.014(10 − MW )3   −2.697 loge Rr up + 32.9458 exp(0.0663 ∗ MW )

Upto 100 km

y = PGA (g)

2

ND10

2 loge y = c1 + c2 M + c3 MW + C4 Rhypo   +c5 ln Rhypo + c6 exp(c7 MW )   +c8 log Rhypo f o + ln ∈

4.0–8.5

Upto 500 km

4.8–8.1

Upto 100 km

3.0–8.7

Upto 300 km

4.0–8.5

Upto 1000 km

4.0–8.5

Upto 300 km

4.0–6.8

Upto 200 km

4.0–9.0

Upto 750 km

y = PGA (g); c1 to c8 are the regression coefficients 3

NA12

4

AN13

loge y = c1 + c2 MW + c3 (10 − MW )3   +c4 loge Rr up + c5 exp(c6 MW ) y = PGA (g); c1 to c6 are the regression coefficients log10 y = c1 + c2 MW √  −1.792 log10 R 2 + h 2 + exp(c3 MW ) y = PGA (g); c1 to c3 are the regression coefficients; R is the closest distance to rupture; h is the focal depth

5

RK14

6

SI16

7

KU17

8

BA19

2 loge y = c1 + c2 M + c3 MW + C4 Rhypo   +c5 ln Rhypo + c6 exp(c7 MW )   +c8 log Rhypo f o + ln ∈  /  f o = max loge (R 100 , 0)

loge y = c1 + c2 (MW − 6) + c3 (MW − 6)2 − loge Rhypo − c4 Rhypo y = PGA (g); c1 to c4 are the regression coefficients log10 y = −1.497 + 0.3882MW   −1.19 log10 Rhypo + exp(0.2876MW ) y = PGA (g) ln y = c1 + c2 (M − 6) + c3 (9 − M)2 + c4 ln R +am ln R(M − 6) + c7 R + σ y = PGA (g); cm = c5 if M W < 6.0, otherwise c6 ; c1 to c7 are the regression coefficients

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of Boore [21] which has been developed for the lower crust zone (depth < 25 km) and upper crust zones. SI16 utilized the stochastic point source model for the development of regional GMPE after utilizing the 30,000 records obtained from 1000 simulated earthquakes. The equation presented is shaped in the functional form used by Iyenger and Raghukant [22]. KU17 utilized 216 records originating from 24 earthquakes to obtain an attenuation relationship for Northeast India. The records obtained from the Indian strong motion network were then regressed using a 2-step stratified regression approach [23]. BA19 developed an attenuation relationship for the whole Himalayan region after dividing it into 4 different regions, i.e., Kashmir Himalaya, Kumaon Himalaya, Bihar-Nepal Himalaya, and Northeastern Himalaya. A separate duration model, geometric attenuation and kappa factor were also developed by BA19 for the respective region which was later used for simulation. This GMPE uses a functional form which is developed particularly for the Himalayan region. All the above GMPEs differ in terms of the parameters and methodology used for the development of the equation which creates confusion among the seismic hazard analysts during the selection of a GMPE. The functional form and the applicability range of each GMPE in terms of magnitude and distance are reported in Table 3. Thus, an effort is made in the present study which will allow an easy selection of a model for hazard analysis.

4 Methodology The development of GMPEs, which are used to forecast the amplitude characteristics like peak ground acceleration (PGA), velocity, or displacement, for an earthquake has been widespread in recent years [24]. This is because of GMPEs have a substantial impact on the modeling of seismic risk and hazard: deriving more precise predictions from Ground Motion Models (GMMs) leads to more precise estimations of seismic risk and hazard. To strengthen the selection of GMMs, various statistical and probabilistic methods have been devised. Several tests, like the Pearson correlation coefficient and Chi-Square Misfit, employ direct observations for model selection, while others, like the chi-square and Kolmogorov–Smirnov tests, analyze the form and distribution of model mismatch residuals. The likelihood (LH) value test [1] employs the assumption of log-normal distribution for each GMPE and calculates the probabilities of residuals; the log-likelihood (LLH) [3] method uses information theory to assess how feasible the model is for the provided data. LLH method is based on the likelihood value of the observed ground motion that will occur if a model is believed to be valid. The match between the data and model is indicated by one value, the negative average log-likelihood (LLH) [25]. Figure 2 shows the working approach of LLH method in which model R represents the actual GMPE and A, B, and C are some other models which try to predict the ground motion as close as possible. However, it should be noted that actual model R is also unknown and only some information is provided which is based on the discrete observations

06-02-1988

EQ02

India-Bangladesh border

21-09-2009

Bhutan

EQ05

06-08-1988

08-05-1997

India-Burma border EQ03

India-Burma border EQ04

10-09-1986

EQ01

NE India

Date (DD-MM-YYYY)

ID

Earthquake

Table 3 Chronicle of earthquakes used in the study

08:53:04

02:53:15

00:36:25

14:50:45

07:50:26

Time (UTC)

6.2

6.0

7.3

5.8

4.5

Magnitude (M W )

MW

MB

MW

MS

MS

Magnitude scale

27.3

24.894

25.149

24.688

25.385

Latitude (°N)

91.5

92.25

95.127

91.57

92.077

Longitude (°E)

8

34

90

15

43

Depth (km)

3

6

12

8

7

No. of stations (Rock)

Ground Motion Predictive Equations and Its Applicability … 93

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P. Kumar and S. S. Kumar

Fig. 2 Working model of LLH-based ranking approach

corresponding to x 1 , x 2 and x 3 . The respective probability density function for each of the predictive models is shown in Fig. 2. Based on this data LLH leads us to a conclusion in regards to the best suitable GMPE model (A, B, C). LLH value is given by Eq. (1). N 1 Σ LLH = − log2 (P(xi )) N i=1

(1)

where P(x) is the probability density function (PDF) of the GMPE models, x i represents the observations from the antecedent earthquakes upto a total no. of N datasets. The total distance has been divided into three distance bins of up to 100, 100–300, and 300–500 km to demonstrate the performance of the GMPEs at various distance ranges. For each of the three distance segments, the average LLH values for each of the 8 GMPEs have been determined wherein, the high LLH value correlates to a model which is less likely to have produced the data and the small LLH suggests that the respective model is near to the model that has generated the data. Further, LLH-based weights for each of the GMPE model is calculated (Eq. (2)) utilizing the respective LLH value according to the method proposed by Delavaud et al. [26] which represents the extent to which data enhances or reduces a model’s weight in correlation to its level of non-informativeness. 2−L L H (Pi (x)) Wi = Σ N −L L H (Pi (x)) i=1 2

(2)

Ground Motion Predictive Equations and Its Applicability …

DS Ii =

Wi − Wu × 100 Wu

95

(3)

where W i is the initial weight, W u (= 1/M) is uniform weight, and M is the number of GMPE model used for calculating LLH. The Data Support Index (DSI) (Eq. (3)) value which represents the percentage by which the weight of a model is reduced or increased, is used for the calculation of the final weight. The final weight is calculated only for GMPEs having a positive DSI value.

5 Results and Discussion Ranking of GMPEs not only helps to select the suitable GMPEs but also leads to a reduction in epistemic uncertainty associated during selection [27]. In the present study, 5 earthquakes from the northeast Indian region were selected to act as the discrete observations of the actual model. Further for each earthquake PGA values were calculated using the respective equation. The records of each event are collected for stations at NEHRP site class A from COSMOS virtual data center [28]. After that ranking is determined using LLH-based approach for each distance bin. Ranking based on LLH value is presented in Fig. 3a, b, c. The respective weight of the selected models is given in Table 4. When a GMPE is not valid for a distance bin it was not considered for the ranking and GMPEs having negative DSI value was also not considered for the weight which can be used for multi-model hazard estimation. For distance bin I, the ND10 model was ranked 1 for EQ01 and EQ04, and BA19 is ranked 2 for two of the earthquakes. For bin II and III ND10 model and BA19 model are ranked 1 and 2, respectively, for all the earthquakes. KU17 acquired the third rank for bin II for EQ02 and EQ04 as it was not considered for EQ03 and EQ05 because the recording station does not fall in the applicability range of the model. Since a low number of data points were employed to determine the ranking, an increase in uncertainty is unavoidable which further increased the variance and also leads to divergence of ranking order. The effect of this can be seen as few of the models developed for Sikkim Himalaya were ranked higher, however, it can be reduced by increasing the data points [25]. Thus, the results obtained in the study can modify given the increment of no. of records used in the analysis. The weights of the model given in Table 4 are significantly different from one another which represent the importance of the use of the weighted model for future seismic hazard analysis. The overall suitability of the GMPE models based on the results obtained (Table 4) is given in Table 5 which classifies the GMPEs as best fit, moderately fit, and poorly fit models. ND10 and BA19 models are the best fit model throughout the distance range. At a higher distance range, the ND10 model was the best-fitted model and its weight is also high when compared with the next best fit model AB19 which is also highlighted in Fig. 3.

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(a) 8 7 6

Rank

5 4 3 2 EQ04 EQ02 EQ01

1 0 NA09

ND10

NA12

AN13

RK14

SI16

KU17

BA19

(b) 6

Rank

5 4 3 2 EQ05 EQ04 EQ03 EQ02

1 0 AN13

RK14

SI16

KU17

BA19

ND10

(c)

Rank

3

2

1

EQ05 EQ03

0 ND10

RK14

BA19

Fig. 3 Ranking of each GMPE for distance bin I (up to 100 km) (a), distance bin II (100–300 km) (b) and distance bin III (300–500 km) (c) considering all the earthquakes mentioned in Table 1

6 Conclusion The GMPE suitable for the northeast region to assess PGA values at the bedrock level has been examined in the present study. Eight GMPE models were selected for the study and the log-likelihood value of each is calculated using the informationtheoretic approach of Scherbaum et al. [3]. The recorded data of the past five earthquakes in the region is used for the analysis after segmenting the data into three different distance segments. Further, the weight of each GMPE is calculated using

Ground Motion Predictive Equations and Its Applicability …

97

Table 4 Weight of the GMPEs against each of the earthquakes used in the study for various distance segments EQ01

Bin I

GMPEs

LLH

DSI

W

Rank

NA09

1.817

3.04

0.17

4

ND10

0.768

113.08

0.35

1

NA12

2.738

− 45.59

NF

NF

AN13

3.241

− 61.60

NF

NF

RK14

21.40

− 100.00

NF

NF

SI16

0.986

83.22

0.30

2

KU17

1.741

8.56

0.18

3

BA19

1.870

− 0.70

NF

NF

EQ02

Bin I

GMPEs

LLH

DSI

W

Rank

LLH

NA09

2.001

92.63

0.26

3

NA

ND10

5.470

− 82.60

NF

NF

0.938

NA12

4.982

− 75.61

NF

NF

NA

AN13

1.930

102.22

0.27

1

RK14

6.591

− 92.01

NF

NF

SI16

8.302

− 97.56

NF

KU17

2.320

54.35

BA19

1.957

98.57

EQ03

Bin II

GMPEs

LLH

NA09

NA

ND10

2.093

NA12

NA

AN13

Bin II DSI

W

Rank

125.32

0.48

1

2.411

− 18.81

NF

NF

9.324

− 99.33

NF

NF

NF

3.176

− 52.25

NF

NF

0.21

4

1.898

15.79

0.25

3

0.27

2

1.739

29.29

0.27

2

DSI

W

Rank

125.83

0.82

1

− 100.00

NF

NF

25.83

0.18

2

DSI

W

Rank

182.12

0.52

1

− 52.63

NF

NF

Bin III DSI

W

Rank

LLH NA

207.31

0.78

1

6.326

4.539

− 37.37

NF

NF

NA

RK14

23.583

− 100.00

NF

NF

41.382

SI16

47.650

− 100.00

NF

NF

NA

KU17

NA

BA19

3.922

EQ04

Bin I

GMPEs

LLH

DSI

W

Rank

LLH

NA09

2.492

8.39

0.17

4

NA

ND10

1.614

99.17

0.31

1

1.055

NA12

4.850

− 78.86

NF

NF

NA

AN13

2.659

− 3.50

NF

NF

3.629

NA

NA 36.94

0.22

2

7.932 Bin II

(continued)

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P. Kumar and S. S. Kumar

Table 4 (continued) RK14

17.232

− 100.00

NF

NF

20.801

− 100.00

NF

NF

SI16

3.660

− 51.77

NF

NF

6.201

− 92.03

NF

NF

KU17

2.112

41.00

0.22

3

2.413

10.09

0.20

3

BA19

1.716

85.57

0.29

2

1.943

52.45

0.28

2

EQ05

Bin II

GMPEs

LLH

DSI

W

Rank

NA09

NA

ND10

0.925

124.850514

0.9

1

NA12

NA

AN13

1.786

20.78

0.25

3

NA

RK14

5.610

− 91.47

NF

NF

4.249

− 86.76018172

NF

NF

SI16

8.697

− 99.00

NF

NF

NA

KU17

NA

BA19

1.469

50.40

0.31

2

21.09033232

0.1

2

Bin III DSI

W

Rank

119.29

0.45

1

LLH NA 0.163 NA

NA 2.024

Note NA—not applicable for respective distance bin, NF = not fit for weighting

Table 5 Sorting the GMPEs based upon their fit to the data

Model fitting

Distance bin 1

Best fit

ND10, BA19, ND10, BA19 KU17

ND10

Moderately fit NA09, AN13, KU17, AN13 SI16

BA19

NA12, RK14

RK14

Poorly fit

Distance bin 2 Distance bin 3

RK14, SI16

the DSI value. Overall, NDMA [5] and Bajaj and Anbazhagan [11] model comes out as the best suitable model for assessing PGA at the bedrock level for the Northeast region. The final weight is calculated only for GMPEs having a positive DSI value. The use of weights as suggested, in the calculation of PGA for hazard analysis can be used to reduce the epistemic uncertainty associated with selection up to a significant extent. The result indicates the use of a segmented ranking system since the use of a single distance range can increase or decrease the rank of the model not fit for the whole range. The use of lesser dataset for the analysis can inherit some uncertainty. However, it can be reduced with the use of larger datasets.

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References 1. Scherbaum, F., Cotton, F., Smit, P.: On the use of response spectral-reference data for the selection and ranking of ground-motion models for seismic-hazard analysis in regions of moderate seismicity: the case of rock motion. Bull. Seismol. Soc. Am. 94(6), 2164–2185 (2004). https:// doi.org/10.1785/0120030147 2. Cotton, F., Scherbaum, F., Bommer, J.J., Bungum, H.: Criteria for selecting and adjusting ground-motion models for specific target regions: application to central Europe and rock sites. J. Seismol. 10(2), 137–156 (2006). https://doi.org/10.1007/s10950-005-9006-7 3. Scherbaum, F., Delavaud, E., Riggelsen, C.: Model selection in seismic hazard analysis: an information-theoretic perspective. Bull. Seismol. Soc. Am. 99(6), 3234–3247 (2009). https:// doi.org/10.1785/0120080347 4. Nath, S.K., Raj, A., Thingbaijam, K.K.S., Kumar, A.: Ground motion synthesis and seismic scenario in Guwahati city-a stochastic approach. Seismol. Res. Lett. 80(2), 233–242 (2009). https://doi.org/10.1785/gssrl.80.2.233 5. NDMA.: Development of probabilistic seismic hazard map of India. In: Technical Report of the Working Committee of Experts (WCE), National Disaster Management Authority (NDMA), Govt. of India New Delhi (2010) 6. Nath, S.K., Thingbaijam, K.K.S., Maiti, S.K., Nayak, A.: Ground-motion predictions in Shillong region, northeast India. J. Seismol. 16(3), 475–488 (2012) 7. Anbazhagan, P., Kumar, A., Sitharam, T.G.: Ground motion prediction equation considering combined dataset of recorded and simulated ground motions. Soil Dyn. Earthq. Eng. 53, 92–108 (2013). https://doi.org/10.1016/j.soildyn.2013.06.003 8. Raghukanth, S.T.G., Kavitha, B.: Ground motion relations for active regions in India. Pure Appl. Geophys. 171(9), 2241–2275 (2014). https://doi.org/10.1007/s00024-014-0807-x 9. Singh, N.M., Rahman, T., Wong, I.G.: A new ground-motion prediction model for Northeastern India (NEI) crustal earthquakes. Bull. Seismol. Soc. Am. 106(3), 1282–1297 (2016). https:// doi.org/10.1785/0120150180 10. Kumar, A., Mittal, H., Kumar, R., Ahluwalia, R.S.: Empirical attenuation relationship for peak ground horizontal acceleration for North-East Himalaya. Vietnam J. Earth Sci. 39(1), 47–57 (2017) 11. Bajaj, K., Anbazhagan, P.: Regional stochastic GMPE with available recorded data for active region–application to the Himalayan region. Soil Dyn. Earthq. Eng. 126, 105825 (2019) 12. Goswami, H.C., Sarmah, S.K.: Probabilistic earthquake expectancy in the northeast Indian region. Bull. Seismol. Soc. Am. 72(3), 999–1009 (1982) 13. Rahman, T.: Seismological model parameters for northeastern and its surrounding region of India. Earthq. Sci. 25(4), 323–338 (2012) 14. Thingbaijam, K.K.S., Nath, S.K., Yadav, A., Raj, A., Walling, M.Y., Mohanty, W.K.: Recent seismicity in Northeast India and its adjoining region. J. Seismol. 12(1), 107–123 (2008) 15. Motazedian, D., Atkinson, G.M.: Stochastic finite-fault modeling based on a dynamic corner frequency. Bull. Seismol. Soc. Am. 95(3), 995–1010 (2005). https://doi.org/10.1785/012003 0207 16. Boore, D.M.: Stochastic simulation of high-frequency ground motions based on seismological models of the radiated spectra. Bull. Seismol. Soc. Am. 73(6A), 1865–1894 (1983) 17. Campbell, K.W., Bozorgnia, Y.: Updated near-source ground-motion (attenuation) relations for the horizontal and vertical components of peak ground acceleration and acceleration response spectra. Bull. Seismol. Soc. Am. 93(1), 314–331 (2003). https://doi.org/10.1785/0120020029 18. Seeber, L., Armbruster, J.G., Jacob, K.H.: Probabilistic assessment of seismic hazard for Maharashtra. Unpublished Report, Government of Maharashtra, Earthquake Rehabilitation Cell Mumbai (1999) 19. Kijko, A., Graham, G.: “Parametric-historic” procedure for probabilistic seismic hazard analysis part II: assessment of seismic hazard at specified site. Pure Appl. Geophys. 154(1), 1–22 (1999). https://doi.org/10.1007/s000240050218

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20. Beresnev, I.A., Atkinson, G.M.: FINSIM–a FORTRAN program for simulating stochastic acceleration time histories from finite faults. Seismol. Res. Lett. 69(1), 27–32 (1998) 21. Boore, D.M.: Phase derivatives and simulation of strong ground motions. Bull. Seismol. Soc. Am. 93(3), 1132–1143 (2003). https://doi.org/10.1785/0120020196 22. Iyengar, R.N., Kanth, S.T.G.R.: Attenuation of strong ground motion in peninsular India. Seismol. Res. Lett. 75(4), 530–540 (2004). https://doi.org/10.1785/gssrl.75.4.530 23. Joyner, W.B., Boore, D.M.: Measurement, characterization, and prediction of strong ground motion. In: Earthquake Engineering and Soil Dynamics II, Proceedings of American Society of Civil Engineers Geotechnical Engineering Division Specialty Conference, pp. 43–102 (1988) 24. Douglas, J.: Ground motion prediction equations 1964–2021 (2021) 25. Delavaud, E., Scherbaum, F., Kuehn, N., Riggelsen, C.: Information-theoretic selection of ground-motion prediction equations for seismic hazard analysis: an applicability study using Californian data. Bull. Seismol. Soc. Am. 99(6), 3248–3263 (2009). https://doi.org/10.1785/ 0120090055 26. Delavaud, E., Scherbaum, F., Kuehn, N., Allen, T.: Testing the global applicability of groundmotion prediction equations for active shallow crustal regions. Bull. Seismol. Soc. Am. 102(2), 707–721 (2012) 27. Anbazhagan, P., Bajaj, K., Patel, S.: Seismic hazard maps and spectrum for Patna considering region-specific seismotectonic parameters. Nat. Hazards 78(2), 1163–1195 (2015). https://doi. org/10.1007/s11069-015-1764-0 28. Archuleta, R., Steidl, J., Squibb, M.: The cosmos virtual data center. In: Directions in Strong Motion Instrumentation. Nato Science Series: IV: Earth and Environmental Sciences, vol. 58, pp. 209–222 (2005). Springer, Dordrecht. https://doi.org/10.1007/1-4020-3812-7_13

Influence of Site-City Interaction on the Response of Buildings on Trapezoidal Basin Neeraj Kumar, J. P. Narayan, Pooja Lohchab, and Sanjay Kumar

Abstract The paper presents the response of cluster of buildings located on soft sediment in double resonance situation. In order to comprehend the influence of Sitecity interaction on the response of the buildings, two symmetrical models–Cluster 1 (9 buildings) and Cluster 2 (25 buildings) situated on trapezoidal shaped basin are simulated using Finite Difference algorithm. The analysis of FDM simulation for 3D SCI reveals a reduction in spectral amplitude at natural frequency of building which increases with the number of buildings. The reduction of spectral amplitude is of the order of 65% in the case of cluster 2 with 25 buildings as compared to standalone building located at the center of closed 3D trapezoidal basin. The S-wave response of buildings shows much larger value of spectral amplitude (approx. 180) for standalone building for 3D analysis in comparison with that of 2D analysis for SH and SV-wave and also there is larger reduction of Spectral Amplification Factor (SAF) at fundamental frequency with the splitting of bandwidth for both Cluster 1 and Cluster 2. These findings call for the urgent need of 3D Site-City Interaction studies in urban environment in development of the seismic resilient and sustainable city. Keywords Site-city interaction · Basin effects · S-wave response

1 Introduction The effects of Site-City interaction were initially pragmatic while investigating the complex response of buildings and the long duration of ground motions with beating phenomenon in the Mexico City during the Michoacan 1985 earthquake [1–3]. The past studies on the site-city interaction (SCI) effects were carried out for N. Kumar (B) · P. Lohchab Central University of Haryana, Mahendergarh, Haryana 123031, India e-mail: [email protected] J. P. Narayan · S. Kumar Indian Institute of Technology Roorkee, Roorkee, Uttrakhand 247667, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_9

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two-dimensional site-city models in double resonance condition and reported that SCI significantly modifies the seismic response of buildings and free-field ground motion as well as splits the frequency band at natural mode of vibration of building [4–10]. The response of a building depends on the sediment parameters of the basin and its geometry. The role of number of the buildings, the city-density and heterogeneity of city on the response of buildings and motions without any structures over ground surface are studied in details [6, 8, 11–13]. The SCI effects are significant when two conditions are satisfied: (1) buildings rest on soft soil and (2) buildings and underlying layer of sediment are in double resonance. The structural parameters (especially mass and stiffness) also play crucial role in dynamic response of buildings during earthquake [8, 13, 14]. Most of the studies are limited to the twodimensional simulations of site-city models and limited literature is available on the three-dimensional SCI effects on free-field motion and response of buildings. There are two major issues with this specific case (1) increase in the natural frequency and amplification of basin due its 3D nature and (2) reduction in the dominant frequency of building as its rest on the very soft sediment resulting in rocking motion. Recently, the study carried by Kumar and Narayan [14, 15] and Chandra and Gueguen [16] reveals that the response of buildings is altered by the presence of other nearby buildings. Kumar and Narayan [12] have simulated the response of buildings situated on closed 2D basins considering the two cities of 3 buildings and 5 buildings with SHand SV-wave. Lu et al. [17] have simulated a 3D site-city model having 9 building group located on 3D trapezoidal basin using vertically propagating S-wave (Ricker wavelet) and also performed a large-scale non-linear time history analysis of buildings at Tsinghua University campus, Beijing on a regional scale. However, a very limited literature is available on 3D SCI effects considering the double resonance situation buildings [14]. Present paper focuses on the response of cluster of buildings situated in 3D trapezoidal basin in double resonance situation and the reduction in spectral amplification of buildings with splitting.

2 Model Parameters In order to comprehend the three-dimensional site-city interaction effects on the behavior of buildings, two groups of buildings: Cluster 1 (comprising 9 buildings) and Cluster 2 (comprising 25 buildings) are considered on trapezoidal basin (Fig. 1). The size of trapezoidal basin in the horizontal plain is taken as 351 m (both NS- and EW-directions) (Fig. 2). The utmost depth of basin is fixed as 51 m. The dimension of flat part of basement of basin is 303 m in both the lateral directions. Generally, the shapes of the ponds/lakes filled with soil are semi-circular and trapezoidal in nature. The parameters and size of trapezoidal basin and building blocks are kept same as that considered in the Kumar and Narayan [13] to compare the results of 3D SCI with that of 2D SCI on the response of building. The buildings are incorporated as a 3D homogenous visco-elastic block with 5% damping [18], instead of real building [12, 14, 19]. The lateral dimensions of the buildings are fixed to 33 m × 33 m and the

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building’s height is accustomed so that the natural frequency of building coincides with that of the underlying basin. The plans of Cluster 1 and Cluster 2 are shown in the Fig. 1. Three-dimensional SCI effects on the S-wave responses of basin and cluster of buildings are studied using three-dimensional staggered grid visco-elastic finite difference algorithm developed by Narayan and Sahar [16]. Time-domain simulation is executed including frequency-dependent damping based on GMB-EK rheological model [20]. To avoid reflection from the edges of FD domain, sponge absorbing boundaries are implemented [21] at bottom and side-faces and a modified vacuum formulation is used for free surface boundary. To simplify the nomenclature, the buildings are named as ‘A’, ‘B’, ‘C’, ‘D’ and ‘E’ in both the directions, where ‘C’ is at the center of the city, as shown in Fig. 1. The gap between two constitutive buildings is kept as 9 m. Recorders are positioned at the top of building blocks, which are located in the NS- and EW-arrays crossing each other at the center of the basin. The rheological parameters for building blocks, soil sediment and bedrock such as P-and S-wave velocity, density, quality factor and unrelaxed moduli are given in Table 1 [10, 13, 14]. The site-city models are excited with a North–South (NS) polarized vertically propagating plane S-wave front. A plane wave front at a depth of 125 m is generated in form of Ricker wavelet using shear stress σxz [14]. To comprehend the SCI effects on the response of cluster of buildings, the response of a standalone building (C1) located at the center of the trapezoidal basin is considered as a reference.

Fig. 1 Plan of Cluster 1 (Left) and Cluster 2 (Right) comprising of 9 buildings and 25 buildings, respectively

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Fig. 2 Sketch of Cluster 2 comprising 25 buildings located in trapezoidal basin

Table 1 The considered parameters for building block, basin and bedrock models Materials Building block Basin Bedrock

V S (m/s) 120

V P (m/s) 204

QS 10

QP 17

Density (kg/m3 ) 350

Unrelaxed Moduli (GPa) μu 0.0064

350

860

35

86

2000

0.2628

1800

3120

180

312

2500

8.2104

Ku 0.0168 1.5218 24.526

λu 0.0039 0.9962 8.1058

3 S-Wave Response of 3D Trapezoidal Basin In order to study the response of cluster of buildings located in closed 3D trapezoidal basin in double resonance situation, first spatial variation of spectral amplifications at different modes of vibrations is required, since spectral amplifications at higher modes can be larger than the natural mode of vibration near the edges. The recorders are placed in two arrays oriented in the East–West (EW) and North–South (NS) directions crossing perpendicular to each other at the center of basin. The spacing between two recorders is kept as 21 m to record the EW, NS and UD (Up-Down) components of the motion at the surface of basins.

3.1 Seismic Response of Basin The S-wave responses of the trapezoidal basin model are recorded in both the NS- and EW-array (Fig. 2). Figure 3a, b describes the NS-components and UD-component of S-wave in the NS-array, respectively, and Fig. 3c describes the NS-components in EW-array of the recorded S-wave in basin model. An increase in the amplitude and the duration of recorded signals toward the center of 3D basins is observed in NS-array. This is possibly due to the interference of basin generated Rayleigh waves, diffracted waves from basin edges and the entrapped bidirectional seismic waves which act in both the horizontal and vertical directions [22]. The obtained signal in the UD-components in NS-array may be due to the bending of the incident

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S-wave, which acts as an SV-wave in the NS direction and the Rayleigh waves generated by basin. Though, the amplitude of Rayleigh wave is small as compared to the amplitude of the incident S-wave. The reverse polarity of the diffracted body wave at the opposite edges of basin and basin generated Rayleigh waves has resulted in zero amplitude of the recorded signal in the vertical component at the center of basin. Whereas in EW-array, an increase in amplitude and duration of recorded signals toward the center of 3D basins is due the interference of basin generated surface waves, diffracted waves from basin edges and the trapping of seismic waves [22]. The recorders in EW-array do not record signal in the UD-components, as the incident S-wave has NS-polarization. Further, along this plane, the incident S-wave will behave as SH-wave so only generated Love waves or diffracted SH-waves will be recorded in EW-array.

3.2 Spectral Amplification in Basin The spatial spectral amplifications of NS-component of ground motion in the NSand EW-array in the trapezoidal basin are presented in Fig. 4. The figure reveals a number of modes of vibrations of the basin in the considered frequency band. Among these modes of vibrations, the fundamental (F 0 ) and first (F 1 ) modes are the dominant one and have highest values of spectral amplifications. Further, the frequency consequent to fundamental and first modes are matching in the NS- and EW-arrays in the respective basin. But the spatial spectral amplification pattern is only same for the fundamental mode and not for the first mode in the EW- and NSarrays. However, other higher modes observed in basins are not matching in the both the arrays. Furthermore, the spatial spectral amplifications corresponding to the first mode (F 0 ) of vibration in the EW-array is larger than that at fundamental mode of vibration near the edges in the basin. The complex interference of the multiples wave like incident S-wave, basin generated surface waves and diffracted waves make it highly complex to recognize that the combination of which modes (horizontal or vertical modes) are responsible for the creating the higher modes of vibrations in basins B (natural frequency [13]. The obtained Spectral Amplification Factor (SAF) and F03D of closed 3D basins) of trapezoidal basins is 19.99 and 1.90 Hz, respectively. The B is largest and decay toward edges of basin. Further, the obtained SAF SAF at F03D patterns subsequent to the fundamental and first mode of vibrations in the NS- and EW-arrays in the basin very much match with those obtained in a 2D basin for the SV- and SH-waves, respectively.

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Fig. 3 a and b The NS- and UD-components of S-wave responses in the NS-array and c NScomponent of S-wave responses in EW-array are shown for Trapezoidal Basin model

4 Site-City Interaction Effects in Basin The responses of buildings of Cluster 1 and Cluster 2 located on basin and their SCI effects in double resonance condition are studied. The parameters of building blocks,

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F0 F1

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F0 F1

Fig. 4 The spectral amplification of NS-components for the NS-array and EW-array corresponding to trapezoidal basin model

basin and bedrock are given in Table 1. The spatial geometries of buildings are taken as 33 m × 33 m in lateral direction. The height of building is considered as 11.1 m using empirical relation given by Narayan and Kumar [14], so that natural frequency S ) matches with that of basin. To simplify the investigation as of building blocks (F03D well as to address the responses of different buildings located in different basins and arrays, a unique name is proposed for each building according to its location. The proposed name for the responses of a standalone building at the center of considered trapezoidal basin is ‘C1’. The proposed nomenclature for the responses of buildings

108 Table 2 The 3D natural S ) frequency of building (F03D B S ), at rock and in basin (F03D and largest SAF in the B S of the frequency band of F03D S-waves SCI models in basin

N. Kumar et al. Building

B = F S (Hz) F03D 03D

B S (Hz) F03D

SAF

C1

1.90

1.88

183.88

Cluster 1 (9 buildings) C9EW

1.81

84.14

B9EW

1.90

1.81

71.31

B9NS

1.81

75.66

Cluster 2 (25 buildings) C25EW

1.77

64.63

B25EW

1.90

1.77

58.08

A25EW

1.77

40.64

B25NS

1.77

59.27

A25NS

1.77

43.97

‘C’, ‘B’ and ‘A’ of the Cluster 2 (25 buildings) in the EW-array are C25EW, B25EW and A25EW, respectively. Similarly, proposed names for the responses of buildings ‘B’ and ‘A’ of the Cluster 2 (25 buildings) in the NS-array are B25NS and A25NS, respectively. In the case of Cluster 1 (9 buildings) located in the trapezoidal basin, the proposed names for the responses of buildings ‘C’ and ‘B’ in the EW-array are C9EW and B9EW, respectively, and proposed names for the responses of building B in the NS-array is B9NS (Table 2).

4.1 Response of Standalone Building The response of standalone building (C1) at the center of trapezoidal basin is shown in Fig. 5a. The maximum value of amplitudes is 28.59 cm/s of building C1. The spectral amplifications of standalone building C1 is shown in Fig. 5b. The analysis SB of standalone building in basin is 1.88 Hz and of this figure depicts that the F03D SB of the corresponding SAFs are 183.88 of C1 building. A minor reduction of F03D standalone building at the center of basins can be inferred as compared to that on the rock [14]. The obtained ASA value is 9.40 in the standalone buildings.

4.2 SCI Effects in Cluster 1 (9 Buildings) The S-wave responses of all the nine buildings of the Cluster1 located in the trapezoidal basin are computed at the center of roof of individual buildings. Figure 5a present the responses of the buildings of Cluster 1 located in the trapezoidal basin. The obtained largest amplitude of the buildings C9EW, B9EW and B9NS of the Cluster1 in the trapezoidal basin are −19.82 cm/s, −17.96 cm/s and 18.63 cm/s,

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Fig. 5 a The NS-component of the standalone building, Cluster 1 buildings and Cluster 2 buildings located in trapezoidal basin model b, c a comparison of spectral amplifications of the NS-component of buildings of Cluster 1 (9 Buildings) and Cluster 2 (25 Buildings) located in the trapezoidal basin with that of a standalone building ’C’ located at the center of the trapezoidal basin

respectively. A comparison of response of standalone building of C1 model with the respective building C9EW of the Cluster 1 depicts a large reduction of amplitude in the Cluster1 due to SCI effects. In addition to this, larger amplitude of building ‘C’ as compared to the building ‘B’ in both EW- and NS-array of Cluster 1 is also observed, that may be due to the larger SAF at FB03D of basin at the location of building ‘C’ in the site-city models. In other words, the 3D resonance phenomenon in the considered basin with a large shape-ratio may be responsible for this observation. Figure 5b present the spectral amplifications of buildings of the Cluster 1 SB (1.81 Hz) of the buildings located in trapezoidal basin. The obtained SAFs at F03D

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C9EW, B9EW and B9NS are 84.14, 71.31 and 75.66, respectively. The obtained SB F03D as 1.81 Hz of the buildings in the trapezoidal basins show that SCI effect due to City1 has reduced the natural frequency of building by about 4.73%, respectively, as compared to that of building on rock. The SCI effects due to the Cluster1 have reduced the value of largest SAF of the order of 54.24%, 61.21% and 58.85% for the buildings C9EW, B9EW and B9NS, respectively, in the trapezoidal basin. The ASA values are 6.95, 6.23 and 6.32 in the case of buildings C9EW, B9EW and B9NS in Cluster 1, respectively, being 26.0%, 33.7% and 32.7% lesser compared with that of the C1 model (9.40). Even though, it is apparent that the SCI effect is least for building located at the center of the Cluster 1, but it is occurring because of the used common reference transfer function corresponding to the standalone building at the SB of the buildings in the center of basin. A splitting of the spectral bandwidth of F03D city can be inferred.

4.3 SCI Effects in Cluster 2 (25 Buildings) The S-wave responses of all the twenty-five buildings of the Cluster 2 located in the trapezoidal basin are computed at the center of roof of individual building. Figure 5a presents the responses of buildings C25EW, B25EW A25EW, B25NS and A25NS of the Cluster 2 in the trapezoidal basin, wherein largest amplitude is 15.81 cm/s, −15.69 cm/s, −12.57 cm/s, 14.87 cm/s and 11.89 cm/s, respectively. The obtained amplitudes of buildings in the EW-array are larger than that in the NS-array at the same distance from the center of the cluster. Finally, it may be hypothesized that the SCI effects is responsible for the reduction of level of vibration of buildings and this minimization in shaking is increasing with the number of buildings. The spectral amplifications of buildings C25EW, B25EW, A25EW, B25NS and A25NS of the Cluster 2 in the trapezoidal basin is shown in Fig. 5c, wherein the obtained largest SB (1.77 Hz) are 64.63, 58.08, 40.64, 59.27 and 43.97, respectively. The SAFs at F03D SCI effects due to Cluster 2 have reduced the value of largest SAF of the order of 64.85%, 68.41%, 77.89%, 67.76% and 76.08% for the buildings C25EW, B25EW, A25EW, B25NS and A25NS, respectively, of the C25EW, B25EW, A25EW, B25NS SB as 1.77 Hz for and A25NS in the trapezoidal basin (Table 2). The obtained F03D SB of building by the trapezoidal basin depicts that SCI effects have reduced the F03D 6.84%, as compared to the standalone building on rock. A splitting of the spectral SB of the buildings in the Cluster 2 can also be inferred. It appears bandwidth of F03D that this splitting is occurring due to the very large drop of spectral amplification at S F03D of building on rock. This drop may be due to the inertial effects of the buildings. S of building on rock in the case of Cluster The spectral amplification drops at F03D 2 is much larger than that in the case of Cluster 1. The ASA values are 5.87, 5.49, 4.85, 5.52 and 4.61 in the case of buildings C25EW, B25EW, A25EW, B25NS and A25NS in Cluster 2, respectively, being 37.5%, 41.5%, 78.4%, 41.2% and 59.5% lesser compared with that of the C1 model (9.40).

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5 Discussion SB A considerable reduction of F03D of buildings is inferred due to the SCI effects as S compared to F03D of standalone building on rock and this reduction is proportional to the number of buildings in the city. For example, the percentage reduction of SB F03D of building were of the order of 5% and 7% in the Cluster 1 (9 buildings) and Cluster 2 (25 buildings), respectively, located in the 3D trapezoidal basin (Table 3). SB of building were of the order of 12% and 14% for However, the % reduction of F02D the SH-wave in the City1 (3 buildings) and City2 (5 buildings), respectively, located in the trapezoidal 2D basin (Table 3). The obtained SAF at natural frequency of standalone 3D building in 3D basin (183.8) in double resonance case is also much larger than that of standalone 2D building in the 2D basin (51.94 and 71.51 for the SH- and SV-waves, respectively). But, at same time, the obtained decrease of largest SAF 3D building of the Cluster 2 (64.85%) wherein 25 buildings are located in 3D trapezoidal basin is much larger than that of 2D building of a city with 5 buildings located in the 2D trapezoidal basin (40 and 40.2 for the SH- and SV-waves, respectively) [13]. Further, this drop of largest SAF of buildings of the city is proportional to the number of buildings in the city. As in practice, the obtained % reduction of largest SAF of building of the Cluster 1 and Cluster 2 were of the order of 54.24% and 64.85%, respectively, in the case of city located in the trapezoidal basin (Table 3). SB A splitting of frequency band at natural mode of vibration (F02D and FSB 03D ) is obtained in the cases of SH-wave and SV-wave responses of buildings of city located in the 2D basins as well as the S-wave responses of buildings of Cluster 1 and Cluster 2 located in the 3D basins. The splitting and a drop in the SAF at the frequency S S F02D /F03D due to the SCI effects can be explained in terms of inertial effects of the S SB and F03D of standalone buildings. The splitting is due to very large drop of SAF at F02D building on rock. This drop in SAF at natural frequency of standalone building on rock is proportional to the number of buildings in the city. For example, the SAF drop S of standalone building on rock in the case of Cluster 2 located in 3D basin is at F03D larger than that in the case of Cluster 1 located in 3D basin. Furthermore, the SAF S of standalone building on rock in the case of Cluster 2 (25 buildings) drop at F03D located in 3D basin is larger than that in the case of City 2 (5 buildings) located in SB of buildings of city 2D basin. The obtained splitting of spectral bandwidth of F02D SB located in the 1D and 2D basins and F03D of buildings of city located in 3D basin due to the SCI effects corroborates with that reported by Kham et al. [6], Semblat et al. [8] and Kumar and Narayan [13] based on the analysis of 2D SCI studies.

6 Conclusions A comparison of the observed 3D SCI effects on the S-wave response of buildings of the Cluster 1 and Cluster 2 reveals that the SCI reduced the amplitude of shaking

= 1.50 1.47

Cluster 1

Cluster 2 40.0

30.5

–

% age

4.73

1.77

Cluster 2

6.84

1.05

1.81

% age

s B = 1.85 Hz F02D = F02D

1.88

s F02D (Hz)

31.2

36.1

51.9

SAF

Cluster 1

s B = 1.90 Hz F02D = F02D

13.5

11.7

7.6

% age

2D SV-wave

Standalone building

3D SV-wave

Building “C”

(Hz)

SCI models

Comparison of 2D and 3D models

1.57

= 1.70 Hz s F02D

Standalone building

B F02D

Building “C”

s F02D

2D SH-wave

SCI models

Comparison of 2D and 3D models

64.63

84.14

183.8

SAF

1.62

1.65

1.72

s F02D (Hz)

12.4

10.8

7.02

42.7

48.4

71.5

SAF

64.85

54.24

–

% age

% age

40.2

32.3

–

% age

S B ) in basin and in largest SAF in the Table 3 A comparison of 2D and 3D response of the building ‘C’, % age reduction in natural frequency of building (F03D frequency band of natural mode in the different SCI models

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to greater extent in all the buildings of the city and this reduction is larger at the outermost buildings of the city and least at the center of city for 3D closed basins as observed in case of 2D closed basin [13]. Further, the reduction effects of SCI are larger on the response of buildings of the Cluster2 than that on the buildings of the Cluster1 in the respective basin, which indicated that the SCI effects increases with the number of buildings of the city, but the effects are more prominent in case of 3D basins as compared to 2D basins. These findings call for the urgent need of 3D SCI studies in urban environment especially in the areas of high seismic hazard due to basin effects.

References 1. Chávez-García, F.J., Bard, P.Y.: Site effects in Mexico City eight years after the September 1985 Michoacan earthquakes. Soil Dyn. Earthq. Eng. 13(4), 229–247 (1994) 2. Wirgin, A., Bard, P.Y.: Effects of buildings on the duration and amplitude of ground motion in Mexico City. Bull. Seismol. Soc. Am. 86(3), 914–920 (1996) 3. Gallipoli, M.R., Mucciarelli, M., Castro, R.R., Monachesi, G., Contri, P.: Structure, soil–structure response and effects of damage based on observations of horizontal-to-vertical spectral ratios of microtremors. Soil Dyn. Earthq. Eng. 24(6), 487–495 (2004) 4. Stewart, J.P., Seed, R.B., Fenves, G.L.: Seismic soil-structure interaction in buildings. II: empirical findings. J. Geotech. Geoenviron. Eng. 125(1), 38–48 (1999) 5. Guéguen, P., Bard, P.Y.: Soil-structure and soil-structure-soil interaction: experimental evidence at the Volvi test site. J. Earthquake Eng. 9(5), 657–693 (2005) 6. Kham, M., Semblat, J.F., Bard, P.Y., Dangla, P.: Seismic site–city interaction: main governing phenomena through simplified numerical models. Bull. Seismol. Soc. Am. 96(5), 1934–1951 (2006) 7. Groby, J.P., Wirgin, A.: Seismic motion in urban sites consisting of blocks in welded contact with a soft layer overlying a hard half-space. Geophys. J. Int. 172(2), 725–758 (2008) 8. Semblat, J.F., Kham, M., Bard, P.Y.: Seismic-wave propagation in alluvial basins and influence of site-city interaction. Bull. Seismol. Soc. Am. 98(6), 2665–2678 (2008) 9. Guéguen, P., Colombi, A.: Experimental and numerical evidence of the clustering effect of structures on their response during an earthquake: a case study of three identical towers in the city of Grenoble, France. Bull. Seismol. Soc. Am. 106(6), 2855–2864 (2016) 10. Sahar, D., Narayan, J.P.: Quantification of modification of ground motion due to urbanization in a 3D basin using viscoelastic finite-difference modelling. Nat. Hazards 81(2), 779–806 (2016) 11. Tsogka, C., Wirgin, A.: Simulation of seismic response in an idealized city. Soil Dyn. Earthq. Eng. 23(5), 391–402 (2003) 12. Sahar, D., Narayan, J.P., Kumar, N.: Study of role of basin shape in the site–city interaction effects on the ground motion characteristics. Nat. Hazards 75(2), 1167–1186 (2015) 13. Kumar, N., Narayan, J.P.: Quantification of site–city interaction effects on the response of structure under double resonance condition. Geophys. J. Int. 212(1), 422–441 (2018) 14. Kumar, N., Narayan, J.P.: Quantification of Fundamental Frequencies of 3D Basins and Structures and Site-City Interaction Effects on Responses of Structures. Pure Appl. Geophys. 176, 4477–4502 (2019) 15. Kumar, N., Narayan, J.P.: Effects of site-city-interaction and polarization of the incident wave on the transfer function and fundamental frequency of structures. Nat. Hazards 97(2), 747–774 (2019) 16. Narayan, J.P., Sahar, D.: Three-dimensional viscoelastic finite-difference code and modelling of basement focusing effects on ground motion characteristics. Comput. Geosci. 18(6), 1023–1047 (2014)

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17. Lu, X., Tian, Y., Wang, G., Huang, D.: A numerical coupling scheme for nonlinear time history analysis of buildings on a regional scale considering site-city interaction effects. Earthquake Eng. Struct. Dynam. 47(13), 2708–2725 (2018) 18. Indian Standard IS–1893:2002 (Part 1), Criteria for earthquake resistant design of structures— Part 1: General provision and buildings, Bureau of Indian Standards, New Delhi (2000) 19. Bard, P.Y., Chazelas, J.L., Gueguen, P., Kham, M., Semblat, J.F.: Site–City Interaction in Assessing and Managing Earthquake Risk, Chapter 5. Springer (2005) 20. Emmerich, H., Korn, M.: Incorporation of attenuation into time-domain computations of seismic wave fields. Geophys. 52(9), 1252–1264 (1987) 21. Israeli, M., Orszag, S.A.: Approximation of radiation boundary conditions. J. Comput. Phys. 41(1), 115–135 (1981) 22. Kamal, N.J.: 3D basin-shape ratio effects on frequency content and spectral amplitudes of basingenerated surface waves and associated spatial ground motion amplification and differential ground motion. J. Seismol. 19, 293–316 (2015)

Assessment of Double Resonance from Microtremor Observations for Jammu Region in India Abdullah Ansari , Falak Zahoor , K. S. Rao , A. K. Jain, Aashi Pal, Neeraj Kumar, Sakib Majid Hajam, Pallavi Shukla, Krishna Sharma, Faizan Fayaz, Mir Akhtar Yousuf, Shakir Riyaz, and Umer Altaf Khan Abstract The Jammu region (JR) in the union territory of India is located in the northwestern Himalayas, affected by moderate to large magnitude earthquakes. To assess the possibility of double resonance effects (DRE) during significant earthquake events, this study examines the influence of Reinforced Concrete (RC) building vibrations in the JR in the context of local seismic response investigations. Using microtremor, a single station seismic ambient noise investigation was conducted at 242 locations to assess the predominant frequency (f p ) and H/V spectra for subsurface soil. During the field survey, building data is collected, and an empirical relationship between building height and vibration period is developed. The findings show that the fundamental frequency (f f ) of 17.35% of structures overlaps with the predominant frequency (f p ) of the site locations in the study area where microtremor testing was done. The vulnerability map divides the city into three discrete zones with high, medium, and low resonance levels, allowing for better hazard mitigation and town planning. Keywords H/V spectra · Double resonance · Predominant frequency · Jammu · Himalayas

1 Introduction Jammu and Kashmir is located in the northwestern Himalayas, one of the world’s most seismically active areas, with major earthquakes, triggered in 1555, 1828, 1885, A. Ansari (B) · F. Zahoor · K. S. Rao Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India e-mail: [email protected]; [email protected] F. Zahoor · A. K. Jain · A. Pal · N. Kumar · S. M. Hajam · P. Shukla · K. Sharma · F. Fayaz · M. A. Yousuf · S. Riyaz · U. A. Khan Department of Civil Engineering, National Institute of Technology Srinagar, Jammu and Kashmir 190006, Srinagar, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_10

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1905, 2005, and 2019 [1–3]. The 2005 Kashmir earthquake (M w = 7.6) was one of the most devastating, killing over one lakh people and destroying infrastructure such as bridges, retaining walls, dams, and building structures. Recent moderate earthquakes in this region include the 2013 Kishtwar earthquake (M w = 5.7) and the 2019 Mirpur earthquake (M w = 5.6). The 2019 Mirpur earthquake in Pakistan was felt in the Jammu region (JR), Punjab region, Uttarakhand, and Delhi killing 50 people and causing property damage in villages surrounding Mirpur city such as Jatlan, Manda, and Afzalpur [4–7]. The slope failure and ground shaking were observed in the Rajouri and Poonch districts due to the 2019 Mirpur earthquake. The post-effect of these earthquake events created the problem of liquefaction in different parts of Jammu and Kashmir [1, 8, 9]. The bridge construction, building development schemes, tunnelling, and national highway expansion-related infrastructure projects as well as urbanisation-oriented construction projects and schemes have advanced at a higher rate in this area in recent [10–12]. In the map shown in Fig. 1, ongoing infrastructure projects in Jammu and Kashmir are highlighted. The Udhampur Srinagar Baramulla Rail Link Project (USBRL) includes the 11.2 km long Pir Panjal railway tunnel, which is located north of Banihal town in Ramban district and travels through the Pir Panjal Range in the central Himalayas. The historical record of earthquakes and seismotectonic settings, as well as urbanisation and industrial growth within the JR, drive the need to quantify local seismic response and investigate the implications of double resonance for the safe design of RC building structures.

Fig. 1 Map of Jammu Region (JR) in Jammu and Kashmir. Red circles indicate the test locations

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2 Single Station Microtremor Testing There are twenty districts in Jammu and Kashmir, ten in the Jammu region (JR), and ten in the Kashmir valley (KV). The Chenab, Ravi, Tawi, and Poonch are the major rivers passing through the JR covering a total area of 26,293 km2 . The near-field as well as far-field earthquakes throughout the Himalayan region, extending up to Hindukush in Afghanistan, have been flicked by both JR and KV [13, 14]. Natural (earthquake, wind, sea waves, etc.) and manmade (traffic, industrial sound, people walking on the street, etc.) sources contribute to ambient seismic noise. Nogoshi [15] developed the Horizontal-to-Vertical Spectral Ratio (HVSR) analysis of microtremor recordings, which was emphasised by [16]. This approach is used to calculate the amplitude spectrum by performing a Fourier transform on the raw data. To exclude the influence of site effects, propagation route, and source effects, the ratio of the squared average of N-S and E-W amplitude spectrum to the vertical amplitude spectra is examined. The maxima for HVSR curves relate to seismic wave amplification induced by a strong impedance difference between underlying strata highlighting the predominant frequency (f p ) for foundation soil. These frequencies represent the approximate values at which the soil might undergo resonance if an earthquake of similar frequency occurs in the region [13, 17]. In this study, a Tromino [18] with a frequency range of 0.1–1000 Hz was employed to record ambient noise and microtremors. The ambient noise measuring instruments provides the level of constant vibration of the Earth interior. This is referred to as seismic noise, and it is produced as a result of both high-frequency manmade events and low-frequency natural occurrences. Nakamura [16] proposed that site response can be anticipated from Horizontal-to-Vertical Spectral Ratio (HVSR) in the frequency domain of seismic microtremors. The amplitude of the H/V curve can be estimated using the mathematical relationship pointed out in Eq. (1). / HVSR(ω) =

HN2 S (ω) + HE2 W (ω) VU P−D O W N (ω)

(1)

By leaving the equipment at the testing position undisturbed for around 30 min, passive noise was captured at a sampling rate of 128 Hz (Fig. 2). Readings were obtained at 242 unique locations covering the whole JR, during peaceful times to avoid influence from undesired anthropogenic noise. The geology of Jammu and Kashmir divides the region into three structural zones: the Pir Panjal, Zanskar, and Tertiary groups. Depending on the local geology and topography, generally clear, multiple, and flat types of curves are observed. For high, medium, and low-risk levels of DRE for sites exhibiting clear peaks for HVSR curves are illustrated in Fig. 3. The field observations for sites showing the clear peak during extensive geophysical testing are reported in Table 1.

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Fig. 2 Single station HVSR microtremor field setup showing all accessories needed to perform the geophysical testing

Fig. 3 HVSR function derived from microtremor for three site locations (43, 159, and 217) indicating the various resonance modes. HVSR curves for site locations 43 and 159 present a high and moderate risk for double resonance, respectively. The magenta colour curve is shown for site location 217 which is subjected to low-level double resonance effect (DRE)

3 Field Survey and Structural Integrity of Building Scenario During the field survey in the Jammu region (JR), RC building data was collected for all test locations where microtremor readings were measured. The southwestern part of JR is flat while the rest is made up of the Jammu hills, with elevations ranging between 330 m at the Jammu to 1638 m at the Kishtwar. Jammu is the winter capital of Jammu and Kashmir; hence, the maximum population is attached to Jammu and neighbouring districts like Udhampur, Doda, Samba, and Kathua. The topography of the northern part of Ramban and Doda, as well as Kishtwar which shares the border

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Table 1 Frequency and amplitudes of HVSR curves with a clear peak in the Jammu region (JR) Site no.

f p (Hz)

H/V

Site no.

f p (Hz)

H/V

Site no.

f p (Hz)

H/V

1

6.15

4.57

74

0.86

2.08

157

5.74

3.34

2

2.1

1.45

75

0.96

2.56

158

6.08

5.09

3

6.05

4.66

76

5.78

3.12

159

5.32

4.78

4

6.26

4.25

77

6.05

6.02

160

7.49

1.92

5

5.94

3.16

78

5.31

7.22

161

6.33

3.55

6

5.74

3.34

79

5.33

6.29

162

6.31

5.51

7

7.58

1.98

81

5.59

3.79

163

7.54

1.87

8

5.64

6.61

82

5.94

3.16

164

7.38

2.48

9

0.89

2.03

84

5.64

6.61

165

7.76

1.85

10

1.18

2.86

85

5.55

5.31

166

7.45

1.95

11

0.84

2.96

86

1.23

1.68

167

7.89

1.82

12

0.85

2.29

91

3.44

2.19

168

7.58

2.15

13

0.92

2.02

93

1.45

3.36

170

7.71

1.98

14

0.86

2.62

94

1.26

2.98

173

7.37

1.71

15

0.94

1.74

96

1.41

2.11

174

7.58

1.92

17

0.38

1.88

97

1.85

1.92

175

7.33

2.11

18

0.87

4.01

100

2.21

2.15

176

7.63

1.66

19

4.28

2.99

103

7.64

2.11

177

0.84

2.07

20

0.51

1.93

104

1.14

2.21

178

0.95

1.93

22

0.99

2.91

105

3.32

2.22

179

0.99

2.91

24

1.02

2.41

108

0.37

1.25

181

5.33

6.29

25

1.26

2.88

111

0.94

2.25

182

6.15

4.23

26

0.63

1.81

112

7.64

1.88

184

1.14

1.16

29

3.53

1.74

113

1.41

2.11

187

1.49

2.13

31

7.64

1.95

114

1.26

2.88

194

5.49

4.69

32

3.18

2.23

115

7.71

1.93

195

1.84

2.05

35

4.09

3.31

120

5.79

2.89

199

7.58

1.82

37

2.52

1.62

121

6.32

4.64

200

7.53

1.99

38

1.18

2.55

122

6.32

4.25

201

7.32

2.39

39

7.51

1.96

123

7.5

1.92

202

6.09

5.11

40

7.45

1.99

124

8.03

1.75

203

6.22

4.43

42

7.58

2.15

128

7.76

1.85

204

6.25

4.88

43

5.32

6.25

129

9.78

3.52

205

6.35

5.51

44

7.58

1.96

132

7.54

2.28

207

7.77

1.91

45

7.64

2.13

133

6.38

4.15

208

7.68

1.98

48

7.38

1.97

134

7.5

2.67

209

7.94

2.55 (continued)

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Table 1 (continued) Site no.

f p (Hz)

H/V

Site no.

f p (Hz)

H/V

Site no.

f p (Hz)

H/V

50

7.38

1.91

135

6.54

4.49

211

8.19

2.31

52

7.45

2.04

136

7.58

1.92

217

5.32

5.48

53

7.71

2.26

137

7.33

2.39

218

1.86

1.92

54

8.03

1.75

138

7.33

2.11

220

2.21

2.16

56

7.71

1.61

140

0.85

2.29

221

1.26

2.98

57

9.79

3.52

142

1.19

2.86

222

1.28

2.58

58

7.64

1.74

143

5.57

3.71

223

1.24

1.67

60

1.41

2.12

144

1.28

2.59

227

1.14

2.21

62

1.12

1.18

147

5.35

6.29

228

1.18

2.55

63

1.25

2.11

148

6.31

5.51

231

1.02

2.41

66

1.28

2.58

150

5.52

6.11

232

5.55

5.31

68

0.9

4.71

151

5.31

7.22

234

1.15

1.68

71

0.67

2.26

153

6.21

5.34

237

6.05

6.02

72

0.94

2.25

154

6.21

4.78

238

1.25

2.11

Table 2 Parametric count taken under consideration while doing field survey in Jammu region (JR) Construction year

No. of buildings

Functionality

Before 1920

37

Public

1920–1950

42

Residential

1950–1980

43

No information

1980–2005

48

After 2005

72

No. of buildings

No. of floors (N f )

No. of buildings

46

2

185

3

105 62

11

4

31

5

29 5+

15

with Ladakh, is extremely harsh and attracts less population. Data for construction year, functionality, and structure height for buildings taken under study are major key components selected while doing the field survey. Details for all these components are enlisted in Table 2.

4 Evaluation of Empirical Relationship Between Vibration Period (T f ) and Building Height (H) The building scenario data collected during the field survey was used to evaluate the empirical relationship between build height (H) and vibration period (T f ) using the regression analysis (Fig. 4). The inverse of vibration period gives the fundamental

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frequency (f f ) of selected RC buildings in the study area. The fundamental frequency (f f ) of important building structures (Fig. 5) is mentioned in Table 3.

Fig. 4 Variation of vibration period with respect to the building height for selected site locations during the field survey

Fig. 5 Buildings in different districts of the Jammu region (JR) highlight the structural integrity and built environment

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Table 3 Fundamental frequency (f f ) of important Reinforced Concrete (RC) buildings for public domain usage in the Jammu region (JR) District

No. of floors Building height Fundamental (H) in m frequency (ff) in Hz

Government Chak Bhalwal College of Engineering and Technology (GCET) (32.8508°N, 74.7787°E)

Jammu

3

13.71

2.98

Euro Kids School (32.5627°N, 75.0991°E)

Samba

3

11.88

3.44

Kathua

4

18.28

2.23

Hotel Patnitop Heights (33.0847°N, 75.3354°E)

Padora enclave, Udhampur 3 Patnitop

12.78

3.19

Jamia Masjid (33.3768°N, 74.3102°E)

Madina Colony Rajouri

2

8.52

4.79

Hazrat Allama Anwar Shah Kashmiri Islamic International Academy (33.7678°N, 74.0956°E)

Nai Basti

Poonch

4

15.84

2.58

Reasi

2

8.52

4.79

Building name

Address

Balour

University of Janglote Road Jammu (Kathua Campus) (32.4161°N, 75.5190°E)

Shri Mata Katra Vaishno Devi Narayana Superspeciality Hospital (33.0308°N, 74.9490°E)

(continued)

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Table 3 (continued) Building name

Address

District

No. of floors Building height Fundamental (H) in m frequency (ff) in Hz

Government High School (GHS) (33.3831°N, 75.1560°E)

Khari

Ramban

3

13.71

2.98

Doda

5

22.85

1.79

Kishtwar

2

7.92

5.15

Government Near Doda Bus Medical stand College (GMC) (33.1415°N, 75.5831°E) Dargah Shah Asrar-ud-Din Baghdadi (33.3229°N, 75.7593°E)

Chowgan

5 Double Resonance Effect (DRE) in Jammu Region (JR) A building oscillates during an earthquake and may sustain significant damage if the fundamental period of oscillation is comparable to the predominant frequency (f p ) of the foundation soil on which it is built [19–22]. The “double resonance effect” is an untoward coincidence that can induce collapse even under minor seismic stimulation. In the present study, the predominant frequency (f p ) of foundation soil is cross-checked with the fundamental frequency (f f ) of the RC buildings in a way to assess the double resonance effect (DRE) [14, 23]. The analysis results showed that more than 50% area falls under the high to moderate level risk zone for the double resonance effect (DRE). For the southwestern towns of Jammu and Kathua and the maximum portion of Samba, complete overlapping of predominant frequency (f p ) of foundation soil with the fundamental frequency (f f ) of the RC buildings were observed (Fig. 6). These buildings show a high risk of DRE and are located in the Indo-Gangetic plain surrounded by young alluvial deposits. The sites located on the right bank of Tawi River specifically in the northern end, Udhampur, Rajouri, and a few sites in Kathua adjacent to Ravi River exhibit an intermediate level of overlapping. A total of 129 sites out of 242 reveal no overlapping and show low risk of DRE (Table 4). In Reasi, Poonch, Doda and Kishtwar, chances of DRE are extremely low and can be declared a safe zone with no chances of DRE.

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Fig. 6 Integrated map dividing the Jammu region (JR) into high, moderate, and low-level risk zones for double resonance effect (DRE) based on predominant frequency (f p ) of foundation soil and fundamental frequency (f f ) of Reinforced Concrete (RC) building structures

Table 4 Zonation of sites to highlight Double Resonance Effect (DRE) during earthquake events in the Jammu region (JR)

Chances of overlapping

No. of buildings

% of total sites

Risk zone

Complete

42

17.35

High

Intermediate

71

29.33

Moderate

129

53.30

Low

No

6 Conclusion Jammu and Kashmir is located in the northwestern Himalayas, one of the world’s most seismically active region, with major earthquakes occurring in 1555, 1828, 1885, 1905, and 2005. In the present study, the resonance frequency (f 0 ) of foundation soil is cross-checked with the fundamental frequency (f f ) of the Reinforced Concrete (RC) buildings in a way to assess the double resonance effect (DRE). More than 50% area falls under the high to moderate level risk zone for the DRE. For the southwestern towns of Jammu and Kathua and the maximum portion of Samba, complete overlapping of f 0 of foundation soil with the f f of the RC buildings was observed. These buildings show a high risk of DRE and are located in the IndoGangetic plains surrounded by young alluvial deposits. The sites located on the right

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bank of Tawi River specifically in the northern end, Udhampur, Rajouri, and a few sites in Kathua adjacent to Ravi River exhibit an intermediate level of overlapping. A total of 129 sites out of 242 reveal no overlapping and show a low risk of DRE. In Reasi, Poonch, Doda, and Kishtwar, chances of DRE are extremely low and can be declared a safe zone with no chances of DRE. These results suggest that soilstructure interaction plays a significant role in expected damage scenarios and must be considered while doing seismic microzonation for urban areas. Acknowledgements Tromino used for geophysical testing is provided by the Geotechnical Division of National Institute of Technology Srinagar, Jammu and Kashmir. The authors thank for the technical and logistical assistance offered by the National Institute of Technology Srinagar. Abdullah Ansari is thankful to Prof. GM Bhat (University of Jammu, Jammu), Dr. Sanjeev Gupta (GCET, Jammu), and Tanzeel Ur Riyaz (RIMT University, Punjab) for their support during data acquisition. The authors are also grateful to the Divisional Commissioner Office of Jammu and Kashmir for granting special permission for fieldwork in Jammu and Kashmir during the COVID-19 pandemic.

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11. Ansari, A., Zahoor, F., Rao, K.S., Jain, A.K.: Deterministic approach for seismic hazard assessment of Jammu region, Jammu and Kashmir. In: Geo-Congress 2022: Geophysical and Earthquake Engineering and Soil Dynamics, GSP 334, pp. 590–598 (2022). https://doi.org/10.1061/ 9780784484043.057 12. Zhong, Z., Shen, Y., Zhao, M., Li, L., Du, X., Hao, H.: Seismic fragility assessment of the Daikai subway station in layered soil. Soil Dyn. Earthq. Eng. 132, 106044 (2020). https://doi. org/10.1016/j.soildyn.2020.106044 13. Ansari, A., Zahoor, F., Rao, K. S., Jain, A. K.: Seismic response and vulnerability evaluation of Jammu Region (Jammu and Kashmir). Indian Geotech. J. 52(6), 1–14 (2022). https://doi. org/10.1007/s40098-022-00694-0 14. Lister, G., Kennett, B., Richards, S., Forster, M.: Boudinage of a stretching slablet implicated in earthquakes beneath the Hindu Kush. Nat. Geosci. 1(3), 196–201 (2008) 15. Nogoshi, M., Igarashi, T.: On the amplitude characteristics of microtremor, Part 2. J. Seismol. Soc. Jpn 24, 26–40 (1971) 16. Nakamura, Y.: A method for dynamic characteristics estimation of subsurface using microtremor on the ground surface. Railway Tech. Res. Inst. Q. Rep. 30(1), 25–33 (1989) 17. Bhandary, N.P., Paudyal, Y.R., Okamura, M.: Resonance effect on shaking of tall buildings in Kathmandu Valley during the 2015 Gorkha earthquake in Nepal. Environ. Earth Sci. 80(13), 1–16 (2021) 18. Micromed s.p.a.: The short Tromino® how to, Ver. 1.1, pp. 1–26 (2009) 19. Adhikari, A., Rao, K.R.M., Gautam, D., Chaulagain, H.: Seismic vulnerability and retrofitting scheme for low-to-medium rise reinforced concrete buildings in Nepal. J. Build. Eng. 21, 186–199 (2019) 20. Alam, M.N., Alam, M.S., Tesfamariam, S.: Buildings’ seismic vulnerability assessment methods: a comparative study. Nat. Hazards 62(2), 405–424 (2012) 21. Ansari, A., Rao, K.S., Jain, A.K.: Damage analysis of seismic response of shallow tunnels in Jammu. In: Das, B.B., Hettiarachchi, H., Sahu, P.K., Nanda, S. (eds.) Recent Developments in Sustainable Infrastructure (ICRDSI-2020)—GEO-TRA-ENV-WRM. Lecture Notes in Civil Engineering, vol. 207, pp. 611–619. Springer, Singapore (2022). https://doi.org/10.1007/978981-16-7509-6_47 22. Ameen, A. M. M. A.: Seismic Hazard Evaluation of Jammu Region and Risk Assessment of Tunnels in the Himalayas. Ph.D. Thesis, Indian Institute of Technology Delhi, India (2023) 23. Ansari, A., Satake K., Malik, J. N.: Modelling the 2004 Indian Ocean Tsunami to estimate tsunami heights and its amplitude and to study its effects on coastal areas. Proceedings of the ERI Earthquake Conference, Tokyo, Japan (2017)

Influence of Epistemic Uncertainty on the Seismic Vulnerability of Indian Code-Compliant RC Frame Building Kaushik Gondaliya , Vishisht Bhaiya, Sandip Vasanwala, Atul Desai, and Jignesh Amin

Abstract The study’s primary goal is to examine the presence of epistemic uncertainty in the RC frame when subjected to lateral force. The seismic response of the RC frame and the impact of uncertainties, namely material (γc , f c , and f y ) and geometrical (Db and bc ) nonlinearity, on the structural elements are determined using nonlinear static analysis. A fragility curve for a 4-storey RC frame structure designed using the most recent IS 1893 for seismic zone-V was created using a simulationbased method. The traditional deterministic technique was also utilised to compare the effect of the structure’s lateral response uncertainty. The classifications for the various damage levels, including slight, moderate, severe, and complete, were established. The random variables are the strength properties of the concrete (f ck ) and steel (f y ) materials, the depth of the beam (Db ), the width of the column (bc ), and the weight density of concrete (γc ). The Monte Carlo technique is used to determine the structural randomness of the RC frames. The gathered data demonstrated a significant increase in the fragility uncertainty and structural responsiveness compared to the deterministic technique. A fundamental conclusion of this study is the critical relevance of tackling issues about the building’s fragility and predicted damage state from a stochastic perspective. K. Gondaliya (B) · V. Bhaiya · S. Vasanwala · A. Desai Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat 395007, India e-mail: [email protected] V. Bhaiya e-mail: [email protected] S. Vasanwala e-mail: [email protected] A. Desai e-mail: [email protected] J. Amin Department of Civil Engineering, Graduate School of Engineering and Technology, Gujarat Technological University, Chandkheda, Gujarat 382424, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_11

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Keywords RC frame · Epistemic uncertainty · Seismic vulnerability · Pushover analysis

1 Introduction With advancements in Earthquake engineering and computing application, it is possible to conduct multiple numbers of analyses to check the wide range of lateral response possibilities. Two forms of uncertainty are involved in determining the fragility curves for every selected RC frame structure [1]. Aleatory uncertainty is the natural phenomenon presenting the randomness in the ground motion in terms of direction, intensity, magnitude, and distance from the site location. The aleatory form of uncertainty is difficult to reduce. However, knowledge of the structure’s material and geometry nonlinearity can reduce the epistemic type. Epistemic uncertainty occurred due to the assumption of material strength and geometry nonlinearity in the analysis [2–5]. It has been observed that epistemic uncertainty can highly influence the RC frame building’s lateral stiffness and nonlinear response. Incomplete scientific knowledge is the source of epistemic uncertainty, which may be addressed in principle by further evidence or more sophisticated scientific concepts. Many researchers have performed stochastic analysis approaches to minimise epistemic uncertainty [6–8]. Seismic fragility analysis is highly influenced by the consideration of epistemic uncertainty during nonlinear static analysis [9, 10]. It has been observed from past literature that various statistical approach is available to conduct the seismic fragility analysis. Capacity-spectrum based analytical approach is the most efficient method for performing the fragility analysis [11]. It is frequently assumed that the following lognormal probability density function can adequately describe the fragility curve:  ρk (Sd ) = Φ

  Sd 1 ln βk Sd,ds

(1)

where S d represents the spectral displacement, S d, ds represents the median value of spectral displacement, and S d represents the threshold limit for a given damage state, which depicts the point at which the probability of equaling or surpassing the damage state is 50%. β k is the standard deviation of the natural logarithm of S d for a given damage state, which provides the notion of scattering, and Φ corresponds to the standard normal cumulative distribution function. This study focuses on determining the seismic vulnerability assessment (SVA) of the 4-storey RC frame building by employing deterministic and stochastic analytical techniques. Before using nonlinear static analysis, a Monte Carlo technique is performed [12]. Utilising the capacity spectrum approach, the seismic vulnerability of the selected RC frame building is determined.

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2 Description of the Study RC Frame IS 456:2000, IS 1893:2016, and IS 13920:2016 serve as a baseline of the 4-storey RC special moment-resisting frame building structure [13–15] (see Fig. 1). According to IS 875:1987 (Part-1) and IS 875:1987 (Part-2) [16, 17], the gravity loads are estimated to be 1 kN/m2 for dead load and 3 kN/m2 for live load. The internal and external masonry walls are each given a thickness of 115 and 230 mm, respectively, while the slab is assigned a thickness of 150 mm. The lateral rigidity of infill walls is not taken into account.

2.1 Material and Geometry Nonlinearity Spatial Variation. Random variables are used to determine the probabilistic seismic vulnerability of structures. The random variables that explicitly affect the epistemic uncertainty of the response were considered in the present study. Table 1 shows the selected five random variables for the stochastic analysis of the 4-storey RC frame building. Here, the characteristic strength of concrete (f c ) is a random variable, and the elastic modulus of concrete √ (E c ) is an explicitly random variable. E c is considered by the equation E c = 5000 f ck . While young’s modulus (E s ) of the steel was taken at the standard rate 2 × 105 N/mm2 [13] guidelines, and the only random variable was the yield strength value of the rebar Fe500. Four groups, one for each floor, make up each floor’s structural component on the building’s floor. The total correlation matrix is employed to consider the correlation between the samples [18]. The sample created for the column or beam belonging to

Fig. 1 a–b Geometry configuration of the selected 4-storey RC frame building with rebar detailing of c beam and d column

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Table 1 Details of selected random variables for the simulation Variable

Mean

fc

25 MPa

fy

500 MPa kg/m3

SDa

CVb

Distribution

3.11

0.12

Normal

17.26

0.04

Lognormalc

γc

25

2.50

0.10

Normal

Db

450 mm

9.38

0.02

Normal

bc

350 mm

7.89

0.02

Normal

SD = Standard deviation b Coefficient of variation c Lognormal random number generated [19] a

the same group is thought to have a strong correlation, whereas the sample of different groups is believed not to correlate. It also accounts for a certain degree of spatial fluctuation of unpredictability. For each iteration of the Monte Carlo algorithms, a random trial of variable rate is created for individual elements of every group [7–9].

2.2 SVA of Selected 4-Storey RC Frame Capacity-Spectrum Method (CSM). SVA of the RC frame building is derived using the CSM. The nonlinear static analysis is commonly referred to as the pushover analysis (PA). Probabilistic analysis is adopted based on the Monte Carlo approach using a random variable of the selected parameters for the RC frame structures. The choice of load distribution is essential when assessing the seismic performance of a structure using PA since it determines how an earthquake’s inertia force will affect a building. The equation calculates the load distribution of seismic force at each floor (Qi ) [14]. Qi =

Wi h i2

n Σ

i=1

Vd

(2)

Wi h i2

where Qi represents the lateral load at the ith floor, lateral loads generate flexural deformations near element borders; hence, flexural hinges are provided to member ends throughout the modelling phase. While hinges M3 accommodating flexure are provided for the beams, hinges P-M2 -M3 accommodating axial and bending moments are specified for the columns. Additionally, the pushover analysis considers the P-delta effect of evaluating the actual reaction of a structure. In beam/column elements, the relative locations of the hinges are established by default SAP2000 software provides [20]. Figure 2 shows the derived PA curves per the deterministic and stochastic approach for the selected 4-storey RC frame building.

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Fig. 2 Comparison of the pushover analysis as per the deterministic and probabilistic approach

In the present work, seismic fragility evaluations are conducted by establishing damage states for each frame and assessing the likelihood of exceeding each damage state by HAZUS criteria. Consideration is given to four damage states: slight, moderate, severe, and complete. The damage state threshold is shown in Fig. 3 to be determined from the bilinear capacity spectrum. Uncertainty (β k ) is calculated in the present study by applying the approximation of normal Assuming that when S d is equal to Sdk , the probability of equaling or exceeding the damage state is 50%, the probabilities of the other damage states are estimated from the corresponding binomial probability distribution [21–23]. Seismic fragility analysis is carried out to find the most probable likelihood of damage under

Fig. 3 Damage threshold of capacity spectrum and fragility curve fitting sample of a 4-storey RC frame structure

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seismic excitation. The most likely damage condition of a building can be expressed by the weighted mean damage index matrix, DSm : DSm =

4 Σ

k pk [N , d]

(3)

i=1

where pk [N, d] is the likelihood that a particular damage state will occur and k is one of the following values: 0, 1, 2, 3, or 4, a zero value of d implies non-damage to a structure, whereas d = 1 denotes destruction. The seismic demand specification calculates the chance of exceeding (Pk ). It is accomplished via a fragility graph by determining the likelihood of exceeding a spectral displacement (S d ) appropriate to the performance point (S d, ds ). Consequently, the probability of occurrence (pk ) of discrete damage grade is derived as shown in Eq. 4. ρk = Pk (k + 1) − Pk (k)

(4)

Furthermore, the mean damage condition of the structure is determined using the probability of occurrence (ρk ). Figure 4 shows the fragility curves produced from the deterministic and stochastic technique for slight, moderate, severe, and complete damage states.

Fig. 4 Fragility graph is derived for material and geometry nonlinearity using the various damage states of the 4-storey RC frame building

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Fig. 5 Comparison of the fragility curve derived using the a deterministic and b stochastic approach of the selected RC frame building

3 Results and Discussion This part discusses the most significant outputs of the stochastic viewpoint. The following are the most important findings of the present study of the 4-storey RC frame. • Comparing the model analysed using a deterministic methodology to a comparable model analysed using a stochastic method, the deterministic technique reveals a 33.38% decrease in the elastic behaviour. • The stochastic analysis of the proposed building indicated considerable changes in the nonlinear response of the structure under seismic force, which considerably impacted the failure mechanism of structural elements. • In a deterministic analysis, an RC frame construction sustains minimal damage. However, moderate damage is observed in a stochastic analytic technique. Figure 5 shows the fragility graph generated per the deterministic and stochastic analysis technique for the sample study building. As the performance point values grow, it was noted that RC frame building evaluation using a deterministic approach produces a conservative estimate of likelihood.

4 Conclusion The 4-storey RC frame building is analysed using the five random variables based on the deterministic and stochastic approach. A stochastic PA technique reveals up to a 10–13% increment in the likelihood of exceedances for complete damage states in the 4-storey RC frame structure. Here, the lateral stiffness of the masonry infill wall

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is not addressed, which could result in a more realistic behaviour of the RC frame building under lateral force. This study can be utilised to build a machine learning model to forecast the mean damage state.

References 1. Bulleit, W.M.: Uncertainty in structural engineering. Pract. Period. Struct. Des. Constr. 13(1), 24–30 (2008) 2. Iervolino, I.: Estimation uncertainty for some common seismic fragility curve fitting methods. Soil Dyn. Earthquake Eng. 152, 107068 (2022) 3. Choudhury, T., Kaushik, H.B.: Seismic response sensitivity to uncertain variables in RC frames with infill walls. J. Struct. Eng. 144(10), 04018184 (2018) 4. Bai, J.W., Hueste, M.B.D. and Gardoni, P.: Probabilistic assessment of structural seismic damage for buildings in mid-America. In: AIP Conference Proceedings, Vol. 1020, No. 1, pp. 1685–1692. American Institute of Physics (2008) 5. Cremen, G., Baker, J.W.: Variance-based sensitivity analyses and uncertainty quantification for FEMA P-58 consequence predictions. Earthquake Eng. Struct. Dyn. 50(3), 811–830 (2021) 6. Gondaliya, K.M., et al.: Seismic vulnerability assessment of Indian code compliant RC frame buildings. J. Vibration Eng. Tech. 1(25), (2022) 7. Gondaliya, K., Bhaiya, V., Vasanwala, S., Desai, A.: Probabilistic seismic vulnerability of Indian code-compliant RC frame. Pract. Period. Struct. Des. Constr. 27(3), 04022028 (2022) 8. Alzate, V., Felipe, Y., et al.: An efficient methodology to estimate probabilistic seismic damage curves. J. Struct. Engi. (New York, NY) 145(4), 04019010–04019011 (2019) 9. Vargas-Alzate, Y.F., et al.: Seismic risk assessment using stochastic nonlinear models. Sustain. 12(4), 1308 (2020) 10. Rosowsky, D.V., Stewart, M.G.: Probabilistic construction load model for multistory reinforced-concrete buildings. J. Perf. Const. Facil. 15(4), 145–152 (2001) 11. Barbat, A.H., Pujades, L.G., Lantada, N.: Performance of buildings under earthquakes in Barcelona, Spain. Computer-Aided Civil Infra. Eng. 21(8), 573–593 (2006) 12. Hurtado, J.E., Barbat, A.: Monte Carlo techniques in computational stochastic mechanics. Archiv. Comp. Methods Eng. 5(1), 3–29 (1998) 13. BIS IS 456.: Plain and Reinforced Concrete-code of practice. Bureau of Indian Standards, New Delhi (2000) 14. BIS IS 1893.: Criteria for earthquake resistant design of structures, Part-1 General Provisions and Buildings. Bureau of Indian Standards, New Delhi (2016) 15. BIS IS 13920.: Ductile detailing of reinforced Concrete-code of practice. Bureau of Indian Standards, New Delhi, India (2016) 16. BIS 875 Part 1.: Design loads (other than earthquake) For buildings and structure. Dead loads— code of practice. Bureau of Indian Standards, New Delhi (1987) 17. BIS 875 Part 2.: Design loads (other than earthquake) for building and structure. Imposed loads—codes of practice. Bureau of Indian Standards, New Delhi (1987) 18. Alzate, V.Y.F., et al.: Probabilistic seismic damage assessment of reinforced concrete buildings considering directionality effects. Struct. Infrastruct. Eng. 14(6), 817–829 (2018) 19. WESSA Homepage: http://www.wessa.net/, last accessed 2022/02/28. 20. Structural Analysis Software (SAP2000), Advance, static and dynamic finite element analysis of structures, Computer and structures, Inc., Berkeley, CA (2015)

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21. Lantada, N., Pujades, L.G., Barbat, A.H.: Vulnerability index and capacity spectrum-based methods for urban seismic risk evaluation: a comparison. Nat. Hazards 51(3), 501 (2009) 22. Barbat, A.H., Pujades, L.G., Lantada, N.: Seismic damage evaluation in urban areas using the capacity spectrum method: application to Barcelona. Soil Dyn. Earthq. Eng. 28(10–11), 851–865 (2008) 23. Manafpour, A.R., Moghaddam, P.K.: Probabilistic approach to performance-based seismic design of RC frames. In: Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA), Vulnerability, uncertainty, and risk: Quantification, mitigation, and management, 1736–1745. American Society of Civil Engineering (ASCE), Liverpool, UK (2014)

Spatial Distribution of the Gutenberg-Richter Parameters and Fractal Dimension and Their Correlations in Northeast India and Its Vicinity R. B. S. Yadav, P. Chauhan, M. Sandhu, R. Kumar, and V. Singh Abstract The study aims to estimate the Gutenberg-Richter parameters (a and b) and fractal dimension (Dc) using the maximum likelihood estimation (MLE) method in 18 shallow (≤70 km) and 5 intermediate (>70 km) depth seismic zones in northeast India and its vicinity. Scaling relations have been developed among the estimated hazard parameters. A unified and comprehensive earthquake catalogue spanning the period from 1897 to 2016 is used for the purpose. The regions associated with the low b-value and high Dc value have been considered the utmost potential regions for the incidence of big events in the examined area. The b-values in the examined region vary from 0.59 to 1.31 in shallow zones and from 0.88 to 0.98 in intermediate zones. Similarly, the Dc values vary from 1.81 to 2.65 in shallow zones and 2.22 to 2.71 in intermediate zones. The low b-values (less than 0.9) and the high Dc values (greater than 2.0) are related to shallow zones 3 (Arunachal Himalaya), 6 (Eastern Himalayan syntaxis), 13 (Burmese region), 17 (south of Shillong Plateau) and all intermediate zones in Indo-Burmese regions. This makes these zones the most vulnerable to high earthquake hazards. The associations between Dc and b, and Dc and a/b illustrate a positive and negative correlation, respectively. The spatial variations of these parameters can be used as important parameters of earthquake hazard levels in the region. Keywords b-value · Fractal dimension · Earthquake hazard · Northeast India

R. B. S. Yadav (B) · P. Chauhan · M. Sandhu · V. Singh Department of Geophysics, Kurukshetra University, Kurukshetra, India e-mail: [email protected] R. Kumar National Centre for Seismology, New Delhi, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_12

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1 Introduction Northeast India and its vicinity are the utmost peculiar seismic dynamic region in the Indian subcontinent, whose seismotectonic is mainly governed by the interaction of three tectonic plates, viz. the Indian, Eurasian and Burmese plates. It has generated the two deadliest earthquakes in 1897 (Mw 8.1), named Shillong earthquake and 1950 (Mw 8.6), named the Assam earthquake. The Bureau of Indian Standards, 2002 [1] demarcated four seismic zones (II, III, IV and V) in the Indian region with the equivalent zonal factors labelled as 0.10, 0.16, 0.24 and 0.36 g, respectively that can unleash an earthquake of magnitudes exceeding the values of 5, 6, 7 and 8, respectively [2–4]. The study region is situated in zone V (highest hazardous zone), which is capable to generate an earthquake of magnitude 8.0 or more. This region can experience peak ground acceleration (PGA) in the range of 0.35–0.40 g for 475 years of return periods [5]. A number of statistical studies have been performed by different researchers in the study region, which include estimation of earthquake hazard indicators, return periods and occurrence probabilities [3, 4, 6–11, etc.]. These studies are based on different statistical and physical models and power-law relations. Two very important earthquake hazard parameters, the b-value of Gutenberg-Richter recurrence relation [12] and fractal dimension (Dc ), have been considered to evaluate the level of earthquake potential in many regions. The b-value is indicative of the comparative share between the number of small and big events in a certain area. A high b-value specifies a growth in the volume of small events and a reduction in the volume of strong events and vice versa. Dc value is a scale-invariant parameter, and it is represented as a well-organized statistical factor to assess the dimensional dispersal of seismicity. The grade of heterogeneity in a region is generally quantified by the measure of fractal dimension. It is governed by the distribution of the differential stress along with the prevailing geological and structural heterogeneity [13]. In the present study, a quantitative approach is adopted to characterize the seismic activity of northeast India in terms of the G-R frequency-magnitude recurrence relation (a- and b-values) and fractal dimension (Dc ). The spatial distribution of these parameters in the study region is also correlated with seismic complexity and tectonics to identify potential regions.

2 Seismotectonics of Study Region The examined area (northeast India and adjacent regions) lies between 20° and 32° N latitude and 87° and 100° E longitude. These areas consist of the Himalayan thrusts (Main Boundary Thrust, MBT and Main Central Thrust, MCT), eastern syntaxis, Arakan-Yoma subduction belt, Shillong Plateau and Bengal basin (Fig. 1). Due to the interaction of Eurasian and Indian plates, the east–west trending Himalayan system has been developed, while the subduction of the Indian plate with the Burmese plate

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generated the north–south trending Arakan-Yoma subduction belt. The part of the Himalayan system in northeast India consists of a number of north-oriented thrust faults, which include mainly MBT, MCT and Himalayan Frontal thrust. The ArakanYoma subduction area is consisting southeast heading thrusts (e.g. Naga and Disang Thrusts). The pop-up tectonic process has generated Shillong Plateau (SP), which has peculiar tectonics. The eastern syntaxis of the Himalayan zone comprises mainly Lohit and Mishmi Thrusts. The northeast Himalaya is a highly seismic active area in the Himalayan system, and its earthquake activity is primarily governed by the interaction of the Indian plate with the Eurasian and Burmese plates [14]. This region has generated two catastrophic events of Mw ≥ 8.0. June 12, 1897, (Mw 8.1) earthquake occurred on SSE dipping Oldham reverse fault in the Shillong Plateau. The August 15, 1950,

Fig. 1 Tectonic map of northeast India and adjacent region depicting main faults and lineaments. Two great earthquakes occurred in 1897 (Shillong) and 1950 (Assam) earthquakes are shown along with the 2016 Manipur earthquake (res star)

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Fig. 2 Map depicts the seismicity of the study region. The intermediate to deep depth seismicity is observed in the Arakan-Yoma subduction zone, which is the collisional boundary of the Indian ad Burmese plates

Assam earthquake (the sixth largest earthquake of the twentieth century) has an instrumentally determined magnitude of 8.6 that occurred approximately the IndiaChina border. The entire region is characterized by shallow depth earthquakes except for the Arakan-Yoma subduction zone, which is characterized by the intermediate to depth earthquake (Fig. 2).

3 Earthquake Data Used and Seismic Zonation The earthquake catalogue compiled by [2] for the period from 1897 to 2007 has been updated up to 2016. The initial catalogue of [2] has been prepared with the

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Fig. 3 Delineation of shallow depth (a) and intermediate depth (b) source zones

help of various published literature and earthquake catalogues provided by national and international seismological agencies viz. the India Meteorological Department (IMD), India; International Seismological Centre (ISC), National Earthquake Information Centre of the United States Geological Survey (USGS) and Global Centroid Moment Tensor Catalogue (GCMT). For the period between 2008 and 2016, the catalogue is updated with the help of ISC data. A full description of compilation, unification of magnitude scales into moment magnitude (Mw), declustering of dependent events (foreshocks and aftershocks) and completeness analysis with magnitude and time of catalogue is given by [2]. The updated catalogue contains 3650 events during the period from 1997 to 2016. The ultimate demarcation of seismic source zones needs comprehensive information on historical and modern seismicity, tectonics, geology and neo-tectonic activities of the examined region. Depending upon the focal depth variation, the seismogenic source zones in the study area have been divided into two parts: 18 shallow focal depth (≤70 km) source zones (Fig. 3a) and 5 intermediate focal depth (>70 km) source zones (Fig. 3b).

4 Methods Applied 4.1 Frequency-Magnitude Recurrence Relation A maximum likelihood method is used to evaluate the parameters a and b of the Guttenberg-Richter frequency-magnitude recurrence relation [12] Log10 N = a − bM

(1)

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where N is the cumulative count of events with size ≥ M. The parameter b is the slope of the frequency-size distribution, and a is the number of events ≥ 0. In most cases, a low b-value is interpreted as the increased frequency of big events, large differential crustal stress and decreased heterogeneity [15, 16]. Generally, its average value is 1.0, which can range between 0.6 and 1.4 [17]. The area associated with high geological complexity and heterogeneity in rock material or crack density, generally, shows increased b-values demonstrating the significance of the multi-fracture system. On contrary, a low b-value is associated with a low grade of heterogeneity, increased stress or strain and a high strain rate. In the present study, an MLE method proposed by [18] is applied to estimate the b-value. b=

log10

[

1 )] , ( M − Mmin −Δm/2

(2)

where M min is the lower cut-off magnitude (magnitude of completeness, M c ), M is the average magnitude and Δm is the bin size of magnitude.

4.2 Fractal Dimension In the present study, Dc is calculated using a method proposed by [19], in which the correlation integral of the distribution of N events yields the following form: C(r ) =

2 N (R < r ) N (N − 1)

(3)

where N(R < r) is the total event couples placed by a distance R less than r. The correlation integral is related to the standard correlation function C(r )r (Dc )

(4)

where Dc is a fractal dimension or the correlation dimension. It is calculated from the log–log plot of C(r) versus r as a slope of the best–fitted straight line. The Dc value decreases if the earthquake events are more clustered. If all the events are clustered on a single point, the Dc value attains zero, while line sources provide Dc close to 1 and the earthquake fractures filled up a crustal volume providing Dc close to 3 [16, 20, 21].

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5 Results and Discussion The seismicity characteristics of northeast India have been evaluated in terms of G-R parameters (a- and b-values) and fractal dimension (Dc ) using a uniform and comprehensive earthquake catalogue spanning the period from 1897 to 2016. For this purpose, 18 shallow and 5 intermediate depth source zones have been identified and variations of these parameters have been correlated in it.

5.1 a- and b-values Variation The G-R frequency-magnitude recurrence relation parameters (a-, a-annual and bvalues) have been estimated using three methods, namely the maximum curvature method, 90% probability and Entire Magnitude Range (EMR) [22]. Among these, the maximum curvature method gives a reliable estimate. The estimated values are listed in Tables 1 and 2 for shallow and intermediate depth source zones, respectively. Some examples of the frequency-magnitude distributions are shown in Fig. 4 for the shallow and intermediate depth zones. The a-value (annual) is also called the activity rate, i.e. the number of earthquakes per year in a given area or zone. The computed b-value ranges between 0.59 and 1.31 in shallow zones with the lowest in Sh-zone-4 (Himalaya Frontal Thrusts region) and the highest in Sh-zone 18 (Shillong plateau). The low b-values (1.0) are observed in Sh-zone 1, 5 (Himalaya Frontal Thrust), 9 (Arakan-Yoma subduction) and 18 (Shillong Plateau). For the intermediate depth, all the seismic zones show a b-value less than 1.0. A high seismic activity rate (a-annual > 4.0 events/per year) is observed in shallow zones 1, 5, 9, 11 and 18, and intermediate depth zones 3, 4 and 5.

5.2 Fraction Dimension Variation Fractal dimension (Dc ) has been calculated for 15 shallow zones out of 18 and all 5 zones of intermediate depth that are listed in Tables 1 and 2, respectively. Some zones show scattered seismicity, and therefore, we were unable to calculate Dc (e.g. Shzone 5, 11 and 16). Some examples of the graphs of correlation integral vs. distance are shown in Fig. 5 for shallow and intermediate depth zones. The calculated Dc values vary between 1.81 and 2.65 with the lowest Dc value found in Sh-zone 12 of the Arakan-Yoma subduction belt, and the highest Dc value was observed in Sh-zone 15 of the Shillong Plateau region. Dc value less than 2.0 was estimated in Sh-zone 12 of the Arakan-Yoma subduction belt region suggesting the source is linear to 2D, and seismicity is clustered. All other zones show a Dc value between 2.0 and 3.0, which

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Table 1 G-R recurrence parameters (a- and b-values), magnitude of completeness (M c ) and fractal dimension (Dc ) for shallow zones 1–18 in the study region S. No

Seismic zones

1

Sh-zone 1 Maximum curvature

2

Methods

4

5

7

8

a-value

a-value (annual)

Fractal dimension

4.8

1.10 ± 0.2

6.96

4.93

2.28 ± 0.02

Mc90

4.7

1.01 ± 0.1

6.54

4.51

4.8

1.10 ± 0.1

6.96

4.93

4.6

0.71 ± 0.1

4.61

2.71

4.5

0.70 ± 0.1

4.60

2.70

4.3

0.77 ± 0.06

5.30

3.40

Sh-zone 2 Maximum curvature

Sh-zone 3 Maximum curvature Mc90

4.8

0.98 ± 0.1

6.36

4.46

EMR method

4.3

0.77 ± 0.06

5.3

3.4

4.6

0.59 ± 0.1

4.07

2.14

Sh-zone 4 Maximum curvature Mc90

4.1

0.56 ± 0.08

3.93

2.0

EMR method

4.6

0.59 ± 0.1

4.07

2.14

4.8

1.01 ± 0.3

6.39

4.57

5.0

0.94 ± 0.3

6.00

4.18

4.4

0.74 ± 0.06

5.29

3.26

Mc90

4.4

0.74 ± 0.06

5.29

3.26

EMR method

4.6

0.81 ± 0.08

5.67

3.63

4.6

0.89 ± 0.1

5.67

3.77

Mc90

4.5

0.79 ± 0.1

5.18

3.29

EMR method

4.6

0.89 ± 0.1

5.67

3.77

4.5

0.87 ± 0.09

5.84

3.89

Mc90

4.3

0.74 ± 0.06

5.22

3.26

EMR method

4.8

0.88 ± 0.1

5.93

3.98

Sh-zone 5 Maximum curvature EMR method

6

b-value

EMR method

EMR method 3

Mc

Sh-zone 6 Maximum curvature

Sh-zone 7 Maximum curvature

Sh-zone 8 Maximum curvature

2.28 ± 0.04

2.4 ± 0.01

2.20 ± 0.03

2.49 ± 0.01

2.43 ± 0.02

2.37 ± 0.02

(continued)

Spatial Distribution of the Gutenberg-Richter Parameters and Fractal …

145

Table 1 (continued) S. No

Seismic zones

9

Sh-zone 9 Maximum curvature

10

Sh-zone 10

Methods

Mc

b-value

a-value

a-value (annual)

Fractal dimension

4.5

1.05 ± 0.1

6.73

4.75

2.65 ± 0.02

Mc90

4.3

0.85 ± 0.07

5.76

3.78

EMR method

4.5

1.05 ± 0.1

6.73

4.75

Maximum curvature

4.5

0.90 ± 0.1

5.58

3.90

EMR method

4.7

1.06 ± 0.2

6.37

4.70

11

Sh-zone 11

Maximum curvature

4.5

1.00 ± 0.3

5.59

4.08

12

Sh-zone 12

Maximum curvature

4.4

0.65 ± 0.1

4.51

2.61

13

14

15

16

17

Sh-zone 13

Sh-zone 14

Sh-zone 15

Sh-zone 16

Sh-zone 17

Mc90

4.3

0.64 ± 0.09

4.45

2.54

EMR method

4.3

0.64 ± 0.09

4.45

2.54

Maximum curvature

4.4

0.75 ± 0.08

5.11

3.15

EMR method

4.4

0.75 ± 0.08

5.11

3.15

Maximum curvature

4.5

0.89 ± 0.1

5.71

3.79

Mc90

4.4

0.86 ± 0.1

5.51

3.59

EMR method

4.6

0.87 ± 0.1

5.55

3.63

Maximum curvature

4.4

0.98 ± 0.2

5.85

3.98

Mc90

4.2

0.82 ± 0.1

5.07

3.21

EMR method

4.5

0.90 ± 0.2

5.48

3.62

Maximum curvature

4.2

0.66 ± 0.07

4.70

2.62

Mc90

4.0

0.57 ± 0.05

4.28

2.21

EMR method

4.6

0.68 ± 0.1

4.82

2.75

Maximum curvature

4.3

0.63 ± 0.07

4.45

2.46

Mc90

4.2

0.63 ± 0.07

4.46

2.47

2.22 ± 0.02

1.81 ± 0.02

2.38 ± 0.01

2.45 ± 0.03

2.65 ± 0.03

2.34 ± 0.02

(continued)

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Table 1 (continued) S. No

18

Seismic zones

Sh-zone 18

Methods

Mc

b-value

a-value

a-value (annual)

EMR method

4.7

0.69 ± 0.1

4.76

2.77

Maximum curvature

4.8

1.31 ± 0.5

7.50

5.71

EMR method

4.6

1.10 ± 0.3

6.44

4.65

Fractal dimension

2.38 ± 0.02

Table 2 G-R recurrence parameters (a- and b-values), magnitude of completeness (M c ) and fractal dimension (Dc ) for intermediate zones 1–5 in the study region S .No

Seismic zones

1

Int-zone 1 Maximum curvature

2

3

4

5

Methods

Mc

b-value

a-value

a-value (annual)

Fractal dimension

4.2

0.90 ± 0.1

5.40

3.36

2.41 ± 0.02

Mc90

4.7

0.75 ± 0.9

4.70

2.66

EMR method

4.1

0.81 ± 0.1

4.97

2.94

4.5

0.92 ± 0.2

5.53

3.61

Mc90

4.2

0.80 ± 0.1

4.92

3.00

EMR method

4.3

0.78 ± 0.1

4.81

2.90

4.5

0.88 ± 0.07

6.13

4.18

Mc90

4.3

0.77 ± 0.5

5.57

3.63

EMR method

4.5

0.88 ± 0.07

6.13

4.18

4.4

0.98 ± 0.08

6.52

4.63

Mc90

4.2

0.87 ± 0.05

5.98

4.09

EMR method

4.7

1.14 ± 0.1

7.28

5.39

4.5

0.92 ± 0.1

5.74

4.03

Mc90

4.3

0.77 ± 0.09

5.00

3.28

EMR method

4.6

0.87 ± 0.1

5.50

3.78

Int-zone 2 Maximum curvature

Int-zone 3 Maximum curvature

Int-zone 4 Maximum curvature

Int-zone 5 Maximum curvature

2.22 ± 0.02

2.57 ± 0.01

2.68 ± 0.04

2.71 ± 0.02

Spatial Distribution of the Gutenberg-Richter Parameters and Fractal …

147

Fig. 4 Some examples of graphs of frequency-magnitude distribution for shallow depth zones a Sh-zone 1, b Sh-zone 4, c Sh-zone 6, d Sh-zone 15, e Sh-zone 16 and f Sh-zone 17; and intermediate depth zones g int-zone 1, h int-zone 2 and i int-zone 3

indicates a uniform or random spatial distribution of seismicity. For the intermediate depth zones, the calculated Dc values lie between 2.22 and 2.71 suggesting that the faults are spatially distributed.

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Fig. 5 Some examples of graphs of fractal dimension for shallow depth zones a Sh-zone 1, b Shzone 4, c Sh-zone 6, d Sh-zone 15, e Sh-zone 16 and f Sh-zone 17; and intermediate depth zones g int-zone 1, h int-zone 2 and i int-zone 3

5.3 Correlation Between G-R Parameters and Dc Values The ratio between b and Dc is an effective indicator of seismic hazards. We correlated seismic b-value with Dc estimated for only shallow depth zones (Fig. 6a) and obtained the following relation: Dc = 0.41b + 2.0

(5)

Another relationship between a/b-values (i.e. modal values) and Dc (Fig. 6b) for shallow depth zones depicts the following relation:

Spatial Distribution of the Gutenberg-Richter Parameters and Fractal …

149

Fig. 6 Graphs show the relationship between a b versus Dc values and b a/b versus Dc value. The straight line is the least square linear regression

Dc = −0.21(a/b) + 3.74

(6)

6 Conclusions The following conclusions can be drawn based on the results obtained in this study: 1. A low b-value ( z|m i r j ⎦. 

(3)

r j =rmin

The term λn (m i ) in Eq. (3) is the annual frequency of occurrence of EQs of magnitude mi on seismic source n. Pn (R = r j |m i ) is the probability of an EQ of magnitude mi on source n occurring at a certain distance r j from the site. P(Z >

Seismic Hazard Analysis Considering the Effect of the Shape, Size, …

157

z|m i r j ) is the probability that ground motion z will be exceeded, given an EQ of magnitude mi on source n at a distance r j from the site. The procedure to obtain these terms can be found in Baro et al. [22]. In the present study, utilising Eq. (2) and Eq. (3), the hazard level for a particular location is calculated for 2% probabilities in 50 years.

4 Results and Discussion For 6 locations (Soreng, Pelling, Namchi, Mangan, Padamchen, and Gangtok. see Fig. 3) distributed in Sikkim, SHA is carried out using all the earlier mentioned SMs. The results obtained are presented in the following subsections.

4.1 DSHA Results Figure 4a–f shows Response Spectra (RS) obtained based on DSHA for Soreng, Pelling, Namchi, Mangan, Padamchen, and Gangtok, respectively. Further, RS based on each of the SMs is also shown individually in each of these figures (Fig. 4a–f). RS obtained considering SM1a, SM1b, SM2a, and SM3 clearly shows minimal variations in Sa values at all the time periods. However, in the case of SM2b, where seismic activity is considered different throughout the faults, the Sa values for all 6 locations are significantly low compared to the values obtained from considering the other SMs (see Fig. 4a–f). Fig. 3 Locations in Sikkim for which SHA are performed (blue lines represent the faults presents in and around Sikkim)

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1.40

1.40

1.20

SM1a SM1b SM2a SM2b SM3 Mean

(a) Soreng

1.00 0.80 0.60 0.40

(b) Pelling

1.20

SM1a SM1b SM2a SM2b SM3 Mean

1.00 0.80 0.60 0.40

0.20

0.20

0.00

0.00 0

1

2

3

4

1.40

0

1

2

3

4

1.40

1.20

SM1a SM1b SM2a SM2b SM3 Mean

(c) Namchi

1.00 0.80 0.60 0.40

1.20

SM1a SM1b SM2a SM2b SM3 Mean

(d) Mangan

1.00 0.80 0.60 0.40

0.20

0.20

0.00

0.00 0

1

2

3

4

1.40

0

1

2

3

4

1.40

1.20

SM1a SM1b SM2a SM2b SM3 Mean

(e) Padamchen

1.00 0.80 0.60 0.40

1.20

SM1a SM1b SM2a SM2b SM3 Mean

(f) Gangtok

1.00 0.80 0.60 0.40

0.20

0.20

0.00

0.00 0

1

2

3

4

X-axis: Time periods (s)

0

and

1

2

3

4

Y-axis: Sa (g)

Fig. 4 Response spectra obtained from DSHA for the 6 locations in Sikkim

Further, mean RS is also calculated at each of the six locations based on RS obtained from different SMs and also shown in Fig. 4a–f. Table 1 presents the PGA values obtained based on different SMs. Further, mean PGA and coefficient of variation for all the locations are also given in Table 1. The coefficient of variation is the ratio of standard deviation and mean of any distribution. It shows the variation of the estimated PGA values obtained from considering different SMs. It can be seen from Table 1 that the highest mean PGA value (0.6 g) is obtained for Namchi location, whereas the minimum PGA value (0.56 g) is obtained for Mangan. Similarly, the highest variability in estimated PGA is seen in case of Mangan and the lowest variability is seen in case of Namchi. In other words, the variation in the estimated PGA value is more when different SMs are used in case of Mangan.

Seismic Hazard Analysis Considering the Effect of the Shape, Size, …

159

Table 1 PGA values for 6 location obtained from DSHA Locations

SMs SM1a

Mean SM1b

SM2a

SM2b

SM3

Standard deviation

Coefficient of variation

Soreng

0.67

0.60

0.64

0.44

0.60

0.59

0.08

0.14

Pelling

0.67

0.60

0.64

0.40

0.60

0.58

0.10

0.17

Namchi

0.67

0.60

0.63

0.49

0.60

0.60

0.06

0.10

Mangan

0.67

0.59

0.64

0.31

0.60

0.56

0.13

0.23

Padamchen

0.67

0.56

0.64

0.40

0.60

0.58

0.10

0.17

Gangtok

0.67

0.57

0.64

0.36

0.60

0.57

0.11

0.19

4.2 PSHA Results Figure 5a–f shows uniform hazard spectra (UHS) for hazard levels of 2% probabilities in 50 years for Soreng, Pelling, Namchi, Mangan, Padamchen, and Gangtok, respectively. In each of these figures, Sa obtained based on different SMs is also shown separately. The Sa values obtained based on different SMs shows variations at all time periods. This variation is less for some time periods (for time periods more than 0.3 s), and the variation is more significant for others (for time periods from 0 s to 0.2 s). Table 2 shows PGA values for all the sites considering different SMs for hazard levels of 2% probabilities in 50 years. From the result, it is found that for the large-scale SM (SM1a), the spatial variation in PGA values is very less. As we can observe (from Table 2) that for all the 6 locations, the PGA value is almost the same (≈0.43 g). It may be attributed to the consideration of uniform seismicity parameters of large areal sources. For the other SMs, however, a significant difference in PGA is observed at different locations. For these SMs, the difference between the PGA values of two locations is as large as 0.1 g (see Table 2). For each of the five SMs, it is observed that the maximum PGA is obtained at distinct locations. The maximum PGA is observed at Mangan, Pelling, Soreng, Soreng, and Pelling for SM1a, SM1b, SM2a, SM2b, and SM3, respectively. Further, the PGA values obtained while considering SM3 are the highest for any particular location. While considering the mean value from all the SMs, Soreng has the highest PGA (0.44 g), and Padamchen has the lowest PGA value (0.36 g). The standard deviation and coefficient of variation values for PGA for all the cities suggest that the variability in PGA value is significant for all the cities. The highest variability is obtained for Mangan, and the lowest variability is seen in case of Soreng. All these observations suggest that the size, shape, type of sources, and activity of past EQs significantly affect the spatial distribution of hazard values in Sikkim region.

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1

1

(a) Soreng

SM1a SM1b SM2a SM2b SM3 Mean

0.8 0.6 0.4 0.2

(b) Pelling

SM1a SM1b SM2a SM2b SM3 Mean

0.8 0.6 0.4 0.2

0

0 0

1

2

3

4

1

0

1

2

3

4

1

(c) Namchi

(d) Mangan

SM1a SM1b SM2a SM2b SM3 Mean

0.8 0.6 0.4

SM1a SM1b SM2a SM2b SM3 Mean

0.8 0.6 0.4

0.2

0.2

0

0 0

1

2

3

4

1

0

1

2

3

4

1 SM1a SM1b SM2a SM2b SM3 Mean

(e) Padamchen

0.8 0.6 0.4

SM1a SM1b SM2a SM2b SM3 Mean

(f) Gangtok

0.8 0.6 0.4

0.2

0.2

0

0 0

1

2

X-

3

4

axis: Time periods (s)

0

and

1

2

3

4

Y-axis: Sa (g)

Fig. 5 UHS obtained from PSHA for hazard levels of 2% probabilities in 50 years Table 2 PGA values for 6 location obtained from PSHA Locations

SMs SM1a

SM1b

SM2a

SM2b

SM3

Soreng

0.43

0.49

0.39

0.35

Pelling

0.43

0.49

0.36

Mean

Standard deviation

Coefficient of variation

0.52

0.44

0.07

0.16

0.31

0.53

0.43

0.09

0.21

Namchi

0.43

0.46

0.36

0.32

0.52

0.42

0.08

0.19

Mangan

0.44

0.41

0.30

0.24

0.49

0.38

0.10

0.27

Padamchen

0.43

0.32

0.34

0.24

0.45

0.36

0.09

0.24

Gangtok

0.43

0.38

0.33

0.28

0.49

0.38

0.08

0.21

Seismic Hazard Analysis Considering the Effect of the Shape, Size, …

161

5 Conclusion The present study performs DSHA and PSHA for 6 locations of Sikkim, considering different seismic SMs. The SMs considered are areal SMs, linear SMs, and hybrid SM. It is found that for the locations of Sikkim, large areal SM (SM1a) is generating the highest value of seismic hazard for all the locations in the case of DSHA. The lowest hazard value is observed in the case of linear SM (SM2b), where seismic activity is considered different throughout a linear source. For a long fault/linear source, it is usually seen that the seismicity is nonuniform throughout the fault. As a result, if the historical EQ information obtained throughout the fault is not adequately captured, the potential of the fault may be underestimated in the case of DSHA. Based on the results obtained from PSHA, it is found that the hazard level is highest for all locations when the hybrid source model is considered. Here, linear SMs give the lowest hazard level for all the locations. In the case of areal SM, for some locations, SM1a is giving higher values, and for other locations, SM1b is giving higher values. However, in the case of linear SM, SM2a gives the highest values for all the locations. The present results show that the selection of seismic SMs significantly affects the outcome of SHA (both DSHA and PSHA) for locations in Sikkim. The hazard value may vary significantly based on the site’s location, type of seismic sources, and distribution of seismicity. Thus, while determining the seismic hazard potential of Sikkim or other nearby locations, it is better to consider more than one SMs during SHA. Besides taking the mean value obtained from multiple hazard values, each value from different SMs should also be examined while determining the seismic hazard level of a location. In other words, variations in hazard level much also examine for a particular location. This will give a better picture of the seismic potential of a particular region. The results of the present SHA can be further improved by incorporating other types of epistemic uncertainties (uncertainty in the selection of methodologies to determine seismicity parameters, selection of GMPE, selection of probability models, etc.) associated with SHA.

References 1. BIS.: IS-1893 2016 part 1: Criteria for Earthquake Resistant Design of Structures Sixth Revision (2016) 2. NDMA.: Development of Probabilistic Seismic Hazard Map of India Technical Report (2010) 3. ISC.: Event CatalogueSearch [Internet]. [cited 2019 May 1]. Available from: http://www.isc. ac.uk/iscbulletin/search/catalogue/ 4. USGS.: Search Earthquake Catalog [Internet]. [cited 2019 May 1]. Available from: https://ear thquake.usgs.gov/earthquakes/search/ 5. Sitharam, T.G., Sil, A.: Comprehensive seismic hazard assessment of Tripura and Mizoram states. J. Earth Syst. Sci. 123, 837–857 (2014) 6. Roe, P., Georgsen, F., Abrahamsen, P.: An uncertainty model for fault shape and location. Math. Geosci. 46, 957–969 (2014)

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7. Gupta, I.D.: Delineation of probable seismic sources in India and neighbourhood by a comprehensive analysis of seismotectonic characteristics of the region. Soil. Dyn. Earthq. Eng. 26, 766–790 (2006) 8. Kolathayar, S., Sitharam, T.G.: Characterization of regional seismic source zones in and around India. Seismol. Res. Lett. 83, 77–85 (2012) 9. Bhukosh.: Bhukosh. Available from: https://bhukosh.gsi.gov.in/Bhukosh/MapViewer.aspx 10. Kundu, B., Gahalaut, V.K.: Tectonic geodesy revealing geodynamic complexity of the IndoBurmese arc region, North East India. Curr. Sci. 104, 920–933 (2013) 11. Chen, B.: Stress-induced trend: the clustering feature of coal mine disasters and earthquakes in China. Int. J. Coal. Sci. Technol. [Internet]. 7, 676–92(2020). Springer Singapore. Available from: https://doi.org/10.1007/s40789-020-00334-z 12. Kijko, A., Sellevoll, M.A.: Estimation of earthquake hazard parameters from incomplete data files, part I: utilization of extreme and complete catalogues with different threshold magnitudes. Bull. Seismol. Soc. Am. 79, 645–654 (1989) 13. Kijko, A., Graham, G.: Parametric-historic procedure for probabilistic seismic hazard analysis. Part I: estimation of maximum regional magnitude m(max). Pure Appl. Geophys. 152, 413–42 (1998) 14. Bahuguna, A., Sil, A.: Comprehensive seismicity, seismic sources and seismic hazard assessment of Assam, North East comprehensive seismicity, seismic sources and seismic. J. Earthq. Eng. Taylor Francis 00, 1–44 (2018) 15. Uhrhammer, R.A.: Characteristics of Northern and Central California seismicity. Earthq. Notes. (1976) 16. Gutenberg, B., Richter, C.F.: Frequency of earthquakes in California. Bull. Seismol. Soc. Am. 185–8 (1944) 17. Guttorp, P.: On least-squares estimation of b values. Bull. Seismol. Soc. Am. 77, 2115–2124 (1987) 18. Borah, N., Kumar, A., Dhanotiya, R.: Seismic source zonation for NE India on the basis of past EQs and spatial distribution of seismicity parameters. J. Seismol. (2021) 19. Borah, N., Kumar, A.: Assessing if foreshocks and aftershocks are dependent events of the mainshock for North–East India. 17th World Conference Earthquake Engineering 17th. (Sendai, 2020) 20. Iyengar, R.N., Ghosh, S.: Microzonation of earthquake hazard in Greater Delhi area. Curr. Sci. 87, 1193–1202 (2004) 21. EM.: Response spectra and seismic analysis for concrete hydraulic structures. Washington, DC, Department of the Army U.S. Army Corps of Engineers (1999) 22. Baro, O., Kumar, A., Ismail-Zadeh, A.: Seismic hazard assessment of the Shillong plateau using a probabilistic approach. Geomatics, Nat. Hazards Risk. [Internet]. Taylor Francis 11, 2210–38 (2020). Available from: https://doi.org/10.1080/19475705.2020.1833989

A Comparative Study on Application of Machine Learning Algorithms in Ground Motion Prediction Equations A. Ahmed and M. Gade

Abstract Ground motion prediction equations (GMPEs) play an important role in the field of earthquake engineering and seismic hazard analysis. With the advancements in the area of artificial intelligence (AI) and machine learning (ML), it becomes critical to have a comparative study on the new methods of obtaining ground motion parameters for a region. Traditional GMPEs developed using parametric regression assume a fixed functional form, and in doing so, it becomes challenging to draw the complex and nonlinear characteristics of the data completely. Adopting nonparametric regression methods, which are independent of predefined equations, helps us to overcome these limitations. This paper investigates a comparative study of some common machine learning algorithms to forecast peak ground acceleration (PGA) associated with ground motion data of NGA West. The prediction model considers nonparametric types of regression and tries to manifest the advantages of these models over conventional GMPEs, derived using parametric regression. The present work explores algorithms like decision trees, random forests, support vector regression, and weighted average ensemble method (WAEM). Also, the performance of the developed models is compared with that of conventional methods. In this study, the earthquake magnitude, rupture distance, average shear wave velocity, and the type of faulting mechanism are used as predictor variables. The algorithms are trained using a database of 13,555 ground motions, with magnitude ranging from 3.0 to 7.9 recorded over the rupture distance range of 0–500 km. Finally, an efficient model is identified using evaluation metrics like mean squared error (MSE), R-squared value, and standard deviation. Keywords Machine learning · GMPEs · Support vector regression · Decision trees · Random forest

A. Ahmed (B) · M. Gade Indian Institute of Technology Mandi, Kamand, Himachal Pradesh, India e-mail: [email protected] M. Gade e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_14

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1 Introduction The ground motion prediction equations (GMPEs) are essential in quantifying seismic hazards and the design of earthquake-resistant structures. Typically, GMPEs use different regression-based methods to predict the ground motion intensity measures (IMs), by giving input variables such as source and path characteristics along with local site conditions [1]. Traditional regression models have been the most classical approach in predicting these IMs, where the dependant variable is a function of various input variables like magnitude M w , distance R, source, and site characteristics, like fault mechanism and Vs30 [2]. These models extract the first-order aspects of the input variables but restrict themselves in drawing the complex nonlinear behaviour of the data. To overcome this limitation, researchers have tried to add more regression coefficients to the models, incorporating different physical phenomenons. This brings into the picture another drawback, called overfitting, where the prediction model ‘overfits’ itself to the existing data and results in an increased variance failing to fit the additional data aptly. Hence, it becomes a challenging task to select such a predefined functional form that incorporates complex source, site, and path effects, without compromising the quality of the fit. Adopting nonparametric regression paves the way for a data-driven technique where the predictor variable does not take a predefined mathematical form but regresses according to the information derived from the data. Various nonparametric prediction models have been developed by researches [3–6], which require a relatively large number of data points as compared to their parametric counterparts. Tezcan and Cheng [7] used a supervised machine learning algorithm called support vector regression (SVR) to estimate PGA, PGV, and spectral accelerations (SAs) using the PEER-NGA database consisting of 2754 records from 135 events. Thomas et al. [8], Tao et al. [9], and Hu and Zhang [10] also used the SVR method to forecast the ground motion parameters for different parts of the world. Researchers have also tried deep learning (DL) models like artificial neural networks (ANNs), Güllü and Erçelebi [11], Kerh and Ting [12], Ahmad et al. [13], Derras et al. [14], Dhanya and Raghukanth [15], and Gandomi et al. [16] to predict PGA, PGV, and SAs using various databases available across the world. Hamze-Ziabari and Bakhshpoori [17], Asencio-Cortés et al. [18], Trugman and Shearer [19], and Kong et al. [20] used other ML algorithms like regression trees and random forests to develop GMPEs. Lately, researchers Fayaz et al. [21] and Asim et al. [22] developed hybrid models in which the prediction parameters were obtained using SVR and a hybrid neural network (HNN)-based classification system. Also, Kubo et al. [23] presented a hybrid strategy that used machine learning technology with a physical model that produced better predictions than either method alone. The present study aims at developing prediction models using three learning algorithms, namely support vector regression, decision trees, and random forests, to forecast PGA. The study also uses the weighted average ensemble method, which results in a better prediction power as compared to the individual use of ML algorithms. The same database is used to train all the models, which is explained in the

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next section. The models are implemented in an open-source programming language, Python 3.7.12. The subsequent portions of the paper are organized in the following order. The earthquake data utilized in the study is described in Sect. 2, which also includes a discussion of input and output variables. A brief discussion on different ML algorithms utilized in this study is done in Sect. 3. The performance of developed models is investigated using different criteria presented in Sect. 4. A comparative assessment is made with the help of existing relations in Sect. 5. Finally, the summary and conclusions from the study are presented in Sect. 6.

2 Dataset In the present study, we have used the updated PEER-NGA-West2 database [24], available at https://peer.berkeley.edu/research/data-sciences/databases. The data is pre-processed as per the elimination criterion followed by Dhanya and Raghukanth [15], and the final database obtained has a total of 13,555 data points recorded from 288 earthquakes. The earthquake magnitude (Mw ), rupture distance (Rr up ), average shear velocity (Vs30 ), and faulting mechanism (FM) are considered as input parameters to develop the models. Here, Vs30 represents the shear wave velocity in the top 30 m of the soil profile in m/s and is one of the most significant features influencing the ground motion parameters. Table 1 lists the classification criteria for defining values of FM, as per the documentation provided by NGA-West2. Vs30 , Rr up , and PGA are converted to their respective logarithmic values before building the models. Hence, the input and output vector, before further pre-processing is       log10 (PGA) = Mw log10 Rr up log10 (VS30 ) FM . The database is divided into training and testing data, with 70% for the training part and 30% for testing. Thus out of 13,555 points, the training subset has 9,488 and the testing subset has 4067 data points. Variables with values at different scales may not contribute equally to the model fitting, which might lead to bias. As a result, we have used feature-wise standardization before model fitting to address this potential problem. Here, the values of a particular feature are centred around the mean with a unit standard deviation. Let x be the variable and μ and σ be the mean and standard Table 1 Values of FM on the basis of rake angle Faulting style

FM

Range of rake angle (λ)

Strike slip

1

−180° < λ < −150°, −30° < λ < 30°, 150° < λ < 180°

Normal and normal oblique

2

−150° < λ < −30°

Reverse and reverse oblique

3

30° < λ < 150°

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deviation of that particular variable. After applying the above-mentioned scaling technique, we get a standardized value z, defined as. z = (x − μ)/σ.

(1)

3 Machine Learning Algorithms 3.1 Support Vector Regression (SVR) Vapnik [25] came up with a major breakthrough in machine learning, by introducing a very efficient yet computationally simpler algorithm called support vector machines (SVMs). In contrast to other classification approaches such as logistic regression, which takes a probabilistic approach to problem solving, SVM takes a statistical approach. Let us first look at the classification done by SVM before understanding the notion underlying SVR. In an N-dimensional space, SVM introduces a concept of a ‘hyperplane’, which acts as a decision boundary to optimally divide the classes, similar to a line and a plane in two-dimensional and three-dimensional space, respectively. The SVR model employs a number of kernel functions, but in our study we will be using three kernel functions: linear kernel, polynomial kernel, and radial basis function (RBF) Kernel. We have built and evaluated the model’s performance using these three kernel functions, as reported in Sect. 4.

3.2 Decision Trees (DT) Decision trees are general-purpose prediction and classification mechanisms that have evolved into extremely cross-disciplinary, computationally intensive methods for prediction and classification, machine learning, and AI problems. The main feature of decision trees is that they create partitions and associated descendent data subsets (called leaves) at any given level of the tree based on the values of associated input variables. Asim et al. [22, 26], Kong et al. [20], and other researchers have made used of this technique to arrive at the prediction of ground motion parameters. The algorithm iterates through each subset, taking into account only those attributes that were not chosen in prior rounds. As a result, a decision tree-based learning model is built, in which the path from the root to the leaf depicts the values of the input variables, and each leaf represents the value of the target variable.

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3.3 Random Forest (RF) Ensemble methods in statistics and machine learning combine many learning algorithms to achieve greater predictive performance than either of the individual algorithms. Ho [27] and Amit and German [28] made use of such an ensemble approach called random forests (RFs) in which multiple decision trees are utilized at a time, and their respective outputs are aggregated to give a final output. RFs works on the concept of ‘Law of Large Numbers’ where randomness in the model is induced by working with multiple DTs. This random selection of rows and columns in the form of individual DTs helps to reduce the chance of model overfitting and hence the generalization error. As the number of DTs increases, although the computational complexity rises, but the generalization error reduces significantly and the model works better on the testing data.

4 Performance Analysis In this section, prediction models for PGA are developed using SVR, DT, and RF, and the performance of the individual models is compared to identify the efficient model. Further, the attenuation characteristics of the developed models are also investigated to verify the models’ ability to predict the PGA values as per physical laws for the given input parameters.

4.1 Evaluation Metrics Firstly, the coefficient of determination (R 2 ) is calculated for each developed model and listed in Table 2. An R 2 value above 0.7 indicates a strong ‘goodness of fit’, which we obtained for all models. Another criteria considered to access the performance is mean square error (MSE), which is the average of the squares of the errors, and a lower value represents a better model. The standard deviation of the residuals (σ ) of log10 PGA also represents the total variability in the ground motion prediction model. The lesser the value of σ , the better the model’s predictive power. Out of all the considered models, the σ of residuals for RF is the least, equal to 0.2426 (log10 PGA units).

4.2 Physical Observed Trends The formation of conventional ground motion prediction equations relies upon the physical relationships between the input and output variables. Their formulation

168 Table 2 Evaluation metrics for different ML algorithms

A. Ahmed and M. Gade Algorithm

R2

MSE

σ

SVR: linear kernel

0.8453

0.1767

0.4263

SVR: polynomial kernel

0.7225

0.3296

0.5694

SVR: RBF

0.9048

0.1147

0.3205

DT

0.9043

0.1153

0.3359

RF

0.9188

0.0964

0.2426

is primarily based on seismological relationships. The data-driven techniques do not need any predefined physical constraints and mechanisms, but they must be investigated for these physical phenomena to check whether the proposed methods are feasible to use or not. The physical trends of predicted PGA values from the SVR model with RBF kernel, for various magnitude and site classes, are presented in Figs. 1 and 2. It is evident from Fig. 1 that even in the absence of any predefined assumption of functional dependence, the results show a significant magnitude reliance, which is consistent with physical observations. As the rupture distance increases, the value of PGA decreases smoothly. The smooth nature of plots adequately depicts the seismological laws, and it should also be noticed that for short rupture distances (R Rup < 6 km), the intensity values are almost constant. It is due to the source dominance in the near-field region. When we move away from the source, the path characteristics start taking dominance over the source, and there is a significant drop in the PGA values. In addition to this, since PGA is a high-frequency ground motion characteristic, it attenuates faster at large distances but does not show much change when the sites are closer to the source. Moreover, it can also be seen from Fig. 1 that as the value of magnitude increases, values of PGA increase, and the degree of attenuation

Fig. 1 Variation of SVR model predictions (PGA) with respect to Rrup and M w with V s30 = 1130 m/s, FM = 1

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Fig. 2 Variation of SVR model predictions (PGA) with respect to Rrup and V s30 with M w = 6, FM =1

also shifts at a higher value of rupture distance. The ‘Local site effects’ significantly influence the intensity of earthquake. The effect of different ‘Soil Classes’ on PGA is illustrated in Fig. 2. It can be observed from the figure that the model is able to capture the amplification of PGA values with respect to soil type. From these observations, we can conclude that the developed data-driven PGA prediction model based on SVR is efficient in following the physics of the GMPEs. A similar analysis was also performed for prediction models developed using DT and RF algorithms, but it was noticed that these relations were not smooth and a significant amount of undulations were present. This can be attributed to the fact that DT and RF algorithms are tree-based in nature and do not involve any mathematical equations in their formulations. Instead, they rely on a discrete tree-based method, which assigns same value of PGA for different values of input parameters. The phenomenon of overfitting takes place in case of these two algorithms; hence, we have not presented the physical trends for these two models. However, recall that the RF model gave an excellent analytical performance, with a very high value of R 2 and the lowest value of the total standard deviation of residuals. In order to take advantage of these observations, we have explored a method known as the ‘weighted average ensemble method’ (WAEM) and the corresponding details are presented in the following sub-section.

4.3 Weighted Average Ensemble Technique It is observed earlier that the support vector regression (SVR) algorithm’s predictions follow the attenuation characteristics of the IMs, but the analytical performance is

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decent. On the other hand, the random forest (RF) gives a better analytical performance than the SVR, but the physical trends do not follow the attenuation characteristics due to overfitting problems. In order to take advantage of these two algorithms, a weighted average of the predictions given by SVR and RF is proposed. Different weights are assigned to SVR and RF predictions in a hit and trial fashion, and their performance has been assessed. A combination of 70% SVR and 30% RF gives satisfactory results, both analytically as well as attenuation characteristics. We noticed that the R 2 value in this weighted average technique comes out to be better than the one given by the individual implementation of SVR model. We proceed with the calculation of other evaluation metrics, and the values of R 2 , MSE, and σ come out to be equal to 0.9057, 0.1137, and 0.2962, respectively. It should be highlighted that the value of total standard deviation, which plays a vital part in the analysis of seismic hazard comes out to be lesser than the individual SVR model, which gives this technique an edge. Now it will be interesting to observe the attenuation patterns of the proposed weighted average ensemble algorithm. The predicted PGA values variation with input parameters are presented in Figs. 3 and 4. It can be seen from Figs. 3 and 4 that the PGA attenuates smoothly as the rupture distance increases. Further, the figures also demonstrate the magnitude and site class dependency on PGA predictions. From these plots, we can conclude that the present ensemble prediction model is superior to the SVR model in terms of analytical performance and ability to capture the physical influence of all input parameters. The above figure shows that the predicted PGA values at the near-field region are very close for different soil classes. This can be attributed to the fact that in the near-field region, the source characteristics are dominant compared to the site characteristic (V s30 ), and hence, these curves, for different site conditions, give almost

Fig. 3 Variation of WA ensemble model predictions (PGA) with respect to Rrup and M w with V s30 = 1130 m/s, FM = 1

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Fig. 4 Variation of WA ensemble model predictions (PGA) with respect to Rrup and V s30 with M w = 6, FM = 1

the same intensity values in near-field regions. Now it will be interesting to compare the performance of the present models with the prediction models available in the literature. In this regard, an attempt is made, and the results are presented in the following section.

5 Comparison with Existing Models Here, we have selected Campbell and Bozorgnia [29] and Dhanya and Raghukanth [14] equations for the comparison purpose, as both equations employed the same database as ours (NGA-WESt2). Here, we have selected the classical parametric regression-based equation (Campbell and Bozorgnia [29]), and another one (Dhanya and Raghukanth [14]) uses the ANN technique to compare our results. A comparison of mean PGA predictions is presented in Fig. 5. It is evident from the figure that the SVR and WA ensembled predictions are comparable with the selected equations. A comparison of the analytical performance of these models is performed, and the results are presented in Table 3. It can be observed from the table that the R 2 and standard deviation values of the SVR model are comparable with selected equations. On the other hand, the standard deviation of the WA ensemble model is considerably lesser than the selected models. Hence, it can be concluded that the WA ensemble model can predict the PGA for a given set of input parameters with lesser variability.

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Fig. 5 Comparison of the PGA predictions of SVR and WA ensemble models with selected equations (M w = 6, V s30 = 1130 m/s, FM = 1)

Table 3 Evaluation metrics given by different models Model

R2

Total standard deviation (σ) (in log10 PGA units)

CB_2014

Not reported

0.3649

DR_2018

0.9005

0.3270

SVR

0.9048

0.3205

WA ensemble

0.9057

0.2962

6 Summary and Conclusions The present study explores three different machine learning (ML) algorithms to develop efficient data-driven GMPEs. These algorithms, SVR, DT, and RF, employ unique learning architectures and extract the complex behaviour of data directly from it. From the initial studies, it is observed that only the SVR algorithm is able to capture the attenuation characteristics of the ground motion parameter, PGA. However, it is observed that the RF model is superior in terms of analytical performance. In order to take advantage of these two ML techniques, a ‘weighted average ensemble method’ is introduced. It is observed from the results that the WA ensemble model’s analytical performance has improved compared to the SVR model and is able to capture the attenuation patterns as well. Further, a comparison study with the existing equation is also attempted to check the ability of the present ML-based prediction equations. The mean predictions of the present two (SVR and WA ensemble) models are comparable with the selected GMPEs (CB_2014, DR_2018). Further, the comparison of analytical performance has shown that the SVR model’s R2 and σ are close to the selected equations. On the other hand, the WA ensemble model’s σ is considerably

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lesser than the selected prediction models. Thus, the present study has demonstrated that the ML algorithms like SVR and RF can be used to develop efficient GMPEs. Further, the same algorithms can be extended to develop prediction models for other IMs like PGV, Ia, and spectral acceleration at several time periods.

References 1. Boore, D.M.: Stochastic simulation of high-frequency ground motions based on seismological models of the radiated spectra. Bull. Seismol. Soc. Am. 73(6A), 1865–1894 (1983) 2. Douglas, J.: Ground-motion Prediction Equations 1964–2010. (Pacific Earthquake Engineering Research Center, 2011) 3. Anderson, J.G.: Nonparametric description of peak acceleration above a subduction thrust. Seismol. Res. Lett. 68(1), 86–93 (1997) 4. Fajfar, P., Peruš, I.: A non-parametric approach to attenuation relations. J. Earthq. Eng. 1(02), 319–340 (1997) 5. Anderson, J.G., Lei, Y.: Nonparametric description of peak acceleration as a function of magnitude, distance, and site in Guerrero, Mexico. Bull. Seismol. Soc. Am. 84(4), 1003–1017 (1994) 6. Brillinger, D.R., Preisler, H.K.: An exploratory analysis of the joyner-boore attenuation data. Bull. Seismol. Soc. Am. 74(4), 1441–1450 (1984) 7. Tezcan, J., Cheng, Q.: Support vector regression for estimating earthquake response spectra. Bull. Earthq. Eng. 10(4), 1205–1219 (2012) 8. Thomas, S., Pillai, G.N., Pal, K.: Prediction of peak ground acceleration using ∈-SVR, v-SVR and Ls-SVR algorithm. Geomatics, Nat. Hazards Risk 8(2), 177–193 (2017) 9. Tao, D., Ma, Q., Li, S., Xie, Z., Lin, D., Li, S.: Support vector regression for the relationships between ground motion parameters and macroseismic intensity in the Sichuan–Yunnan region. Appl. Sci. 10(9), 3086 (2020) 10. Hu, J., Zhang, H.: Support vector machine method for developing ground motion models for earthquakes in western part of China. J. Earthq. Eng. 1–16 (2021) 11. Güllü, H., Erçelebi, E.: A neural network approach for attenuation relationships: an application using strong ground motion data from Turkey. Eng. Geol. 93(3–4), 65–81 (2007) 12. Kerh, T., Ting, S.B.: Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system. Eng. Appl. Artif. Intell. 18(7), 857–866 (2005) 13. Ahmad, I., El Naggar, M.H., Khan, A.N.: Neural network based attenuation of strong motion peaks in Europe. J. Earthq. Eng. 12(5), 663–680 (2008) 14. Derras, B., Bard, P.-Y., Cotton, F., Bekkouche, A.: Adapting the neural network approach to PGA prediction: an example based on the KiK-net data. Bull. Seismol. Soc. Am. 102(4), 1446–1461 (2012) 15. Dhanya, J., Raghukanth, S.T.G.: Ground motion prediction model using artificial neural network. Pure Appl. Geophys. 175(3), 1035–1064 (2018) 16. Gandomi, A.H., Alavi, A.H., Mousavi, M., Tabatabaei, S.M.: A hybrid computational approach to derive new ground-motion prediction equations. Eng. Appl. Artif. Intell. 24(4), 717–732 (2011) 17. Hamze-Ziabari, S.M., Bakhshpoori, T.: Improving the prediction of ground motion parameters based on an efficient bagging ensemble model of M5 and cart algorithms. Appl. Soft Comput. 68, 147–161 (2018) 18. Asencio-Cortés, G., Morales-Esteban, A., Shang, X., Martínez-Álvarez, F.: Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure. Comput. Geosci. 115, 198–210 (2018)

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19. Trugman, D.T., Shearer, P.M.: Strong correlation between stress drop and peak ground acceleration for recent M 1–4 earthquakes in the San Francisco bay area. Bull. Seismol. Soc. Am. 108(2), 929–945 (2018) 20. Kong, Q., Trugman, D., Ross, Z., Bianco, M., Meade, B., Gerstoft, P.: Machine learning in seismology: turning data into insights. Seismol. Res. Lett. 90, 11 (2018) 21. Fayaz, J., Xiang, Y., Zareian, F.: Generalized ground motion prediction model using hybrid recurrent neural network. Earthq. Eng. Struct. Dyn. 50(6), 1539–1561 (2021) 22. Asim, K.M., Idris, A., Iqbal, T., Martínez-Álvarez, F.: Short term earthquake prediction model using support vector regressor and hybrid neural networks. PloS One 13(7), e0199004 (2018) 23. Kubo, H., Kunugi, T., Suzuki, W., Suzuki, S., Aoi, S.: Hybrid predictor for ground-motion intensity with machine learning and conventional ground motion prediction equation. Sci. Rep. 10(1), 1–12 (2020) 24. Ancheta, T.D., Darragh, R.B., Stewart, J.P., Seyhan, E., Silva, W.J., Chiou, B.S-J., Wooddell, K.E., Graves, R.W., Kottke, A.R., Boore, D.M., et al.: NGA-West2 database. Earthq. Spectra 30(3), 989–1005 (2014) 25. Vapnik, V.: The Nature of Statistical Learning Theory. (Springer Science & Business media, 1999) 26. Asim, K.M., Idris, A., Martínez-Álvarez, F., Iqbal, T.: Short term earthquake prediction in Hindukush region using tree based ensemble learning, in 2016 International Conference on Frontiers of Information Technology (FIT) (2016), pp. 365–370. https://doi.org/10.1109/FIT. 2016.073 27. Ho, T.K.: Random decision forests, in Proceedings of 3rd international conference on document analysis and recognition, vol. 1. (IEEE, 1995), pp. 278–282 28. Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Comput. 9(7), 1545–1588 (1997) 29. Campbell, K.W., Bozorgnia, Y.: NGA-West2 ground motion model for the average horizontal components of PGA, PGV, and 5% damped linear acceleration response spectra. Earthq. Spectra 30(3), 1087–1115 (2014)

Study of an Anomalous Behavior of Atmospheric Parameters—As an Earthquake Precursors for Himalayan Region Earthquakes M. Senthil Kumar

and Natarajan Venkatanathan

Abstract This research work concentrates on how the atmospheric parameters act as a precursor of earthquakes occurred in the Himalayan region. In the past decade, several literature results suggest there is a link between the observation of atmospheric phenomenon and the earthquake occurrence. In this study, we have chooses to analyze the outgoing longwave radiation (OLR) and relative humidity (RH) scenario prior to the occurrence of earthquakes. Eight notable earthquakes (M ≥ 5.0) are selected that happened in the Himalayan region, along the Main Central Thrust (MCT). The Himalayas are emphasized by one of the world’s largest continental megathrusts, which causes the occurrence of massive seismic events that produce significant destruction. The atmospheric parameters like OLR and RH data sets were derived from satellite observation, and variations were identified using the Z-factor method. The temporal analysis of the atmospheric parameters was done for six months before the occurrence of the earthquake. An abnormal deviation was observed in OLR and RH before the occurrence of the earthquake. From the observation, all the anomalies present in the atmospheric parameters were observed ten days prior to the occurrence of the earthquake except for two events, which shows the heterogeneous nature of the earthquake process. For all earthquakes, the RH value dropped predominantly before observing the anomaly rise of the OLR. Similarly, the pattern of these atmospheric parameters indicates sudden drops of RH value followed by the sudden rise in the OLR, which is proposed by the LAIC model. Thus, the authors concluded that atmospheric parameters like OLR and RH are the important short-term earthquake precursor to forecast the earthquakes. Keywords Outgoing longwave radiation (OLR) · Relative humidity (RH) · Earthquake · Earthquake precursors

M. Senthil Kumar · N. Venkatanathan (B) SASTRA Deemed to Be University, Thanjavur, Tamil Nadu 613401, India e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_15

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1 Introduction Considerable variations in relative humidity and temperature in the atmosphere are detected before the occurrence of earthquakes. The abnormalities in these atmospheric parameters have been identified for prolonged and short-term durations ahead of the earthquakes. The short-term abnormalities show how the air temperature and relative humidity patterns have changed before the event [1]. Analysis of meteorological data before the strong earthquakes around the world gives a pathway for the specific repetitive trend of humidity and air temperature changes [2]. Recent researches revealed a strong link between radon levels and relative humidity [3]. Surface and near-surface temperature variations were reported before earthquakes [4]. Relative humidity was observed 10 to 15 days prior to a significant earthquake strike, according to Pulinets et al., in the zone of earthquake occurrence. The studies of large earthquakes confirm an enhancement in air temperature and a decrease in relative humidity observed over the seismic prone zone before the earthquake [5]. In this study, we investigate an atmospheric phenomenon like outgoing longwave radiation (OLR) and relative humidity (RH) since these parameters have shown anomalous deviation for a chosen earthquake. For this, the Himalayan belt region earthquakes were selected for the analysis. Because this region is among the most vulnerable to continental collisions in the world [6–8]. Significant earthquakes (M ≥ 5.0) that occurred in the Himalayan region were chosen for the analysis shown in Fig. 1.

Fig. 1 Epicenters of the earthquakes that occurred on the Himalayan belt region and red concentric circles are represents the epicenter of the earthquakes

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2 Methodology 2.1 Atmospheric Parameters (Outgoing Longwave Radiation and Relative Humidity): Outgoing longwave radiation is the thermal radiation due to the reflection of incoming solar radiation on the earth’s surface into the atmosphere (OLR). The energy flux carried by OLR leaving the earth’s surface into the atmosphere can be measured in terms of W/m2 . With the help of satellite technology, the OLR has been measured at the height of 10–12 km from the earth’s surface [9, 10]. An abnormal rise in OLR flux was found around or over seismically active zones and extending 1000 sq. km for short periods. Before the occurrence of major earthquakes, several experts have identified these short-lived thermal anomalies. The unprocessed OLR data is filtered for the clear sky scenario in the 10–13 μm wavelength range [9, 11]. Anomaly variations were reported about a few days to months before the deadly earthquakes, according to multiple studies on OLR flux. The abnormal fluctuations of OLR flux at earthquake preparation zone has been identified by computing with the mean OLR flux of preceding ten years are as follows: Let us consider that “U” is the energy of the OLR left from the earth’s surface. And “a” is latitude, and “b” is longitude where the energy was calculated, and “t” is a time. m Σ

Ua,b,t =

Ua,b,t

t=1

m

,

(1)

where “m” is the number of defined past years for which mean OLR flux is measured for a given location (a, b) and time (t). dEindex flux (ΔUabt ) =

Ua,b,t − Ua,b,t , σa,b,t

(2)

where ΔUabt dE_index flux for a given latitude (a), longitude (b) and time (t). Uabt current OLR and RH flux computed for a given location (a, b) and time (t). Uabt mean OLR and RH flux calculated for a given location (a, b) and time (t). To efficiently show the anomalous spikes, we have to use the filtering formula to filter the index value other than the anomalous one. The anomalous deviation in energy flux index “[ΔUabt ]∗ ” is recognized by masking the energy flux index (dE_index flux) value below + 2σ level of mean OLR flux. If ΔUa,b,t ≥ Ua,b,t + 2,

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then ∗  ΔUa,b,t = ΔUa,b,t ELSE ΔUa,b,t = 0,

(3)

where  ∗ ΔUa,b,t Anomalous energy flux index of OLR and RH observed for a given location (a, b) & time (t). Similarly, the above methodology is used for identifying the anomalous drop of RH. The impending earthquake zone can be determined using the “anomalous energy flow index” analysis. In this study, the authors have used OLR data sets with a resolution of 1° × 1° and RH data sets with a resolution of 2.5° × 2.5° provided by the National Climatic Data Centre (NOAA) [11].

3 Results The anomalous variations in relative humidity (RH), and the outgoing longwave radiation (OLR) of the earthquakes that occurred on the Himalayan belt region were analyzed and reported in this section. An earthquake with a magnitude of 6.8 Mw occurred on October 19, 1991, in the Garhwal Himalayas of Northern India, at the location 30.78N latitude and 78.774E longitude [12]. The anomalous RH drop was observed on July 22, 1991, which was 89 days prior to the occurrence of the event has a dE_index flux is −3.102 W/m2 , and the anomalous variations of OLR was observed on August 12, 1991, i.e., 68 days before the event with the dE_index flux value 3.0873 W/m2 shown in Fig. 2a. The duration of the RH and OLR anomaly is 21 days. The Chamoli Earthquake, which occurred on March 28, 1999, with an epicenter in 30.512N latitude and 79.403E longitude, which is another significant earthquake in Northern India from the perspective of the Himalayan region [13]. The anomalous drop in RH was observed on March 04, 1999, has a dE_index flux of −2.997 W/m2 which was 24 days before the main event, and the anomalous variations in OLR were recorded on March 19, 1999, it is found nine days prior to the earthquake and has a dE_index flux was 2.992 W/m2 as shown in Fig. 2c. The duration of these anomalous variations is 17 days. On December 14, 2005, a significant earthquake occurred the Garhwal district with the epicenter of the event being 30.476N in latitude and 79.225E in longitude, which has been recorded with a magnitude of 5.1 Mw. The anomalous variations in RH were observed on August 05, 2005, has a dE_index flux is −2.006 W/m2 which was nearly four months prior to the occurrence of the earthquake, and the maximum OLR anomaly was recorded on August 08, 2005, has a dE_index flux is 4.4262 W/m2 , i.e., just three days after the observation of the RH drop. The last OLR

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Fig. 2 Anomalous variations of RH and OLR for the earthquake occurred on a October 19, 1991, with a magnitude of 6.8, b January 05, 1997, with a magnitude of 5.6, c March 28, 1999, with a magnitude of 6.6, d December 14, 2005, with a magnitude of 5.1, September 21, 2009, with a magnitude of e 6.1 and f 5.1, g April 04, 2011, with a magnitude of 5.3, and h February 06, 2017, with a magnitude of 5. 1

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anomaly observation was found on September 06, 2005, which was 99 days before the main event as shown in Fig. 2d. Two earthquakes that occurred on September 29, 2009, with different magnitude lies on the MCT regions. One has occurred in Uttaranchal, India with the latitude of 30.879°N and longitude of 79.057°E, an earthquake with a magnitude of 5.0Mw, and another one that happened in Bhutan has a 27.332°N latitude and 91.437°E longitude with the magnitude of 6.1Mw [14]. The anomaly drop of the RH was found on June 25, 2009, which was just 88 days before the event, with the value of −2.536 W/m2 for the earthquake having a magnitude of 6.1Mw (Fig. 2e). The anomaly index of RH was found on August 28, 2009, which has the dE_index is − 2.624 W/m2 for a magnitude of 5.0 Mw earthquake, which was observed 24 days before the earthquake. For the 6.1 Mw magnitude earthquake, the OLR anomaly was found one month after the anomalous drop in RH, i.e., on August 02, 2009, with the dE_index is 2.1911 W/m2 . The OLR anomalous was found on September 13, 2009, with the value of 2.2994 W/m2 , which was found 08 days before the occurrence of the earthquake and 16 days after the anomalous variations in RH for a 5.0 Mw magnitude earthquake (Fig. 2f). On April 04, 2011, the earthquake was occurred with the magnitude of 5.3Mw, which was located on 29.698 latitude and 80.754 longitude [15]. The anomalous drop in RH was observed on October 20, 2010, which was detected 166 days before the occurrence of the earthquake, the anomaly drop index is −1.964 W/m2 . The anomalous variation of OLR was found on November 30, 2010, with the dE_index of 2.255 W/m2 (Fig. 2g) which observed 41 days after the occurrence of the RH anomalous drop. On February 06, 2017, an earthquake happened in Uttarakhand district, India with a magnitude of 5.1 Mw. The epicenter of the event was reported to be 30.6544 latitude and 79.1645 longitude. This event occurred, which is recognized as the seismic gap in Type-1 is located in the Garhwal area of the NW Himalayas with the possibility to give a rise of a magnitude Mw ~ 8.0 earthquake [16]. The anomalous variations of RH and OLR were detected between the 12 days duration. The RH anomalous drop found on October 10, 2016, and the OLR anomaly was observed on October 22, 2016, which have the values of −2.243 W/m2 and 2.0532 W/m2 , respectively (Fig. 2h).

4 Discussion Our research into these earthquakes revealed certain connections between atmospheric parameters and seismicity. We were unable to draw any conclusions from the overall correlation between earthquake parameters and atmospheric parameters because of the earth’s heterogeneity. The correlation matrix is presented in Table 1, which illustrates clearly that there is no correlation between atmospheric parameters and earthquake parameters such as magnitude, depth, and epicenter. Therefore, we opted for one of the earthquake parameters as a constant to establish a cluster among

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these earthquakes. The cluster is separated according to the strike of the fault. The same strike of the earthquakes followed one another in the same cluster. There are three clusters among these eight earthquakes, which are detailed in Table 2. Table 2 is the information and values of the various earthquake clusters’ atmospheric parameters. After analyzing the correlation matrix for each cluster, it is clear that the earthquake parameters and atmospheric parameters have a strong inverse relationship. The relationship between the magnitude of the earthquake and the atmospheric parameter is shown in Fig. 3a–f. The atmospheric parameters include the maximum dE index flux of RH & OLR, as well as the day difference. The day difference is measured by the difference between the day of the earthquake and the last day of the anomalous variations observed in RH and OLR. The correlation matrix between earthquake characteristics and atmospheric parameters is presented in Table 3 for Table 1 Correlation between the magnitude and dE_Index flux value of OLR and RH and time duration of an anomaly as well as the time gap between the event date from the last observation of the anomaly of the earthquake precursor for the earthquakes that occurred on the Himalayan belt region Latitude Magnitude

−0.04311

dE_Index_OLR

0.043027

dE_Index_RH

−0.22288

EQ_RH

−0.13734

EQ_OLR

0.003871

Longitude 0.122678 −0.09683 0.065747 0.064785 −0.04972

Depth

Strike

Magnitude

−0.67553

0.365569

1

0.672893

0.40198

−0.33843

0.444199

0.436736

−0.05299

0.38025

0.653079

−0.42218

0.296356

0.661857

−0.40744

Table 2 Earthquakes cluster list based on the strike of the fault EQ_Date

Lat

Long

Strike

Mag

dE Index OLR

dE Index RH

EQ_RH

EQ_OLR

02–06–2017

30.65

79.16

279

5.1

2.540

−3.978

58

46

01–05–1997

29.84

80.53

280

5.6

2.777

−2.442

43

4

03–28–1999

30.51

79.40

290

6.6

2.992

−2.977

24

7

12–14–2005

30.47

79.25

293

5.1

4.426

−2.835

131

99

09–21–2009

27.33

91.43

297

6.1

3.075

−2.984

88

50

04–04–2011

29.69

80.75

318

5.3

3.818

−1.964

166

125

10–19–1991

30.78

78.77

320

6.8

3.087

−3.102

89

67

Cluster-1

Cluster-2

Cluster-3

EQ_DATE Earthquake occurrence date EQ_RH Day difference of the anomaly observed in RH and earthquake date EQ_OLR Day difference of the anomaly observed in OLR and earthquake date

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Fig. 3 Relation between the magnitude of the earthquake and the atmospheric parameters

three different clusters. And it is evident that there is a strong negative correlation between earthquake magnitude and atmospheric parameters. Figure 4 describes the day difference between the anomalous observation of RH and OLR. In this bar diagram, the gray color indicates the day difference between the anomaly of RH and the event date and the yellow color exhibits the day difference between the day of the anomaly present in the OLR and the event date. From this graph, we clearly understand that the anomaly of the RH was found first before the anomaly was observed in the OLR. Changes in relative humidity and air temperature are the atmospheric phenomena experienced closest to the surface of the earth. As a result of the release of gas/radon from the underneath subsurface, the rise in the temperature is instantly apparent on the ground’s surface. This could be possibly due to the increased radon, and other gas emissions are caused by air ionization, which leads to thermal effects such as changes in relative humidity, air temperature and outgoing longwave radiation. Horizontal air movements, air mixture, a rise in air temperature, and the temperature difference between the fault’s region and the region distant from the faults would be the possible triggering effects for originating the entire earthquake preparation zone [17]. And our model obeys the LAIC model [18].

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Table 3 Correlation between the magnitude and dE_Index flux value of OLR and RH and time duration of an anomaly as well as the time gap between the event date from the last observation of the anomaly of the earthquake precursor for the earthquakes that occurred on the Himalayan belt region with different clusters Latitude

Longitude

Depth

Strike

Magnitude

Magnitude

−1

1

1

1

1

dE_Index_OLR

−1

1

1

1

1

dE_Index_RH

−1

1

1

1

1

EQ_RH

1

−1

−1

−1

−1

EQ_OLR

1

−1

−1

−1

−1

Magnitude

1

−1

−1

1

1

dE_Index_OLR

−1

1

1

−1

−1

dE_Index_RH

−1

1

1

−1

−1

EQ_RH

−1

1

1

−1

−1

EQ_OLR

−1

1

1

−1

−1

Magnitude

−0.179

0.199

−0.935

−0.249

1.000

dE_Index_OLR

0.446

−0.464

0.997

−0.031

−0.960

dE_Index_RH

0.531

−0.548

1.000

−0.128

−0.929

EQ_RH

−0.122

0.102

0.786

0.526

−0.954

EQ_OLR

0.028

−0.048

0.870

0.393

−0.988

Cluster_1

Cluster_2

Cluster_3

EQ_RH

RH VS OLR

EQ_OLR

Number of days

300

125 200

100

99

46 58

0

4 43

131

67

50

89

88

166

7 24

8 24

Earthquake date

Fig. 4 Reveals the time duration of occurrence of the RH, and OLR before the events for all earthquakes

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5 Conclusion We all know that the earth is diverse characteristics in each and every place, and the parameters used to measure the earth’s features vary from place to place due to the earth’s heterogeneous nature. So, we cannot say for certain that this will be a factor for this cause. However, in this case, we attempted to have atmospheric parameters work as one of the precursors for earthquake forecasting techniques for the entire region but we cannot due to the divergent character of the earth. The value of the precursors may vary due to the earth’s nature, however, when there is an anomaly in the atmospheric parameters like RH and OLR in the atmosphere, there is a cause earthquake near the region. The atmospheric parameters data sets were derived from satellite observation, and the anomalous variations were identified using the Zfactor method. The temporal analysis of the atmospheric parameters was done for six months before the occurrence of the earthquake. The anomalous variations were detected for RH is below the −2sigma level and for OLR the anomalous variations are the above 2sigma level. We found there was an anomalous deviation in RH and OLR before the occurrence of the earthquake. From the observation, for all earthquakes, the RH value dropped predominantly before observing the anomaly rise of the OLR. Similarly, in our model, the pattern of these atmospheric parameters indicates sudden drops of RH value followed by the sudden rise in the OLR, which is proposed by the LAIC model. The duration period for these two atmospheric parameters is within a month. Due to the divergent nature of the earth, the overall correlation matrix does not have any conclude evidence between the atmospheric parameters and earthquake variables. So, we have divided the earthquakes based on the strike of the fault and got 3 different clusters. For these three different clusters, we found that there is relationship using correlation matrix. The magnitude of the earthquake is strong negative correlation between the dE_index flux value of the RH and OLR as well as the day difference also negatively correlated with the magnitude of the earthquake. Thus, the authors concluded that atmospheric parameters like OLR and RH are the important short-term earthquake precursor to forecast the earthquakes.

6 Funding and Acknowledgments The authors are thankful to the Ministry of Earth Sciences, India for financial assistance (Project No: MoES/P. O(seismo)/1(343)/2018) to carry-out research on earthquake forecasting. We would like to acknowledge SASTRA Deemed to be University for facilitating and encouraging to do this research work.

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References 1. Dunajecka, M.A., Pulinets, S.: Atmospheric and thermal anomalies observed around the time of strong earthquakes in Mexico. Atmosfera 18, 233–247 (2005) 2. Pulinets, S.A., Dunajecka, M.A.: Specific variations of air temperature and relative humidity around the time of Michoacán earthquake M8.1 Sept 19, 1985 as a possible indicator of interaction between tectonic plates. Tectonophysics 431, 221–230 (2007) 3. Prasad, B.S.N., Nagaraja, T.K., Chandrashekara, M.S., Paramesh, L., Madhava, M.S.: Diurnal and seasonal variations of radioactivity and electrical conductivity near the surface for a continental location Mysore, India. Atmos. Res. 76, 65–77 (2005) 4. Tronin, A.A., Hayakawa, M., Molchanov, O.A.: Thermal IR satellite data application for earthquake research in Japan and China. J. Geodyn. 33(2004), 519–534 (2004) 5. Oikonomou, C., Haralambous, H., Pulinets, S., Khadka, A., Paudel, S.R., Barta, V., Muslim, B., Kourtidis, K., Karagioras, A., Inyurt, S.: 2021 Investigation of pre-earthquake ionospheric and atmospheric disturbances for three large earthquakes in Mexico. Geosciences 11, 16 (2021). https://doi.org/10.3390/geosciences11010016 6. Bilham, R.: Himalayan earthquakes: a review of historical seismicity and early 21st century slip potential. Geol. Soc., Lond., Spec. Publ. 483 (2019) 7. Mishra, O.P.: Intricacies of the Himalayan seismotectonics and seismogenesis: need for integrated research. Curr. Sci. Assoc. 106(2), 176–87 (2014). http://www.jstor.org/stable/240 99798 8. Kornei, K.: Geodetic data pinpoint earthquake-prone regions of the Himalayas. Eos. 101 (2020). https://doi.org/10.1029/2020EO141610. Published on 19 March 2020 9. Fu, C.-C., Lee, L.-C., Ouzounov, D., Jan, J.-C.: Earth’s outgoing longwave radiation variability prior to M ≥ 6.0 earthquakes in the Taiwan area during 2009–2019. Front. Earth Sci. 8 (2020). https://doi.org/10.3389/feart.2020.00364 10. https://www.weather.gov/jetstream/energy 11. Lee, H.-T.: NOAA CDR Program, 2011: NOAA Climate Data Record (CDR) of Daily Outgoing Longwave Radiation (OLR), Version 1.2. [indicate subset used]. NOAA National Climatic Data Center. https://doi.org/10.7289/V5SJ1HH2 [access date] 12. Rajendran, K., Rajendran, C.P., Jain, S.K., Murty, C.V.R., Arlekar, J.N.: The Chamoli earthquake, Garhwal Himalaya: field observations and implications for seismic hazard. Curr. Sci. Bangalore 78(1), 45–51 (2000) 13. Shrikhande, M., Rai, D., Narayan, J.P., Das, J.: The March 29, 1999 earthquake at Chamoli, India, 12 WCEE 2000: 12th world conference on earthquake engineering; Auckland, New Zealand, Sunday 30 January–Friday 4 February 2000. Structural engineering 0779–1131, vol. 10 (2000) 14. Kayal, J.R., Arefiev, S.S., Baruah, S., Tatevossian, R., Gogoi, N., Sanoujam, M., Gautam, J.L., Hazarika, D., Borah, D.: The 2009 Bhutan and Assam felt earthquakes (Mw 6.3 and 5.1) at the Kopili fault in the northeast Himalaya region geomatics. Natural Hazards Risk (2010) ISSN 1947-5705 Print/ISSN 1947-5713 Online@2010 Taylor & Francis http://www.tandf.co.uk/jou rnals https://doi.org/10.1080/19475705.2010.486561 15. Earthquake catalogue NEIC, USGS, https://earthquake.usgs.gov/earthquakes/search/ 16. Srivastava, H.N., Verma, M., Bansal, B.K., Sutar, A.K.: Discriminatory characteristics of seismic gaps in Himalaya. Geomatics Nat. Hazards Risk 6, 224–242 (2013). https://doi.org/ 10.1080/19475705.2013.839483 17. Ouzounov, D., Pulinets, S., Hattori, K., Kafatos, M., Taylor, P.: Atmospheric Signals Associated with Major Earthquakes. A Multi-Sensor Approach, Electromagnetic Phenomena Associated with Earthquakes, Transworld Research Network 18. Pulinets, S., Ouzounov, D.: Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) model— an unified concept for earthquake precursors validation. J. Asian Earth Sci. 41(2011), 371–382 (2011). https://doi.org/10.1016/j.jseaes.2010.03.005

Probabilistic Seismic Hazard Assessment of North East India C. Lallawmawma, M. L. Sharma, and J. Das

Abstract Probabilistic seismic hazard assessment of the North East Indian region is carried out considering three models of seismic source, i.e., areal sources, line sources, and smoothed gridded seismicity model. Between 87°–98°E and 20°–30°N, earthquake catalogs and various tectonic features are collected from multiple sources which produce a seismotectonic map. Based on seismicity, tectonic provinces, and fault rupture mechanism, the study region is divided into six area source zones. Thirtytwo identified fault sources are modeled as linear source models; the gridded seismicity model is modeled as a point source model. For each source model, seismicity parameters are calculated by considering only the completed earthquake catalog. Four next-generation (NGA) ground motion models are applied to estimate the hazard at the reference rock condition. The logic tree framework is implemented in the source models and GMPEs to account for the epistemic uncertainties. Peak ground acceleration (PGA) and spectral acceleration (Sa) are estimated at different cities of Northeastern states for a 2% and 10% probability of exceedance in 50 years. The hazard curves and uniform hazard spectra are also presented. Keywords PSHA · Logic tree · OpenQuake

1 Introduction The North East Indian region being overthrust by the Eastern Himalaya in the NorthNortheast and the Burmese arc in the East-Southeast forms one of the most complex C. Lallawmawma (B) · M. L. Sharma · J. Das Department of Earthquake Engineering, IIT Roorkee, Roorkee, India e-mail: [email protected] M. L. Sharma e-mail: [email protected] J. Das e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_16

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tectonic provinces in the world [1]. With a complex tectonic and geological setup, this region has experienced several moderate-large earthquakes in the past, including January 10, 1869, Cachar (Mw 7.4), June 12, 1897, Shillong (Mw 8), August 15, 1950, Assam (Mw 8.7) and September 18, 2011, Sikkim (Mw 6.9). With high population, rapid urbanization, and poor construction practices in this high earthquakeprone region, it becomes imperative to quantify the severity of ground shaking that can be expected in the area, which incorporates a great deal of earthquake uncertainty in location, size, and resulting ground motion of future earthquakes. The PSHA of any region needs to be revisited or updated by accommodating the latest knowledge about the seismotectonic of the area, incorporating improved attenuation relations, and taking advantage of the prevailing advanced analysis techniques [2]. In this study, seismicity parameters and seismic hazards are estimated for seven cities in NE states using different source models and GMPEs. To account for epistemic uncertainties, logic tree procedure is applied. OpenQuake engine [3] is used to perform the hazard analysis. The comparison of results obtained from each source model and GMPEs along with the combined model is also presented.

2 Seismicity and Seismotectonics 2.1 Tectonic Provinces Northeastern India and its neighborhood comprise a highly seismically active region, which contains many major tectonic provinces. The seismically active northeast Indian region falls at the junction of NS trending Burmese arc and EW trending Himalayan arc resulting in numerous geological structures [4]. As per the seismic zoning map of India given in the earthquake-resistant design code of India [BIS 1893(Part I) 2016], most of the northeastern states have been placed in seismic zone V except for Sikkim, which falls in zone IV. The primary tectonic background includes the eastern Himalayan structures, the Mishmi massif, the Indo-Myanmar arc, the Surma Basin, and the Shillong plateau [5]. As a collision plate boundary, the Himalayan tectonic zone is characterized by a series of north dipping thrusts that are exposed at the surface [6]. The Himalayan structures mainly consist of the thrust planes namely the Main Central Thrust (MCT), Main Boundary Thrust (MBT), Main Frontal Thrust (MFT), and their subsidiaries [5, 7]. The MBT separates the lesser Himalaya from the sub Himalayan belt, whereas the MCT separates the higher Himalaya from the lesser Himalaya [8]. The eastern syntaxis zone, where the Burmese arc and the Himalaya arc meet the Mishmi Block, is a significant tectonic domain. The Indo-Myanmar is formed by the Indian plate subducting beneath the Sunda plate in the east. The region is characterized by high seismicity. Surma Basin is an area of folded sediments characterized by westerly convex, sinuous structural ridges, and valleys [9]. Lineaments/faults running NE-SW and NW–SE are quite prevalent. Shillong Mikir Plateau, which is considered the plate-boundary zone,

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major geotectonic structures connected in the region are the EW Dauki fault, the Brahmaputra lineament to the north, NE-SW to the east is the Naga thrust, while to the west is the Dhubri fault. In this study, the seismotectonic map has been prepared using ArcGIS [10] mapping tool platform, the tectonic features (faults and lineaments) given in SEISAT (2000) and from published literature [11–14] were digitized and imposed with a collected earthquake catalog to prepare seismotectonic map as shown in Fig. 1.

2.2 Earthquake Database and Treatment of Catalog Study region ranging between 20°–30° and 87°–98°, earthquake catalog is compiled from different agencies namely United States Geological Survey (USGS) [15], Indian Meteorological Department (IMD) [16], International Seismological Center (ISC) [17], and Global Centroid Moment Tensor (GCMT) [18]. Historical earthquake records are collected from the published study of Raghukanth [19]. A total of 4792 events are collected from 825 to 2020 with detailed information about the date and time of occurrence, magnitude, and location with specified coordinates. Compiled catalogs are declustered and homogenized in a unified moment magnitude scale using Scordilis [20] and Das [21]. Magnitude of completeness (Mc) is estimated to assess for completeness in terms of size, and to ensure completeness in terms of time, Stepp’s [22] approach is used.

3 Seismic Source Models 3.1 Areal Sources Area sources are the most widely used type of source zone in the PSHA studies. In many regions, it is known that active faults exist, but it is not possible to characterize individual faults as sources. In such cases, it is common to define an area source that aggregates the seismic activity over some spatial region. Area sources are typically a polygon in which earthquakes can occur anywhere and at any time inside the designated zone. In terms of time and space, these zones are believed to have consistent source properties. Based on seismicity, tectonic features, fault rupture mechanism, and published literature [11–14], the region is grouped into six seismogenic source zones as shown in Fig. 2. Source Zone 1 corresponds to the central low lands of Myanmar. The primary tectonic feature is the Sagaing Fault, which runs for nearly 1000 km from north to south and is one of Myanmar’s most notable active strike-slip faults. The largest magnitude observed is Mw 7.8 occurred on June 1, 1787; most of the events show a strike-slip mechanism.

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Fig. 1 Seismotectonic map of the study area showing major fault such as ShanSagaining fault, Churachanpur Mau fault (CMF), Eastern boundary thrust (EBT), Kabaw fault, F1, Mat fault, Chittagong coastal fault (CCF), Eocene hinge zone (EHZ), Kaladan, Madhupur blind fault, Sylhet fault, Barapani shear zone, Dauki fault, Dhubri fault, Dudhnoi fault, Bomdila fault, Kopili and F2 fault, Naga and Disang thrust, Oldham fault, Samin and Chedrang fault, Arun and Purnea fault, BNS fault, Dudhkosi fault, Indus Suture fault (IST), main boundary thrust (MBT), Main central thrust (MCT), Lohit thrust, and Mishmi thrust

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Fig. 2 Six seismogenic source zones considered

Source Zone 2 represents the Indo-Myanmar subduction zone, about 15 significant earthquakes (Mw > 6.5) have struck the region in the previous 100 years, making it a seismically active region. The main tectonic features are the Eastern Boundary Thrust (EBT) or Kabaw fault and the Churachanpur Mao fault (CMF). Reverse and Strike-Slip mechanisms are largely observed. Source Zone 3 corresponds to the Bengal Basin and part of the Mizoram and Tripura fold belt. Bengal basin is characterized by low seismicity, while Mizoram

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and Tripura folded belt shows moderate seismic activity. Srimangal earthquake (Mw 7.6) of July 8, 1918, occurred in this zone. The main tectonic features are Chittagong Coastal fault (CCF), NE trending Sylhet fault, Kaladan fault, Eastern Hinge zone, and other NW trending faults. Events show strike-slip and reverse mechanisms. Source Zone 4 represents Meghalaya Plateau, Mikir Hills, and Naga-Disang thrust. Meghalaya plateau is bounded to the north by the Arunachal Himalaya, Bengal basin, and Surma basin to the southern part, and to the western part by Dhubri fault. Seismic activity is considered plate-boundary zone activity. The Mikir Hill is surrounded by Bomdila Fault to the east and the Kopili Fault to the west, characterized by a strike-slip mechanism. Large magnitude earthquake Mw 8.1 occurred on June 26, 1897. Source Zone 5 represent Eastern Himalaya zone Indo-Tsangpo suture zone. The main tectonic features are Main Boundary Thrust (MBT) and Main Central thrust (MCT). Most of the seismicity in the Himalayan region is concentrated around the MBT and MCT [23]. The maximum magnitude observed in this zone is Mw 7.7. Source Zone 6 is the Mishmi Massif region, an NW–SE trending feature. The main tectonic features are Lohit thrust, Mishmi thrust tiding suture, and Pochu fault. The maximum magnitude observed in this area is Mw 8.5 occurred on August 15, 1950.

3.2 Linear Sources A fault source is the most prominent representation of a seismic source. Tectonic features for North East India and surrounding areas are primarily obtained from the Seismotectonic Atlas (SEISAT 2000) prepared by the Geological Survey of India (GSI) and published literature [11–14]. The information about fault focal mechanisms is obtained from the literature and catalogs extracted from GCMT [18]. A total of 32 linear sources are used in this study.

3.3 Gridded Seismicity Sources To overcome the subjectivity that the traditional area source model introduces and to model sources in the lack of clear recognized seismic sources, the gridded seismicity model, Frankel [2] suggested a zoneless spatially smoothing approach based on the seismic activity rate obtained from the catalog. In this study, this approach is applied. The study region is divided into grids (in this case, 0.2° × 0.2°). The number of earthquakes with a magnitude higher than a cutoff magnitude in each grid is counted, and the activity rate for each grid cell is smoothed utilizing a Gaussian function. The background smoothed activity rates (10a values) obtained using the Frankel (1995) approach is shown in Fig. 3.

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Fig. 3 Smoothed activity rate 10a values

4 Seismicity Parameters and Maximum Magnitude The recurrence model parameters determine the number of earthquakes of various magnitudes that occur each year. The level of seismicity of a region is assessed from the seismic parameters a, b and Mc. In this study, the Gutenberg–Richter (GR) magnitude–frequency relationship [24] to obtain the parameters of seismicity ‘a’ value and ‘b’ value. For the area source zone, after determining the completeness of

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the catalog reported in each zones, seismicity parameters are estimated. For linear sources, 30–40 km is measured from the traced fault line, creating a polygon for each fault. In a similar approach to the area source, seismicity parameters are estimated. The seismicity rates (10a ) at each position in a grid of 0.2° × 0.2° are obtained using the Frankel [2] spatially smoothing technique for gridded sources. The value of ‘b’ is calculated using the complete earthquake catalog within the boundary of the study region. For background seismicity modeling, the computed value of b = 0.87 is considered to be consistent. Maximum magnitude is computed using the equation given by KijKo [25] (Tables 1 and 2).

5 Ground Motion Prediction Equation A ground motion prediction equation (GMPE) is one of the prerequisites for seismic hazard evaluations, since it transforms event information (such as earthquake location and magnitude) into site parameters that characterize the seismic hazard at a site (e.g., peak ground acceleration, PGA) [26]. The ground motions are estimated using GMPEs, and essential parameters are the source-to-site distance, magnitude, type of faulting, and site response. Generally, the GMPEs are empirically derived from the recorded earthquake ground motion data using statistical regression analysis. Region specific GMPEs are preferred and are desirable to use for ground motion estimates. For the regions where strong motion data is not available for such analysis, the GMPEs developed for other regions are used based on the resemblance of the characteristics of both regions [27]. Five sets of ground motion prediction equations (GMPEs) developed by PEER next-generation (NGA) models updated in 2014 implementing more latest and worldwide ground motion data are selected and used in this study. The GMPEs are Abrahamson, Silva, and Kamai (ASK14) [28], Boore, Stewart, Seyhan, and Atkinson (BSSA14) [29], Campbell and Bozorgnia (CB14) [30], Chiou and Youngs (CY2014) [31], and Idriss (I14) [32]. However, Chiou and Youngs (CY2014) overestimate the hazard. In the study, the other four GMPEs are used for hazard analysis.

6 Logic Tree Structure Logic trees provide a convenient form for formal and quantitative treatment of uncertainties [33]. The main uncertainties associated are modeling the seismogenic sources and the ground motion prediction models used in estimating the hazard. A logic tree is made of a series of nodes and branches, these branches represent several models that have been given a pre-determined weight, with the overall weighting factor at the end branch equaling the total of the weights allocated to individual branches. In the study, the logic tree comprises of three seismic source models and four GMPEs

283

472

609

177

7.3

7.8

8.1

7.7

8.5

SZ2

SZ3

SZ4

SZ5

SZ6

140

181

8

SZ1

Duration of catalog

Mmax (obs)

Source

4.2

4.1

4.3

4.3

4.2

4.3

3.029

2.704

2.856

2.925

4.659

3.108

8.5 ± 0.15

7.8 ± 0.15

8.30 ± 0.18

8.6 ± 0.25

8.2 ± 0.22

9.0 ± 0.25

0.64 ± 0.03

0.85 ± 0.03

0.69 ± 0.05

0.8 ± 0.05

0.66 ± 0.04

0.68 ± 0.06

0–50

0–90

0–50

0–60

0–100

0–100

Depth range (km)

86/−11/35–0.34 24/72/119–0.33

30/0.34 50/0.33

63/−51/86–0.33

72/−12/216–0.33 1.0

83/0.33 10/0.33

45/−90/21–0.34

50/0.34

62/179/109–0.33

55/47/85–0.33 1.0

46/0.33 12/0.33

41/83/91–0.34

25/0.34

53/44/89–0.33

37/168/163–0.33 1.0

55/0.33 10/0.33

34/−82/179–0.34

85/−2/63–0.33

30/0.34

1.0

54/120/148–0.33

10/0.33

90/0.3

75/178/5–0.33 28/99/44–0.34

1.0

50/0.34

10/0.3

68/166/2–0.33

98/0.33

78/−30/122–0.33

Dip/strike/rake-probability

Focal mechanism

90/74/186–0.34

1.0

Aspect ratio

50/0.34

10/0.33

Hypo depth (km)/probability

Source parameters Mmax

B

Mc

a

Zmap (maximum likelihood)

Table 1 Seismicity and other required parameters for area sources hazard calculation

Probabilistic Seismic Hazard Assessment of North East India 195

5.4

5.7

All

SS

Dudhnoi

Bomdila

7.1

7.1

R&SS

SS

Dauki

5.7

Dhubri

Barapani

Thrust

7.6

SS

Source 4

Sylhet

7

All

Madhupur

72

94

192

320

50

158

68

424

320

7.8

7.5

All

Reverse

EHZ

380

338

121

296

280

305

520

4.66

3.21

50.58

41.02

5.16

118.58

40.74

73.28

145.21

100

35.48

3.93

9.75

59.98

59.98

166.72

Rupture length

Rupture characteristics Total fault length (Km)

7.5

Kaladan

CCF

SS

7

Thrust

EBT (portion)

Source 3

5.6

SS

Mat

7.2

6.1

SS

All

EBT or Kabaw

7.2

7.8

Observed Mw

F1

SS

SS

Faulting

CMF

Source 2

ShanSagaining

Source 1

Name of fault

6.47

3.41

26.34

12.82

10.32

75.05

59.91

17.28

45.38

26.32

10.41

3.25

3.29

21.42

19.67

32.06

Rupture percent (%)

Table 2 Rupture percent and source model parameters for considered faults in the study area

0–50

0–50

0–50

0–50

0–40

0–50

0–50

0–100

0–60

0–40

0–100

0–80

0–100

0–80

0–60

0–40

Depth range (Km)

83

55

87

55

79

88

77

37

34

51

54

70

89

32

78

68

Dip

3.81

0.717

7

88

162

88

158

166

171

2.18

3.25

2.125

2.444

2.218

2.062

1.875

2.378

−82 168

2.376

3.497

3.001

87

120

175

3.07

−8 160

3.063

2.002

a

42

166

Rake

(continued)

0.63 ± 0.07

0.86 ± 0.14

0.65 ± 0.14

0.73 ± 0.16

0.82 ± 0.22

0.57 ± 0.08

0.68 ± 0.14

0.63 ± 0.05

0.44 ± 0.09

0.76 ± 0.10

0.69 ± 0.02

0.78 ± 0.10

0.76 ± 0.05

0.76 ± 0.03

0.72 ± 0.06

0.53 ± 0.04

b

196 C. Lallawmawma et al.

6.5

8.1

Reverse

Reverse

Reverse

Naga Disang Thrust

Oldham

Samin and Chedrang

All

Indus Suture 540

8.5

7

Thrust

Thrust

Lohit Thrust 279

208

790

7.7

7.2

Mishmi Thrust

Source 6

MBT and MCT Thrust

43

274

136

22

128

400

300

35.48

312.61

47.42

123.88

5.16

140

8.32

26.55

174.98

17.18

59.98

Rupture length

Rupture characteristics Total fault length (Km)

5.7

7.7

SS

All

BNS

6

All

Dudhkosi

Arun and Purnea

Source 5

7.2

SS

Kopili and F2

6.8

Observed Mw

Faulting

Name of fault

Table 2 (continued)

12.72

150.29

6

22.94

12

51.09

6.12

120.68

136.70

4.30

19.99

Rupture percent (%)

0–50

0–50

0–60

0–80

0–60

0–40

0–80

0–40

0–50

0–50

0–50

Depth range (Km)

87

24

72

76

45

42

52

40

79

55

55

Dip

99

72

2.334

2.279

1.421 2.437

97

0.874 3.578

−94 −46 −12

2.278

2.186

2.287

1.407

2.017

a

−62

83

167

88

47

Rake

0.62 ± 0.04

0.60 ± 0.08

0.60 ± 0.02

0.51 ± 0.08

0.89 ± 0.10

0.48 ± 0.06

0.62 ± 0.06

0.71 ± 0.18

0.72 ± 0.15

0.64 ± 0.14

0.63 ± 0.08

b

Probabilistic Seismic Hazard Assessment of North East India 197

198

C. Lallawmawma et al.

Fig. 4 Logic tree framework used in PSHA for source models and GMPEs

shown in Fig. 4. By allocating proper weightage to each branch, a total of twelve branches are adopted.

7 Probabilistic Seismic Hazard Analysis and Results Seismic hazard computes the expected ground motion at a given site considering the seismicity rate, geology, seismotectonic, and the attenuation characteristics of the seismic waves in the region. Probabilistic seismic hazard calculation has been performed based on classical Cornell–McGuire method implementing the total probability theorem, OpenQuake engine [3] is used to perform the hazard analysis. For seismic hazard computations, the whole study area is divided at a grid intervals of size 0.1° × 0.1°, and the values of seismic hazard at the center point of each grid cell are calculated. The peak ground acceleration (PGA) and spectral acceleration (SA) at periods of 0.1, 0.2, 0.4, 0.7, 1.0, and 2.0 s at a 5% damping ratio at 10% (475 years return period) and 2% (2475 years return period) probability of exceedance in 50 years are estimated. The peak ground acceleration and spectral acceleration at different periods are calculated using a reference bedrock condition of 760 m/s average shear wave velocity in the top 30 m (Vs30). This reference site class relates to a boundary between sites B and C specified by the National Earthquake Hazard Reduction (NEHRP). Figure 5 presents the hazard map at 2% and 10% probabilities of exceedance in 50 years. Figures 6 and 7 present the mean hazard curve and uniform hazard spectra at seven cities of North East states, respectively (Table 3).

Probabilistic Seismic Hazard Assessment of North East India

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Fig. 5 Peak ground acceleration 2% and 10% exceedance in 50 years

8 Conclusions This paper presents a probabilistic seismic hazard assessment of North East India. The seismic hazard has been computed by performing hazard computations at a grid interval of 0.1° × 0.1° using three source models. To account for epistemic uncertainties in source models and GMPEs, the logic tree procedure is adopted. The calculated PGA for a 10% exceedance in 50 years, which is equivalent to return period of 475 years is compared with different source models and previous studies. It is observed that the estimated seismic hazard is higher in Imphal than in other cities due to the hazard contribution from the Churachanpur Mao fault and eastern subduction seismic sources. Choice of GMPEs also greatly affects the hazard value so selecting appropriate GMPEs is crucial in the hazard analysis. The seismic hazard curve and uniform hazard spectrum have been evaluated for bedrock rock level. Corresponding to 10% and 2% probability of exceedance in 50 years uniform hazard spectrum for major cities at the NE states are also presented.

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Fig. 6 Mean hazard curve for a Sa (2.0), b Sa (0.1), and c (0) seconds in 50 years

Probabilistic Seismic Hazard Assessment of North East India

Fig. 7 Uniform hazard spectra at seven cities of NE states at a 10%, b 2% PE in 50 years

201

202

C. Lallawmawma et al.

Table 3 Estimated peak ground acceleration and comparison with other studies in seven cities NE cities (location)

Present study

Previous studies

PGA (in 10% PE in 50 years) Area

Linear

Grid

Combine source

Das et al. [21]

Nath et al. [11]

NDMA [12]

Sharma and Malik [34]

Agartala (91.318, 23.785)

0.09

0.13

0.16

0.13

0.217

0.5

0.18

0.3

Aizawl (92.69, 23.785)

0.25

0.15

0.19

0.20

0.114

0.6

0.18

0.3

Imphal (93.929, 24.774)

0.25

0.46

0.19

0.30

0.144

0.99

0.35

0.4

Itanagar (93.569, 27.113)

0.09

0.15

0.13

0.12

0.182

0.7

0.3

0.44

Shillong (91.884, 25.584)

0.08

0.13

0.12

0.11

0.316

1.10

0.25

0.5

Kohima (94.083, 25.674)

0.20

0.22

0.15

0.19

0.148

0.6

0.2

0.48

Despur (91.807, 26.123)

0.08

0.13

0.15

0.12

0.2

0.75

0.2

0.4

References 1. Das, S., Gupta, I.D., Gupta, V.K.: A probabilistic seismic hazard analysis of Northeast India. Earthq. Spectra 22, 1–27 (2006) 2. Frankel, A.: Mapping seismic hazard in the central and eastern United States. Seismol. Res. Lett. 66, 8–21 (1995) 3. Pagani, M., et al.: Openquake engine: an open hazard (and risk) software for the global earthquake model. Seismol. Res. Lett. 85, 692–702 (2014) 4. Sitharam, T.G., Kolathayar, S., James, N.: Probabilistic assessment of surface level seismic hazard in India using topographic gradient as a proxy for site condition. Geosci. Front. 6, 847–859 (2015) 5. Kumar Nath, S., Kumar, K., Thingbaijam, S., Raj, A.: Earthquake hazard in Northeast India-A seismic microzonation approach with typical case studies from Sikkim Himalaya and Guwahati city. J. Earth. Syst. 117, 809–831 (2008) 6. Choudhary, C., Sharma, M.L.: Global strain rates in western to central Himalayas and their implications in seismic hazard assessment. Nat. Hazards 94, 1211–1224 (2018) 7. Nandy, D.R.: Geodynamics of Northeast India and the Adjoining Region, 3rd edn. Abc Publication, Kolkata (2001) 8. Harbindu, A., Sharma, M.L., Kamal.: Stochastic ground-motion simulation of two Himalayan earthquakes: seismic hazard assessment perspective. J. Seismol. 16, 345–369 (2012)

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9. Tiwari, R.P., et al.: No evidence for shallow shear motion on the Mat fault, a prominent strike slip fault in the Indo-Burmese wedge. J. Earth Syst. Sci. 124, 1039–1046 (2015) 10. ESRI.: ArcGIS Desktop: Release 10. (Environmental Systems Research Institute, Redlands 2011) 11. Nath, S.K., Thingbaijam, K.K.S.: Probabilistic seismic hazard assessment of India. Seismol. Res. Lett. 83(1), 135–149 (2012) 12. National Disaster Management Authority.: Development of Probabilistic Seismic Hazard Map of India, Government of India, New Delhi (2011) 13. Baro, O., Kumar, A., Ismail-Zadeh, A.: Seismic hazard assessment of the Shillong Plateau, India. Geomat. Nat. Haz. Risk 9(1), 841–861 (2018) 14. Bahuguna, A., Sil, A.: Comprehensive seismicity, seismic sources and seismic hazard assessment of Assam, North East India. J. Earthquake Eng. 24(2), 254–297 (2018) 15. United States Geological Survey (USGS).: https://earthquake.usgs.gov/earthquakes/search/. Accessed 2020 16. Indian Meteorological Department (IMD).: www.imd.gov.in/, New Delhi, India (Through Personal communication) 17. International Seismological Center (ISC).: http://www.isc.ac.uk/iscbulletin/search/catalogue/. Accessed 2020 18. Ekström, G., Nettles, M., Dziewonski, A.M.: The global CMT project 2004–2010: centroidmoment tensors for 13,017 earthquakes. Phys. Earth Planet. Inter. 200–201, 1–9 (2012) 19. Raghukanth, S.T.G.: Estimation of seismicity parameters for India. Seismol. Res. Lett. 81, 207–217 (2010) 20. Scordilis, E.M.: Empirical global relations converting MS and mb to moment magnitude. J. Seismol. 10, 225–236 (2006) 21. Das, R., Sharma, M.L., Wason, H.R.: Probabilistic seismic hazard assessment for Northeast India region. Pure Appl. Geophys. 173, 2653–2670 (2016) 22. Stepp, J.C.: Analysis of completeness of the earthquake sample in the Puget sound area. Contrib. Seism. Zo. 16–28 (1973) 23. Bajaj, S., Sharma, M.L.: Modeling earthquake recurrence in the himalayan seismic belt using time-dependent stochastic models: implications for future seismic hazards. Pure Appl. Geophys. 176, 5261–5278 (2019) 24. Gutenberg, B., Richter, C.F.: Earthquake magnitude intensity energy and acceleration. B. Seismol. Soc. Am. 46, 105–145 (1956) 25. Kijko, A.: Estimation of the maximum earthquake magnitude, mmax . Pure Appl. Geophys. 161, 1655–1681 (2004) 26. Sharma, M.L., Douglas, J., Bungum, H., Kotadia, J.: Ground-motion prediction equations based on data from the Himalayan and Zagros regions. J. Earthq. Eng. 13, 1191–1210 (2009) 27. Sharma, M.L.: Attenuation relationship for estimation of peak ground vertical acceleration using data from strong motion arrays. World Conf. Earthq. Eng., NZ Soc. Earthq. Eng., Auckland, NZ 88, 1–8 (2000) 28. Abrahamson, N.A., Silva, W.J., Kamai, R.: Summary of the ASK14 ground motion relation for active crustal regions. Earthq. Spectra 30, 1025–1055 (2014) 29. Boore, D.M., Stewart, J.P., Seyhan, E., Atkinson, G.M.: NGA-West2 equations for predicting PGA, PGV, and 5% damped PSA for shallow crustal earthquakes. Earthq. Spectra 30, 1057– 1085 (2014) 30. Campbell, K.W., Bozorgnia, Y.: Campbell-bozorgnia NGA-West2 horizontal ground motion model for active tectonic domains. NCEE 2014—10th U.S. Natl. Conf. Earthq. Eng. Front. Earthq. Eng. (2014). https://doi.org/10.4231/D3MS3K235

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Experimental Investigation of Seismic Response of Hybrid Shear Wall with External Energy Dissipating Reinforcement Ankhiparna Guha, S. R. Dash, and Goutam Mondal

Abstract A conventional shear wall has negligible reusability capacity if it gets severely damaged during any strong earthquake. This forms a major drawback for many civil installations, which are prone to frequent seismic activities. Often, a posttensioned (PT) shear wall having self-centering capacity is used as one of the effective ways of resisting such large lateral shear forces, however, it has limited energy dissipation capability. Therefore, in situations having a higher force and limited drift demand, PT shear walls with internal energy dissipating reinforcements (EDRs) are that, once they get damaged, they cannot be replaced. Hence to solve this problem, use of external energy dissipating reinforcement has been introduced due to its merit of easy replacement. A new configuration of external energy dissipating device has been introduced in this study, where notch plates are used as external energy dissipating devices. The suitability and functionality of such external energy dissipating notched bars (EDNB) have been checked experimentally and reported. Keywords Post-tensioned shear wall · External energy dissipating notched bars (EDNBs) · Self-centering

1 Introduction Adequate seismic resistance and correct seismic design are crucial for ensuring little structure disturbance and minor or trivial structural damage. One of the most successful and widely acknowledged methods of such application is the introduction to structural shear walls. However, the construction is unsuitable due to issues like residual drifts, localized concrete damage, bar yielding, etc. Using post-tensioned shear walls can be a workable approach to get around these restrictions. High strength A. Guha (B) IIT Kanpur, Kanpur, India e-mail: [email protected] S. R. Dash · G. Mondal IIT Bhubaneswar, Bhubaneswar, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_17

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tendons that remain elastic during the seismic deformation and restore the wall to its original position once the loads are withdrawn are a component of post-tensioned shear walls, which exhibit self-centering effects. To preserve the structural form in good working order, the greatest amount of energy that can be released through the PT shear wall is restricted to energy release through the elastic behavior of the PT tendons and pure wall shaking. Therefore, PT shear walls with internal energy dissipating reinforcements (IEDR) are used in practice in conditions with a greater force and restricted drift demand. The issue with this internal EDR, however, is that once it is destroyed, it cannot be repaired. Complete destruction of such an element necessitates total replacement of the building. Due to its benefit of easy replacement, external energy dissipating reinforcement (EEDR) has thus been used to alleviate this issue. The work of some researchers on numerical analysis, experimental investigation and the design of PT shear walls is included in this review of the literature. Additionally, it describes the field research done to improve the performance of PT shear walls by the use of viscous dampers or steel energy dissipators. Smith et al. [1] reviewed the lateral load behavior of three precast specimens intended to mimic monolithic cast-in-place RC shear walls, as well as the performance of two 0.40-scale hybrid precast concrete shear wall test specimens. While the emulative wall only used mild steel bars, the hybrid walls used both mild steel bars and high strength unbonded post-tensioning (PT) strands to provide lateral resistance. The hybrid wall with continuous mild steel bars demonstrated superior restoring, energy dissipation and ductile behavior over larger lateral displacements. The findings highlight the possibilities for precast walls in seismic areas while also highlighting crucial detailed issues. Guo et al. [2] proposed and implied a seismic rehabilitation method by using a self-compacting (SC) self-centering concrete wall with friction dampers, aiming at providing self-centering capacity and supplemental energy dissipation to a structure. The proposed SC wall was applied in the seismic resilience upgrade of a five-story RC frame building located in a high-seismicity city in China. Watkins et al. [3] carried conducted an analysis to investigate the influence of PT wall to floor interaction on global response of the E Defense test facility, taking the 3D numerical modeling one step further. As a result, the modeling took into account how each structural component will affect the structure as a whole. The two-bay bonded PT moment frames in the longitudinal direction and the unbonded PT shear walls in the transverse direction made up the lateral force resisting systems for the test buildings. The Kobe earthquake of 1995 caused increasingly powerful strong vibrations to be applied to the test building. In this current study, experimental investigation has been performed to check the suitability of two different external energy dissipating device configurations, namely external energy dissipating reinforcement configuration (EEDR) and external energy dissipating notched bar configuration (EDNB). The comparison of the effect of various configurations of energy dissipaters has been represented using finite element analysis by Guha et al. [4] and Prachi et al. [5]. To get more accurate idea

Experimental Investigation of Seismic Response of Hybrid Shear Wall …

207

about the response of the shear wall, experimental investigation was done in this work. The best suitable model has been concluded on the basis of their performances.

2 Description of the Experimental Model The test wall considered in the experimental study was having a height of 2.83 m and a length of 1 m, at 1/3rd scale, thus satisfying the minimum height to length ratio requirement of 0.5, according to ACI-ITG 5.2. The design and reinforcement detailing of the wall is referred in Guha [6]. The thickness of the wall was 0.1 m, and it was resting on a foundation beam of 0.14 m high. The foundation beam was consisting of an ISMB 300 section lying horizontally, with two ISMC 100 channel sections welded to its web. The space between the channel sections was filled with fiber-reinforced concrete to provide a strong bearing surface for the rocking of the wall. A depression of 2 cm was provided in fiber-reinforced concrete at the top of the foundation beam inside which the wall was placed to prevent horizontal sliding of the wall at the base while rocking. To prevent the uplifting of the foundation beam, angle sections were placed on the top of the foundation, which was anchored to the ground with the help of anchor bolts. Two holes were made in the web of the I section for the connection of PT tendons with the foundation beam. The PT tendons were placed with a straight profile inside the wall, anchored to the top of the wall at one end and to the bottom of the foundation beam at the other end. The main objective of testing the PT hybrid shear wall was to check the feasibility of placing two different external energy dissipating devices which are referred as external energy dissipating reinforcement bars (EEDRs) and external energy dissipating notched bars (EDNBs) in this study. The devices are fitted externally to facilitate its easy replacement without compromising its energy dissipation and selfcentering behavior. For establishing an external connection of the two devices, two angle sections were attached to the wall at 0.20 m height from the base. The angle sections were fitted to the wall with the help of bolted connections. The devices were connected to the angle section at one end and the channel sections of the foundation beam at the other with the help of bolted connections, thus spanning between the two with an unbonded length of 0.20 m. The reinforcement detailing of the PT shear wall along with the schematic diagram and actual view of the EDNB connection is shown in Fig. 1. The middle half portion of EDNB having a length of 10 mm was milled to a smaller cross-section of dimensions 8 mm × 10 mm and then tapered to a cross-section of 15 mm × 10 mm to prevent failure of anchorage and to shift the weak zone to the center of the bar. The two ends of the bar were threaded so that they could be attached to the channel and the angle sections with the help of bolted connections. The connection of EDNB with the angle and channel sections was made in such a way that it could resist the slippage of angle sections so that the EDNBs can be subjected to proper tension and compression. The connection made was such that

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Fig. 1 Reinforcement detailing of PT shear wall along with sketch showing the experimental arrangement for EDNB connection

the EDNB could be easily replaced after it is damaged by simply removing the bolts from the angle and channel sections. The detailing of the EEDR connection is shown in Fig. 2. To prevent anchorage failure and to move the weak zone to the center of the bar, the middle half part of EDR is machined to a smaller diameter of 9 mm, as shown in Fig. 3. By removing the bolt connection from the angle and channel portions, it would be simple to replace the EDR before it yields. In order to submit the EEDRs to the necessary tension and compression, the connection of the EDNB with the angle and channel sections was built in a way that it could resist the slippage of the angle sections. The bolts from the angle and channel parts could be easily removed, allowing the EDNB to be replaced once it is damaged.

Fig. 2 Actual connection and schematic diagram of the connection of EEDR

Experimental Investigation of Seismic Response of Hybrid Shear Wall …

209

Fig. 3 EEDR with reduced diameter of the middle portion

To control the out-of-plane displacement of the shear wall, two numbers of column supports were given at the front and the backside of the wall (Fig. 4a). Apart from this, an additional frame arrangement was also attached to the wall which is connected to the end of the load actuator. To resist the development of additional moments due to the rigidness of the actuator end, a hinged swivel head has been attached, which allows rotation at the actuator end while displacement-controlled loading is being applied.

2.1 Material Properties Stress–strain properties of the various steel used in the experimental model, based on tensile testing, are given in Table 1. The average compressive strength of concrete used in the shear wall model was 40 MPa. To obtain the stress–strain curve of the PT tendon, a tensile test has been carried out as per ASTM A416. The tendon satisfied the criteria of the minimum breaking strength of 160 kN and a total elongation value of greater than 3.5% as required by the code.

2.2 Instrumentation Two load cells have been placed, one, in front of the hydraulic actuator and another, at the top of the PT tendon, to measure the load applied to the structure and the amount of PT stress generated in the tendon during the test. The actuator was fitted with an lateral variable differential transducer (LVDT) to measure the displacement

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(a)

(b)

Fig. 4 a PT shear wall experimental setup and b the schematic diagram showing experimental setup with instrumentation Table 1 Actual stress–strain properties of reinforcement bars and PT tendon used in the experiment Reinforcement type

Diameter of bar (mm)

E (GPa)

f y (MPa)

f u (MPa)

Eu

Distribution steel

6

210

528

622

0.15

EEDR

Bar of variable dimensions

206

550

650

0.14

EDNB

Plate of variable dimensions

210

550

650

0.14

Tie rod

12

208

502

563

0.14

PT tendon

12.7

200

1678

1750

0.02

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211

of its piston with reference to its body. However, another LVDT has been attached at the back of the actuator loading frame to correct the actual lateral movement of the actuator piston with reference to the fixed ground by applying a correction for any displacement happening to the loading frame at actuator level, although it was very small. Another LVDT was placed at the bottom of the wall on one side to measure the horizontal slip of the structure. Two potentiometers have been attached at the reaction wall side of the PT wall, one horizontally and another vertically, to measure the horizontal slip and vertical uplift, respectively. All the sensors were calibrated before fixing to the experimental model. Figure 4 shows the schematic diagram of the instrumented experimental setup of the hybrid PT shear wall with external energy dissipating notched bars (EDNB).

2.3 Post-tensioning The post-tensioning of the tendons was done with the help of a post-tensioning jack. Due to the physical limitations of the mechanical application of prestress to tendons, the exact prestress in the tendon could not be precisely controlled and applied as designed. However, after prestressing, the stress in the tendon was measured to be about 797 MPa, which was corresponding to 74 kN of prestressing force as compared to 83 kN of design prestress in each tendon. Safety arrangements are provided at the top of the anchorage by connecting them firmly to the wall with the help of steel channel sections.

2.4 Loading The actuator was placed at the height of 2.3 m from the base of the wall, where the resultant force of the first modal inertia forces is expected, as per the design. Displacement controlled slow cyclic loading was applied at the rates of 0.4 mm/sec to avoid the dyamic effect of the structural response, as shown in Fig. 5. The lateral load was applied cyclically in the pushing direction to avoid the inconvenient connection of the bulky swivel head.

3 Experimental Results and Discussions To check the feasibility of the proposed method of placing external energy dissipating reinforcing bars (EEDR) and energy dissipating notched bars (EDNB) in a PT shear wall for energy dissipation, experimental testing has been performed. Various responses of the model were recorded, including backbone curves, stiffness

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Displacement (mm)

212 45 40 35 30 25 20 15 10 5 0

0

500

1000 Time (sec)

1500

2000

Fig. 5 Displacement controlled load applied to the structure

degradation and energy dissipation which is analyzed and presented in the following sections.

3.1 Comparison of Backbone Curves The backbone curves of the experimental models with two different configurations namely EEDR and EDNB are plotted in Fig. 6. It has also been observed that in the initial drift values, the backbone curve of EDNB is steeper as compared to that of the EEDR model. At design drift 0.384%, the load carried by the EDNB model is 22.5 kN whereas for EEDR model the value is 14.15 kN. But with the increase in the amplitude of displacement, the backbone curve of EDNB model becomes flatter. For a drift value of 1.35%, the load carrying capacity of EDNB model becomes equal to the EEDR model, i.e., 34.25 kN. For drift values greater than 1.35%, the load carrying capacity of EEDR model becomes higher than the EDNB model. At maximum drift value, the load carried by the EDNB model is 39.15 kN and for EEDR model the value is 40.21 kN. 45 40 35

Load (kN)

Fig. 6 Comparison of backbone curves of experimental models with EEDR and EDNB

30 25 20 15 10

EEDR

5

EDNB

0

0

0.5

1

Lateral Drift (%)

1.5

2

Stiffness (kN/m)

Experimental Investigation of Seismic Response of Hybrid Shear Wall … 1800 1600 1400 1200 1000 800 600 400 200 0

213

EDNB EEDR

0.3

0.5

0.7

0.9

1.1

1.3

1.5

Lateral Drift (%)

Fig. 7 Comparison of stiffness degradation curves of EEDR and EDNB models

3.2 Comparison of Stiffness Degradation The comparison of stiffness of both the models corresponding to their lateral drifts is shown in Fig. 7. It can be perceived that the initial stiffness of the EDNB model is relatively higher as compared to the EEDR model (which is evident from the backbone curve also). At design drift of 0.384%, the stiffness of EDNB model is 1623 kN/m. In case of EEDR mode, the value is 1104 kN/m. Although, as the lateral drift increases the rate of stiffness degradation is higher in case of EDNB model as compared to the other one. That’s why, near maximum drift value, the stiffness of both the model becomes almost similar. At 1.45% lateral drift ratio, the stiffness of EDNB is 855 kN/m whereas in case of EEDR, the value is 800 kN/m.

3.3 Comparison of Energy Dissipation The amount of cumulative energy dissipated by the two experimental models has been compared, as shown in Fig. 8. Both the models are showing almost similar amount of energy dissipation corresponding to its lateral drift values. Although, if the graph can be observed closely, it is seen that for lateral drift values up to design drift, i.e., 0.384%, the amount of energy dissipated by the EDNB model is slightly higher that of EEDR model. At design drift, the energy dissipated by EDNB is 17.64 kN-m, whereas in case of EEDR, the value is 11.02 kN-m. At maximum drift, i.e., 1.45%, the amount of energy dissipation for EDNB is 287.1 kN-m and for EEDR the value is 292.34 kN-m. If the behavior of both the models is compared, it can be seen that at same drift values the EDNB model is showing higher load taking capacity as compared to EEDR initially. Therefore, while calculating the amount of energy dissipation, despite of high stiffness values, the EDNB model was able to dissipate almost equivalent amount of energy as EEDR.

Fig. 8 Comparison of energy dissipation of numerical model and experimental model

A. Guha et al.

Energy Dissipated (kN-m)

214 350 300 250 200 150 100

EEDR

50 0

EDNB

0

0.5

1

1.5

2

Drift Value (%)

4 Summary and Conclusions The above discussion explains a comparative experimental study of shear wall models with two different external energy dissipating devices which are external energy dissipating reinforcing bars (EEDRs) and external energy dissipating notched bars (EDNBs). Various aspects of the two models have been compared such as comparison of backbone curves, stiffness degradation and energy dissipation. It has been perceived that although there is not much difference in ultimate load capacity of the two models, the initial stiffness of the EDNB model is higher as compared to the EEDR model. This can be observed in the stiffness degradation curve as well. The initial stiffness of the EDNB model is quite high. But with increase in lateral drift, the rate of stiffness degradation is high in case of EDNB model, hence at ultimate lateral drift values both the models showed almost similar stiffness. While comparing the energy dissipation, both the models are showing almost similar amount of energy dissipation. From the above discussions, it can be said that both the models are showing almost similar behavior, but the EDNB model is showing relatively higher stiffness as compared to the EEDR model, hence having higher load carrying capacity at initial small lateral drift values. Therefore, the EDNB model can be more efficient in terms of lateral load taking capacity as compared to EEDR model. It can be concluded that the hybrid shear wall model with external energy dissipating notched bar can be a better option as an energy dissipating device.

References 1. Watkins, J., Sritharan, S., Nagae, T., Henry, R.S.: Computational modelling of a four storey post-tensioned concrete building subjected to shake table testing. Bull. N. Z. Soc. Earthq. Eng. 50(4), 595–607 (2017) 2. Smith, B.J., Kurama, Y.C., McGinnis, M.J.: Design and measured behavior of a hybrid precast concrete wall specimen for seismic regions. J. Struct. Eng. 137(10), 1052–1062 (2011)

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3. Guo, T., Xu, Z., Song, L., Wang, L., Zhang, Z.: Seismic resilience upgrade of RC frame building using self-centering concrete walls with distributed friction devices. J. Struct. Eng. 143(12), 04017160 (2017) 4. Guha, A., Taori, P., Dash, S.R., Mondal, G.: Efficient numerical analysis of hybrid shear wall with energy dissipating reinforcements. In: Proceedings of the Indian Structural Steel Conference2022, IIT Hyderabad, Hyderabad, India 5. Taori, P., Dash, S.R., Mondal, G.: Seismic response of post tensioned hybrid shear walls with external energy dissipating reinforcement (EEDR). J. Earthq. Eng. (2020). https://doi.org/10. 1080/13632469.2020.1778587 6. Guha, A.: Seismic response of post-tensioned hybrid shear walls with external energy dissipating reinforcement. M. Tech Thesis, IIT Bhubaneswar, India (2020)

1D Velocity Model for NW India in and Around Delhi Deepak Kumar , G. Suresh, S. C. Gupta , M. L. Sharma , and Hasbi Ash Shiddiqi

Abstract We derive a 1D crustal velocity model based on P- and S-wave residual time arrivals at the national seismological network maintained by National Center for Seismology (NCS) for NW India around Delhi between latitude range 22º N to 33º N and longitude range 73ºE to 80ºE, which separates interplate area in the north and intraplate area in the south. Available models are mostly from regional studies having low crustal resolution. We use 413 earthquakes in all, which are chosen based on the maximum number of stations, minimum azimuthal gap, and root mean square travel time. We use VELEST code to invert the 1D velocity model, hypocenter locations, and station delays. The Indo-Gangetic (IG11) model is used as a starting model, and in this model, we further created 500 initial models by adding random perturbation ± 10%. We also evaluate the effect of different Moho depths (i.e., 38, 43, and 48 km). We selected the preferred model based on how the models converge and on the travel time residuals. The minimum residual travel time of P- and S-arrivals indicated crustal thickness as 43 km. Further, this model can be used for seismicity and 3D seismic tomographic studies of the area. Keywords Earthquake location · 1D crustal velocity model

D. Kumar (B) · S. C. Gupta · M. L. Sharma Department of Earthquake Engineering, IIT Roorkee, Roorkee, India e-mail: [email protected]; [email protected] S. C. Gupta e-mail: [email protected]; [email protected] M. L. Sharma e-mail: [email protected]; [email protected] G. Suresh National Center for Seismology, New Delhi, India e-mail: [email protected] H. A. Shiddiqi Department of Earth Science, University of Bergen, Bergen, Norway e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_18

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1 Introduction The Delhi falls on the western edge of the Ganga basin, east of the Delhi–Hardwar Ridge, surrounded by the Himalayas in the north, and the Aravalli ranges in the south. The rocks of Delhi belong to both the earliest and latest chapters of the geological history of the earth: the Pre-Cambrian and the Quaternary. The Pre-Cambrian rocks belong to the Alwar series of the Delhi System. Geologically, 95% of the area of the Delhi region is covered by the Quaternary sediments, and the rest 5% comprises the Meso- to Neo-Proterozoic rocks of the Delhi Supergroup present in south and southwest intruded by more than one phase of acidic and basic intrusives of Neo-Proterozoic age (Post Delhi intrusives) and the tertiary rocks in the northeast. Numerous local tectonic features surround the Delhi region, including the Aravalli–Delhi fold, Mahendragarh–Dehradun Fault, Moradabad Fault, Great Boundary Fault, Sohna Fault, Delhi–Hardwar Ridge, Mathura Fault, Delhi–Lahore Ridge, Sohna Fault, Mahendragarh–Dehradun Fault, and Moradabad Fault, as well as other less significant lineaments and distinct tectonic features such as the Main Boundary Thrust (MBT) and Main Central Thrust (MCT) of the Himalaya, as well as various minor lineaments and far-off tectonic structures. Several significant earthquakes have occurred close to Delhi in the past: Delhi earthquake (1720) M: 6.5, Mathura earthquake (1803), M: 6.8, Bulandshahar earthquake (1956) M: 5.8, Delhi NCR earthquake (1960) M: 4.8, and small magnitude earthquakes frequently occur in the area. Many small (1.0 ≤ Mw ≤ 3.0) and moderate earthquakes occur on the Delhi NE border adjoining Uttar Pradesh. The Hazard in Delhi originated due to the Himalayan thrust fault system which was further associated with the collision between the Indian and Eurasian plates [1–4] (Fig. 1). From seismological studies, the estimated average Moho depth is 43 km [5] and 46 km [6] from receiver function studies beneath the Delhi region,the joint inversion of receiver function and Rayleigh wave group velocity shows crustal thickness variation from 40 to 44 km beneath Delhi Fold Belt (DFB). Mandal et al. (2013) used the common reflection stack method variation in crustal thickness across the Aravalli Delhi fold Belt and found a variation from 38 to 50 km. Although lots of models have been provided by different researchers for this area, those models have a low resolution in the crust. The VELEST program provides a P and S wave highresolution velocity structure. Various seismological researches have been conducted by considering the reference velocity model as the initial starting velocity model, and this initial model is modified until there is a strong and positive correlation between the majority of observed and predicted values. The impact of the initial reference model on tomographic inversion results has not been properly grasped in many earlier investigations. An incorrect initial reference model can affect the quality of the solution, and it can also introduce large uncertainties in the final results. To overcome this kind of problem, we evaluated the 1D model for the Delhi region by using the joint inversion of travel time data, revised hypocenter coordinates, and station corrections. The evaluated minimum 1D velocity model can use as a reference model for the 3D topography of the Delhi region.

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Fig. 1 Map of Delhi with the major regional tectonic features of Himalaya: Himalaya frontal thrust (HFT), main boundary thrust (MBT), South Tibetan Detachment (STD), Delhi–Hardwar ridge (DHR), Moradabad fault (MF), Sohna fault (SF), Mathura fault (MTF), and great boundary fault (GBF). The distribution of earthquakes (red star) and NCS seismic observatories are shown in black triangles

2 Data The hypocenter parameters, P- and S-wave arrivals for the study region bounded by between latitude 22ºN–33ºN and longitude 73ºE–80ºE, are collected from the seismological bulletins of the International Seismological Center (ISC) for the region (http:// www.isc.ac.uk/cgi-bin/collect?Reporter=NDI) and the website of NCS (https://sei smo.gov.in/bulletins) for the period July 2000 to June 2020, i.e., 20 years of data. We selected 413 earthquakes based on the criteria, (a) record of a minimum of eight stations, (b) having an azimuthal gap less than 180º, and (c) root mean square (RMS) travel time residuals less than 2.0s. There are 3191 P-arrivals and 2986 Sarrivals on 51 recorded stations in the dataset that covers the whole study region (Fig. 2).

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Fig. 2 Different ray path directivities of 413 earthquakes (circles according to their focal depth distribution) used for velocity inversion a P-wave ray paths and b S-wave ray paths

3 Methodology The evaluation of travel time from seismic velocity models along their ray paths and the location of earthquakes is an inverse nonlinear problem, and the problem is linearized to solve it in the least-square sense [7]. The arrival time of a seismic wave from the source to recoding station is a nonlinear function of station coordinates (s), hypocentral location parameters (h), and velocity model (m) as given by [7, 8]. tobs = f (s, h, m)

(1)

To solve Eq. (1) we start with the initial guess for these unknown parameters and we calculated theoretical arrival times (tcal ). The difference between observed and calculated arrival time is the residual travel time (tr es ) and can be expressed as tr es = tobs − tcal =

 k=1,4

 ∂f ∂f h k + m i + e i=1,n ∂h k ∂m i

(2)

where in matrix notation [t] = [H ][h] + [M][m] + [e] = [A][d] + [e]

(3)

[t] is a vector of travel time residuals, [H] is a matrix of partial derivatives of travel time with respect to hypocenter parameters, [h] is a vector of hypocenter parameter

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adjustment, [M] is a matrix of partial derivatives of travel times with respect to model parameters, [m] is a vector of model parameters adjustment, [e] is a vector of travel time error, [A] is a matrix of all partial derivatives, and [d] is a vector of hypocentral and model parameter adjustments. The mean residual time and RMS error are used to drive the velocity model where the final model is defined in the least square sense. The initial models are generally adopted from previous similar works for the regions, and refined models are then recommended for their further use in 3D tomography.

3.1 Inversion of the Velocity Model Bhattacharya [9], has evaluated Indo-Gangetic (IG11) velocity model from surface wave dispersion data. We used this IG11 velocity model (Table 1) for our present study area to evaluate the minimum 1D velocity model. We started with two different velocity models with and without the sedimentary layer and consisting of six (the first layer is the sedimentary layer) and five layers up to 100 km of depth, respectively. The depth of the top of the 4th layer is Moho which is being tested for different depths. We started with two different models, one is the model (without sedimentary layer) in which the top layer is above 0, the depth of the 2nd layer is 3.5 km, the depth of the 3rd layer is at 20 km, the depth of the top of the 4th layer is Moho, and 5th layer is fixed at 100 km from IG11 model. In the second model (with sedimentary layer), one sedimentary layer at depth of 0.5 km has been introduced in addition to the layers considered in the first model. We inverted for seismic velocities (P and S waves) but not thicknesses of layers, and we divided the crust into 5 km layers. As suggested by Kissling et al. [10], we also tested Moho depth by ± 5 km to the average depth of Moho, i.e., 38, 43, and 48 km3 . We relocated the hypocenter in the first iteration and inverted for the velocity model in the second iteration; in this way we evaluated the hypocenter location and velocity model simultaneously for up to 20 iterations and the layers with almost the same velocities are combined. We finally determined with this process the average Table 1 Velocity model of Indo-Gangetic plain Depth of top of layer (km) Above 0.0

Thickness of layer (km) 3.5

Vp (km/s)

Vs (km/s)

Density (gm/cm3 )

3.40

2.00

2.00

3.5

16.5

6.15

3.55

2.60

20.0

23.0

6.58

3.80

3.00

43.0

57.0

8.19

4.60

3.30

100.0

20.0

8.30

4.60

3.30

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Fig. 3 a Initial 1D velocity model for Vp and Vs from IG11 model. b Generated random initial models from inversion. c The best models and random models are depicted by black and gray lines, respectively

crustal thickness is 43 km in the Delhi region. Inversion steps’ example has been shown for Moho 43 km using the IG11 model in Fig. 3. We tested several randomly generated models to get the best velocity model. For each model, we create 500 perturbations and further modify VP and VS for each layer in the ± 10 range. We use VP/VS ratio range of 1.6–1.9, and then for each model, we inverted using VELEST. The inversion process has been followed by Kissling et al. [7], in which origin, hypocenter, and depth damping are fixed at 0.01, but the velocity and station correction damping is fixed at 1.0. For each initial model, we have accepted the models with the lowest (10%) travel time. We repeat this process for 20 iterations. The best models are averaged to get the final velocity model. The inversion result distributions for all the three tested Moho depths 38, 43, and 48 km3 with and without sedimentary layer are shown in Fig. 4. We used New Delhi Station (NDI) as a reference station for station correction which is located at the center of our study area. The station corrections are refined further by choosing a higher damping value (=10.0) for the final velocity model. This further results in there being no change in the velocity model, but we will get the updated station corrections and hypocenters. To check the quality of solutions, we used different velocity models with station corrections and located events with the HYPOCENTER program [11]. Then for each velocity model, we compared travel time residuals and found the relocated models with sedimentary layers have higher means of travel time than that of initial locations (Table 2).

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Fig. 4 All accepted results from the random test are shown by gray color lines, with the final velocity models shown by a red line, and the best models are represented by black lines with 38, 43, and 48 km3 Moho depth beneath the Delhi region from left to right: a outcomes of the model having no sedimentary layer and b outcomes of the model having sedimentary layer Table 2 Comparison of mean residuals from the initial and final models S. No.

Velocity model (km)

Initial mean residual (s)

Final mean residual (s)

Without sedimentary layer

With sedimentary layer

Without sedimentary layer

With sedimentary layer

1

38

2.620509

1.166025

0.950155

0.988897

2

43

2.446367

1.165017

0.890986

0.967577

3

48

2.448025

1.370489

0.944641

1.017219

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4 Results and Discussion We used the earthquake catalog from July 2000 to June 2020 with their updated location obtained by simultaneous inversion through the VELEST program of SEISAN software [12]. The shallower (focal depth less than 50 km) earthquakes are used for the processing of travel times. A total of 313 earthquakes are used in the present study which is recorded at least by eight stations. We have selected the velocity model having Moho depth at 43 km with P and S wave velocities of 8.18 km/s and 4.71 km/s as our final 1D velocity model (Table 3 Fig. 5a) based on the minimum travel time residual (0.890986s and 0.967577s) travel time. As the error difference due to the presence of a sedimentary layer is negligible in comparison to the resolution of the processing so no definite conclusions may be drawn about the presence of a sedimentary layer in this region. Several studies show the Moho depth in the Delhi region varies from 38 to 45 km. Rai et al. [13] computed Moho depth variation around 40 to 75 km for Delhi and Taksha at the Karakorum fault by using joint inversion of Rayleigh wave dispersion data and receiver function analysis. The P and S wave velocities found at Moho were 7.6 km/s and 4.4 km/s respectively which is comparable to the present study Table 3 Final velocity model with Moho 43 km

Top of the layer (km)

Vp (km/s)

Vs (km/s)

Above 0

1.50

0.83

0.5 (sedimentary layer)

3.77

1.96

3.5

6.06

3.54

20

6.46

3.78

43

8.18

4.71

100

8.56

5.01

Fig. 5 a Initial and final models’ seismic velocities plots. b The regional comparison of seismic velocities with the present study

1D Velocity Model for NW India in and Around Delhi Table 4 Regional comparison of Moho with different models

Models

225 V P (km/s)

V S (km/s)

Moho (km)

Kumar et al. [14]

7.79

4.50

43

Rai et al. [15]

7.79

4.50

36

Rai et al. [13]

7.61

4.40

40

Julià et al. [16]

7.61

4.40

40

Present study

8.19

4.73

Note Vp has been calculated by using the relation

43 Vp Vs

=

√

3

(Fig. 5b). Different methods have been used to obtain the 1D velocities models for the Delhi region, and we compared our results with these studies (Table 4, Fig. 5b). The crust beneath the south shield has a similar Moho of ~36 km and similar seismic velocities [15], and the Indian shield has 33–39 km of Moho depth [14]. Reference [16] has also evaluated the seismic velocity structure with crustal thickness 40 km beneath Delhi for the Indian shield. The P and S wave velocities of the present study at Moho are 8.19 km/s and 4.73, respectively, which is a little bit higher than all other comparison models (Table 4, Fig. 5b). Variation in the crustal velocity and thickness can cause significant arrival time delays [17]. Singh et al. [18] showed that the crustal velocity in the Indian shield is higher than in the Himalayan region. The stations located in the northern part of our study area show delayed P and S arrivals (positive delays gray color triangles), and this may be due to lower crustal velocity around the Himalayas and the stations located in the southern part of the area which show early P and S arrivals (negative delays red color triangles) and have higher crustal velocity (Fig. 6).

Fig. 6 P and S wave’s travel-time station corrections were obtained from VELEST inversion

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5 Conclusion In the present study, a 1D velocity model of the Delhi region has been estimated using 413 earthquakes recorded at 51 stations. We observe the negative delays or early arrivals observed in the center and the southern part of the study area are probably related to this higher crustal velocity, and the lower velocity around the Himalayas can cause positive delays or late arrivals. We confirm the new velocity model, which consists of 6 layers up to 100 km of depth with P and S wave velocities varying from 1.5 km/s to 8.56 km/s. The sedimentary layer seems to be at a depth of top 0.5 km with P and S wave velocities of 1.50 km/s and 0.83 km/s, respectively; however, the error difference due to the presence of the sedimentary layer is negligible in comparison to the resolution of the processing so no definite conclusions may be drawn about the exact thickness of the sedimentary layer in this region. Moho depth appears to be at 43 km having minimum residual time and maximum precision in earthquake location from the tested models with the P and S velocities of 8.18 km/s and 4.71 km/s, respectively. The final estimated velocity model can be used to determine source parameters and as an input for the studies of 3D tomography of the Delhi region. Data and Resources The phase data of earthquakes have been taken from the monthly seismological bulletins published on the NCS website (https://seismo.gov.in/bulletins) and International Seismological Center (ISC) (http://www.isc.ac.uk/cgi-bin/collect?Report er=NDI) for the region. The figures were made using Generic Mapping Tools and ArcGIS software. Acknowledgements The authors are thankful to the Director of National Center for Seismology, New Delhi, India for making the data available to carry out the study. Acknowledgments are due to THDCIL, Rishikesh, for help and data.

References 1. Chandra, U.: Seismotectonics of the Himalayas. Curr. Sci. 62, 40–71 (1992) 2. Joshi, G.C., Sharma, M.L.: Strong ground-motion prediction and uncertainties estimation for Delhi, India. Nat. Hazards 59(2), 617–637 (2011). https://doi.org/10.1007/s11069-011-9783-y 3. Sharma, M.L., Wason, H.R., Dimri, R.: Seismic zonation of the Delhi region for bedrock ground motion. Pure Appl. Geophys. 160(12), 2381–2398 (2003). https://doi.org/10.1007/s00 024-003-2400-6 4. Sharma, M.L.: Seismic hazard in the Northern India region. Seismol. Res. Lett. 74(2), 141–147 (2003). https://doi.org/10.1785/gssrl.74.2.141 5. Mitra, S., Kainkaryam, S.M., Padhi, A., Rai, S.S., Bhattacharya, S.N.: The Himalayan foreland basin crust and upper mantle. Phys. Earth Planet. Inter. 184(1–2), 34–40 (2011). https://doi. org/10.1016/j.pepi.2010.10.009 6. Borah, K., Kanna, N., Rai, S.S., Prakasam, K.S.: Sediment thickness beneath the Indo-Gangetic Plain and Siwalik Himalaya inferred from receiver function modelling. J. Asian Earth Sci. 99, 41–56 (2015). https://doi.org/10.1016/j.jseaes.2014.12.010

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7. Kissling, E., Ellsworth, W.L.L., Eberhart-Phillips, D., Kradolfer, U.: Initial reference models in local earthquake tomography. J. Geophys. Res. 99(B10), 19635–19646 (1994) 8. Kissling, E.: Geotomography with local earthquake data. Rev. Geophy. 26, 659–698 (1988) 9. Bhattacharya, S.N.: Crustal and upper mantle structure of India from surface wave dispersion. Curr. Sci. 62, 94–100 (1992) 10. Kissling, E., Kradolfer, U., Maurer, H.: VELEST User’s Guide–Short Introduction. (Institute of Geophysics and Swiss Seismological Service, ETH Zurich, 1995), p. 25 11. Lienert, B.R., Havskov, J.: A computer program for locating earthquakes both locally and globally. Seismol. Res. Lett. 66(5), 25–36 (1995). https://doi.org/10.1785/gssrl.66.6.26 12. Havskov, J., Voss, P.H. , Ottem¨ oller, L.: Seismological observatory software: 30 Yr of SEISAN. Seismol. Res. Lett. 91(3), 1846–1852 (2020) 13. Rai, S.S., Priestley, K., Gaur, V.K., Mitra, S., Singh, M.P., Searle, M.: Configuration of the Indian Moho beneath the NW Himalaya and Ladakh. Geophys. Res. Lett. 33(15), 3–7 (2006). https://doi.org/10.1029/2006GL026076 14. Ravi Kumar, M., Saul, J., Sarkar, D., Kind, R., Shukla, A.K.: Crustal structure of the Indian shield: New constraints from teleseismic receiver functions. Geophys. Res. Lett. 28, 1339–1342 (2001). https://doi.org/10.1029/2000GL012310 15. Rai, S.S., Priestley, K., Suryaprakasam, K., Srinagesh, D., Gaur, V.K., Du, Z.: Crustal shear velocity structure of the south Indian shield. J. Geophys. Res.: Solid Earth 108(B2) (2003). https://doi.org/10.1029/2002jb001776 16. Julià, J., Jagadeesh, S., Rai, S.S., Owens, T.J.: Deep crustal structure of the Indian shield from joint inversion of P wave receiver functions and Rayleigh wave group velocities implications for Precambrian crustal evolution. J. Geophys. Res.: Solid Earth 114(10), 1–25 (2009). https:// doi.org/10.1029/2008JB006261 17. Shiddiqi, H.A., Ottemöller, L., Rondenay, S., Halpaap, F., Gradmann, S., Michálek, J.: Crustal structure and intraplate seismicity in Nordland, Northern Norway: insight from seismic tomography. Geophys. J. Int. 230(2), 813–830 (2022). https://doi.org/10.1093/gji/ggac086 18. Singh, A., Ravi Kumar, M., Mohanty, D.D., Singh, C., Biswas, R., Srinagesh, D.: Crustal structure Beneath India and Tibet: new constraints from inversion of receiver functions. J. Geophys. Res.: Solid Earth 122(10), 7839–7859 (2017). https://doi.org/10.1002/2017JB013946

Anomalous Deviations in Atmospheric Parameters as Pre-earthquake Signals-A Case Study on Sumatra Region Earthquakes (M ≥ 6.0) Ramya Jeyaraman

and N. Venkatanathan

Abstract One of the most demanding disciplines in geoscience is earthquake forecasting, which is widely debated due to its heterogenetic nature. Several strategies for forecasting the destructive earthquake events have been developed in recent decades. This work focuses on exploring and understanding of the precursory signals which is associated with the occurrence of earthquakes. The earthquake precursors are identified by anomalous deviation in outgoing longwave radiation (OLR) and relative humidity (RH) before strong magnitude earthquakes. This work concentrates to study the correlation of anomalous variations in atmospheric parameters such as RH and OLR flux index associated with the earthquakes through lithosphere–atmosphere–ionosphere coupling (LAIC) model. The retrospective analysis of temporal variations is done on the daily datasets of RH, and OLR is carried out for eight earthquakes with magnitude ≥ 6.0 occurred during 1991–2021 within 75 km radius of December 26, 2004. The exploratory data analysis is carried out based on the investigation of earthquake activity in the study region taken from United States Geological Survey (USGS), anomalous OLR flux index, anomalous RH index for the period of six months. According to our preliminary findings, the relationship among the anomalous drop of RH and anomalous increase in OLR flux index anomalies with the occurrence of earthquakes were observed near epicentral region as proposed by the LAIC model. This anomalous behavior was observed 3 days to 3 months prior to the occurrence of earthquakes. Hence, the authors inferred that there exists a possible link between the abnormal deviations in atmospheric parameters and the occurrence of earthquakes. Further incorporation of various precursors along with OLR and RH studies can help us to develop an effective tool for forecasting the earthquake on a short-term basis. Keywords Earthquake precursors · Outgoing longwave radiation · Relative humidity · Earthquake forecasting R. Jeyaraman · N. Venkatanathan (B) School of Electrical and Electronics Engineering, SASTRA Deemed to Be University, Thanjavur, Tamil Nadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_19

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1 Introduction Identification of potential earthquake precursors poses challenging research in shortterm earthquake forecasting studies due to its heterogeneous nature. Numerous investigations revealed anomalous changes in space–time transients related to OLR and RH during the earthquake preparation process. Recent advances in remote sensing technologies have improved our understanding of the earth’s surface seismic signals. The change in OLR radiation can reflect the evolution of the critical state of ground stress [1]. Satellite thermal imaging data can detect anomalies in OLR [2]. The energy emitted by the earth is electromagnetic radiation that travels through the atmosphere and into space as OLR [3]. The release of energy into the atmosphere causes a rise in air temperature. Even if the radon quantity in the air is negligible, the energy efficiency of the ionization process is exceptionally high [4, 5] which leads to anomalous variations in atmospheric parameters such as RH, OLR (more than 108 ) [6]. OLR is affected by the temperature near the surface and in the atmosphere, air humidity, and cloud presence [7]. Analyzing longwave radiation data using time series, comparison, and eddy field methods have been performed. The association between abnormality and earthquake occurrence has shown that seismic anomalies can be detected using OLR singularities [8]. The parameters of the atmosphere and ionosphere are jointly analyzed with variations in both temporal and spatial aspects. It has been demonstrated that abnormalities over the atmosphere and ionosphere frequently develop close to an earthquake’s epicenter [9]. A potential coupling mechanism between the lithosphere and the ionosphere of the earth is proposed by the LAIC model. According to the model, radon gas emissions from the earth’s crust are caused by pre-earthquake seismic activity triggered by the movement of tectonic plates in the lithosphere. Radon gas emissions cause the atmosphere’s air to ionize, which renders possible anomalies [10–12]. Using an interdisciplinary approach, pre-earthquake signals such as proton (H+ ion) permeability, RH, radon gas emission, and OLR can be employed as a precursor. The movement of H+ ions from the earth’s core to the surface causes an increase in tectonic activity, leading to the emission of radon gas. It causes the air molecules in the atmosphere to ionize, which lowers the RH on the surface of the earth. Latent heat release caused by the upward migration of ionized air molecules from the earth’s surface causes the anomaly in OLR flux [13–15]. The possible relationship between TEC, air temperature, and RH and Mexico M7.4 earthquake is studied and shows that RH displayed a negative anomaly above the earthquake’s epicenter [16]. Sumatra is vulnerable to earthquakes due to its location in an active subduction zone between the Indo-Australian plate and the Eurasian plate and its location on a fault along the Sumatra fault [17]. In this work, the possible link with the atmospheric parameters such as RH and OLR with respect to the occurrence of the earthquakes in the Sumatra-Indonesia region is explored.

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1.1 Study Area The study area taken for this work is Sumatra-Indonesia. Indonesia is located at the intersection of three massive tectonic plates: the Indo-Australian Plate on the southern side, the Eurasian Plate on the Northern side, and the Pacific Plate from northeast to southwest [18]. Due to its location on the plate boundary between the Indo-Australian plate to the north and the Eurasian plate to the south, the island of Sumatra is situated in an active seismic subduction zone. Initially, the Indo-Australian plate was moving at 86 mm per year, but due to the collision event, this fell to 40 mm per year. At the beginning of a new tectonic configuration process, this drop continues to occur up to 30 mm per year. In addition, the rate of return has dramatically risen to 76 mm per year [19]. Sumatra resides in an active subduction zone between the Indo-Australian and Eurasian plates, making it prone to earthquakes [17]. The data from the 75 km radius of the Sumatra-Indonesia earthquake period from 1991 to 2021 is taken from USGS. The earthquake magnitude employed in this study is magnitude > 6 Mw, with a configuration depth of 0–35 km [20]. As shown in Fig. 1, the following map denotes the eight earthquakes in the Sumatra-Indonesia region taken for study as mentioned in Table 1.

2 Methodology OLR represents energy emitted to space by the earth’s surface and atmosphere. The energy flux of OLR from the earth’s surface is measured in Wm − 2. The raw data are converted into a standard anomaly index. The data collected is in a grid resolution of 1° × 1° (latitude × longitude). OLR data are time-series data that cover the entire earth. Fig. 1 Map shows eight earthquakes within a 75 km radius of the December 26, 2004 earthquake from 1991 to 2021. Here the red star denotes the epicenter

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Table 1 List of earthquakes taken for study Date

Latitude

Longitude

Depth

Magnitude

01–09–1993

2.986

96.122

34

6.3

02–11–2002

2.824

96.085

30

7.4

26–12–2004

3.295

95.982

30

9.1

07–04–2007

2.916

95.7

30

6.1

20–02–2008

2.768

95.964

26

7.4

09–12–2009

2.759

95.91

21

6

09–05–2010

3.748

96.018

38

7.2

25–07–2012

2.707

96.045

22

6.4

Exploratory data analysis is carried out in the OLR raw data, and Z-factor normalization is applied to the raw OLR data to find the anomalous OLR flux index (E Flmτ ). From the mean OLR flux of the previous “n” years, anomalous variations in OLR flux have been computed by, E Flmτ =

n 

E Flmτ

(1)

τ =1

where For a given location (l, m) and time (τ ), “n” is the number of predefined ten years for which mean OLR flux is determined dE_index(Flux index)(E Flmτ ) =

E Flmτ − E Flmτ σlmτ

(2)

where [Elmτ ] Flux index for a given latitude (l), longitude (m), and time of data acquisition (τ ). Elmτ Current OLR flux for a given location (l, m) and time (τ ). E Flmτ Mean OLR flux for a given location (l, m) and time (τ ). Anomalous flux index energy “[Elmτ ]∗ ” can be determined by removing the energy flux index value below the +2σ level of mean OLR flux, which helps maintain the duration of anomalous change observed. If Elmτ ≥ Elmτ + 2σ Then, Elmτ = [Elmτ ]∗ ELSE Elmτ = 0

(3)

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where [Elmτ ]∗ Anomalous energy flux index observed for given location and time. A similar approach can be used to measure RH. Keeping the flux index energy “[Elmτ ]∗ ”, the anomalous RH index can be determined by removing the energy index value below −2σ level of mean RH.

3 Results and Discussion 3.1 01–09–1993: M6.3 Simeulue, Indonesia Earthquake On September 1, 1993, a magnitude 6.3 earthquake struck Simeulue, Indonesia, at 2.986°N 96.122°E occurred due to a thrust fault with 34.0 km depth. The anomalous variations in RH were observed on 29–07–1993, which is 34 days before the occurrence of the earthquake, followed by 02–08–1993 (30 days prior) with an anomalous index of −2.378 and −2.253, respectively (Fig. 2). The anomalous variations in OLR were observed on 01–08–1993 which is 31 days before the occurrence of earthquake and anomaly occurred for three days consecutively on 02–08–1993, 03–08–1993, and 04–08–1993 with an anomalous index of 2.468, 3.167, 2.071, respectively (Fig. 2). 5 4

Date: 01-09-1993 Lat. Long: 2.986 N 96.122E Magnitude: 6.3Mw

RH OLR

3

dE_Index

2 1 0 -1 -2 -3 -4 -5 01-03-1993 01-04-1993 01-05-1993 01-06-1993 01-07-1993 01-08-1993 01-09-1993

DATE Fig. 2 Anomalous variations of RH and OLR before the 01–09–1993, Simeulue, Indonesia earthquake (6.3 Mw). The other anomalies are suppressed to understand better the pattern of anomalous variations corresponding to this particular earthquake

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Date: 02-11-2002 Lat. Long: 2.824 N 96.085E Magnitude: 7.4 Mw

RH OLR

3

dE_Index

2 1 0 -1 -2 -3 -4 -5 02-05-2002 02-06-2002 02-07-2002 02-08-2002 02-09-2002 02-10-2002 02-11-2002

DATE Fig. 3 Anomalous variations of RH and OLR before 02–11–2002, Simeulue, Indonesia earthquake (7.4 Mw). The other anomalies are suppressed to understand better the pattern of anomalous variations corresponding to this particular earthquake

3.2 02–11–2002: M7.4 Simeulue, Indonesia Earthquake A magnitude 7.4 Mw earthquake struck Simeulue, Indonesia, at 2.824°N latitude, 96.085°E longitude on 02–11–2002, caused by a shallow depth of 30 km. This earthquake is considered a foreshock of the great 26–12–2004 Sumatra–Andaman Islands earthquake [21]. The anomalous variations in RH were started 42 days before the main shock on 21–09–2002 with an anomalous index of −3.4385, followed by a consecutive anomaly on 25–09–2002 with an anomalous index of −2.688. On 20–10– 2002, the anomalous OLR flux was found to be 2.6667, followed by a further increase in tectonic activity has shown consecutive abnormal emissions on 21–10–2002 with an anomalous index of 2.7181, followed by 24–10–2002 with an anomalous OLR index of 2.4553 (Fig. 3).

3.3 26–12–2004: M9.1 Sumatra–Andaman Islands Earthquake A devastating subduction zone earthquake occurred on December 26, 2004, with a magnitude of 9.1 at a depth of around 30 km and is located at 3.295N latitude and 95.982E longitude. The anomalous variations in RH started on 05–10–2004, which occurred 82 days before the mainshock with an anomalous index of −2.21802, followed by 11–10–2004 with an anomalous index of −3.04569. The anomalous

Anomalous Deviations in Atmospheric Parameters as Pre-earthquake … 5 4

Date: 26-12-2004 Lat. Long: 3.295 N 95.982 E Magnitude: 9.1Mw

235

RH OLR

3

dE_Index

2 1 0 -1 -2 -3 -4 -5 26-06-2004 26-07-2004 26-08-2004 26-09-2004 26-10-2004 26-11-2004 26-12-2004

DATE Fig. 4 Anomalous variations of RH and OLR before the 26–12–2004, Sumatra–Andaman Islands earthquake (9.1 Mw). The other anomalies are suppressed to understand better the pattern of anomalous variations corresponding to this particular earthquake

variations in OLR are observed on the same day on 05–10–2004, where anomalous RH is observed with an anomalous index of 5.1214. The anomalous variations appear continuously on 06–10–2004 with an anomalous index of 2.77629, followed by 16–10–2004, 20–10–2004, 21–10–2004, 22–10–2004 with an anomalous index of 2.78658, 2.46929, 4.6173, 3.70549, respectively, from 82 to 67 days before the mainshock. Again, the anomalous pattern started 50 days before the mainshock RH anomaly was observed on 06–11–2004 with an anomalous index of −2.49715, followed by an OLR anomaly on 19–11–2004 with an anomalous index of 2.33085 which is 37 days before the main shock (Fig. 4).

3.4 07–04–2007: M6.1 Simeulue, Indonesia Earthquake On April 7, 2007, a shallow thrust fault earthquake hit Simeulue, Indonesia, with a magnitude 6.1 Mw. At a focal depth of 30.0 km, the epicenter was at 2.916°N and 95.700°E. The anomalous variations in RH and OLR were observed on the same day on 29–03–2007, nine days before the main shock, with an anomalous index of − 2.37585. It is followed by a continuous anomalous variation in RH on 30–03–2007, 31–03–2007, 01–04–2007, and 04–04–2007 with an anomalous RH index on −3.14, −3.4205, −2.7417, −3.98304, respectively, followed by OLR anomalous variations on the day of the main event with an anomalous index of 3.02711 (Fig. 5).

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Date: 07-04-2007 Lat. Long: 2.916 N 95.7E Magnitude: 6.1

RH OLR

dE_Index

2 1 0 -1 -2 -3 -4 -5 07-10-2006 07-11-2006 07-12-2006 07-01-2007 07-02-200707-03-2007 07-04-2007

DATE Fig. 5 Anomalous variations of RH and OLR before the occurrence of 07–04–2007, Simeulue, Indonesia earthquake (6.1 Mw). The other anomalies are suppressed to understand better the pattern of anomalous variations corresponding to this particular earthquake

3.5 20–02–2008: M7.4 Simeulue, Indonesia On February 20, 2008, a magnitude 7.4 earthquake occurred in Simeulue, Indonesia, at 2.76°N 95.964°E. The earthquake was caused by shallow thrust faulting along the boundary between the Australia and Sunda plates. The anomalous variation in RH started to occur on 08–01–2008, which is 43 days before the mainshock with the anomalous index of −2.22351. The anomalous variations appear continuously on 09–01–2008, 13–01–2008 with an anomalous index of −2.27413, −2.39277 respectively, from 42 to 38 days before the mainshock. The anomalous variations succeed in OLR on 18–01–2008, which is 33 days before the main shock with an anomalous index of 2.3788 (Fig. 6).

3.6 09–12–2009: M6.0 Simeulue, Indonesia A 6.0 Mw magnitude earthquake due to a shallow thrust fault occurred in Simeulue, Indonesia, on December 9, 2009, located at 2.759°N, 95.910°E at a depth of around 21.0 km. The continuous anomalous variations in RH occurred on 14–10–2009 which is 56 days before the main shock with an anomalous index of −2.0964 followed by 16–10–2009 with an anomalous RH index of −4.25346, respectively. The anomalous OLR is observed on 01–11–2009, anomalous index of 2.06218, which is 38 days before the main shock (Fig. 7).

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5 4 3

Date: 20-02-2008 Lat. Long: 2.768 N 95.964E Magnitude: 7.4

RH OLR

dE_Index

2 1 0 -1 -2 -3 -4 -5 20-08-2007 20-09-2007 20-10-2007 20-11-2007 20-12-2007 20-01-2008 20-02-2008

DATE Fig. 6 Anomalous variations of RH and OLR before the 20–02–2008, Simeulue, Indonesia earthquake (7.4 Mw). The other anomalies are suppressed to understand better the pattern of anomalous variations corresponding to this particular earthquake 5 4

Date: 09-12-2009 Lat. Long: 2.759 N 95.91E Magnitude: 6

RH OLR

3

dE_Index

2 1 0 -1 -2 -3 -4 -5 09-06-2009 09-07-2009 09-08-2009 09-09-2009 09-10-2009 09-11-2009 09-12-2009

DATE Fig. 7 Anomalous variations of RH and OLR prior to the occurrence of 09–12–2009, Simeulue, Indonesia earthquake (6.0 Mw). The other anomalies are suppressed for better understanding of the pattern of anomalous variations corresponding to this particular earthquake

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Date: 09-05-2010 Lat. Long: 3.748 N 96.018E Magnitude: 7.2

RH OLR

3

dE_Index

2 1 0 -1 -2 -3 -4 -5 09-11-2009 09-12-2009 09-01-2010 09-02-2010 09-03-2010 09-04-2010 09-05-2010

DATE Fig. 8 Anomalous variations of RH and OLR before the M7.2 Northern Sumatra, Indonesia earthquake on 09–05–2010. The other anomalies are suppressed to understand better the pattern of anomalous variations corresponding to this particular earthquake

3.7 09–05–2010: M7.2 Northern Sumatra, Indonesia The earthquake that hit northern Sumatra on May 9, 2010 was occurred by shallow thrust faulting on or near the subduction interface plate boundary between the IndoAustralian and Sunda plates. The magnitude 7.2 earthquake occurred at 3.748°N latitude and 96.018°E longitude, about 38 km depth. The anomalous variations in RH were observed on 22–03–2010 with an anomalous index of −2.5504 which is 48 days prior to the occurrence of earthquake. The anomalous variations in OLR appear on 12–04–2010 with an anomalous index of 4.12514 which is 27 days prior to mainshock (Fig. 8).

3.8 25–07–2012:M6.4 Simeulue, Indonesia Earthquake On July 25, 2012, an earthquake with a magnitude of 6.4 occurred in Simeulue, Indonesia, which has a latitude of 2.707°N and a longitude of 96.045°E with a depth of 21.0 km. The anomalous variations in RH were observed on 04–07–2012 with an anomalous index of −2.5504, which is 48 days before the occurrence of an earthquake. The anomalous variations in OLR appear on 12–04–2010 with an anomalous index of 4.12514, which is 27 days before the mainshock (Fig. 9).

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5 4 3

Date:25-07-2012 Lat. Long: 2.707 N 96.045E Magnitude: 6.4

RH OLR

dE_Index

2 1 0 -1 -2 -3 -4 -5 25-01-2012 25-02-2012 25-03-2012 25-04-2012 25-05-2012 25-06-2012 25-07-2012

DATE

Fig. 9 Anomalous variations of RH and OLR before 25–07–2012, Simeulue, Indonesia Earthquake (6.4 Mw). The other anomalies are suppressed to understand better the pattern of anomalous variations corresponding to this particular earthquake

3.9 Discussion From the above results, it is inferred that 1. The anomalous variations in RH and OLR appear three days to 3 months before the occurrence of the earthquake. 2. The anomalous variations of OLR succeed the anomalous variations in RH, which indicates the possible radon activity on atmospheric gases due to air ionization and ion-formed condensation leading to latent heat exhalation from water vapor condensation causes thermal anomalies. Hence, these anomalous variations could be potential precursors for the impending earthquakes. 3. There is a strong correlation between the strike of the fault with the number of days prior to the earthquake events and the anomalous drop in RH observed as mentioned in Table 2. 4. Similar type of relation between the strike of the fault and the number of days for which the OLR anomalies are observed. 5. Based on the clusters mentioned in the above two statements (3 and 4), the number of days of OLR anomalies observed in the past six months has a positive correlation with the magnitude of the earthquakes (Table 2). 6. Our results vindicate the proposal given by the LAIC model–LAI coupling). The most important mechanism is the ionization of the air, which is caused by an increase in the emission of gases such as radon from the earth’s crust in the area of active tectonic faults. The enhanced gas emission sparked a series of physical processes and chemical reactions from the ground surface, including the ionization of air molecules, which led to the development of complex molecular

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Table 2 Earthquakes grouped based on near similar strike value Date

Latitude Longitude Depth Magnitude Strike Rh-EQ event No. of OLR anomalies in 6 months

01–09–1993 2.986

96.122

34

6.3

131

34

11

02–11–2002 2.824

96.085

30

7.4

133

42

17

26–12–2004 3.295

95.982

30

9.1

132

82

18

09–12–2009 2.759

95.91

21

6

135

56

10 15

09–05–2010 3.748

96.018

38

7.2

128

48

07–04–2007 2.916

95.7

30

6.1

138

9

4

25–07–2012 2.707

96.045

22

6.4

137

21

12

20–02–2008 2.768

95.964

26

7.4

137

43

18

ions, and the formation of massive ion clusters that can grow up to the size of aerosols. The rise in surface temperature is causing a significant increase in outgoing thermal radiation, particularly in the epicentral regions. Ionization of the air also results in thermal effects, which cause the temperature and humidity of the air, which might lead to an anomalous drop in RH and an anomalous increase in OLR.

4 Conclusion The time-series analysis is performed on the daily datasets of RH and OLR for eight earthquakes of magnitude 6.0 that occurred between 1991 and 2021 within a 75-km radius of the epicenter of the December 26, 2004 earthquake. This study investigates and comprehends the precursory signals related to earthquake occurrence. The observation of anomalous deviations in OLR and RH is analyzed before the event of each earthquake. Through the LAIC model, the correlation between anomalous variations in atmospheric parameters such as RH and OLR flux index concerning the occurrence of earthquakes is investigated. The anomalous variations in RH and OLR arise between 3 days to 3 months before an earthquake. Anomalous variations follow the anomalous variations in RH in OLR, which suggests possible radon activity on atmospheric gases due to air ionization and ion-formed condensation, which in turn leads to a drop in humidity and increase in thermal anomalies, especially around the epicentral region. Our findings are consistent with the LAIC model, which explains the majority of these events as a synergy between various processes and anomalous variations that are referred to as short-term earthquake precursors. The interval between anomalous variations in RH and the occurrence of an earthquake increases as the magnitude of earthquakes with the near similar strike value. Additionally, the number of OLR anomalies in the preceding six months increases as the magnitude

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increases. Consequently, these unusual variations could be considered precursors of impending earthquakes to forecast on a short-term basis. Acknowledgements The authors are thankful to the Ministry of Earth Sciences, India, for financial assistance (Project No: MoES/P. O(seismo)/1(343)/2018) to carry out research on earthquake forecasting. We would like to acknowledge SASTRA Deemed to be University for facilitating and encouraging to do this research work.

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Spatial Distribution of Stress Orientation by Inversion of Focal Mechanism Solutions Using MSATSI: A Case Study Across Japan Trench Sucheta Das, Sandeep, Sonia Devi, Himanshu Mittal, Praveen Kumar, and Monika Abstract Estimation of stress field orientations is a necessary aspect for recognition of crustal mechanics as well as the physics behind occurrence of earthquakes. A case study employing the new MATLAB software package Spatial And Temporal Stress Inversion (SATSI) for stress inversion utilizing the focal mechanism data is presented here to produce stress orientation models in Northeast (NE) Japan. In this work, the study region is divided into 49 small sub-regions so that the stress tensors and focal mechanisms can independently fit in each sub-region. Determination of any stress variation is strongly needed by the data while eliminating the artifacts due to overfitting of noisy or nonuniquely fitting data. To resolve it, a damped inversion procedure was applied which inverted the stresses in all sub-regions, while at the same time reducing the difference in stress between adjacent sub-regions. Earthquake focal mechanisms have been used to determine the stress patterns at depths capable of generating earthquakes in NE Japan since 1960–2021. In this work, 0D, 1D, and 2D stress inversion using the MSATSI (MATLAB package for Spatial And Temporal Stress Inversion) routine was performed and examined the spatial variation of stress orientations over NE Japan along the Japan Trench and put forward recent knowledge about the stress pattern. From the obtained 2D inversion results, a spatially varying stress regime is observed in the crust which demonstrates normal faulting on the subducting Pacific plate which changes to reverse faulting on the Okhotsk plate through an intermediate state of oblique faulting. S. Das · Sandeep (B) · S. Devi · Monika Department of Geophysics, Banaras Hindu University, Varanasi, India e-mail: [email protected] Monika e-mail: [email protected] H. Mittal National Centre for Seismology, MoES, Noida, India P. Kumar · Monika Wadia Institute of Himalayan Geology, Dehradun, India S. Das Discipline of Earth Sciences, IIT Gandhinagar, Gandhinagar, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_20

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Keywords MSATSI · Stress inversion · Focal mechanism solution

1 Introduction Occurrence of earthquake takes place when tectonic stresses in the crust release fragmentarily due to abrupt brittle rupture. Earthquakes alter the stress field as they reshuffle the stresses that have developed throughout the interseismic period. Therefore, to understand the physical process of earthquake initiation and also the up-todate tectonic movement, particulars about the stress state are necessary. In the 1980s, the usual procedures of inverting stress (e.g., Gephart and Forsyth [12]; Michael [14], 1987) replaced the classical methods due to the efficacy to determine the average stress orientations of already separated blocks using the Focal Mechanism Solution (FMS) data of occurred earthquakes by the means of least square procedure. The study area extends from 37.2° to 40° north and 142–144.8° east, i.e., across a part of the NE Japan islands governed by the Japan Trench. Japanese islands have a very complex tectonic setting. The Pacific plate is going below the North American plate (NAP) across the Kuril–Japan Trench, whereas the Philippine Sea plate across the Izu-Bonin Trench, and the Philippine Sea plate underneath the NAP across the Sagami trough and the Eurasian plate across the Suruga-Nankai trough and its South-Western (SW) prolongation, the Ryukyu Trench. The system of tectonic stress fields in Japan and its adjacent regions is caused by this type of complex tectonic background. Various scholars (e.g., Yoshii [15]; Huzita [16]; Wesnousky et al. [17]; Nishimura et al. [18] and Townend [19]) have separately evaluated the stress regime of the seismically active area in and around Japan over the last three decades by analyzing the geophysical and geological data. Subduction of the oceanic plate has disfigured the seaward edge of the continental plate near the Japan Trench convergent plate margin. A multichannel seismic survey reported that the northern Japan Trench is bounded by the strong continental substructure by a reflector dipping landward noticeably. Due to the interaction of two planes by subduction phenomena, there is a development of progressive distortion or demolition of a horst structure across the upper part of the oceanic crust which is being subducted and also deformation in the oceanic plate [1]. An even reflector indicating a steady slip plane is seen across and over the oceanic crust at greater than 45 km toward the land from the trench axis. From this location, seismicity of interplate earthquakes swiftly increases landward. The data of Japan islands used here are collected from Global CMT Catalog Search (https://www.globalcmt.org/CMTsea rch.html/) and USGS Earthquake catalog 7 (https://earthquake.usgs.gov/) during the temporal variation of 61 years (11th March 1960–11th March 2021). The evaluation of the stress field across Japan Trench is prepared by compiling the data from these networks. In this work, we have performed 0D, 1D, and 2D stress inversion using the MSATSI (Matlab package for Spatial And Temporal Stress Inversion) by Patricia Martínez-Garzón, Grzegorz Kwiatek, Michèle Ickrath, and Marco Bohnhoff routine

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and examined the spatial changes of stress patterns over NE Japan along the Japan Trench and put forward recent knowledge about the stress pattern.

2 Stress Inversion Stress inversion procedures are applied to assess the state of stress from numerous focal mechanisms of earthquakes (established from the Bott hypothesis, which presumes that the slip vector rests on the fault plane and the maximum resolved shear stress in the same plane is parallel to it). It does not appraise non-double-couple components of source. After being selected and discretized, the focal mechanisms are feasible for inversion of the stress field orientation (Fig. 1). In this method, a given area is divided into a number of sub-regions so that the stress tensor and focal mechanism data can be independently fitted. This method might supposedly give rise to spatial variability which is basically an artifact due to overfitting of data embedded with noise or nonuniquely fitting data. Therefore, in order to remove all these artifacts while determining any stress variation which is strongly needed by the data, a damped inversion procedure is introduced to for stress inversion in all sub-regions while minimizing the difference in stress between neighboring sub-regions at the same time. The nonlinear stress inversion problem is changed into a linear one by presuming that the shear traction magnitude of every fault plane is nearly equal. This method focuses on searching the stress regime which reduces the inconsistency between the direction of resolved shear stress and the slip for the total number of seismic events in the dataset. If the FMSs only provide the fault orientation and the slip direction but not the relative displacement magnitude (Δu), then just a segment of the regional stress tensor is approximated concerning the direction of the principal axes and also their relative magnitudes [3]. To be more specific, the inversion gives us a ‘reduced’ deviatoric stress tensor (T ), which is specified by four parameters. Among them, three parameters explain the principal stress axes orientations (S1, S2, S3), and the fourth one is stress ratio R (known as shape factor or shape ratio, stress shape, aspect ratio or stress magnitude parameter) [12]: R=

σ2 − σ1 σ3 − σ1

Here σ 1 is the magnitude of maximum stress axis, σ 2 is the magnitude of the intermediate stress axis, and σ 3 is the magnitude of the minimum stress axis derived from the deviatoric stress tensor. Value of R states whether the magnitude of σ 2 is closer to the magnitude of σ 1 or σ 3 . If R is increased up to 1 from 0, σ 2 will decrease in the limit between σ 1 and σ 3 , where R having value near 0 denotes σ 2 ≈ σ 1 and R having value near 1 indicates σ 2 ≈ σ 3 (Fig. 2). Both of the ranges stand for the conditions of uniaxial extension (where R = 0) and uniaxial compression (where R = 1), respectively, while R = 0.5

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Fig. 1 Flowchart abridging various steps made up of a the selection of data and b inversion of the stress tensor from FMS (after Martínez-Garzón et al. [2])

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Fig. 2 Stress ellipsoids and the corresponding Mohr diagram for various stress orientations specified by the stress ratio (R), for: a uniaxial extension (R = 0, oblate ellipsoid), b triaxial stress (R = 0.5), and c uniaxial compression (R = 1, prolate ellipsoid). It is to be noted that here the stress magnitudes seem to have all positive values. Figure after Lejri [20]

indicates that σ 2 = (σ 1 + σ 3 )/2. If the principal stresses combinedly give rise to either R > 1 or R < 0, then they are inaccurate [13]. A comparable parameter Φ is put forward as given below (Angelier et al. [11]; Angelier [9, 10]; Michael [14], 1987): R=

σ2 − σ3 σ1 − σ3

The combination of the above two equations provides the relation Φ = 1 − R. Both Lame’s stress ellipsoid and Mohr diagram represent a specific state of stress and also provide us a geometrical portrayal of R which shows the connection with stress magnitudes. The bootstrap resampling procedure implemented to the input FMS causes uncertainties not only in the stress axes patterns, but also in the relative stress magnitude.

3 Methodology To estimate the spatial and temporal stress orientation from FMS data, several algorithms are accessible. The algorithm of the method used here involves the collection of FMS data (dip direction, dip angle, and rake) followed by division of study area in grids and then running the MSATSI routine in MATLAB as per our need of 0D, 1D, and 2D inversion. SATSI is an alteration of Michael’s code which inverts FMS for stress field varying both in space and/or time. The stress orientations using SATSI are determined by splitting an area in a number of sub-areas and then inverting the FMS at the same time for each sub-areas, looking for the optimal stress through every cell during the process of reducing the difference in stresses between neighboring areas [4]. Other than the problem regarding noise, it also smoothens out disruption of abnormal stresses which might be insignificant for the arrangement of regional stress states.

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A damped least-squares inversion strategy was employed to invert a stress tensor for every grid point at the same time to acquire a smooth solution. SATSI was executed because of the robustness of the formal stress inversion (FSI) procedure along with the damped least squares optimization and also due to its advantages over processing of datasets. The input FMS can be assembled earlier to the inversion into a number of ‘grid points’ which are dispersed over a number of dimensions, from 0D (single FSI in one grid point is conducted) and 1D (e.g., temporal changes of stress field) up to 4D which gives the spatiotemporal distribution (Fig. 3), with time considered as an extra coordinate. The study area extends from 37.2°–40° north and 142–144.8° east, i.e., across a part of the NE Japan islands. The selected area is divided into 7 × 7, i.e., 49 grids having the dimension of 0.4° × 0.4°. The data of Japan islands used here are collected from Global CMT Catalog Search (https://www.globalcmt.org/CMTsearch.html/) and USGS Earthquake catalogue (https://earthquake.usgs.gov/) during the temporal variation of 61 years (11th March 1960–11th March 2021). The input matrix for

Fig. 3 Demonstration of feasible dimensions of the FSIs conducted with SATSI and MSATSI. (1) 0D gives a single static stress field. (2) 1D gives a set of stress tensors scattered over single coordinate (e.g., temporal distribution). (3) 2D forms a map of stress-tensor outputs changing over two coordinates (e.g., the surface distribution of stress). (4) 3D performs the spatial distribution of the stress field. (5) 4D performs a spatiotemporal distribution of stress-tensor orientations. (From Martinez-Garzon et al. [5])

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stress tensor inversion changes depending on the type of inversion performed, i.e., whether 0D/1D/2D. In case of 0D, 1D, and 2D, we have used the n-by-5 size input matrix where n denotes the number of input data (i.e., focal mechanisms). There are no first-hand details on the location of certain earthquakes, but only the details about the grid point the specified FMS are associated with and then the solutions from each grid point are inverted for the stress tensor. The different types of the plot are described in user manual of MSATSI-([5], Sect. 4.7). For 0D inversion, a set of 106 focal mechanisms in a single grid point [0 0] was selected. A damped inversion cannot be performed, and because it was only one grid point, neighboring grids were absent. Hence, the ‘Damping’ was naturally selected as ‘off’. Therefore, there was not any trade-off curve output. P and T axes for this grid (‘PTPlots’) were also activated, and the uncertainty was calculated by 2000 bootstrap resamplings. For 1D inversion, the FMSs are arranged into seven different grid points which vary over Y coordinate; this makes the case 1D. Each grid has 20 focal mechanisms. A damped inversion which was settled by default was performed, and uncertainty was calculated with 2000 bootstrap resamplings. Here ‘ConfidenceIntervals’ was turned off so that the pictures only include the trajectory marked out by the best solution across the grid points. For 2D inversion, the FMSs were divided into 49 grid points in total which changes over both X and Y coordinates and thus makes the case 2D. Every grid has 20 focal mechanisms. Here also a damped stress inversion was performed by default, and uncertainty was settled by 2000 bootstrap resamplings. Furthermore, a minimum limit of 30 events per node was set.

4 Estimated Stress Patterns and Their Comparison with Tectonic Motions The FMS data inversion method is applied to around 1000 earthquakes inside the magnitude range of 0.3–9.0 in the NE Japan along the Japan Trench (11th March 1960–11th March 2021). 0D inversion of the grid point with X = 0, Y = 0 gave the following stereonet for a set of 106 FMS, and also best R value = 0.59 is obtained (Fig. 4). Another stereonet shows the uncertainties mapped as colored dots straight from the bootstrap resampling, where red, green, and blue, respectively, denotes σ 1, σ 2, and σ 3. This type of stress orientation indicates reverse faulting regime (Fig. 5). Results from the 1D inversion (Fig. 6) are in good agreement with the work of Terakawa [6], Asano et al. [7] and Yoshida et al. [8] indicating reverse faulting mechanism caused by compressive shortening of the crust. In reverse faulting mechanism, the minimum principal stress σ 3 (blue) is vertical and at the center of lower hemisphere projection, whereas the maximum principal stress σ 1 (red) is horizontal. Trend is the azimuth direction measured from north (−360 to 360 degrees), and plunge is

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Fig. 4 Stereonet for [0 0]. Blue dots denote tension and red dots denote pressure

Fig. 5 Stereonet for σ 1 , σ 2 , and σ3 . Best stress orientation is indicated by a + sign

the downward angle measured from horizontal (−90 to 90 degrees). To further look into the spatial development of stress field orientation, plunge and trend of σ 1−3 are plotted with their 95% confidence interval (Fig. 7). The trend of σ 1 almost remains constant at 90°, whereas the plunge drops from 45° to 30°. Trend and plunge of σ 2 show a variation of 180° and 60°, respectively. Trend of σ 3 starts with 112.5°, drops to 40°, and then gradually increases and retains at 112°. Almost similar drift is seen for the plunge of σ 3 which varies between 10° and 60°. 2D damped inversion is performed on 49 grids (0.4° × 0.4°) covering the total area from 37.2–40° north to 142–144.8° east, i.e., approximately 96,600 km2 which consists of a part along the Japan Trench. The uncertainty evaluation was carried out

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Fig. 6 1D inversion results of study area

by setting bootstrap resampling at 2000 and confidence interval at 95%. Figure 8 shows the orientation of the maximum horizontal stress for each of the FSIs carried out by making use of the World Stress Map (WSM) plot. For most grid points, the orientation of σ 1 is in well accordance with the regional stress field along Japan Trench [6]. Figure 9 shows the location of principle stress axes using Stereomap in Universal Transverse Mercator (UTM) coordinates. For the cases where bootstrap resampled points are dispersed across huge regions (e.g., σ 2 axes with Easting = 589 km and Northing = 4274 km), the portrayal of the confidence interval with bootstrap resampling should come up with a better practical solution. In the southwestern (SW) segment of the area (inversions with Easting = from 589 to 448 km, Northing = from 4118 to 4222 km), the bootstrap resampling for σ 2 axis is scattered across a large number of azimuths, this suggests that in this area the magnitude of σ 2 axis is less than σ 1 and σ 3 . Therefore, trend of σ 1 and σ 3 has higher degree of uncertainty. The results are also in good agreement with the distribution of R values for every grid where comparatively low R value (0.43, 0.29, 0.37, etc.) for the SW part is seen. According to the stress pattern in Fig. 10, it can be seen that the Pacific plate exactly before being subducted is experiencing tensile stress perpendicular to stress axis which gives rise to normal faulting in the eastern part of study area. Also, in Fig. 11, it can be seen that the stress patterns corresponding to oblique (grey)type faulting across plate boundaries indicate longstanding slip motion due to the resistance caused by friction. These type of stress orientations specifying the zones of plate boundary are appreciably prominent in Fig. 10. The stress orientation in the Pacific plate going underneath the NE Japan arc alters from tensional forces (red spheres denoting normal faulting regime) to compressional forces (blue spheres with opposite mechanism, i.e., reverse faulting) in the Okhotsk plate through a neutral

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Fig. 7 Stress tensor results from 1D inversion for selected grids in lower hemisphere projection. Colored dots indicate sampled bootstrap points in the 95% confidence interval, and best stress orientation is indicated by a + sign. Variations with trend and plunge in each grid are shown for maximum compressive stress-σ 1 (red), intermediate compressive stress-σ 2 (green), and minimum compressive stress-σ 3 (blue) along the grid points. Uncertainties in trend and plunge are represented with error bars

state (gray spheres denoting oblique faulting pattern). The outcomes are also in good agreement with those of WSM (Fig. 8).

5 Conclusion With the MSATSI inversion routine based on Michael’s grid search algorithm [4], we estimated the 2D tectonic stress pattern covering the total area from 37.2–40° north to 142–144.8° east, i.e., approximately 96,600 km2 which consists a part along the Japan Trench for around 1000 seismic events (0.3 ≤ M ≤ 9.0) from 11th March

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Grid along y coordinate

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Grid along x coordinate

Northing (km)

Fig. 8 2D inversion results employing WSM. For every grid maximum horizontal stress is illustrated. Red signifies normal faulting regime according to best solution

Easting (km) Fig. 9 2D inversion results using Stereomap in UTM coordinates. Best stress orientation is indicated by a + sign

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Fig. 10 Principle stress orientation in Stereomap of study area

Fig. 11 2D stress inversion results indicating the type of faults. Red denotes normal faulting; blue denotes reverse faulting, and gray denotes oblique faulting

1960–11th March 2021. The goal of the method was to find the model with minimal complex structure which is compatible with the selected data. The method also includes the concept of damping which is used for minimizing the difference between components of stress tensor in neighboring sub-regions, accompanied by the expected reduction of the misfit of stress tensor to FMS of seismic events. A systematic stress spatially varying stress regime is observed in the crust. The stress orientation denotes

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normal faulting on the subducting Pacific plate which changes to reverse faulting on the Okhotsk plate through an intermediate state of oblique faulting. Thus, it is concluded that Pacific plate exactly before being subducted is experiencing tensile stress. The calculated stress pattern has an acceptable correspondence with the latest (Quaternary) complex tectonics of the islands in and around Japan from regional to local scale which confirms efficacy of the method used. For most grids, the orientation of σ 1 is in well accordance with the stress field along Japan Trench. Therefore, it is concluded that the MSATSI routine can further be utilized for inverting the damped models of stress by the means of temporal changes through performing 3D as well as 4D inversion.

References 1. Tsuru, T., Park, J.-O., Takahashi, N., Kodaira, S., Kido, Y., Kaneda, Y., Kono, Y.: Tectonic features of the Japan trench convergent margin off Sanriku, northeastern Japan, revealed by multichannel seismic reflection data. J. Geophys. Res.: Solid Earth 105, 16403–16413 (2000) 2. Martínez-Garzón, P., Ben-Zion, Y., Abolfathian, N., Kwiatek, G., Bohnhoff, M.: A refined methodology for stress inversions of earthquake focal mechanisms. J. Geophys. Res.: Solid Earth 121, 8666–8687 (2016) 3. Kassaras, I.G., Kapetanidis, V.: Resolving the tectonic stress by the inversion of earthquake focal mechanisms. Application in the Region of Greece. A Tutorial. Moment Tensor Solutions (2018) 4. Hardebeck, J.L., Michael, A.J.: Damped regional-scale stress inversions: methodology and examples for southern California and the Coalinga aftershock sequence. J. Geophys. Res.: Solid Earth 111, n/a–n/a (2006) 5. Martinez-Garzon, P., Kwiatek, G., Ickrath, M., Bohnhoff, M.: MSATSI: a MATLAB package for stress inversion combining solid classic methodology, a new simplified user-handling, and a visualization tool. Seismol. Res. Lett. 85, 896–904 (2014) 6. Terakawa, T., Matsu’ura, M.: The 3-D tectonic stress fields in and around Japan inverted from centroid moment tensor data of seismic events. Tectonics 29, n/a–n/a (2010) 7. Asano, Y., Saito, T., Ito, Y., Shiomi, K., Hirose, H., Matsumoto, T., Aoi, S., Hori, S., Sekiguchi, S.: Spatial distribution and focal mechanisms of aftershocks of the 2011 off the Pacific coast of Tohoku earthquake. Earth, Planet. Space 63(7), 669–673 (2011) 8. Yoshida, K., Hasegawa, A., Okada, T.: Spatial variation of stress orientations in NE Japan revealed by dense seismic observations. Tectonophysics 647–648, 63–72 (2015) 9. Angelier, J.: From orientation to magnitudes in paleostress determinations using fault slip data. J. struct. geol. 11, (1–2), 37–50 (1989). https://doi.org/10.1016/0191-8141(89)90034-5 10. Angelier, J.: Tectonic analysis of fault slip data sets. J. Geophys. Res.: Solid Earth 89(B7) 5835–5848 (1984). https://doi.org/10.1029/JB089iB07p05835 11. Angelier, J., Tarantola, A., Valette, B., Manoussis, S.: Inversion of field data in fault tectonics to obtain the regional stress ? I. Single phase fault populations: a new method of computing the stress tensor. Geophys. J. Int. 69(3) 607–621 (1982) 12. Gephart, J.W., Forsyth, D.W.: An improved method for determining the regional stress tensor using earthquake focal mechanism data: Application to the San Fernando Earthquake Sequence. J. Geophys. Res. 89(B11), 9305–9320 (1984) 13. Gephart, J.W.: Principal stress directions and the ambiguity in fault plane identification from focal mechanisms. Bulletin of the Seismol. Society of America, 75(2), 621–625 (1985) 14. Michael, A.J.: Determination of stress from slip data: faults and folds. J. Geophys. Res.: Solid Earth, 89(B13), 11517–11526 (1984)

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15. Yoshii, T.: A detailed cross-section of the deep seismic zone beneath northeastern Honshu, Japan. Tectonophysics 55(3–4), 349–360 (1979) 16. Huzita, K.: Role of the median tectonic line in the Quaternary tectonics of Japanese islands. Memoirs Geol. Soc. Japan, 18, 129–153 (1980) 17. Wesnousky, S.G., Scholz, C.H., Shimazaki, K.: Deformation of an island arc: rates of moment release and crustal shortening in intraplate Japan determined from seismicity and Quaternary fault data. J. Geophys. Res.: Solid Earth 87(B8), 6829–6852 (1982) 18. Nishimura, T., Hirasawa, T., Miyazaki, S.i., Sagiya, T., Tada, T., Miura, S., Tanaka, K.: Temporal change of interplate coupling in northeastern Japan during 1995–2002 estimated from continuous GPS observations. Geophys. J. Int. 157(2), 901–916 (2004) 19. Townend, J., Zoback, M.D.: Stress, strain, and mountain building in central Japan. J. Geophys. Res.: Solid Earth 111(B3) (2006) 20. Lejri, M.: Subsurface stress inversion modeling using linear elasticity: sensivity analysis and applications, Université de Montpellier (2015)

Seismic Landslide Hazard Assessment of Mandi Town, Himachal Pradesh A. Kothiala , P. S. Nayek , Maheshreddy Gade , and U. V. Kala

Abstract This study performs slope displacement-based probabilistic seismic landslide hazard assessment for Mandi town. Here, the slope angles of the concerned region are obtained from the digital elevation map (DEM), and the material properties are obtained from lithological information and literature. The critical acceleration of the slopes is estimated by combining the obtained slope angle and material properties. In this work, hazard assessment is performed considering the arid and fully saturated condition of the soil mass of the slopes. The PGA values are estimated for 100, 475, and 2475 years return periods by performing probabilistic seismic hazard analysis of the study region. Further, the PGA values and the slope displacement prediction equation are used to estimate Newmark’s sliding displacement. Finally, the hazard map in terms of the probability of slope displacement (SD) value exceeding the threshold values of 5 cm is presented. The developed seismic landslide hazard map highlights the areas that may experience co-seismic landslides in the future. The probability of occurrence of co-seismic landslides gets as high as 93.4% for saturated soil and 86.3% for arid soil for a return period of 2475 years. This hazard map will help local authorities and planners with tools for assessing the seismic landslide risk associated with land use and taking necessary measures to minimize the damages. Keywords Co-seismic landslide · Seismic landslide hazard · Newmark sliding displacement · Mandi

A. Kothiala (B) · M. Gade · U. V. Kala School of Civil and Environmental Engineering, IIT Mandi, Kamand, India e-mail: [email protected] M. Gade e-mail: [email protected] U. V. Kala e-mail: [email protected] P. S. Nayek Department of Civil Engineering, Sikkim Manipal Institute of Technology, East Sikkim, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_21

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1 Introduction The Himalayan region is one of the most seismically active regions in the world [3, 4, 24]. It has endured many destructive quakes in the past, including the 1897 Shillong earthquake (M w 8.1), 1905 Kangra earthquake (M w 7.8), 1934 Bihar–Nepal Earthquake (M w 8.2), 1950 Assam Earthquake (M w 8.6), 1991 Uttarkashi Earthquake (M w 6.8), 1999 Chamoli Earthquake (M w 6.4), 2005 Kashmir Earthquake (M w 7.6), 2011 Sikkim Earthquake (M w 6.9), and 2015 Gorkha Earthquake (M w 7.8), etc. All these earthquakes are known to have caused massive societal damage, including loss of life. The earthquakes in the hilly terrain are accompanied by co-seismic landslides [15, 26], and these co-seismic landslides further aggravate the catastrophe caused by the earthquakes. Earthquake-induced landslides have been a cause of concern for a long time and have been widely studied in various regions. Among many models available for performing the seismic landslide hazard analysis, the Newmark sliding block displacement [23] is popular in the scientific community because of its simplicity and ability to provide slope displacement values. Several researchers used slope displacement prediction equations to perform regional-level co-seismic landslide hazard assessments for various regions. Refice and Capolongo [27] performed slope displacement-based landslides susceptible studies for the Sele Valley of southern Italy using a pseudo-probabilistic approach. Nayek and Gade [22] developed a probabilistic seismic landslide hazard map for the central seismic gap region based on the Newmark sliding block method. These studies highlight the importance of identifying the areas prone to co-seismic landslides to minimize societal loss in case of any such event. The 1905 Kangra earthquake was the major earthquake that occurred in the Himachal Pradesh state, and this event claimed around 20,000 lives [33]. Further, this earthquake triggered massive landslides in the near-field region and created two landslide-dammed lakes in Tirthan and Sainj gorges [19]. The Himalayan region has not experienced a major earthquake (> M w 8) since 1952 [11], hence, the region is long overdue for such an event. Gupta and Sabnis [12] estimated that in case of a hypothetical midnight M w 8 earthquake, with Sundernagar in District Mandi as the epicenter, around 1 million people will lose their lives across Himachal Pradesh, Punjab, Haryana, and UT of Chandigarh. Chand and Sharma [5] prepared a landslide hazard vulnerability map of district Mandi, highlighting the prone areas. However, within the authors’ knowledge, the co-seismic landslide zonation of Mandi town is not performed. Mandi town lies in the vicinity of the Main boundary thrust [31] and is densely populated, as shown in Fig. 1 (Google Earth, n.d.). The Manali– Ropar strike-slip fault also passes through Sundernagar–Mandi [32]. These faults in its vicinity indicate the town’s vulnerability to such an event. The vulnerability of the town to both earthquakes and landslides makes it imperative for a study on earthquake-induced landslides in the town. The study region extends from 31° 41, 16.71,, N to 31° 42, 14.59,, N latitude and 76° 54, 31.20,, E to 76° 58, 12.47,, E longitude. The Google Earth, n.d. image

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Fig. 1 Google Earth image of Mandi town, highlighting the study area

(Fig. 1) shows the present-day town in red. It is evident from Fig. 1 that the presentday town has been swamped with urban construction. The town is likely to expand in the future into the surrounding area. Keeping the future expansion of the town, a 1 km wide buffer area, shown in blue (Fig. 1), is selected for the present study. The town is the district headquarter of District Mandi and houses the office of the district collectorate, district hospital, and office of the district disaster management authority. It serves as a vital road link to the tourist towns of Kullu and Manali. Given that the city is located in the seismically active Himalayan region and has the potential to experience a major earthquake in the near future, it is important to perform the coseismic landslide hazard analysis to identify the vulnerable regions. Thus, an attempt is made in this work to develop earthquake-induced landslide hazard maps for the Mandi town of Himachal Pradesh. The present results will help the administration identify the town’s at-risk locations and devise a plan to minimize the seismic risk of any such event.

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2 Methodology During seismic loading, as the shear strength mobilizes, permanent displacement builds up due to plastic strain on the sliding surface [29]. The Newmark sliding block approach [23] has been widely used to estimate the sliding displacement in seismic landslide hazard assessment over the pseudo-static approach and the stress deformation analysis. This method assumes a rigid block to slide down an inclined plane when its base acceleration exceeds the critical acceleration (k y ). The block continues to slide until its relative velocity with respect to the inclined plane becomes zero. Now, to estimate the sliding displacement of the block, the relative velocity time history is integrated. However, the Newmark sliding block model cannot be used in its original form for co-seismic hazard mapping as we are predicting displacements due to ground motions that might occur in the future. Therefore, various predictive models based on the Newmark sliding block approach have been developed to estimate the sliding displacement [2, 8, 13, 14]. Several researchers have used these predictive models and prepared landslide-susceptible maps, in terms of the probability of slope displacement exceeding a threshold value (mostly 5 cm), for different regions of the world [27, 28]. The present study uses the slope prediction equation proposed by Jibson, [13], which was developed using 2270 ground motions data from 30 earthquakes.      k y 2.341 k y −1.438 log D N = 0.215 + log 1 − ± 0.510 PGA PGA

(1)

DN = Newmark sliding displacement (cm); k y = critical acceleration of the slope (g); PGA = Peak ground acceleration (g). Further, the above equation is used to estimate the slope displacement, and the probability of the displacement exceeding a threshold value of 5 cm is calculated at each grid point. The estimation of k y and PGA values at each grid point of the selected region is discussed below.

2.1 Estimation of Critical Acceleration (ky ) The critical acceleration depends on slope angle (β), saturation level of the ground (m), and material properties such as cohesion (c), unit weight of soil (γ ), angle of internal friction (ϕ), sliding mass thickness (t) enumerated in Eqs. 2 and 3. FS =

tan ∅ mγw tan ∅ c + − γ t sin β tan β γ tan β k y = (FS − 1)g sin β

(2) (3)

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First, we have estimated the slope angle at each grid point using a digital elevation map (DEM) of 12 m resolution obtained from ALOS PALSAR—ASF, n.d. The slope angle distribution (β) of the study is estimated from the DEM map using the ArcMap software, and the corresponding results are shown in Fig. 2. It is evident from Fig. 2 that the present-day town has extended up to the area covered by gentler slopes. Future expansion toward the steep slopes is inevitable. Next, we require the material properties (c, φ, t, and γ ) of the study region. The area around Mandi town consists of the Shali and Sundernagar formations of weathered rocks [6], granite and mandi granite, phyllite/schists, and basalt formations [16]. Mali et al. [17] have listed the presence of the Shiwalik group, Dharamshala/Kasauli formation, and Shali/Sundernagar/Kullu formation Jutugh group consisting of siltstones, sandstones, shale, phyllites, slate, quartzite, gneisses, etc. in the Mandi area. The above-mentioned is the geology of the neighboring areas of Mandi town. The Geological Survey of India (GSI) digital lithological map for the study area is used in the present study. The area can be classified into two groups. In Group I, we club together medium rocks (Calcareous sandstone, Argillaceous limestones, and Marls), and in Group II, we club hard rocks (Slate, Quartzites, and Gneiss), as shown in Fig. 3. These classifications are broad and may not include the spatial variation in the study area. Mali et al. [17] have carried out laboratory experiments in the vicinity of Mandi town toward IIT Mandi and found the dry unit weight to range from 15.5 to 17.30

Fig. 2 Slope angle distribution of the study area

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Fig. 3 Lithological groups of the study area (Group I represents medium rocks such as calcareous sandstone, argillaceous limestones, and marls, and Group II represents hard rocks such as slate, quartzites, and gneiss)

kN/m3 , cohesion value to range from 5 to 45 kPa, and angle of internal friction to range from 9.5 to 24°. The geotechnical investigation carried out by Ghosh et al. [9] along the Rishikesh–Uttarkashi–Gangotri National Highway in the vicinity of Agrakhal village produced a proctor density of 1.74 g/cc, cohesion varied from 0.02 to 0.11 kg/cm2 and friction angle as 40°–46°. Sharma et al. [30] carried out a geotechnical investigation of Kotropi soil (near Mandi) and found the maximum dry density as 1.69 g/cc, average cohesion 26.66 kN/m2 , and average friction angle as 32.66°. Nayek and Gade [22] utilized the most likely values of strength parameters of the lithological groups obtained from the literature for the landslide hazard assessment of the central seismic gap region of the Himalayas, given in Table 1. For this study, we considered the strength parameters given in Table 1 for our calculations. Martha et al. [18] show that the sliding mass thickness (t) ranges from 3 to 8 m in the Himalayan region of Nepal. Nayek and Gade [22] considered the sliding mass thickness as 5 m for the central seismic gap region in the Himalayas. Considering Table 1 Shear strength parameters for the study area

Lithological group

Unit weight (γ) (kN/m3 )

Cohesion (c) (kN/m2 )

Angle of friction (φ) (°)

Group I

24

36

28

Group II

27

46

29

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Fig. 4 Critical acceleration (k y ) values for the study region for a dry soil (m = 0), b saturated soil (m = 1)

that our study region also falls in the lesser Himalayas, we considered the depth of the sliding mass to be equal to 5 m. The estimation of critical acceleration has been carried out for two saturation levels, arid soil (m = 0) and completely saturated soil (m = 1), and the corresponding results are shown in Fig. 4. In the present study, the PGA values are estimated by performing the PSHA. Here, the PGA values are estimated for 100, 475, and 2475 return periods using the recurrence parameters reported by Muthuganeisan and Raghukanth [20] and the GMPE proposed by Dhanya and Raghukanth [7]. Further, the site condition is assumed as C-type [21]. From PSHA studies, we have observed that the PGA values estimated from PSHA are almost constant at each grid point. It can be attributed to the fact that these points are located very close to each other. The estimated PGA values for three return periods are 0.112 g, 0.272 g, and 0.564 g, respectively.

3 Results and Discussion Here, the estimated critical acceleration, PGA, along with Eq. 1, are used to prepare the landslide susceptibility maps of the study region. Initially, the mean prediction of slope displacement at each grid point is estimated for two saturation conditions and PGA values corresponding to three return periods. Further, the probability of slope displacement exceeding a threshold value is estimated by assuming lognormal distribution with a standard deviation value of 1.175 (given in Eq. 1). In this study, the threshold displacement causes a landslide during the earthquake which is considered as 5 cm. The seismic landslide hazard maps are shown in Fig. 5. It can be observed from the hazard maps that the area prone to co-seismic landslides falls in the buffer zone, attributing to higher slope angles in that region. The probability gets as high as 93.4% for a return period of 2475 years for saturated soil. It

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Fig. 5 Seismic landslide hazard map of Mandi town for a return period 100 years for dry soil (m = 0), b return period 475 years for dry soil (m = 0), c return period 2475 years for dry soil (m = 0), d return period 100 years for saturated soil (m = 1), e return period 475 years for saturated soil (m = 1), and f return period 2475 years for saturated soil (m = 1)

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is a major concern as new tourism projects like Shiv Dham are coming up in the buffer zone, and the population settlement here is likely to increase rapidly. The study also highlights the high probability of co-seismic landslides along the existing national highways towards Kullu, Jogindernagar, and Bilaspur. The landslides on these roads might obstruct rescue and relief operations, evacuation, and medical aid in case of an earthquake. The co-seismic landslides along the riverbanks of Beas and Suketi Khud, flowing through the study region, might lead to the formation of landslide-dammed lakes. The formation of such lakes on the upstream end of the town will increase the possibility of flash flooding on the downstream end. In addition, the helipad lies within the zone of high probability for co-seismic landslides, reducing the chances of aerial assistance. The study presented in this paper can help the administration plan the town’s expansion wisely, keeping in mind the population’s best interest and minimizing any societal losses in case of such an event.

4 Conclusions In this study, the co-seismic landslide hazard maps have been prepared for Mandi town of Himachal Pradesh. The study region extends from 31° 41, 16.71,, N to 31° 42, 14.59,, N and 76° 54, 31.20,, E to 76° 58, 12.47,, E. The Newmark sliding displacement-based hazard analysis is adopted in the present study. At each grid point of the study region, the probability of slope displacement exceeding 5 cm is estimated for two saturation (dry and fully saturated) and PGA values corresponding to three return periods. The probability of co-seismic landslides is low in the existing city region. However, the probability gets as high as 93.4% for saturated soil and 86.3% for arid soil for a return period of 2475 years in the buffer region. These observations can be attributed to the steep slopes in the buffer region. The present study intends to help the administration in the detailed planning of the development projects. One can explore effective mitigation techniques to minimize the vulnerability of the infrastructure located in the buffer zones. Further, we would like to add that the buffer zone microzonation studies using the finer resolution DEM and geotechnical material characterization will be the future scope of the present work. Funding This research is supported by Indian Institute of Remote Sensing (IIRS), ISRO, Government of India, under the grant no. IIRS/DO/DMSP-ASCB/2022/14.

References 1. ALOS PALSAR—ASF: Retrieved July 28, 2022, from https://asf.alaska.edu/data-sets/sar-datasets/alos-palsar/ (n.d.) 2. Ambraseys, N.N., Menu, J.M.: Earthquake-induced ground displacements. Earthquake Eng. Struct. Dynam. 16(7), 985–1006 (1988). https://doi.org/10.1002/eqe.4290160704

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3. Ambraseys, N., Bilham, R.: A note on the Kangra Ms = 7.8 earthquake of 4 April 1905. Curr. Sci 79(1), 45–50 (2000) 4. Bilham, R.: Himalayan earthquakes: a review of historical seismicity and early 21st century slip potential. Geol. Soc. 483(1), 423–482 (2019). https://doi.org/10.1144/SP483.16 5. Chand, D.K., Sharma, D.D.: Spatial pattern of landslide vulnerability at block level in district Mandi of Himachal Pradesh: a GIS based approach. J. Clim. Change Water 2(1), 17 (2017) 6. Choubey, V.M., Mukherjee, P.K., Bajwa, B.S., Walia, V.: Geological and tectonic influence on water–soil–radon relationship in Mandi-Manali area, Himachal Himalaya. Environ. Geol. 52(6), 1163–1171 (2007). https://doi.org/10.1007/s00254-006-0553-1 7. Dhanya, J., Raghukanth, S.T.G.: Ground motion prediction model using artificial neural network. Pure Appl. Geophys. 175(3), 1035–1064 (2018). https://doi.org/10.1007/s00024-0171751-3 8. Gade, M., Nayek, P.S., Dhanya, J.: A new neural network–based prediction model for Newmark’s sliding displacements. Bull. Eng. Geol. Env. 80(1), 385–397 (2021). https://doi. org/10.1007/s10064-020-01923-7 9. Ghosh, A., Sarkar, S., Kanungo, D., Jain, S., Kumar, D., Kalura, A., Kumar, S.: Slope instability and risk assessment of an unstable slope at Agrakhal, Uttarakhand. In: Proceedings of the India Geotechnical Conference, Guntur, India (2009) 10. Google Earth: Retrieved July 28, 2022, from https://earth.google.com/web/ (n.d.) 11. Gupta, H.K., Gahalaut, V.K.: Can an earthquake of Mw ∼9 occur in the Himalayan region? Geol. Soc. 412(1), 43–53 (2015). https://doi.org/10.1144/SP412.10 12. Gupta, H.K., Sabnis, K.A.: Developing an earthquake resilient society in the vicinity of Himalaya. J. Geol. Soc. India 97(12), 1593–1602 (2021). https://doi.org/10.1007/s12594-0211918-5 13. Jibson, R.W.: Regression models for estimating coseismic landslide displacement. Eng. Geol. 91(2–4), 209–218 (2007). https://doi.org/10.1016/j.enggeo.2007.01.013 14. Jibson, R.W.: Predicting earthquake-induced landslide displacements using Newmark’s sliding block analysis. Transp. Res. Record 1411 (1993). https://trid.trb.org/view/384547 15. Keefer, D.K.: Investigating landslides caused by earthquakes—a historical review 38 (2002). https://doi.org/10.1023/A:1021274710840 16. Kumar, A., Sharma, R.K., Bansal, V.K.: GIS-based landslide hazard mapping along NH-3 in mountainous terrain of Himachal Pradesh, India using weighted overlay analysis. In: Singh, H., Garg, P., Kaur, I. (eds.) Proceedings of the 1st International Conference on Sustainable Waste Management Through Design, vol. 21 (pp. 59–67). Springer International Publishing (2019). https://doi.org/10.1007/978-3-030-02707-0_9 17. Mali, N., Shukla, D.P., Kala, V.U.: Identifying geotechnical characteristics for landslide hazard indication: a case study in Mandi, Himachal Pradesh, India. Arab. J. Geosci. 15(2), 144 (2022). https://doi.org/10.1007/s12517-022-09475-8 18. Martha, T.R., Roy, P., Mazumdar, R., Govindharaj, K.B., Kumar, K.V.: Spatial characteristics of landslides triggered by the 2015 Mw 7.8 (Gorkha) and Mw 7.3 (Dolakha) earthquakes in Nepal. Landslides 14(2), 697–704 (2017). https://doi.org/10.1007/s10346-016-0763-x 19. Middlemiss, C.S.: The Kangra Earthquake of 4th April, 1905, vol. 38. Geological survey of India (1910) 20. Muthuganeisan, P., Raghukanth, S.T.G.: Site-specific probabilistic seismic hazard map of Himachal Pradesh, India Part II. Hazard estimation. Acta Geophys. 64(4), 853–884 (2016) 21. Muthuganeisan, P., Raghukanth, S.T.G.: Site-specific probabilistic seismic hazard map of Himachal Pradesh, India. Part I. Site-specific ground motion relations. Acta Geophysica 64(2), 336–361 (2016a) 22. Nayek, P.S., Gade, M.: Seismic landslide hazard assessment of central seismic gap region of Himalaya for a Mw 8.5 scenario event. Acta Geophysica 69(3), 747–759 (2021). https://doi. org/10.1007/s11600-021-00572-y 23. Newmark, N.M.: Effects of earthquakes on dams and embankments. Géotechnique 15(2), 139–160 (1965). https://doi.org/10.1680/geot.1965.15.2.139

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24. Oldham, R.D.: Report of the Great Earthquake of 12th June, 1897. Office of the Geological Survey (1899) 25. PH and HP State Unit, Chandigarh: Report on preliminary assessment of the landslide, team of GSI office. https://employee.gsi.gov.in/cs/groups/public/documents/document/b3zp/mtyx/ *edisp/dcport1gsigovi161798.pdf. Accessed 1 Nov 2017 26. Parker, R.N., Densmore, A.L., Rosser, N.J., de Michele, M., Li, Y., Huang, R., Whadcoat, S., Petley, D.N.: Mass wasting triggered by the 2008 Wenchuan earthquake is greater than orogenic growth. Nat. Geosci. 4(7), 449–452 (2011). https://doi.org/10.1038/ngeo1154 27. Refice, A., Capolongo, D.: Probabilistic modeling of uncertainties in earthquake-induced landslide hazard assessment. Comput. Geosci. 28(6), 735–749 (2002). https://doi.org/10.1016/ S0098-3004(01)00104-2 28. Rodriguez-Peces, M.J., Garcia-Mayordomo, J., Azanon, J.M., Jabaloy, A.: Regional hazard assessment of earthquake-triggered slope instabilities considering site effects and seismic scenarios in Lorca Basin (Spain). Environ. Eng. Geosci. 17(2), 183–196 (2011). https://doi. org/10.2113/gseegeosci.17.2.183 29. Rollo, F., Rampello, S.: Probabilistic assessment of seismic-induced slope displacements: an application in Italy. Bull. Earthq. Eng. 19(11), 4261–4288 (2021). https://doi.org/10.1007/s10 518-021-01138-5 30. Sharma, P., Rawat, S., Gupta, A.K.: Study and remedy of Kotropi landslide in Himachal Pradesh, India. Ind. Geotechn. J. 49(6), 603–619 (2019). https://doi.org/10.1007/s40098-0180343-1 31. Singh, P., Ao, A., Thakur, S., Rana, S., Sharma, R., Krishnakanta Singh, A., Singhal, S.: Geology, Structural, Metamorphic and Mineralization Studies Along the Mandi-Kullu-ManaliRohtang Section of Himachal Pradesh, NW-India (pp. 437–460) (2021). 32. Thakur, V.C., Jayangondaperumal, R., Joevivek, V.: Seismotectonics of central and NW Himalaya: plate boundary–wedge thrust earthquakes in thin- and thick-skinned tectonic framework. Geol. Soc. 481(1), 41–63 (2019). https://doi.org/10.1144/SP481.8 33. Thakur, V.C., Sriram, V., Mundepi, A.K.: Seismotectonics of the great 1905 Kangra earthquake meizoseismal region in Kangra-Chamba, NW Himalaya. Tectonophysics 326(3–4), 289–298 (2000). https://doi.org/10.1016/S0040-1951(00)00126-8

Infill Wall Effect on Seismic Analysis of Reinforced Concrete Buildings C. H. Sirajudheen and Behera Dibyadarshi

Abstract Infill walls are used to fill the gap between the structural elements in a framed structure. It protects the structure from external environment and also acts as partition wall to create separate rooms according to the requirements. Different types of infills are used to create masonry wall like red clay brick, fly ash brick, and AAC blocks. In seismic analysis, the infill wall contribution is ignored as it is considered as non-structural element. But in recent years, much research has been conducted by many researchers to study the behavior of the unreinforced masonry during seismic event. Here, the study is to find the infill wall effect on seismic analysis and design. In our study, bare frame model (BFM), hybrid frame model (HFM) and infilled frame model (IFM) are taken to perform the seismic analysis. Equivalent diagonal strut method is used to include the unreinforced masonry infill wall effect in seismic analysis using codal provisions of IS1893:2016. In this method, the wall is converted as a diagonal strut which only takes compressive force when the structure is subjected to some lateral forces such as seismic load. We performed few experiments on red clay brick masonry to obtain the data required for designing the diagonal strut dimension. Both static and dynamic analyses (response spectrum method) are performed using ETABS software. G + 4, G + 9, and G + 14 models are designed to study the infill wall contribution in earthquake analysis by varying the elevation of the structure. Storey drift, storey displacement, member forces, and time period are taken as the parameters. After performing both the analysis, this is observed that the infill wall significantly affect the member forces and stiffness of the structure during seismic event. So, the practicing engineers must include infill wall effect while designing real structures. Keywords Diagonal strut method · Hybrid frame · Infilled frame · Bare frame · Storey drift · Storey displacement · Response spectrum method C. H. Sirajudheen Assistant Professor, Department of Civil Engineering, College of Engineering and Technology, Bhubaneswar, India B. Dibyadarshi (B) College of Engineering and Technology, Bhubaneswar, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_22

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1 Introduction The infill wall is the unreinforced masonry (URM) panel used to fill the reinforced concrete frames which accomplish the necessity of making separate rooms and protect the buildings from the environment. There are different types of infills available to create wall, viz. AAC blocks, red clay brick, fly ash brick. Red clay bricks are the oldest and most extensively used as building materials worldwide. Still, it is the most common and ruling construction material because these are cheap, durable, and easy to handle and to work with. Generally, infill walls are constructed without leaving any gap between structural elements. But nowadays, gaps are given in infill walls to build large windows for the aesthetical view. These soft storey shows relatively less stiffness than other floors. Also, open-ground storey buildings are built to provide parking area in the ground floor. From the past earthquake instances, it is found that the irregularities in elevation due to the infill wall caused severe damage to the building. Like in Bhuj earthquake (2001), the collapse of more than a hundred RC frame buildings with open ground stories at Ahmedabad has emphasized that such buildings are extremely vulnerable under earthquake shaking [1]. So, the contribution of infills on the overall capacity of structures is strongly dependent on regularity of their distribution in plan and over the height [2]. Due to the lack of knowledge of the complex behavior of the infilled frame, it is neglected in the design. However, under seismic loading infill walls tend to interact with the frame when the structure is subjected to lateral loads and also exhibit energy-dissipation characteristics. Masonry walls contribute to the stiffness of the infill under the action of lateral load [3]. The mass distribution and height of the building are the parameters which contributes in fundamental time period. Therefore, dynamic forces like earthquake depend on dynamic characteristics like mass and stiffness of the structure. As per research, regular distribution of infill wall reduces the fundamental time period [4]. Displacement profiles for both equivalent static method (ESM) and response spectrum method (RSM) have a sudden change of slope at first storey level. The inter-storey drift demand is largest in the ground storey for all the models for both ESM and RSM. The mode shape changes significantly when infill is present in the building. Thus, the infill wall significantly increases the total base shear in both static and dynamic analyses [5]. Infilling panels are found to increase stiffness of the structure. The increase in initial stiffness, obtained for small strains, can reach seven times that of bare frame. Experimental data show that after the first shear cracking, appreciable stiffness degradation occurs. This decrease continues when the displacement increases. However, the stiffness of the infill frame remains higher than that of the bare frame even after the collapse of the masonry panel [6]. Presently in India, the URM wall is considered as non-structural element even though masonry infills give strength and stiffness to the reinforced frame. But in higher seismic zone, the infill wall design should be taken into consideration because the presence of infill wall significantly affects the lateral displacement of the building, bending moments, shear force, and axial forces in the beams and columns. In revised IS Code 1893:2016, the codal provisions are also there for infill wall [7].

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Infill wall behaves like compressive strut in between beam and column. From the modeling approaches of infill walls, there are two main different methods have been used. Micromodel based or finite element techniques while macromodel is the equivalent strut method. The genuine reason of using the equivalent strut method which is a part of macromodeling is very simple while the computation based on the physical understanding of the behavior of infills [8]. The infill walls are analytically replaced by equivalent diagonal struts which only carry compressive force. The endpoints of the strut connected to the frame are pin-jointed to avoid the moment from frame to infill. In this method, the infill wall is idealized as diagonal strut, and the frame is modeled as a truss element [9].

2 Methodology 2.1 Theoretical Background In this study, three models are taken like bare frame model, infill frame model, and hybrid frame model. Hybrid frame model signifies the open ground storey. To compare the infill wall effect in seismic event, G + 4, G + 9, G + 14 buildings are modeled with and without infill walls. The equivalent model with a single strut is not able to describe the “local” interaction between the infill panel and the surrounding structural elements, but it provides a rational basis for estimating the lateral strength and stiffness of the infilled frames as well as the infill diagonal-cracking load. The width of the strut has been calculated by equivalent strut method referring IS code 1893:2016. For URM infill walls without any opening, width wds of equivalent strut (Fig. 1) shall be taken as wds = 0.175αh−0.4 L ds where / αh = h

4

E m t sin 2θ 4E f Ic h



The modulus of elasticity E m (in MPa) of masonry infill wall shall be taken as: E m = 550 f m where f m is the compressive strength of masonry prism (in MPa) obtained as per IS 1905 or given by expression: 0.36 f m = 0.433 f b0.64 f mo

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Fig. 1 Equivalent diagonal strut of URM infill wall

where f b = the compressive strength of brick in MPa; and f mo = the compressive strength of mortar in MPa. The experiments are conducted to in the laboratory (Fig. 2) to get the compressive strength of red clay brick and mortar as per IS 3495-1992 and 2250-1981, respectively. The linear static and dynamic analyses were performed using ETABS. This is an engineering software product that serves to multi-storey building analysis and design, modeling tools and templates, analysis methods, solution techniques, and code-based load prescriptions. In dynamic analysis, the response spectrum method has been adopted in which n modes are taken into consideration to get the maximum response. In ETABS, 12 modes have been taken to get the results. The modes used in the analysis for earthquake vibration along a specific direction has maintained that sum total of modal masses of these modes should be at least 90% of the total seismic mass. The peak responses of different modes have been combined by complete quadratic combination method (CQC) in ETABS. To get the contribution of the infill wall in seismic action storey displacement, storey drift, natural period, base shear, and storey stiffness are taken into consideration. The storey displacement is the total displacement of ith storey with respect to ground, and there is a maximum permissible limit prescribed in IS codes for buildings. Maximum storey displacement limit = H/250, where H is the total height of the building according to codal provisions. Storey drift is the relative displacement of one level above or below between the floors above and below under the storey consideration. Codal provision says that the storey drift in any storey due to the minimum specified design lateral force, with a partial load factor of 0.1 shall not exceed 0.004 times the storey height. While designing according to code in RC and masonry structures, for beams 35% is considered as it is stiff and the rest 65% is vulnerable to cracks. Similarly, for columns 70% is stiff and

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Fig. 2 Compression test on brick and mortar

the rest 30% is vulnerable to cracks. The value for columns is more because they can undergo compression and cracking is not easy. These factors need to be considered as this results in more flexure value and more stiffness. The moment of inertia needs to be reduced in order to prevent the concrete outside the stirrups from coming out.

2.2 Details of the Building Models See Table 1.

2.3 Modelling of Infill Wall In bare frame model (BFM), infill walls were not modeled as it is considered as a non-structural elements although the masses of infill walls are included in the model. In such a case, the mechanism to resist the lateral loads known as frame action mechanism in which bending moments and shear forces are developed in beams and column by means of rigid joint action. The infill walls are modelled as a diagonal strut in infilled frame model (IFM) and hybrid frame model (HFM). The width of the strut was calculated using IS 1893 provisions, and then it is modeled as a strut member in ETABS. The strut only assigned to compressive load and the ends of the strut made pin jointed not to take any moments. The only difference between IFM and HFM is infill walls are distributed throughout the structure in case of IFM, whereas in HFM, infill walls are present all stories except ground storey. The properties of the building which is mentioned in Table 1 are based on IS 1893:2016 provisions [7] (Figs. 3, 4 and 5).

Grade and thickness of slab = M 25 and 165 mm 300 mm * 500 mm (primary beam), 300 mm * 400 mm (plinth beam)

Grade of concrete = M 25

Grade of steel = Fe 500 and Fe 415

Density of concrete = 25 kN/m3

Density of masonry wall (brick) and plaster = 20 kN/m3

SMRF (special moment resisting frame)

Plinth level = 2 m

Floor to floor height = 3.35 m

Type of building = residential building

Internal wall thickness = 145 mm External wall thickness = 270 mm

Column size 400 mm * 400 mm (G + 4), 600 mm * 600 mm (G + 9, G + 14)

Member properties

Material properties

Details of building

Table 1 Properties of building

SDL on roof = 1.5 kN/m2 Parapet = 6.48 kN/m

SDL on floor = 2.5 kN/m2

Live load on roof = 1.5 kN/m2

Live load on floor = 3 kN/m2

Type of loads and their intensities

Soil type = I Damping ratio = 5%

Response reduction factor (R) =5

Importance factor (I) = 1.2

Zone factor (Z) = 0.36, Zone =V

Seismic properties

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Fig. 3 Bare frame model of G + 14 building

Fig. 4 Infill frame model of G + 14 building

3 Results and Discussion 3.1 Experimental Results Compressive strength of the red clay brick (24 × 11.5 × 7 cm) = 6.9 N/mm2 .

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Fig. 5 Hybrid frame model of G + 14 building

28 days compressive strength of mortar cube of side 7.06 cm = 14.76 N/mm2 . Using these values, we calculated the compressive strength of masonry prism (f m ). As per IS 1893, 0.36 f m = 0.433 f b0.64 f mo = 0.433 × 6.90.64 × 14.760.36

= 3.9 ≈ 4 N/mm2

3.2 Results from Numerical Analysis 3.2.1

Natural Period

In the static analysis for BFM, natural period of the building is obtained corresponding to the fundamental translational mode as per IS 1893:2016. So here we got a higher value in BFM and same value for both HFM and IFM. Static analysis is an approximate method, and it gives the results independent of the structural properties of the building. However, it is mainly depending on an empirical formula which is influenced by the total height as well as the type of structure and its plan. From the dynamic analysis in which we consider the multiple modes and its combined effect, we found that modeling of infill with equivalent diagonal strut significantly affects natural period of the building. In BFM, we found a higher natural period as compared to HFM and IFM for all three models. The captured results indicate that the time period of open storey building model, which is considered as hybrid frame structure

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is in between time period of bare frame model and infilled frame model. In all three models, the natural period of infilled frame is reducing almost 50% compared to bare frame models. So, from the analysis of results, it is concluded that the presence of infill wall in the structure increases its lateral stiffness and hence reduces the time period of the structure (Figs. 6, 7, 8). From all the above results, it is also found that if we increase the height of the structure then structure will become more flexible and hence the time period also increases. Hence, the height also affects the time period in dynamic analysis.

Fig. 6 G + 4 buildings

Fig. 7 G + 9 buildings

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Fig. 8 G + 14 buildings

3.2.2

Maximum Storey Displacement

From Figs. 9, 10, and 11, we observed that maximum storey displacement in all the models is below the limit value of (H/250) where H is the total height of the building. For G + 4, G + 9, and G + 14 models, the limit values are 75, 142, and 209 mm, respectively. Bare frame storey displacements are much higher than frame with infill effect in all the cases. From this, we can understand that infill wall contributes in lateral load resisting action. If we compare the values of HFM and IFM, the displacement of hybrid frame is bit higher than infilled frame, and it is due to the soft storey or open ground storey effect. Displacement of soft storey is much higher compared to frame with infill effect. From this, we can understand that soft storey effect is much higher in small buildings compare to medium or high rise buildings. Displacement of Storey-2 of HFM is higher than that of IFM & BFM in all the models due to presence of soft storey in all the cases. So, by comparing that relative storey displacement (storey drift) of storey-2 to storey-1 for G + 4 models (Fig. 9), we found that for IFM value of storey drift is 4.144 mm which is within limit of 0.002 times of h, i.e., 6.7 mm mentioned in Table 6 of IS 1893:2016. In the case of HFM, value of drift is 21.851 mm which is significantly higher than the limit value 6.7 mm. For BFM, we found a drift of 14.437 mm which is greater than limiting value 13.4 mm, i.e., 0.004 times of h. In G + 9 storey drift for IFM is 4.792 mm which is within the limit. For HFM 10.749 greater than limiting value and for BFM 9.755 mm which is within limiting value. In G + 14, IFM’s storey drift is 4.773 within the limiting value. HFM’s value is 10.2 mm greater than the limiting value (Fig. 11). BFM’s value is 10.41 within the limiting value. Hence, if we see storey drift, then storey drift of HFM is higher than limit value mentioned in the code. But

Storey Displacement in mm

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MAX. STOREY DISPLACEMENT

100 50 0

storey 6

storey 5

storey 4

storey 3

storey 2

storey 1

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Fig. 9 G + 4 buildings

Fig. 10 G + 9 buildings

maximum storey displacement is coming within the limit. But, comparing all the models by Storey drift and storey displacement IFM shows better result. To avoid failure of building due to drift in HFM, shear wall should be designed or the column dimension should be increased.

3.2.3

Base Shear

From Fig. 12, we can understand that presence of infill walls in the structure leads to an increase in base shear force in all buildings. If we compare all the models, the base shear also increases as the increase of number of stories. Among all models, G + 14 building has highest value of base shear. If we observe, all the three models, there is not much difference between HFM and IFM in terms of base shear values. The small difference due to the infill wall is not present in ground storey of HFM.

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Fig. 11 G + 14 buildings

Fig. 12 Base shear for G + 4, G + 9, and G + 14 buildings

Hence, it is proved that the presence of infill wall increases the lateral stiffness of the building which attracts more seismic forces to the structure.

3.2.4

Storey Stiffness

From Fig. 13, we found that ground storey stiffness of IFM is much higher than BFM and HFM. Also stiffness of HFM is higher than BFM. In G + 4 model, storey

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Fig. 13 Storey stiffness of G + 4, G + 9, and G + 14 buildings

stiffness of HFM is 1.2 times that of BFM, and similarly, storey stiffness of IFM is 5.82 times that of BFM. In G + 9 model, storey stiffness of HFM is 1.56 times that of BFM and similarly storey stiffness of IFM is 3.57 times that of BFM. In G + 14 model, storey stiffness of HFM is 1.6 times that of BFM and similarly storey stiffness of IFM is 3.5 times that of BFM. In both G + 9 and G + 14 models, soft storey stiffness is about 55% less compared to with infill effect and for G + 4 model soft storey stiffness is about 80% less compared to IFM. Hence, storey stiffness is significantly increases from G + 4 to G + 9 building, but the increase from G + 9 to G + 14 building is very small. So, stiffness not only affected by infill but also height of the building. High-rise building will be more flexible than medium rise building.

3.2.5

Comparison of Bending Moment Diagrams

The variation of bending moment is shown in Fig. 14. The value of bending moment in BFM is higher than HFM and IFM for all stories. From the analysis results of G + 4, G + 9, and G + 14 buildings, we found that in G + 4 models, GF column bending moment demand in infilled frame model (IFM) is about 72–76% less when compared to BFM and HFM. In G + 9 models, GF column bending moment demand in infilled frame model (IFM) is about 78.3–88% less when compared to BFM and HFM. In G + 14 models, GF column bending moment demand in infilled frame model (IFM) is about 73–80% less when compared to BFM–HFM. It is also found that the interior

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BFM (G+14)

IFM (G+14)

HFM (G+14)

Fig. 14 Variation of bending moment in BFM, IFM, and HFM

column has the higher value of bending moment than corner and exterior columns due to seismic loads.

3.2.6

Comparison of Axial Force Diagrams

The columns are experiencing axial force in all the models shown in Fig. 15. But the axial force in columns of IFM is higher than other models due to truss action. From the analysis results of G + 4, G + 9, and G + 14 buildings, we found that, in G + 4 building, the axial force of IFM is 173.83% higher than BFM and axial force of HFM is 110.7328% higher than BFM. In G + 9, IFM is 152% and HFM is 128% higher than BFM while comparing to axial force of corner column. Similarly in G + 14, IFM is 104% higher than BFM, and HFM is 93.81% higher than BFM.

4 Conclusion By comparing bending moment and axial force diagram, we can conclude that in bare frame models (BFMs), the bending moment and shear force demand are much higher in all the stories compared to infilled frame and hybrid frame models. Axial forces are also present but less than that of infilled frames. Hence, it is a clear indication of predominant frame action in BFM. In infilled frame models (IFM), the bending moment and shear force demand are very less in comparison with bare frame and soft storey HFM. At the same time, the axial forces are much higher than BFM and HFM. Hence, it is a clear indication of predominant truss action in IFM. In hybrid frame models (HFM), the bending moment and shear force demand are significant

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BFM (G+14)

IFM (G+14)

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HFM (G+14)

Fig. 15 Variation of axial force in BFM, IFM, and HFM

only in ground storey, and all other upper stories are having much lesser moments and shear forces compared to BFM. But the axial forces are present in all columns which is significantly higher than BFM. Hence, it is a clear indication of predominant frame action in ground storey and truss action in all other upper stories. So, we can call it as a hybrid action in HFM. It is also analyzed that the presence of infills in building decreases the time period by which the building will be less affect by the earthquake. From maximum storey displacement values, it is found that the bare frame model has a higher value of displacement than the infilled frame and hybrid frame models. But in the hybrid frame model, maximum displacement is found in the soft storey as compared to other stories. Hence, if any irregularities of infill wall present in any storey then on that level damage will occur. As we observed, the storey displacement varied from 62 to 22% if we consider the infill wall. In all the cases, the relative displacement is coming within limit but by considering infill wall the drift value decreases. But at the soft storey drift value is higher all than other floors, as it attracts more earthquake forces. Seismic analysis of the bare frame model leads to underestimations of base shear. This underestimation of base shear may lead to the collapse of the structure during earthquake shaking. As buildings are more capable of taking compressive load than shear force and bending moment, so infilled frame model is effective in seismic events. The stiffness of the infilled frame is higher than the others. The higher the stiffness lesser will be the deflection. The presence of infill reduces the lateral deflection of the building, displacement, bending moments in the frame, and increasing axial forces in columns. Hence, there is a need to include the infill wall effect in the seismic analysis of reinforced concrete buildings to predict the correct behavior of buildings during an earthquake. So, it is concluded that we should analyse at least three models—BFM, HFM, and IFM for the design of a reinforced concrete buildings.

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References 1. Murty, C.V.R.: Earthquake Tips. National Information Center of Earthquake Engineering. Indian Institute of Technology Kanpur (2005) 2. Trapani, F.D., Macaluso, G.: Masonry infills and RC frames interaction: literature overview and state of the art of macro modelling approach. Euro. J. Environ. Civ. Eng. (2015) 3. Davis, R., Krishnani, P.: Effect of infill stiffness on seismic performance of multi-storey RC framed buildings in India. In: 13th World Conference on Earthquake Engineering Vancouver, B.C., Canada (2004) 4. Kose, M.M., et al.: Parameters affecting the fundamental period of RC buildings with infill walls. Eng. Struct. (2009) 5. Alam, T., Amanat, K.M.: Seismic response of randomly infilled reinforced concrete frames with soft ground storey. Austr. J. Civ. Eng. (2020) 6. Elouali, T.: Effect of infill masonry panels on the seismic response of frame buildings. In: 14th World Conference on Earthquake Engineering (14WCEE) (2008) 7. IS 1893:2016: Criteria for Earthquake resistant design of structure 8. Amalia, A.R., Iranat, D.: Comparative study on diagonal equivalent methods of masonry infill panel. AIP Conf. Proc. (2017) 9. Abd-Elhamed, A., Mahmoud, S.: Linear and nonlinear dynamic analysis of masonry infill RC framed buildings. Civ. Eng. J. (2017)

Geotechnical Seismic Base Isolation Using Rubber Sand Mixtures—Review S. L. Divyasree , K. M. Jithin , and Renjitha Mary Varghese

Abstract Automobile industry has achieved an exponential increase in the sales of vehicles during the past decades. Consequently, the problem with the disposal of vehicle tires has become a potential threat especially among the developing countries. Geotechnical research community has resolved the problem by employing the tire shreds for vibration isolation applications, retaining wall construction, as a sound barrier, cushioning foams, etc. Current work discussed the previous studies related to the historical background, engineering properties, mechanical behavior, dynamic characteristics, and numerical modeling of rubber–sand mixtures. This review paper will provide the researchers a general insight toward the factors influencing the static and dynamic properties of rubber–sand mixtures, advantages, disadvantages, contradictory results, and the areas which require future studies. Keywords Automobile industry · Tire shreds · Vibration isolation · Dynamic properties · Rubber sand mixtures · Contradictory

1 Introduction Waste product disposal is a serious threat faced by the modern world due to industrialization. According to the reports of World Health Organization during 2015, estimated percentage of motorized vehicles in developing countries is 53% and that in developed countries is 46% [7]. Tire industry currently manufactures almost close to 1.6 billion tires globally. The outbreak of COVID-19 has led to tremendous increase in the sales of vehicles [7]. After the end of life of vehicles, the disposal of the waste tire becomes a serious concern especially for the middle-income countries. Possible disposal methods of vehicle tires practiced by the developing countries are stockpiling and dumping in landfill. Severe environmental as well as health risks are associated with these disposal methods. Sometimes, the stock piles of rubber might S. L. Divyasree (B) · K. M. Jithin · R. M. Varghese NIT Calicut, Calicut, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_23

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cause fire outbreak and lead to the emission of toxic gases. Water accumulated in the scrap tires become breeding places for mosquitos. Potential hazards due to improper disposal of end-of-life tires have resulted in the exploration of alternate means to utilize waste tires. Scrap tires have the advantage of greater sorption capacity for volatile organic compounds, and it is found to be useful in solid waste landfill as leachate drainage layer [9]. End-of-life tires can be used for the production of tire derived fuel (TDF) and tire pyrolysis oil (TPO). Every year, 100 million waste tires are being recycled. Retreading and regrooving are the current methods exercised in India for the recycling and reuse of waste tires. Tread, bead, and side walls are the secondary materials obtained due to the shredding of whole tires. Further grinding of the shredded parts may lead to a more valuable product known as rubber crumb. Crumb rubber or rubber crumb finds applications as a fine aggregate for concrete manufacturing, raw material for the production of floor mats, rubber sheets, hose pipes [7], surfacing of play grounds and for making sports field. End-of-life tires have also found to be suitable for applications such as lightweight backfill for embankments, retaining walls and slopes in order to counteract bearing capacity failures, slope-stability problems, and excessive settlement and to reduce the lateral earth pressure [1]. Seismic isolation using rubber–sand mixtures has been a topic of interest for the researchers in Geotechnical Earthquake Engineering for the past few years. Large numbers of numerical as well as experimental investigations have been done in order to identify the seismic isolation characteristics of rubber–sand mixtures. Rubber– sand mixtures are characterized by their high compressibility, large void ratio, high damping, low shear modulus, low bulk unit weight, high friction angle, low liquefaction potential, etc. Studies conducted by [4, 30] have also proved that scrap rubber tire is a non- hazardous material which do not produce any kind of contamination to ground water. The problem with self-heating can be avoided by mixing it with soil. Current work discusses the historical background, mechanical, and engineering properties of RSM, static and dynamic properties of RSM, and the numerical studies related to the modeling of RSM.

2 Historical Background Yegian and Catan [34] performed experiments on several geotextiles and suggested that a polyethylene geotextile layer placed immediately below the footing can act as an ideal geotechnical seismic isolation interface. These geotextile liners dissipate the energy of the incoming seismic waves by slip deformations. Xu and Fatahi [33] further investigated the efficacy of using geotextile layers with in the soil below the foundation and were found to be quite satisfactory for seismic isolation because of its high slip resistance nature. Tsang et al. [31] reported that compacted sand and pebble layers can also serve the purpose of energy dissipation below the foundation. Increasing demand for the application of vibration isolation technology to common

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residential buildings in developing countries led to the development of many alternative methods. Among them seismic isolation using rubber–sand mixtures proposed by Tsang H. H gained much popularity due to the enormous advantages offered by the end-of-life tires. Shivaprakash and Dinesh [29] introduced the concept of vibration isolation using scrap rubber soil mixtures. Effectiveness of the suggested method was checked by means of finite element analysis. Use of RSM can substantially reduce the horizontal ground accelerations by 60–70% and vertical accelerations by 80–90%. Proposed method can be categorized under distributed seismic isolation system since the rubber–sand mixtures isolate the entire surface of the foundation structure. Advantages of rubber include its fire resistance, termite-proofing, durability, no significant outgas emission on burial, etc. Stockpiles of scrap tires can be avoided by effectively using these scrap tires for engineering applications.

3 Mechanical and Engineering Properties of Sand-Tire Chip Mixtures Anbazhagan et al. [3] studied about the influence of size of rubber, rubber content, gradation of sand on the shear, and volume behavior of sand–rubber mixtures (SRM). Major factors which influence the shear strength of SRM are size of rubber and rubber content. SRM with 30% Group VI granulated rubber (passing through 12.5 mm and retained on 9.5 mm) possessed maximum shear strength with greater cohesion and angle of friction values. With increase in rubber content, shear strength of SRM increased for all sizes of rubber and their percentage content. Senthen Amuthan et al. [28] determined the influence of fines content, plasticity, and relative density on shear modulus and damping ratio of sand-fine mixtures at 0.005 to greater than 1% strain levels. For low shear strains, shear modulus increased with increase in confining pressure. At constant relative density, shear modulus increased with increase in confining pressure. For larger shear strain amplitudes, shear modulus was found to be independent of relative density. At low shear strains, shear modulus decreased with increase in fines due to the increase in plasticity. But damping ratio increased with increase in the percentage of fines. Effect of plasticity index on shear modulus was insignificant at larger strains. Also damping properties of samples increased with increase in plasticity index. Granulated rubber is shown in Fig. 1. Boominathan and Banerjee [5] have carried out a work regarding the compressibility and permeability characteristics of sand–rubber tire shred mixtures. Sand– rubber tire shred mixtures exhibit ductile behavior under shearing. Up to 30% rubber content, peak angle of internal friction reduced. Beyond which its effect was insignificant. Compressibility characteristics increased with increase in rubber tire shreds, whereas inverse effect was observed for brittleness index. Permeability reduced with increase in rubber content. Similar results were obtained for [13]. But contradictory results were obtained for [22]. Rubber is a hydrophobic material. Resistance to the flow of water along the paths with in the rubber matrix was high as compared

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Fig. 1 Granulated rubber [19]

to the flow resistance offered in sand matrix. That may contribute to the reduced permeability. Ghazavi [12] have determined the efficiency of using sand-tire crumb mix as infill materials in geocell pockets of coir and HDPE geotextiles. They observed that soil reinforced with HDPE geocell increased the bearing capacity of soil up to three times. When the soil was reinforced with coir geotextiles, failure was gradual unlike HDPE geocell reinforced soil. For HDPE geocell reinforcement, tire-crumb was found to be a better infill material. For coir geocell, sand was found to be effective for initial loads. But for higher loads and high strain values, sand-tire crumb infill materials performed better. Also surface heave was considerably reduced when sandtire crumb infill materials were used with coir geotextiles. Foose et al. [10] explored the possibility of using shredded waste tires to improve the shear strength of soil. Their findings suggest that factors like normal stress, sand matrix unit weight, and the amount of shreds in the mixture affected the shear strength of soil. Non-linear strength envelope was obtained for sand having higher unit weight, whereas linear strength envelope was obtained for loose and medium dense sand. This result was in contrast to the result obtained by [11] but it is in agreement with the observations reported by [18]. That may be due to the longer size of the reinforcing elements used in the study conducted by [11].

4 Static and Dynamic Properties of Rubber–Sand Mixtures Pincus et al. [23] have determined the dynamic characteristics of various combinations of sand-granulated rubber mixtures and gravel-granulated rubber mixtures. For 50,r greater than 1, as the confining pressure increased, constant rubber content and D D50,s

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more rubber like behavior was observed. But less rubber like behavior was observed less than 1 and for same rubber content. Influence of void ratio on the for DD50,r 50,s damping behavior of sand-granulated rubber mixtures or gravel-granulated rubber mixtures was ascertained to be negligible. For low shear strain values less than 10–2 %, damping was mainly due to the friction between the sand grains. So damping ratio was observed to be higher than the parent soil. For intermediate strain 10–2 – 0.5%, damping ratio was less than that of unmixed soil. For high shear strain, i.e., greater than 0.5%, damping ratio of the mixtures was higher than the parent soil. At higher shear strain of 0.8%, greater damping ratio was obtained for mixtures greater than 1 and with rubber content less than 20%. For mixtures having DD50,r 50,s greater than 1, increase in damping was found to be less prominent. Li having DD50,r 50,s et al. [14] conducted a series of consolidated undrained cyclic triaxial tests to understand the dynamic behavior of rubber–sand mixtures subjected to large shear strain. At constant rubber volume content and relative density, as the rubber particle size reduced, shear modulus decreased. Reason can be attributed to the increased number of contacts between the rubber particles due to the addition of small sized rubber particles. Thus, it may increase the number of sand–rubber–sand and rubber–rubber force transmission chains. At small shear strain amplitude, high deformation ability of the rubber particles will predominate in the process of damping and subsequent energy dissipation. Hence, sand–rubber mixtures under shear strain levels less than critical strain exhibit higher damping ratio. Whereas at high shear strain levels, inter granular friction between the particles causes dissipation of energy. Thus, damping ratio reduces. Also, the damping ratio is inversely proportional to rubber particle size and confining pressure. Under large shear strain, sand–rubber mixtures exhibit strain hardening behavior. Anastasiadis [2] have carried out torsional resonant column tests on pure sand, gravel, sand–rubber mixtures, and gravel rubber mixtures under confining pressures in the range of 25–400 kPa. As the percentage of soft rubber inclusions increased, void ratio and dry unit weight of the mixture decreased. Dynamic response of the soilrubber mixtures is largely influenced by three factors, mean grain size of rubber to soil grains, rubber fraction, and uniformity co-efficient. As the rubber fraction in the mixtures increased, there will be more rubber-to-rubber contacts, hence the behavior of the mixture changed from sand like to rubber like. Liu et al. [16] have examined the behavior of tire shred-sand mixtures under dry conditions using direct shear, triaxial, and dynamic triaxial tests. Different gravimetric proportions of rubber such as 0, 10, 30, 50, and 100% were mixed with sand. With the increase in tire shred content, angle of internal friction corresponding to both the peak and large strain deviatoric strength reduced. Internal friction angles were found to be independent of the rate of shearing and confining pressure. Dilative behavior of rubber–sand mixtures decreased with increase in rubber content due to the flexible nature of rubber particles. Number of loading cycles has less impact on the shear modulus and damping ratio of dry sandrubber tire shred mixtures. Neaz Sheikh et al. [21] inspected the influence of ground rubber additions on the shear modulus and damping ratio of sand-clay mixtures. They concluded that the seismic isolation efficiency of sand can be improved by

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mixing with rubber particles. Shear stiffness and damping ratio of sand-clay-rubber mixtures can be increased by mixing the granulated sand with granulated rubber up to 20% volume fraction. Soil rubber inclusions showed a greater improvement in the damping behavior of mixtures than soil-acetal inclusions. When materials such as rubber and sand having different elastic and thermal properties are blended together, thermo elastic damping occurs at the sand–rubber interfaces. Hence, the low strain damping ratio increased by three times. Rate of improvement decreased at higher volume fraction of rubber due to the replacement of sand–rubber contacts by sand-to-sand or rubber-to-rubber contacts. Rao and Dutta [27] have performed direct shear tests on particulate rubber–sand mixtures (PRSM) and particulate rubber fly ash mixtures (PRFM) at various normal stresses 50, 100, and 150 kPa. Both PRSM and PRFM can be used for base isolation applications due to its ductile nature. Compressible behavior of PRSM can be significantly reduced by replacing sand with fly ash. Reason may due to the filling of pores present on the surface of the rubber by the fly ash particles. Shear strength and angle of internal friction were higher for PRFM as compared to PRSM. This may be due to the increased particle contacts between particulate rubber and fly ash. With the progressive replacement of sand with fly ash in PRSM50, shear strength improved. Also, with the increase in confining pressure and relative density, shear properties eventually got improved. Madhusudhan et al. [17] have determined static and dynamic properties of sand–rubber shred mixtures. Rubber shreds were varied in percentage of 0, 10, 30, 50, and 100% by weight of sand. Shear modulus and damping ratio decreased with increasing rubber content. Both were decreased with increase in strain. The behavior of the mixture changed to ductile due to the introduction of rubber shreds. Mixtures with 10% rubber content can be effectively used for base isolation applications since it possessed satisfactory static and dynamic properties. Moghaddas Tafreshi et al. [20] have evaluated the shear and compressibility behavior of sand-tire crumb mixtures. Sand-tire crumb mixtures were prepared by varying the volume of tire crumb by 0%, 10, 20, 30, and 40%. They have concluded that the peak shear strength and the axial strain capacity of sand were largely influenced by the tire crumb content and the confining pressure. Greater reduction in shear strength was imparted due to the higher volume fraction of tire crumb in the mixture. With respect to the increase in confining pressure, peak shear strength and axial capacity of the mixture got improved. Enhancement in the axial strain capacity was due to the ductile nature of tire particles.

5 Numerical Studies on Soil-Scrap Tire Mixtures Yegian and Lahlaf [35] have analyzed the seismic response of high-rise buildings isolated by a layer of RSM and geotextile layer. Numerical analysis was done using Plaxis 2D. Peak ground acceleration reduced by 18–48% by the use of RSM layer. As the amplitude of input motion increased, reduction in peak acceleration due to the inclusion of RSM layer also increased, whereas lateral displacement of soil

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increased due to the inclusion of RSM. Tsang [30] have investigated the dynamic response of soil-foundation-structure system reinforced with RSM by means of finite element model created in Strand 7. Raft, pile foundations, and soil were modeled using quadrilateral plane-strain elements. They have found that by means of RSM 40–60% reduction in acceleration can be attained. As the thickness of the RSM layer increased, percentage reduction in peak roof acceleration increased from 47 to 73%, and the percentage reduction in peak footing acceleration increased from 35 to 65%. The suitability of using granulated rubber–sand mixtures for the enhancement of seismic performance of reinforced concrete moment resisting frames (RCMRF) was also checked by [25]. Sand with 30% by rubber weight was considered for the study. OpenSees finite element platform was employed for modeling the soilstructure interaction. The use of RSM was beneficial in the case of medium rise and high-rise buildings since it reduced the base shear force, bending moment, and inter-story drift to a considerable percentage. The efficacy of RSM-based isolation method in terms of overturning stability was determined by [24]. Three-dimensional analysis of the soil-structure system has been enabled with the help of Abacus. That model can be regarded as the most effective model for predicting the dynamic behavior of rubber–sand mixtures since it incorporated the non-linear behavior of the RSM through shear modulus and damping ratio degradation curves. RSM-based geotechnical seismic isolation layer benefitted the seismic response of structure by reducing the base shear and displacements when the rubber content was up to 15– 20% by weight. When the rubber content increased beyond 40%, rocking behavior of the structure increased. Slenderness ratio of the structure and characteristics of the input motion also affected the performance of RSM-based GSI. Xiao et al. [32] have determined the effect of stiffness, length, amount, and the spacing of geotextile layers on the dynamic response of 15 story medium rise building under maximum considered earthquake (MCE) loading using FLAC3D. With the increase in stiffness, length, and number of geotextile layers and with the decrease in spacing of reinforcing layers, base shear forces increased due to the decrease in energy dissipation. Most of the stresses induced by rocking of footing were sustained by the reinforcing layers placed close to the edges of footing. Rocking induced damage and the post-earthquake settlements can be considerably reduced by increasing the stiffness, length, number, and spacing of geotextile layers. Dhanya et al. [8] carried out numerical studies using Abacus to assess seismic isolation potential of geotechnical seismic isolation (GSI) system composed of sand–rubber mixture (SRM) positioned between the footing and soil. GSI system composed of SRM with 30% rubber content and one-tenth of the foundation width times thick and reinforced with double layered geogrid layer were found to be cost-effective solution for reducing the dynamic response of low-rise buildings. Proposed GSI system can decrease the peak spectral acceleration (PSA) by 40% for low and medium frequency earthquakes and 30% for high frequency earthquakes. When the GSI system was reinforced with double layered geogrids, settlement can be reduced by about 55%. Due to the high damping nature of rubber particles, amplitude of input motions reduced when it passes through the GSI layer. Inter-story drift of the building reduced by 20% due to the presence of reinforcements in the GSI system. Created FEM model has been depicted in Fig. 2.

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Fig. 2 Finite element model of building isolated with GSI layer modeled using Abacus [8]

6 Limitations of Rubber–Sand Mixtures Even though, RSM mixtures are considered as environmentally friendly, some studies pointed out contradictory remarks regarding the environmental impact of end-of-life tires. Under acidic condition, leaching from rubber occurs, leading to the release of harmful metals into the soil. Also, chemicals such as methyl ethyl ketone and toluene are also found as part of leachate from rubber. Although chemicals and metals are present in leachate, its effect on ground water quality and thereby on human health is considered to be marginal. However, long-term studies are required to accurately predict the environmental effect of rubber–sand mixture [15]. Another disadvantage is related to the effectiveness of RSM. Seismic isolation of RSM was found to be effective for peak ground acceleration (PGA) in excess of 0.2–0.25 g. For lower levels of PGA, non-linear response was not induced resulting in lesser seismic isolation properties [6].

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One of the possible demerits of geotechnical seismic isolation (GSI) system using scrap tire sand mixtures is the greater amount of settlement induced due to the high compressibility of rubber fraction. Concept of soil reinforcement can be implemented to enhance the load carrying capacity and to decrease the excessive deformation characteristics of RSM. Physical, mechanical, and chemical methods are widely used to improve the behavior of weak ground. Over the past few decades, reinforcing the soil by using natural or artificial fibers in order to enhance its strength and rigidity is gaining more popularity.

7 Conclusions A thorough literature review has been carried out related to the historical background, engineering and mechanical properties, numerical modeling, and static and seismic isolation properties of sand tire shred mixtures. The following conclusions have been derived from the current study. • Shear properties of rubber–sand mixtures were influenced by the size and amount of rubber in the mixture, normal stress, and bulk unit weight of sand. During shearing, sand tire mixtures exhibit ductile behavior. Compressible behavior of sand tire shred mixture increased with increase in percentage of rubber in the mixture. Whereas, brittleness index reduced with the increase in amount of rubber particles in the matrix. Contradictory results have been obtained with respect to the influence of rubber content on permeability properties of sand–rubber mixture. • Contradictory results have been obtained related to the peak shear strength of sand containing different percentages of shredded tires or tire chip mixtures. Some of the studies suggested that a peak shear stress was obtained with the increase in normal stress or horizontal displacement [10, 11], whereas some other works showed that no such peak can be obtained with respect to the gradual increase in normal stress [26]. • More rubber like behavior was observed with the increase in confining pressure greater than 1. at the same rubber content and DD50,r 50,s • For shear strain values less than critical strain, damping of sand–rubber shred mixtures was mainly due to the deformability of the rubber particles. At high shear strain levels, friction between rubber particles was responsible for the reduction in damping ratio. • Uniformity co-efficient, percentage of rubber, and mean grain size of rubber to sand are the factors which affects the dynamic response of sand–rubber mixtures. • Peak ground accelerations can be significantly reduced by introducing a layer of sand–rubber mixture between the foundation and surrounding soil. • Possible demerit of rubber-based seismic isolation system was the excessive amount of settlement imposed due to the high compressibility of rubber particles. This can be avoided by reinforced the geotechnical seismic isolation system using single or double-layer geogrid. Only few numerical studies have been conducted

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on this regard. Future studies should be carried out in this direction to explore the feasibility of GSI systems for field applications in developing countries.

References 1. Ahmed, I., Lovell, C.W.: Rubber soils as lightweight geomaterials. Transp. Res. Rec. 1422, 61–70 (1993) 2. Anastasiadis, A., Senetakis, K., Pitilakis, K.: Small-strain shear modulus and damping ratio of sand-rubber and gravel-rubber mixtures. Geotech. Geol. Eng. 30(2), 363–382 (2012) 3. Anbazhagan, P., Manohar, D.R., Rohit, D.: Influence of size of granulated rubber and tyre chips on the shear strength characteristics of sand–rubber mix. Geomechan. Geoeng. 12(4), 266–278 (2017) 4. Bosscher, P.J., Edil, T.B., Eldin, N.N.: Construction and performance of a shredded waste tire test embankment. Transp. Res. Rec. 1345, 44–52 (1992) 5. Boominathan, A., Banerjee, S.: Engineering properties of sand–rubber tire shred mixtures. Int. J. Geotechn. Eng. 15(9), 1061–1077 (2021) 6. Brunet, S., de La Llera, J.C., Kausel, E.: Seismic Isolation Using Recycled Tire-Rubber (2017) 7. Chaturvedi, B., Handa, R.R.: Circulating Tyres in the Economy. 1–68 (2017) 8. Dhanya, J.S., Boominathan, A., Banerjee, S.: Response of low-rise building with geotechnical seismic isolation system. Soil Dyn. Earthq. Eng. 136, 106187 (2020) 9. Edil, T.B., Park, J.K., Kim, J.Y.: Effectiveness of scrap tire chips as sorptive drainage material. J. Environ. Eng. 130(7), 824–831 (2004) 10. Foose, G.J., Benson, C.H., Bosscher, P.J.: Sand reinforced with shredded waste tires. J. Geotechn. Eng. 122(9), 760–767 (1996) 11. Ghazavi, M.: Shear strength characteristics of sand-mixed with granular rubber. Geotech. Geol. Eng. 22(3), 401–416 (2004) 12. Kolathayar, S., Chitrachedu, R.K.: Model Footing Tests on Sand Bed to Evaluate Efficiency of Tire Crumb as Infill Materials in Geocells. Springer, Singapore (2022) 13. Li, B., Huang, M., Zeng, X.: Dynamic behavior and liquefaction analysis of recycled-rubber sand mixtures. J. Mater. Civ. Eng. 28(11), 04016122 (2016) 14. Li, J., Cui, J., Shan, Y., Yadong, L., Ju, B.: Dynamic shear modulus and damping ratio of sand-rubber mixtures under large strain range. Materials 13(18), 12113–12120 (2020) 15. Liu, L., Cai, G., Zhang, J., Liu, X., Liu, K.: Evaluation of engineering properties and environmental effect of recycled waste tire-sand/soil in geotechnical engineering: a compressive review (2020) 16. Madhusudhan, B.R., Boominathan, A., Banerjee, S.: Factors affecting strength and stiffness of dry sand-rubber tire shred mixtures. Geotech. Geol. Eng. 37(4), 2763–2780 (2019) 17. Madhusudhan, B.R., Boominathan, A., Banerjee, S.: Static and large-strain dynamic properties of sand-rubber tire shred mixtures. J. Mater. Civ. Eng. 29(10), 04017165 (2017) 18. Maher, M.H., Gray, D.H.: Static response of sands reinforced with randomly distributed fibers. J. Geotechn. Eng. 116(11), 55 (1990) 19. Moghaddas Tafreshi, S.N., Joz Darabi, N., Tavakoli Mehrjardi, G., Dawson, A.: Experimental and numerical investigation of footing behaviour on multi-layered rubber-reinforced soil. Eur. J. Environ. Civ. Eng. 23(1), 29–52 (2019) 20. Neaz Sheikh, M., Mashiri, M.S., Vinod, J.S., Tsang, H.-H.: Shear and compressibility behavior of sand-tire crumb mixtures. J. Mater. Civ. Eng. 25(10), 1366–1374 (2013) 21. Pamukcu, S., Akbulut, S.: Thermoelastic enhancement of damping of sand using synthetic ground rubber. J. Geotechn. Geoenviron. Eng. 132(4), 501–510 (2006) 22. Pincus, H., Edil, T., Bosscher, P.: Engineering properties of tire chips and soil mixtures. Geotech. Test. J. 17(4), 453 (1994)

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23. Pistolas, G.A., Anastasiadis, A., Pitilakis, K.: Dynamic behaviour of granular soil materials mixed with granulated rubber: influence of rubber content and mean grain size ratio on shear modulus and damping ratio for a wide strain range. Innov. Infrastruct. Sol. 3(1) (2018) 24. Pistolas, G.A., Pitilakis, K., Anastasiadis, A.: A numerical investigation on the seismic isolation potential of rubber/soil mixtures. Earthq. Eng. Eng. Vib. 19(3), 683–704 (2020) 25. Pitilakis, K., Karapetrou, S., Tsagdi, K.: Numerical investigation of the seismic response of RC buildings on soil replaced with rubber-sand mixtures. Soil Dyn. Earthq. Eng. 79, 237–252 (2015) 26. Rao, G.V., Dutta, R.K.: Compressibility and strength behaviour of sand-tyre chip mixtures. Geotech. Geol. Eng. 24(3), 711–724 (2006) 27. Senthen Amuthan, M., Boominathan, A., Banerjee, S.: Density and shear strength of particulate rubber mixed with sand and fly ash. J. Mater. Civ. Eng. 30(7), 04018136 (2018) 28. Shivaprakash, B.G., Dinesh, S.V.: Dynamic properties of sand-fines mixtures. Geotech. Geol. Eng. 35(5), 2327–2337 (2017) 29. Tsang, H.: Seismic isolation by rubber—soil mixtures for developing countries. Earthq. Eng. Struct. Dyn. 37(2), 283–303 (2008) 30. Tsang, H., Lo, S.H., Xu, X., Sheikh, M.N.: Seismic isolation for low-to-medium-rise buildings using granulated rubber—soil mixtures: numerical study. Earthq. Eng. Struct. Dyn. 41(14), 2009–2024 (2012) 31. Xiao, H., Butterworth, J.W., Larkin, T.: Low-technology techniques for seismic isolation. NZSEE Conf. 36, 36–43 (2004) 32. Xu, R., Fatahi, B.: Influence of geotextile arrangement on seismic performance of mid-rise buildings subjected to MCE shaking. Geotext. Geomembr. 46(4), 511–528 (2018) 33. Yegian, M.K., Catan, M.: Soil isolation for seismic protection using a smooth synthetic liner. J. Geotechn. Geoenviron. Eng. 130(11), 1131–1139 (2004) 34. Yegian, M.K., Lahlaf, A.M.: Dynamic interface shear strength properties of geomembranes and geotextiles. J. Geotechn. Eng. 120(2), 462–465 (1994) 35. Yildiz, Ö.: Geotechnical seismic isolation method using rubber-soil mixtures. NATURENGS MTU J. Eng. Nat. Sci. Malatya Turgut Ozal Univ. 1, 34–43 (2021)

Development of Soil Amplification Factors Using 1D and 2D Ground Response Analysis A. Sharma

and S. Adhikary

Abstract 1D ground response analysis is quite popularly used for determining soil amplification numerically. 1D GRA works well for layered soil material with infinitely extending boundaries. However, in case of irregular ground, complex site stratigraphy, river basin, etc., 2D analysis or even 3D analysis is preferable where soil properties in all the corresponding direction are incorporated and thus depicts the real behaviour when it is subjected to an earthquake motion. In the present study, an attempt has been made to determine soil amplification using the 1D and 2D GRA. The analysis is carried out on numerous multilayer real soil profiles taken from author’s past work. As a first step, 1D EL GRA is conducted for 8 real sites from India, using the DEEPSOIL software. Spectrum compatible time histories matched with the TypeI spectra of Indian code, corresponding to effective peak ground acceleration (EPGA) of 0.36 g, are used. Deconvoluted motion of earthquakes is applied at the bottom of strata/ preferably on bedrock, and the response throughout the thickness of the strata is recorded in order to get the soil amplification profile. Later, using commercially available FEM software, ABAQUS 1D and 2D analysis are done where the layered soil medium is modelled using continuum approach. The results from 1 and 2D are compared together in terms of response spectra and amplification factors. This study brought forth the comparison of GRA results using discrete and continuum approach in 1D and 2D analysis. Keywords Soil amplification · Seismic response · Ground response analysis · 1D · 2D

1 Introduction Earthquake is a naturally occurring phenomenon, which releases massive amount of energy when triggered and takes place due to tectonic movement of earth crust, A. Sharma · S. Adhikary (B) Department of Civil Engineering, VNIT Nagpur, Nagpur, Maharashtra 440010, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_24

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incurring tremendous loss of infrastructure and lives. The earthquake strong ground motions vary from place to place across the surface of earth depending on the seismic zone and type of soil bed underneath. The local soil conditions play a very important role in changing the dynamic characteristics of an earthquake. The properties of different soil layers may amplify or deamplify the earthquake ground motion at the soil surface. Due to this nature of soil, several structures resting on soft soils suffered tremendous damage [1]. The primary features that influence the local alterations to the applied strong ground motions are the stratigraphy of deposited soil, location of bedrock, etc. [2]. Many researchers showcased their expertise in this direction and contributed along the way. Desai and Choudhury [3] performed the ground response analysis of a few crucial sites and ports of Mumbai city, India. SHAKE2000 [4] was used to perform 1D equivalent linear GRA on typical soil profiles from the ports and crucial locations. They concluded that a few sites were highly susceptible to earthquakes amongst the selected sites. Amorosi et al. [5] conducted ground response analyzes of a cohesive deposit using finite element computer software PLAXIS2D [6]. This FE method adopted a linear cum visco-elastic and visco-elasto-plastic constitutive soil models. The finite element results from PLAXIS2D were later compared with 1D equivalent linear, visco-elastic, and frequency-domain analyzes obtained from SHAKE91 [7] computer software. GovindaRaju et al. [2] presented a case study by conducting a ground response analysis of a site in Ahmedabad city, India during the Bhuj earthquake. They enlightened the engineering importance and difficulties faced whilst performing GRA and proposed the method to perform the same. A total stress analysis approach [8] of ground response analysis was performed on a major earthquakehit site Lotung, Taiwan (M6.5 and M7.0) in 1986, using widely used equivalent linear analysis code SHAKE91 [7] and time domain finite element nonlinear code SPECTRA. These two algorithms were compared by analyzing records of major earthquakes on selected sites in their research work. Bard and Bouchon [9] have given an empirical relation to compute the fundamental frequency of the soil deposits. Also, Kumar and Narayan [10] have worked on the response of different shapes of the subsoil basins (namely rectangular and elliptical). Admirable correlation has been obtained between their observed fundamental frequency and the computed frequency using empirical relation by [9]. Later, with these results, a new empirical relation has been generated via regression analysis in order to predict the fundamental frequency of elliptical soil basins. And the results from this relation have shown an increase in the fundamental frequency of soil with increase in depth to width ratio of the basin. Ranjan [11] gathered soil profile data from an Indian city, Dehradun, by measuring shear wave velocity of soil using multi-channel analysis of surface waves method (MASW). He collected this data from a total of 31 number of sites located across the city. SHAKE91 [7] was extensively used for the GRA. The study classified the entire city into different zones depending on the measured shear wave velocity and observed spectral acceleration for the purpose of deterministic seismic hazard analysis and probabilistic seismic hazard analysis.

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299

Shukla and Choudhury [12] prepared consistent seismic ground motions for the structures situated closer to the ports located in Gujarat. The aim of their study was to evaluate the potential of seismic hazard for the few chosen ports, viz. Hazira, Kandla, and Mundra ports in the state and to develop site-specific ground motions for a scale of three-level of ground shaking, viz. operational level earthquake, contingency level earthquake, and maximum considered earthquake. Similar study has been conducted for a few other sites in Kolkata city, India [13] and Bangalore city, India [14] where they have collected data from several typical soil sites and conducted equivalent linear ground response analysis using computer programme SHAKE2000 [7] with a few typical earthquake ground motions. These profiles were elaborated using MASW test to get their shear wave velocity profile along the depth. Recently, Deoda and Adhikary [15, 16] developed the site classification and amplification factors for Indian site conditions using equivalent linear and nonlinear GRA using DEEPSOIL software [17]. The present study aims to investigate the equivalent linear GRA using discrete and continuum approach. For this purpose at first, 1D equivalent linear GRA is carried out in DEEPSOIL for 8 sites considered, and then, the same problem is considered in the commercially available FEM software ABAQUS [18] in 1D and 2D using plane strain elements and proper boundary conditions. The results are compared in terms of response spectra and amplification curves. It is observed that there is significant difference between the approaches considered.

2 Selected Sites and Earthquake Ground Motions 2.1 Sites Selected for the Study The aim of this study is to carry out ground response analysis of a few selected sites of India chosen from the literature. The sites were classified into different soil classes (I, II, and III as per IS: 1893-2016 [19]). These sites are spread across India and are a good representation of a variety of soil profiles. This classification of soils in Indian Standards can be easily understood from Table 1. These sites were taken from various literature [15, 16] listed in Table 2 along with all the required information to conduct equivalent linear ground response analysis in DEEPSOIL and ABAQUS. Information like the complete profile of the site with thickness of individual layers, their unit weight, shear wave velocity, and the dynamic properties (strain dependent properties), viz. modulus reduction curve and damping ratio curve is mentioned. For any particular site, depth of profile, average shear wave velocity, fundamental period of profile, and site class as per Indian seismic code IS: 1893 (part-1) 2016 is mentioned in Table 2. The Indian seismic code classifies the soil only on the basis of one parameter, i.e. the standard penetration test (SPT) value. However, Deoda and Adhikary [15] suggest that only SPT value does not provide the complete understanding of the soil underneath, but average shear wave velocity

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Table 1 Classification of soil type as per IS 1893:2016 Site class-I

Site class-II

Site class-III

Name

Hard soil

Medium soils

Soft soils

N SPT

> 30

10–30

< 10

Table 2 Soil profiles considered in the present study and classified as per IS 1893:2016 and Deoda and Adhikary [15] Author(s)

Site

Depth of profile (m)

Avg. shear wave velocity, V s,avg (m/s)

Fundamental period (s)

Site class as per IS 1893:2016

Site class as per Deoda and Adhikary [15]

31.7

406.97

0.31

I

B

Site 2

29.96

334.65

0.36

I

B

Site 4

31.94

318.69

0.4

I

B

Shukla and Choudhury [12]

Hazira

30

234.32

0.51

II

C

Kandla

32

236.45

0.45

II

C

Mundra

30

237.26

0.42

II

C

Chatterjee and Choudhury [13]

BH-1 Park Street

31

258.54

0.48

III

D

BH-3 Rajarhat

40.5

228.00

0.71

III

D

Ranjan [11] Site 1

is a more reliable parameter for the classification of soil. They suggest a few more parameters must be taken into account to observe the behaviour of the soil. In their study, fundamental period of profile, T o and average shear wave velocity, V s,avg were dominantly used to assess the soil class, and the same has been followed in this study. All these sites were selected from the literature carried out before conducting this computational study. These sites are distributed across Indian subcontinent, viz. Hazira, Kandla, and Mundra are located on the very western part of the country, i.e. Gujarat. Site 1, Site 2, and Site 4 are located in the northern part of the country, i.e. Dehradun, Uttarakhand. Whereas BH-1 Park Street and BH-3 Rajarhat are situated in the eastern side of the country, i.e. Kolkata, West Bengal.

2.2 Ground Motion Selection Selection of suitable ground motions has a very prominent aspect whilst performing ground response analysis for any site. The ground motion we use to apply as a base excitation motion to any soil profile should have the same frequency as that of the soil class the site belongs. Hence, a total of ten numbers of natural recorded

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301

Table 3 Earthquake ground motions considered in this study Earthquake parameters

Recording station

Year of occurrence

Magnitude, Mw

Distance from source (km)

India-Burma Border

Umsning

1988

6.1

343.8

70.52

India-Burma Border

Umsning, India

1997

5.6

106.8

27.28

NE India

Pynursla

1986

4.5

48.2

18.52

Loma Prieta

Gilroy Array #1 1989

6.93

8.84

79.89

Landers

Lucerne

7.28

2.19

96.16

Kobe, Japan

Kobe University 1995

6.9

0.9

31.96

India-Bangladesh Border

Nongkhlaw

1988

5.8

117.3

45.2

Uttarkashi

Uttarkashi

Earthquake

1992

Duration of earthquake (s)

1991

7

34

El Mayor-Cucapah, Blythe Mexico

2010

7.2

164.38

120.01

San Simeon, CA

2003

6.52

37.92

29.43

Diablo Canyon Power Plant

39.84

ground motions were made compatible with Type-I spectra of IS: 1893 (part-1) 2016 for 0.36 g (i.e. Zone V) were used in this study. These earthquake motions were taken from databases, viz. Pacific Earthquake Engineering Research Centre and The Consortium for Strong-Motion Observation Systems-Virtual Data Centre. The necessary information of these ground motions is specified in Table 3. Spectral plot of these spectrum compatible earthquakes is plotted in Fig. 1 along with the target spectra of Type-1 spectra of IS: 1893 (part-1) 2016 for 0.36 g. The procedure has been explained in detail [15].

3 Methodology 3.1 Ground Response Analysis Ground response analysis is a simple and common test which is performed before any construction and before conducting any computational research work. Seeking this as a need, a few renowned and easy-to-use softwares were formulated using codes in different computer language to perform this study, viz. SHAKE91 [7] and DEEPSOIL [17]. The 1D GRA is widely used since early days for design projects using the same methodology but often gives conservative results [2]. GRA is performed for various

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Fig. 1 5% damped acceleration response spectra of selected ground motions with the target of Type-1 spectra of IS: 1893-2016

1.25

PSA (g)

1 0.75 0.5 0.25 0 0

0.5

1

1.5

2

2.5

3

3.5

4

Period (s)

reasons, viz. to calculate natural period of site, to assess ground amplification, to prepare spectra for the site, and to evaluate liquefaction potential [2]. 1D equivalent linear, visco-elastic, and frequency-domain analyzes with DEEPSOIL include the use of shear wave velocity profile along the depth, modulus reduction curve which gives the variation of shear modulus with shear strain, and the damping ratio curve which gives the variation of damping with shear strain. Ground response analysis (GRA) may be addressed via linear, equivalent linear, or nonlinear approach in one, two, or three dimensions (1D, 2D, or 3D). The nonlinear technique makes use of cyclic stress–strain models (backbone curves) in order to describe the irregularity in the material properties shear modulus (G) and damping ratio (ξ), whereas the linear approach asks for constant values of G and ξ for an induced level of tiny shear strain in each layer. In the equivalent linear method, the nonlinearity of the shear modulus and damping with cyclic shear strain is taken into account by employing the equivalent linear soil characteristics through an iterative technique, to get values of the G and ξ that are consistent with the effective shear strains in each layer [2]. In this study, one-dimensional equivalent linear analysis is performed in DEEPSOIL and later compared with the FEM model of the 1D analysis in ABAQUS.

3.2 GRA Using DEEPSOIL (Discrete Soil Modelling) DEEPSOIL [17] is a one-dimensional ground response analysis tool that can perform 1D equivalent linear frequency dependent analysis and 1D nonlinear time dependent analysis even with pore water pressure generation. The software works on KelvinVoigt model which can be easily represented by a parallel assembly prepared with a viscous spring and a viscous damper hereby having the property of elasticity and viscosity. The source code utilizes the fundamental idea of successive iterations

Development of Soil Amplification Factors Using 1D and 2D Ground …

(a)

(b)

303

(c)

Fig. 2 Soil profiles prepared in different softwares, a DEEPSOIL, b ABAQUS 1D model and c ABAQUS 2D model

to solve the 1-dimensional wave equations to represent the vertical propagation of S–H waves. Bedrock is considered elastic half-space, and the input motions are deconvoluted before been applied. Figure 2a shows the representative model of 1D equivalent linear GRA using DEEPSOIL.

3.3 GRA Using ABAQUS (Continuum Soil Modelling) For the purpose of 1D continuum modelling, ABAQUS software has been used. The dynamic soil properties, i.e. modulus reduction curve and damping ratio curve, of each soil layer were determined using the strain observed whilst performing the 1D DEEPSOIL analysis. A similar study was conducted by Nautiyal et al. [20]. They chose ABAQUS to perform a 1D, 2D, and 3D comparison of 2 sites with their soil properties and shear wave velocity profile along the depth and found to be in exact correspondence with each other. The model for two-dimensional (2D) study is prepared with 4-noded, plane strain elements with the individual element size kept 1 m × 1 m. The element size and width of the 2D analysis used in this model were chosen so as to get the correct solution after performing sensitivity analysis.

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The prepared model is 36 m wide with element size of 1 m × 1 m (refer Fig. 2c) and its total depth and thickness of individual layers vary as per the chosen soil profile. Several different such models were prepared, and dynamic analysis was conducted for ten numbers of earthquake motions for all the selected sites constituting eighty individual analysis. The chosen earthquake motions and the soil profile for this study were specified in the earlier sections. The earthquake motion is applied at the base of the profile where the horizontal boundary is fixed for the movement of the model, and non-reflecting infinite boundary is provided for smoothly passage of the earthquake waves in the vertically downward direction. Similar non-reflecting infinite boundary is provided at both the sides of the profile in horizontal direction giving the model an adequate length to dissipate the energy of the earthquake and restricting the waves to reflect and enter again in the model. The model for one-dimensional (1D) study is only two metre wide plane strain elements, slender, and multilayer soil column (refer Fig. 2b), that is why, the nonreflecting boundaries could not be employed here rather 1D dashpots are attached to the nodes at both the sides of the model to create an artificial boundary to dissipate the earthquake energy. These dashpots attached at the edges of the model which is commonly referred as viscous boundary condition which helps in smooth passage of the earthquake waves. This dashpot boundary condition works well with two degree of freedom at the edge of the model, i.e. ux and uy . The stiffness coefficient for these dashpots is required to be provided which were determined using the procedure given in [21]. These calculated values of dashpot-stiffness changes with the material, i.e. calculations have to be repeated for each layer and for all the soil profiles. Structural damping is considered in the numerical modelling (1D and 2D) via Rayleigh damping in terms of mass proportional coefficient, α (unit = s−1 ) and stiffness proportional coefficient, β (unit = s) correspond to the first and the second modal natural frequency of the soil profile obtained after performing a modal analysis for a particular soil profile, and the eigenvalues are evaluated using Lanczos iteration method in ABAQUS. The procedure to calculate the values of these coefficients is given in Eq. (1), where ωi and ωj are the first and second modal natural frequencies (Hz), respectively, and ξ is the damping ratio considered for this study. The values of first and second modal natural frequencies and their corresponding α and β coefficients for a few sites are shown in Table 4 and are compared with the profile’s natural frequency obtained in DEEPSOIL. Table 4 Natural modal frequencies and stiffness coefficients obtained for two typical sites using different softwares Software

DEEPSOIL

ABAQUS 1D

Site

ω

ωi

ωj

ABAQUS 2D α (× 10–4 )

β (× 10–4 )

ωi

ωj

α (× 10–4 )

β (× 10–4 )

Site- 1

3.21

2.86

3.60

2005

4.92

3.20

3.29

2041

4.9

Site- 2

2.49

2.46

3.05

1714

5.770

2.46

2.51

1563

6.4

Development of Soil Amplification Factors Using 1D and 2D Ground …

305

It was observed that the frequencies obtained using DEEPSOIL, ABAQUS 1D, and ABAQUS 2D are in very good agreement with one another, and it should be, because the soil profile and material properties are the entities required to perform this study, and they were kept same in all the softwares. Also it was noted that the two modal frequencies obtained using ABAQUS 2D are closer to each other compared to the two modal frequencies obtained using ABAQUS 1D. And natural frequency of soil column obtained using DEEPSOIL also lies very much between the two modal frequencies obtained using ABAQUS 2D. It suggests modelling in 2-dimension gives better result when performing analysis of soil profile and possibly for combined soil-structure profiles too. α=ξ

2ωi ω j 2 ,β = ξ ωi + ω j ωi + ω j

(1)

4 Results and Discussion The deconvoluted ground motion time histories are applied at the base of each soil profile considered and the output time history, commonly known as response time history, at the surface of the soil layer is observed. This analysis of observing the behaviour of soil layer on the surface by applying earthquake motion at the base is termed as ground response analysis. This analysis is performed several times by applying the ten selected earthquake motions on the eight selected sites, thus constituting total eighty number of analyzes for 1D case model in ABAQUS. The results from these many analyzes are compared with the similar number of eighty analyzes results performed in DEEPSOIL software. Similar eighty number of analyzes is also performed in ABAQUS with the 2D model. The response history observed at the surface and at the surface of all the adjacent layers of the soil profile is recorded for all the soil profiles, and the peak acceleration value for all these layers was determined and plotted against the depth of the soil profile. This value for every response at the surface of all layers is known as peak ground acceleration PGA (g). The variation of the PGA (g) along the depth of the profile is plotted for all the selected sites and for all the earthquake motions as shown in Fig. 3. These curves are plotted considering the mean of the response of all earthquake motions for all eight sites for DEEPSOIL, ABAQUS 1D, and ABAQUS 2D. As it is evident from these plots, the curves representing ABAQUS 1D and 2D are in very good agreement with the 1D analysis results obtained from DEEPSOIL. The spectra for the response observed at the surface of soil profile are plotted alongside the spectra of input earthquake motion for all the analyzes conducted in different softwares which are shown in Fig. 4. It can be clearly seen from these figures that the spectra obtained from DEEPSOIL depict larger period than ABAQUS 1D and ABAQUS 2D in all cases. It can also be stated as 1D discrete model analysis procedure gives larger period than 1D or 2D continuum model analysis. One good explanation

306

A. Sharma and S. Adhikary Site- 2

Site- 1 PGA (g) 0.35 0.4

0.3

0.45

0.25 0

0.5

0.45

0.5

0.15 0

4

8

8

8

12

12

12

16 20

20

24

24

24

28

28

Abaqus 2D

Abaqus 2D

36

Site- Hazira PGA (g) 0.45 0.55

Site- Mundra

Site- Kandla 0.65

0.25 0

0.75

0.35

PGA (g)

0.45

0.55

0.2

8

8

12

12

12

16 20

20

24

24

24

28

28 DeepSoil

32

DeepSoil

32

Abaqus 1D

Abaqus 2D

Abaqus 2D

36

36

Site- BH-3 Rajarhat

Site- BH-1 Park Street 0.4

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can be given for this like, DEEPSOIL predicts the natural period of any strata by simply considering the average shear wave velocity, V s,avg and depth of the profile, H referring Eq. (2), whereas ABAQUS keeps into account the material properties also whilst determining the natural frequency of the soil column. Hence, gives better results for natural period and for all other types of analyzes. Also it is noted from these figures that the peak spectral acceleration obtained from DEEPSOIL, ABAQUS 1D,

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and ABAQUS 2D is equal for all the soil profiles. VS,avg 4H

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Figure 5 depicts the curves known as the amplification factor curves which are plotted for all the sites with respect to the applied input motion. These plots simplify the way of understanding the spectral amplification of soil response with respect to the input motion for all the considered sites. Each curve represents the amplification of the corresponding ordinates of the response spectra at the surface of soil to the response spectra of the applied input motion. It can be clearly observed that the amplification in the amplification factor curve representing “DEEPSOIL 1D Amplification Factor” is highly amplified in all the cases compared to the amplification factor curve representing “ABAQUS 1D Amplification Factor” and “ABAQUS 2D Amplification Factor”. The simple reason behind this huge amplification is that the peak of the corresponding DEEPSOIL spectra has large period of natural oscillation compared to the ABAQUS 1D and ABAQUS 2D spectra. Also the peak attained by ABAQUS 2D curve is higher in all the cases compared to ABAQUS 1D. Similar Site- 1

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conclusion can be observed here as well that the peak of ABAQUS 2D is earlier than the peak of ABAQUS 1D when comparing ABAQUS 1D and 2D modelling. Also the ABAQUS 2D curve is highly deamplified compared to ABAQUS 1D and DEEPSOIL, and the reason for this could be the nonlinearity of the soil layers which significantly damped out the seismic waves.

5 Conclusion In this study, several numerical analyzes were performed on various real sites considered from India with various earthquake motions, to get the PGA of the response for all those sites along the depth. The selected sites and chosen earthquake ground motions with the criteria of their selection are specified in the earlier sections. These analyzes were performed in different softwares, i.e. DEEPSOIL as a discrete approach and ABAQUS as a continuum approach and are summarized as a comparative study. This comparison was shown in the results and discussion section and

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that is in very good correspondence with each other, and the appropriate reasoning is provided for all the deviations. The study reflects the difference in the considered approaches for modelling ground response analysis. The results obtained from the discrete approach are on the conservative side. Further, it is observed that for 2D continuum approach, there is significant deamplification, and the GRA is not captured efficiently for the considered site profiles. Nevertheless, for spatial variation and heterogeneity in soil profile, 2D continuum approach is recommended.

References 1. Kramer, S.L.: Geotechnical Earthquake Engineering. Pearson Education, Inc. (2013) 2. GovindaRaju, L., Ramana, G.V.: Site-specific ground response analysis. JSTOR 87(10), 1354– 1362 (2004) 3. Desai, S.S., Choudhury, D.: Site-specific seismic ground response study for nuclear power plants and ports in Mumbai. Nat. Hazards Rev. 16(4), 04015002 (2015). https://doi.org/10. 1061/(ASCE)NH.1527-6996.0000177 4. Ordóñez, G.A.: SHAKE2000—A Computer Program for the 1-D Analysis of Geotechnical Earthquake Engineering Problems. GeoMotions, LLC, Lacey, Washington, USA (2012) 5. Amorosi, A., Boldini, D., Elia, G.: Parametric study on seismic ground response by finite element modelling. Comput. Geotech. 37(4), 515–528 (2010). https://doi.org/10.1016/j.com pgeo.2010.02.005 6. Bentley Systems, “PLAXIS” 7. Schnabel, P.B., Lysmer, J.: SHAKE91: a computer program for earthquake response analysis of horizontally layered sites 8. Borja, R.I., Duvernay, B.G., Lin, C.-H.: Ground response in Lotung: total stress analyses and parametric studies. J. Geotech. Geoenviron. Eng. 128(1), 54–63 (2002). https://doi.org/10. 1061/(ASCE)1090-0241(2002)128:1(54) 9. Bard, P.-Y., Bouchon, M.: The two-dimensional resonance of sediment-filled valleys. Bull. Seismol. Soc. Am. 75(2), 519–541 (1985). https://doi.org/10.1785/bssa0750020519 10. Kumar, N., Narayan, J.P.: Study of 2D basins and site-city interaction effects on ground motion characteristics. J. Indian Geophys. Union 22(1), 16–23 (2018) [online]. Available https://www. researchgate.net/publication/320345540 11. Ranjan, R.: Seismic Response Analysis of Dehradun City, India. M.Sc. thesis, International Institute for Geo-Information Science and Earth Observations, Enschede (2004) 12. Shukla, J., Choudhury, D.: Seismic hazard and site-specific ground motion for typical ports of Gujarat. Nat. Hazards 60(2), 541–565 (2012). https://doi.org/10.1007/s11069-011-0042-z 13. Chatterjee, K., Choudhury, D.: Influences of local soil conditions for ground response in Kolkata city during earthquakes. Proc. Natl. Acad. Sci. India Sect. A Phys. Sci. 88(4), 515–528 (2018). https://doi.org/10.1007/s40010-016-0265-1 14. Anbazhagan, P., Sitharam, T.G.: Site characterization and site response studies using shear wave velocity. J. Seismol. Earthq. Eng. 10(1), 53–67 (2008) 15. Deoda, V.R., Adhikary, S.: A preliminary proposal towards the revision of Indian seismic code considering site classification scheme, amplification factors and response spectra. Bull. Earthq. Eng. 18(6), 2843–2889 (2020). https://doi.org/10.1007/s10518-020-00806-2 16. Deoda, V.R., Adhikary, S.: Revision of IS 1893: proposal for an alternative soil classification scheme and associated intensity-dependent spectral amplification factors. Nat. Hazards Rev. 23(2) (2022).https://doi.org/10.1061/(asce)nh.1527-6996.0000554 17. Hashash, Y.M.A., Musgrove, M.I., Harmon, J.A., Ilhan, O., Xing, G., Numanoglu, O., Groholski, D.R., Phillips, C.A.: DEEPSOIL v7.0, User Manual. Urbana, IL, Board of Trustees of University of Illinois at Urbana-Champaign (2020)

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18. Dassault Systemes: Abaqus/CAE, Simulia v. 2017 19. IS 1893 (Part-1): Criteria for Earthquake Resistant Design of Structures. Part 1: General Provisions and Buildings. Bureau of Indian Standards, New Delhi (2016) 20. Nautiyal, P., Raj, D., Bharathi, M., Dubey, R.: Ground response analysis: comparison of 1D, 2D and 3D approach. Proc. Indian Geotechn. Conf. 2021, 607–619 (2019). https://doi.org/10. 1007/978-981-33-6564-3_51 21. Tidke, A.R., Adhikary, S.: Seismic fragility analysis of the Koyna gravity dam with layered rock foundation considering tensile crack failure. Eng. Fail. Anal. 125, 105361 (2021). https:// doi.org/10.1016/j.engfailanal.2021.105361

Probabilistic Seismic Hazard Assessment for Assam, North-East India M. Borah, M. L. Sharma, and R. N. Dubey

Abstract In this study, probabilistic seismic hazard assessment (PSHA) is performed for Assam, North-East (NE) India. NE India being bounded by latitude 20°–30° N and longitude 87°–98° E is considered as one of the most earthquakeprone areas in the world. As per seismic zoning map of India [12], most of the states in NE region have been placed in seismic zone V, which has the highest zone factor in the country. Amongst the eight north-eastern states, Assam serves as the gateway to the other seven states. As this region lies on one of the most vigorous tectonic plates in the world, it has experienced several devastating earthquakes in the past. The most devastating earthquakes include 1869 Cachar Earthquake (M w = 7.5), 1897 Assam-Shillong Earthquake (M w = 8.1), and 1950 Assam Earthquake (M w = 8.7). Considering the seismicity of this region, seismic hazard assessment plays a significant role to assess the seismic risk for the future. The NE India region is broadly divided into four seismogenic sources and further sub-divided into eleven seismogenic sources based on the tectonic features and seismicity characteristics. For the study of hazard assessment, a unified moment magnitude catalogue has been used, where the events are assembled from various databases (ISC, IMD, USGS-NEIC). The catalogue has been declustered, and the seismicity parameters are calculated for each source zone. The hazard maps have been presented at the bedrock level, in terms of peak ground acceleration (PGA) and spectral acceleration (S a ) values. The PGA values vary in between 0.16 and 0.57 g, whilst the S a values are obtained in the range of 0.12–0.77 g. These hazard maps are expected to give insight to the local site-specific seismic hazard variation for the Assam region and would be useful for the preparedness of risk and disaster mitigation measures.

M. Borah (B) · M. L. Sharma · R. N. Dubey Department of Earthquake Engineering, IIT Roorkee, Roorkee, India e-mail: [email protected] M. L. Sharma e-mail: [email protected] R. N. Dubey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_25

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Keywords PSHA · NE India · Assam · PGA · Hazard map

1 Introduction Earthquake, considered as one of the most damaging natural phenomena, has been destroying human life and human-made structures up to a great extent. Due to earthquakes, numbers of shakings ranging from small to great have occurred from time to time, and to date, there are no such measures developed to predict earthquakes accurately. Indian sub-continent is prone to natural hazards due to its predominant geo-climatic conditions. It has experienced numerous destructing disasters leading to loss of lives and properties. Developing countries have more vulnerability to hazards, and the lack of proper disaster management leads to increase in risk in these countries. Most of the civil structures and infrastructures concentrate in these developing countries which are directly affected by earthquakes. For this very reason, seismic hazard assessment plays an important role in the urban areas to minimize the damage due to earthquakes up to a possible extent. The NE India is one of the most seismically active regions around the globe. This region has been witnessing a number of devastating earthquakes, including Cachar earthquake (1869, M w = 7.8), Assam EQ (1897, M w = 8.1), Meghalaya EQ(1923, M w = 7.1), Dhubri EQ (1930, M w = 7.1), Assam EQ (1943, M w = 7.6), Arunachal Pradesh EQ (1947, M s = 7.7), Assam EQ (1950, M w = 8.7), Manipur EQ (1988, M s = 7.3), Assam EQ (2009, M w = 5.1), and Sikkim EQ (2011, M w = 6.9). Recently, starting from 28th April, 2021, Assam has been shaken by more than 100 earthquakes in only a month duration, ranging from small to large, highest (main shock) being 6.4 in Richter magnitude followed by a number of aftershocks up to magnitude 2.0 in Richter scale. These records of past earthquake data indicate the vulnerability of this region to high magnitude earthquake; hence, seismic hazard assessment is really essential for the future preparedness of the important structures and affected community. Till date, various researchers have conducted seismic hazard assessment for India, using both the approaches deterministic seismic hazard assessment (DSHA) and probabilistic seismic hazard assessment (PSHA), such as [1, 3–5, 8, 9, 11, 13, 16– 23]. These researchers have either studied one specific state amongst the eight northeastern states or the entire NE region all together. The various studies display the hazard maps in the form of PGA and S a with an exceedance probability of 0.5, 2, 5, and 10% in 50 years for fixed return periods such as 50, 100, 475, 1000, 2475, and 10,000 years. Assam, being the gateway for all the NE states, it must be given special attention to study the seismic activities throughout the last decades. The earthquake catalogue has been collected from 1897 to 2022, including instrumental as well as pre-instrumental data, provided by various agencies such as ISC, IMD, and USGS-NEIC. The catalogue is first unified to moment magnitude (M w ) and further declustered before using it as the input for finding the seismicity parameters. The region is sub-divided into eleven source zones, and the seismicity parameters for each source zone have been estimated. Various ground motion prediction equations (GMPE) have been used for performing PSHA. The seismic hazard curves in terms

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of PGA values have been developed for 2% and 10% probability of exceedance in 50 years, corresponding to return periods of 475 and 2475 and years, respectively. The spectral acceleration values (S a ) are also presented for 10% of probability of exceedance in 50 years, for time 0.2 and 1.0 s.

2 Geological and Tectonic Setting The NE India region has high seismicity due to its diverse geological and tectonic setting. Assam is located between latitude 24–28° N and longitude of 89–96° E and at an average altitude of around 170 ft above mean sea level. As per seismic zoning code of India [12], the states in North-East (NE) region have been considered in seismic zone V having the highest zone factor (0.36 g) in the country. Amongst the eight north-eastern states, Assam serves as the gateway to the other seven states. According to Geological Survey of India, Assam is broadly divided into three physiographic domains: Brahmaputra Valley, Central Assam Hills, and Barak Valley covering 78,523 km2 areas. In the NE region, mainly, three tectonic belts are present with two convergent boundaries, viz. the Himalayan arc to the north and the IndoBurmese region to the east, interacting with the Assam syntaxis. The Himalayan belt is associated with the Indian plate and at the same time under-thrusts beneath the Eurasian plate. The seismic activity of the Indo-Burmese arc is related to the subduction of the Indian plate. Moreover, intra-plate activities also take place within this region, which comprise Naga thrusts, the Arakan-Yoma Fold Belt in the Indo-Burma ranges, and the Shillong plateau. These thrusts are tectonically and geologically interesting, as the subducted front of Indian plate is under Tibetan plate to the north and Burmese plate to the southeast and it is associated with thrusts and shear along the plate boundaries. Figure 1 shows the tectonic features of NE India. For better understanding of the tectonic setting of the NE India region, it has been broadly divided into four zones, such as Eastern Himalaya region, Assam valley, Indo-Burmese region, and Shillong-Mikir Plateau [4].

2.1 Seismic Source Models One of the basic pre-requisites to seismic hazard assessment is the seismic source zonation of the study area. Several studies such as Sitharam and Sil [23] and Borah et al. [6] have been performed to sub-divide the NE India region into various source zones. The source zones are modelled based upon the parameters, such as the past seismicity, earthquake location, and tectonic features of the region. In the present study, the NE India region is sub-divided into eleven source zones as shown in Fig. 2. The source zones are taken as similar to the source zonation performed by Borah et al. [6]. The NE India region has been categorized into eleven different source zones based on the past seismicity and spatial variation of the seismicity parameters.

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Fig. 1 Tectonic features of NE India

It is assumed that the source zones act as a single point source model for earthquake occurrence, and earthquakes may arise anywhere arbitrarily near the source zone. The source zones 1, 2, 3, and 4 constitute the Eastern Himalayan region. Some important thrusts such as MCT, MBT, MFT, and IST are present in these four zones. Moreover, some more significant faults, namely Mishmi thrust and Lohit thrust, are covered by Zone 4. Zone 5 covers Dhubri and Chedrang fault. Zone 6 contains a part of the Assam valley, Shillong Plateau, and the Mishmi hill; having some important faults such as Dudhnoi shear, Oldham fault, Kopili fault, and Dauki fault. Zone 7 contains a part of Assam valley and the Indo-Burmese range, with a segment of Naga thrust, Mishmi thrust, and Lohit thrust. Zone 8 comes under the Bengal basin region with Eocene Hinge zone, and Debagram-Bogra fault. Zone 9 covers partially Bengal basin and the Indo-Burmese range, with important faults such as Sylhet fault and a segment of the Dauki fault. Zone 10 entails the Indo-Burmese range with a segment of Naga thrust, a segment of Dauki Wanding fault, and Mat fault. Zone 11 is situated to the east of Zone 10 with Shan-Sagaing fault.

3 Earthquake Catalogue For the execution PSHA, preparation of a reliable and homogenized earthquake catalogue is one of the basic inputs. The catalogue has been collected from the seismological agencies such as ISC, IMD, and USGS-NEIC; from 1897 to 2022

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Fig. 2 Source zonation of NE India

for the region of latitude 20–30° N and longitude 87–98° E. The magnitudes are obtained in different scales, such as M b , M L , M s , M d , and M w . For further processing, the catalogue is first homogenized to moment magnitude, M w using the regression relationship proposed by Sitharam and Sil [23] for NE India as follows: Mw = 0.862 m b + 1.034

(1)

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3.1 Declustering of Catalogue A total number of 6550 events of earthquake are obtained from where the foreshock and the aftershock events (dependent events) are declustered from the main shocks,

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Fig. 3 Seismicity map of NE India

and finally, a total number of 6010 events are left in the catalogue. The declustering is performed by the algorithm developed by Uhrhammer [25], in the ZMAP programme. Figure 3 shows the seismicity map of NE India with the declustered catalogue showing events with magnitude greater than or equal to 4.

3.2 Evaluation of Seismicity Parameters The seismicity parameters considered for PSHA include ‘a’ and ‘b’ values, seismic activity rate (λ), β, M max , and threshold magnitude (M C ). These parameters are evaluated for each source zone. The two parameters (‘a’ and ‘b’ values) relate the seismic activity rate (λ) with the magnitude of earthquake for a particular region. Gutenberg and Richter [10] developed the regression relationship as follows: log λ = a − bM

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where ‘a’ and ‘b’ are the constants of the regression relationship, λ is the mean annual rate of exceedance of a particular magnitude, M. The values of ‘a’ and ‘b’ show the seismicity of a region, acting as the main inputs for PSHA. The high values of these two parameters indicate a high level of seismicity with a larger proportion of small events. ZMAP software package developed by Wiemer [26] is used for calculating

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the ‘a’, ‘b’ values and M c . The M c is the completeness magnitude, termed as the minimum magnitude or the threshold magnitude. At this magnitude, 100% of the earthquakes are perceived in the time scale. This value is considered for obtaining the sub-catalogues for each source zone to evaluate the values of λ, β (2.303b), and M max .

3.3 Maximum Magnitude (Mmax ) The information about the maximum possible earthquake magnitude (M max ) is essential for the further processing of PSHA, which is a key parameter for seismic design. It is the maximum magnitude or upper limit that can be generated by a seismic source zone. There are several methods available to calculate the maximum magnitude of seismic sources. In the present study, the programme developed by Kijko [14] has been used to calculate the maximum magnitude. The seismicity parameters obtained from the procedures by ZMAP and Kijko [14] have been tabulated for each zone as shown in Table 1. These parameters are further considered for performing PSHA.

4 Ground Motion Prediction Equation (GMPE) Selecting the appropriate ground motion prediction equations (GMPE) is a crucial step for performing PSHA. A noteworthy variation can occur between the predicted and the actual values provided by the attenuation models for the area. These attenuation models account for the seismic energies for the tectonic zones. Thus, to reduce the uncertainties in the prediction models, as many as possible attenuation models are taken into account for the study. In this study, three attenuation models are used for performing PSHA. GMPE labels the seismic function (Y ) such as the peak ground acceleration (PGA) or S a , in terms of earthquake magnitude (M) and distance (r) as: Y = F(M, r )

(6)

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1.22

1.25

7.3

7.6

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0.73

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2.71

1.62

1.56

1.89

2.94

1.57

COV of β

0.21

0.05

0.08

0.00

0.11

0.13

0.11

0.07

0.09

0.46

0.06

1.354

15.215

2.733

0.192

2.996

3.342

1.235

3.692

2.726

0.277

2.745

λ

Std. deviation of λ

0.329

2.356

0.583

0.073

0.606

0.708

0.293

0.750

0.567

0.080

0.531

M max

8.10

7.41

6.94

6.80

7.26

9.20

7.08

7.88

6.57

6.99

6.90

Std. deviaton of M max

0.00

0.27

0.29

0.00

0.30

0.00

0.38

0.31

0.26

0.83

0.27

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[2] for the active intra-plate regions; and [15] for the subduction zone regions; are used.

5 Computation Model for PSHA The probability of exceeding an IM intensity level, given the occurrence of a future earthquake from a single source, can be computed as: m max rmax

P(IM > x) = ∫ ∫ P(IM > x|m, r ) f M (m) f R (r )dr dm

(7)

m min 0

where P(IM > x) is obtained from the ground motion prediction model; f M (m) and f R (r) are the probability density functions for magnitude and source to site distance, respectively. The integration operation adds up the conditional probabilities of exceedance associated with all possible magnitudes and distances. In this present study, the area source method is assumed where all the seismicity is distributed to the area source as a single point source model, and integration is performed all over the source area. The software ‘R-Crisis’ is used to perform the PSHA, where the G-R distribution is considered, in terms of M min , M max , β, and λ values, as shown in Table 1. The study area is divided into different grids of size 0.10 × 0.10, and hazard curves are generated at bedrock level where V s = 1100 m/s, and distance of 300 km is taken from each grid. The curves are developed for ten time periods such as 0.01, 0.1, 0.2, 0.3, 0.5, 0.7, 1.0, 2.0, 3.0, and 4.0 s. The hazard maps are generated at bedrock level in terms of PGA, for 2% and 10% probability of exceedance in 50 years corresponding to return periods of 475 and 2475 years, respectively. Besides, for time period of 0.2 and 1.0 s, the hazard maps are presented for 10% probability of exceedance in 50 years.

5.1 Results and Discussion PSHA for Assam state of NE India is performed by dividing the entire area into grids of size 0.1° × 0.1°, and the results are presented in the forms of seismic hazard curves. The hazard curves are generated at bedrock level for ten different time periods. These maps are generated for 2% and 10% exceedance probabilities in 50 years, corresponding to return periods of 475 and 2475 years, respectively. Figures 4 and 5 show the hazard maps in terms of PGA for 2 and 10% exceedance probabilities. The hazard maps in terms of S a values are also presented for 0.2 and 1.0 s, for 10% probability of exceedance (Fig. 6). The PGA and S a values are shown in Tables 2 and 3 for different return periods. Also, the PGA values for six major cities of Assam have been presented in Table 4. A comparison is done amongst the

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Fig. 4 Hazard map of PGA values for 2% probability of exceedance in 50 years

obtained results and the existing PSHA studies by various researchers, as presented in Table 5. It is observed that the PGA values vary from 0.24 to 0.57 g, and 0.16–0.33 g; for 2% and 10% probability of exceedance, respectively. The PGA spectra developed are generally considered for the design of the structures. The results show a significant variation with the other existing studies conducted for the Assam region. The major cities show higher values, indicating high seismicity concentrated around the populated areas of the state.

6 Conclusions In this study, an attempt has been made for generating the updated seismic hazard curves for Assam, NE India using PSHA approach. The hazard maps show a significant variation with the existing studies available for the region. The PGA values have been evaluated at bedrock level, for 2% and 10% exceedance probabilities in 50 years corresponding to 475 and 2475 years of return periods, respectively. The results are validated with the expected values as per the given codes for the region. These results are expected to give insight for the design of the engineering structures and future preparedness of disaster mitigation for the community. Further, site-specific studies logic tree approach is recommended for the improvement of the seismic hazard scenario of the region.

Probabilistic Seismic Hazard Assessment for Assam, North-East India

Fig. 5 Hazard map of PGA values for 10% probability of exceedance in 50 years

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Fig. 6 Hazard maps of S a values for 10% probability of exceedance in 50 years for: a t = 0.2 s and b t = 1.0 s Table 2 PGA values for obtained for Assam region

2% probability of exceedance in 50 years

0.24–0.57 g

10% probability of exceedance in 50 years

0.16–0.33 g

Probabilistic Seismic Hazard Assessment for Assam, North-East India Table 3 S a values obtained for Assam region

10% probability of exceedance in 50 years

323 t = 0.2 s

0.24–0.77 g

t = 1.0 s

0.12–0.20 g

References 1. Ameer, A.S., Sharma, M.L., Wason, H.R., Alsinawi, S.A.: Probabilistic seismic hazard assessment for Iraq using complete earthquake catalogue files. Pure Appl. Geophys. 162(5), 951–966 (2005). https://doi.org/10.1007/s00024-004-2650-y 2. Atkinson, G.M., Boore, D.M.: Earthquake ground-motion prediction equations for Eastern North America. Bull. Seismol. Soc. Am. 96(6), 2181–2205 (2006). https://doi.org/10.1785/ 0120050245 3. Bahuguna, A., Sil, A.: Comprehensive seismicity, seismic sources and seismic hazard assessment of Assam, North East India. J. Earthq. Eng. 24(2), 254–297 (2020). https://doi.org/10. 1080/13632469.2018.1453405 4. Bandyopadhyay, S., Parulekar, Y.M., Sengupta, A.: Generation of seismic hazard maps for Assam Region and incorporation of the site effects. Acta Geophys. (2022). https://doi.org/10. 1007/s11600-022-00846-z 5. Baro, O., Kumar, A., Ismail-Zadeh, A.: Seismic hazard assessment of the Shillong plateau using a probabilistic approach. Geomat. Nat. Haz. Risk 11(1), 2210–2238 (2020). https://doi. org/10.1080/19475705.2020.1833989 6. Borah, N., Kumar, A., Dhanotiya, R.: Seismic source zonation for NE India on the basis of past EQs and spatial distribution of seismicity parameters. J. Seismolog. 25(6), 1483–1506 (2021). https://doi.org/10.1007/s10950-021-10037-w 7. Chiou, B.-J., Youngs, R.R.: Update of the Chiou and Youngs NGA model for the average horizontal component of peak ground motion and response spectra. Earthq. Spectra 30(3),

Table 4 PGA values for six major cities of Assam for 10% probability of exceedance in 50 years Cities

2% probability of exceedance in 50 years (g)

10% probability of exceedance in 50 years (g)

Guwahati

0.37

0.28

Tezpur

0.38

0.28

Jorhat

0.38

0.28

Dibrugarh

0.55

0.32

Silchar

0.28

0.21

Nagaon

0.38

0.28

Table 5 Comparison of the present study with the existing studies for Assam region

Sitharam et al. Bahuguna and Present study [24] (g) Sil [3] (g) (g) 0.4–0.65 2% probability of exceedance 10% 0.2–0.36 probability of exceedance

0.44–0.77

0.24–0.57

0.16–0.33

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1117–1153 (2014). https://doi.org/10.1193/072813EQS219M 8. Das, R., Sharma, M.L., Wason, H.R.: Probabilistic seismic hazard assessment for Northeast India Region. Pure Appl. Geophys. 173(8), 2653–2670 (2016). https://doi.org/10.1007/s00024016-1333-9 9. Das, S., Gupta, I.D., Gupta, V.K.: A probabilistic seismic hazard analysis of Northeast India. Earthq. Spectra 22(1), 1–27 (2006). https://doi.org/10.1193/1.2163914 10. Gutenberg, B., Richter, C.F.: Frequency of earthquakes in California. Bull. Seismol. Soc. Am. 34, 185–188 (1944) 11. Harbindu, A., Sharma, M.L., Kamal: Stochastic ground-motion simulation of two Himalayan Earthquakes: seismic hazard assessment perspective. J. Seismol. 16(2), 345–69 (2012).https:// doi.org/10.1007/s10950-011-9247-6 12. IS-1893: Criteria for Earthquake Resistant Design of Structures, Part-1. Bureau of Indian Standards, India (2016) 13. Jena, R., Pradhan, B., Naik, S.P., Alamri, A.M.: Earthquake risk assessment in NE India using deep learning and geospatial analysis. Geosci. Front. 12(3), 101110 (2021). https://doi.org/10. 1016/j.gsf.2020.11.007 14. Kijko, A.: Estimation of the maximum earthquake magnitude, m Max. Pure Appl. Geophys. 161(8), 1655–1681 (2004). https://doi.org/10.1007/s00024-004-2531-4 15. Lin, P.-S., Lee, C.-T.: Ground-motion attenuation relationships for subduction-zone earthquakes in Northeastern Taiwan. Bull. Seismol. Soc. Am. 98(1), 220–240 (2008). https://doi. org/10.1785/0120060002 16. Mahajan, A.K., Thakur, V.C., Sharma, M.L., Chauhan, M.: Probabilistic seismic hazard map of NW Himalaya and its adjoining area, India. Nat. Hazards 53(3), 443–457 (2010). https:// doi.org/10.1007/s11069-009-9439-3 17. Nath, S.K., Raj, A., Thingbaijam, K.K.S., Kumar, A.: Ground motion synthesis and seismic scenario in Guwahati city—a stochastic approach. Seismol. Res. Lett. 80(2), 233–242 (2009). https://doi.org/10.1785/gssrl.80.2.233 18. Sarmah, T., Das, S.: Earthquake vulnerability assessment for RCC buildings of Guwahati city using rapid visual screening. Procedia Eng. 212, 214–221 (2018). https://doi.org/10.1016/j.pro eng.2018.01.028 19. Shanker, D., Sharma, M.L.: Estimation of seismic hazard parameters for the Himalayas and its vicinity from complete data files. Pure Appl. Geophys. 152(2), 267–279 (1998). https://doi. org/10.1007/s000240050154 20. Sharma, M.L.: Seismic hazard in the Northern India Region. Seismol. Res. Lett. 74(2), 141–147 (2003). https://doi.org/10.1785/gssrl.74.2.141 21. Sharma, M.L., Lindholm, C.: Earthquake hazard assessment for Dehradun, Uttarakhand, India, including a characteristic earthquake recurrence model for the Himalaya Frontal Fault (HFF). Pure Appl. Geophys. 169(9), 1601–1617 (2012). https://doi.org/10.1007/s00024-011-0427-7 22. Sharma, M.L., Malik, S.: Probabilistic seismic hazard analysis and estimation of spectral strong ground motion on bed rock in North East India (2006) 23. Sitharam, T.G., Sil, A.: Comprehensive seismic hazard assessment of Tripura and Mizoram States. J. Earth Syst. Sci. 123(4), 837–857 (2014). https://doi.org/10.1007/s12040-014-0438-8 24. Sitharam, T.G., Kolathayar, S., James, N.: Probabilistic assessment of surface level seismic hazard in India using topographic gradient as a proxy for site condition. Geosci. Front. 6(6), 847–859 (2015). https://doi.org/10.1016/j.gsf.2014.06.002 25. Uhrhammer, R.J.E.N.: Characteristics of northern and central California seismicity. Earthq. Notes 57, 21 (1986) 26. Wiemer, S.: A software package to analyze seismicity: ZMAP. Seismol. Res. Lett. 72, 373–382 (2001)

Effect of Randomness of Slip and Source Time Function on Pseudo-Dynamically Simulated Ground Motion Characteristics Vishal, J. P. Narayan, and L. Joshi

Abstract This paper presents the effects of randomization of slip and the parameters of source time function on the pseudo-dynamically simulated ground motion characteristics. In the case of numerical simulations, the radiation of seismic energy from the rupture plane as per Brune’s model as well as to avoid the coherency effects is a challenging job for the simulators. The randomization of slip, rise time, and peak time of the source time function and the rupture arrival time, as well as the incorporation of fault roughness and damage zone, play important roles in seismic energy release from the rupture plane as well as in the reduction of coherency effects on the high-frequency seismic radiations. Inversion of earthquake data or statistical analysis of dynamic rupture simulations is used to estimate the slip distribution. The statistical approach assumes that the earthquake slip follows a random distribution on the fault plane. The simulation of pseudo-dynamic ground motion has been carried out using a fourth-order accurate staggered-grid time-domain 3D finite sdifference method. The ground motions are simulated taking ten different slip patterns for a hypothetical strike-slip M w 6.0 earthquake. In addition, for each slip pattern, a stochastic perturbation in the parameters of the source time function is introduced. The simulated results have been analyzed based upon some important parameters such as arias intensity, peak ground acceleration, peak ground velocity, and peak ground displacement. Considerable variation in the computed values for the aforesaid parameters is obtained with the change of slip patterns and parameters of the STF. A good match of the computed average pseudo-spectral acceleration (PSA) using the simulated ground motion with that obtained using NGA West2 GMPEs is obtained in the frequency range 0.1–5.0 Hz and at an epicentral distance of 11 km. Keywords Pseudo-dynamic rupture · Finite difference method · 3D ground motion simulation

Vishal (B) · J. P. Narayan · L. Joshi Department of Earthquake Engineering, IIT Roorkee, Roorkee, Uttarakhand, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_26

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1 Introduction The physics-based numerical simulation of strong ground motion (SGM) characteristics is very important for site-specific SGM prediction, cost-effective earthquakeresistant designs, and assessment of vulnerability and risk. The numerical strong ground motion can be simulated using either the kinematic approach or the dynamic approach. The dynamic approach is not often used for the SGM simulations because of the lack of the required rheological and physical parameters around the source volume as well as the need for very large computational memory and speed [1–3]. However, it is possible to simulate ground motion using a kinematic approach, which is easier to implement and requires less computing power. Different kinematic dislocation models have been used in the past, such as the Haskell model [4], Brune’s model [5], and asperity and barrier models. The main difference between dynamic and kinematic simulation models of earthquake rupture is whether or not the physics of the fault breaking is taken into account. The former takes into account the physics of the fault rupturing, while the latter ties the earthquake to a certain slip distribution. In kinematic models, the rupture is described by a fault slip defined as a function of position and time. In dynamic models, the fault slip is determined dynamically, taking into account the size of the earthquake, how its focal mechanism works, and the geology of the area. Guatteri et al. [6] proposed pseudo-dynamic rupture implementation using parameters like a slip, rise time, and rupture arrival times derived from a wide range of dynamic rupture simulations [7, 8]. The pioneer research works [9, 10] explain how to improve broadband SGM using a kinematic approach. This is a mix of a deterministic approach for low-frequency (≤ 1.0 Hz) simulations and a stochastic method for high-frequency (> 1.0 Hz) simulations. Incorporating a so-called “pseudodynamic rupture” into the numerical grid is the key to getting rid of the need for a stochastic method for high-frequency ground motion [6, 7, 11, 12]. The characterization and inter-correlations of kinematic parameters in the case of pseudo-dynamic rupture are directed by the rules produced from the statistical analysis of suites of dynamic rupture simulations. These rules were constructed in order to lead the process of pseudo-dynamic rupture. The incorporation of stochastic crustal velocity perturbations [12], near-fault damage zone [13, 14], and the effects of fault roughness [15] is some of the significant recent developments that have been made in the pursuit of the reduction of coherency in the high-frequency radiations. In most of the studies incorporating the pseudo-dynamic rupture for simulation of earthquake ground motions [12, 16], the spatial and temporal distribution of slip is considered random. This randomness of slip affects simulated ground motion. Therefore, the primary objective of our research work is estimation of effect of randomization of slip on the different characteristics of earthquake ground motion. The important characteristics like peak ground acceleration, peak ground velocity, peak ground displacement, and arias intensity are considered.

Effect of Randomness of Slip and Source Time Function …

327

2 Methodology Adopted We have used the fourth-order staggered-grid viscoelastic time-domain 3D finite difference code written by [17]. The capability of this code to simulate the SGM as per physics of the rupture propagation was enhanced by [18] by means of implementation of pseudo-dynamic rupture, wherein there is stochastic perturbation of slip, rise time, and rupture arrival times correlated with slip distribution, peak time of the STF, and fault roughness [7, 9, 12, 15, 19]. First, we computed the seismic moment release for the postulated earthquake (M w 6.0) using Eq. 1. Mw =

log10 Mo − 10.71 1.5

(1)

where M w is the moment magnitude, M o is seismic moment is dyne-cm. In the next step, the dimensions of faults length and width in the strike and down dip directions are calculated using the scaling laws given by [20]. For the postulated magnitude M w = 6.0, the seismic moment, length, and width of the rupture come out to be 1.122 * 1025 dyne-cm, 14 km, and 11 km, respectively. The average slip comes out to be 0.21 m. Once the initial model parameters have been defined, the next step in the simulation process is prescribing a random spatial and temporal distribution of slip and rupture arrival times at all the point sources distributed over the rupture plane. The randomization of spatial distribution of slip on rupture plane is achieved using the methodology given by [21]. The temporal evolution of slip/slip velocity at each point source in the fault plane is assumed to be known and is described using an STF having rise time partially correlated with the slip at that particular point source. The STF used in this study is a Kostrov-like pulse proposed by [19], as given below: ⎧      t ⎪ 0 ≤ τ < τ1 ⎪ C N 0.7 − 0.7 cos πτ1t + 0.6 sin 0. 5π ⎪ τ ⎨  1     1) τ1 ≤ τ < 2τ2 s˙ (t) = C N 1.0 − 0.7 cos πτ1t + 0.3 cos π (t−τ τ2 ⎪    ⎪ ⎪ π ) (t−τ 1 ⎩ C N 0.3 + 0.3 cos 2τ1 ≤ τ < τ τ2

(2)

where s˙ (t) is the slip velocity, CN = π/(1.4π τ1 + 1.2τ1 + 0.3π τ2 ), τ is average rise time, τ1 is peak time and τ2 = τ − τ1 . The average rise time comes out to be 0.355 s. The rupture arrival times at each point source are calculated using [22] methodology after assigning the hypocenter location within the rupture plane and with a background rupture velocity equal to 80% of the local S-wave velocity to which perturbations are further added. The random distributions of parameters such as slip, rupture arrival time, and rise time are done using an amalgamation of the procedures devised by [7, 19]. The stochastic perturbations to the rupture arrival time and rise time partially correlated with the slip distribution reduce the coherency effects on the high-frequency radiations. Near fault damage zone and the effects of fault roughness have been incorporated to reduce

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the coherency effect in the radiation of high frequency (> 1.0 Hz). The two cases of peak times have been considered. In first case, the peak time has been kept constant at 0.13 times of average rise time, while perturbation in peak time has been added for the second case. The methodology developed by [23] has been used to represent the source in a deterministic manner. According to this theory, the whole rupture plane may be broken down into multiple sub-faults, and each of them can be thought of as an independent point source. On the rupture plane, there are a total of 61,600 point sources that have been taken into consideration, and the distance that separates any two consecutive point sources in either the horizontal or vertical plane is 50 m. By utilizing the STF and stress tensor components, a specific point source may be introduced into the numerical grid that constitutes the velocity-stress scheme. STF(t)∗Δt ∗ Mx x V

(3)

n STF(t)∗Δt ∗ M yy n − (σ yy )i+ 1 1 1 = σ yy i+ 21 , j+ 21 ,k+ 21 , j+ ,k+ 2 2 2 V

(4)

STF(t)∗Δt ∗ Mzz V

(5)

n n = (σx x )i+ − (σx x )i+ 1 1 , j+ 1 ,k+ 1 , j+ 1 ,k+ 1 2

2

2

2

2

2

n n (σzz )i+ = (σzz )i+ − 1 1 , j+ 1 ,k+ 1 , j+ 1 ,k+ 1 2

2

2

2

2

2

n STF(t)∗Δt ∗ Mx y (σx y )i,n j,k+ 1 = σx y i, j,k+ 1 − 2 2 V

(6)

n STF(t)∗Δt ∗ Mzy n (σzy )i+ = σzy i+ 1 , j,k − 1 , j,k 2 2 V

(7)

STF(t)∗Δt ∗ Mx z V

(8)

(σx z )i,n j+ 1 ,k = (σx z )i,n j+ 1 ,k − 2

2

where Mx x = −m o (sin δ cos γ sin 2φs + sin 2δ sin γ sin 2φs )

(9)

Mx y = m o (sin δ cos γ cos 2φs + 0.5 sin 2δ sin γ sin 2φs ) = M yx

(10)

Mx z = −m o (cos δ cos γ cos φs + cos 2δ sin γ sin φs ) = Mzx

(11)

M yy = m o (sin δ cos γ sin 2φs − sin 2δ sin γ cos 2φs )

(12)

M yz = −m o (cos δ cos γ sin φs − cos 2δ sin γ cos φs ) = Mzy

(13)

Mzz = m o (sin 2δ sin γ )

(14)

Effect of Randomness of Slip and Source Time Function … Table 1 Rheological parameters of considered layered earth model

Thickness (km)

V S (m/s)

329 V P (m/s)

Density (kg/m3 )

0–2

1500

3123

2000

2–7

2000

3742

2200

7–11

2600

4500

2400

> 11

3200

5543

2600

where δ, γ , and φs represent dip, strike, and rake angle taken as 90, 90, and 160 degree, respectively. mo is the seismic moment for each sub-fault. σ ij is the respective stress components, STF(t) is the value of the STF at time instant “t”, Δt is the time step adopted in the FD simulation. M ij is the moment tensor component as described earlier, and V is the volume of the finite difference grid used in the FD simulation. The essential wave propagation effects, which include viscoelastic damping and a heterogeneous velocity structure, are simulated by a three-dimensional finite difference algorithm, the specifics of which are outlined in detail in [17]. The model is surrounded on three sides by absorbing boundary conditions [24], and the free surface effect is simulated on the uppermost layer using a technique called VGRstress imaging [25]. Further in both the horizontal directions grid spacing was taken as 50 m to get reliable results up to 5 Hz. Finally, a time step of 0.005 s was chosen to keep in the mind the stability criteria for fourth-order finite difference simulations. Table 1 provides the velocity model used in the simulation.

3 Ground Motion Simulations In order to estimate the effect of randomization of the spatial distribution of slip, ten different PBS1-PBS10 slip models, rise time, and rupture arrival times on the rupture plane have been considered. Figure 1a–j shows the slip patterns, rise times, and rupture arrival times for the PBS1-PBS10 slip models. A considerable change in the rise time distribution can be inferred since it is correlated with the slip distribution. The rupture for the considered strike-slip earthquake extend from a depth of 1.0– 12 km. The focus of the earthquake is considered at a depth of 6.5 km and at distance of 7.0 km from the left-edge of the rupture. The right panels of Fig. 1 depict the variation of rupture arrival time accordingly. All the horizontal distances are measured with respect to the epicenter of the earthquake. All the three components of the ground motion are simulated on a circular array with its radius as 11.0 km, and center of this array is collocated with the epicenter (Fig. 2). There are 881 receiver points on the circular array at a horizontal distance of 50 m. The R1 and R441 receiver points are located on the eastern and western edge of a line passing along the diameter of the circular array, which is oriented in the EW-direction. So, R2 to R440 receiver points are on the circular array and north of this diameter oriented in EW-direction.

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Fig. 1 a–j depicts the spatial variation of slip (left panel), rise time (middle panel), and rupture arrival time (right panels) on the rupture plane of the considered PBS1-PBS10 slip models

Effect of Randomness of Slip and Source Time Function …

Fig. 1 (continued)

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Fig. 2 Sketch for a circular array around the epicenter at a distance of 11.0 km with 881 receiver points at a horizontal distance of 50 m (Note: rupture plane is oriented in the EW-direction)

The left, middle, and right panels of Fig. 3 depict the NS-, EW-, and vertical components of the simulated ground motion at 12 recording stations (R1–R441) at a horizontal distance of 2 km (at an interval of 15° in an azimuthal range − 90° to 90°). The effects of rupture directivity can be observed in the NS-component at the R1 and R441 recording points. A decrease of directivity effects can be inferred on rest of the receiver points. Also, there is more amplitude in the EW-component as compared to NS-component for receiver’s perpendicular to the rupture plane. There is a lot variation in ground motion amplitude and duration with the change of azimuth of the receiver points. The vertical components have also relatively comparable amplitude to those in the two horizontal components due to recordings made in the epicentral zone. The duration of ground motion is of the order of 12–15 s at different receiver points. Similarly, the strong ground motions are simulated on the same circular array for all the considered PBS1-PBS10 slip models.

Fig. 3 NS-(left panel), EW-(middle panel), and vertical (right panel) components of the simulated ground motion on the circular array corresponding to the PBS1 slip model

Effect of Randomness of Slip and Source Time Function …

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Fig. 4 Comparison of simulated PSA with the average of PSA obtained using NGA West2 GMPEs. Gray lines show the PSA computed for each R1–R441 using ROTD50 of NS- and EW-components

3.1 Validation of Pseudo-Dynamic Rupture Implementation The NGA West2 GMPEs are well constrained for epicentral distances less than 20–30 km for the moderate earthquakes (M6.0–M6.5) due to an adequate number of earthquake records [26–29]. We computed pseudo-spectral acceleration (PSA) with 5% damping for both the horizontal components at each receiver points with epicentral distances 11 km after band-pass filtering in a frequency bandwidth of 0.1– 5.0 Hz. Figure 4 shows the median of pseudo-spectral acceleration (PSA) obtained using average of NGA West2 GMPEs (black color) with standard deviation (red and blue) in a period range of 0.2–10 s. The gray lines indicate the PSA for all the R1– R441, while green line indicates the average PSA of 441 receivers. The median of PSA using NS- and EW-components of each receiver is obtained using ROTD50 as described by [30]. The analysis of Fig. 4 shows that the average PSA for all receivers matches very well with the results derived using NGA West2 GMPEs. It validates the efficiency of code in simulating the strong ground motion. The good matching of the obtained average PGA as 0.13 g with that computed (0.11 g) using NGA West2 GMPEs further validates the capability of the enhanced 3D FD code by [18] for the broadband ground motion.

3.2 Effect of Randomization of Slip Pattern Figure 5 shows the variation of peak ground acceleration (PGA) in the NS-, EW-, and vertical components in the case of used slipping PBS1-PBS10 models. The obtained range of PGA is 0.05–0.33 g, 0.07–0.44 g, 0.06–0.35 g, 0.06–0.35 g, 0.08–0.51 g, 0.06–0.36 g, 0.07–0.36 g, 0.07–0.45 g, 0.07–0.48 g, and 0.06–0.38 g in the case of PBS1-PBS10 slip models, respectively. Similarly, the obtained average PGA is 0.13 g, 0.13 g, 0.11 g, 0.11 g, 0.14 g, 0.11 g, 0.12 g, 0.12 g, 0.15 g, and 0.11 g in the case of PBS1-PBS10 slip models, respectively. Although, the computed PGA at different recording stations is highly variable with the change in the slip pattern, but,

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Fig. 5 Variation of PGA in the NS-component (left), EW-component (middle), and vertical component (right) with receiver positions

in most of the considered slip models, there is good match of the average PGA with that computed using NGA West2 GMPEs. However, the obtained up to 30% larger average PGA in some of the slip models may be due to the rupture directivity effect. Similarly, Figs. 6 and 7 show the variation of peak ground velocity (PGV), peak ground displacement (PGD), Arias intensity for NS-, EW-, and vertical components for PBS1–PBS10 slip models, respectively. The analysis of these results shows that the spatial variation PGV and PGD also match with the variation of PGA. The obtained very large PGV of the order of 0.26–0.40 m/s in the NS-component at receivers R1 and R441 in the different slip models as compared to that computed as 0.04 m/s using NGA West2 GMPEs also validates the efficacy of numerical modeling to simulate the rupture directivity effects as per the physics of the rupture propagation. Figure 8 depicts the variation of Arias intensity for the NS-, EW-, and vertical components for the PBS1-PBS10 slip model models, respectively. The obtained Arias intensity is very high at receivers R1 and R441 due to rupture directivity effects in the NS-component.

Fig. 6 Variation of PGV in the NS-component (left), EW-component (middle), and vertical component (right) with receiver positions

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Fig. 7 Variation of PGD in the NS-component (left), EW-component (middle), and vertical component (right) with receiver positions

Fig. 8 Diagram showing the variation of Arias intensity for NS-component (left), EW component (middle), and vertical component (right) with receivers

4 Conclusions A good match of the computed average PSA in a period range of 0.2–10 s. with NGA West2 GMPEs for a hypothetical strike-slip earthquake of M w = 6.0 reveals the efficiency of 3D FD code to appropriately radiate seismic energy in a broadband and avoids the occurrence of coherency effect on the high-frequency radiation [12, 15]. The obtained very large PGV of the order of 0.26–0.40 m/s in the NS-component at receivers R1 and R441 in different slip models as compared to that computed as 0.04 m/s using NGA West2 GMPEs also validates the efficacy of numerical modeling to simulate the rupture directivity effects as per the physics of the rupture propagation. It also validates the implementation of strike-slip rupture in this study. A lot of variation in the ground motion characteristics as well as PGA, PGV, PGD, and the Arias intensity was observed with the change of slip pattern. For example, the obtained range of PGA was 0.05–0.33 g, 0.07–0.44 g, 0.06–0.35 g, 0.06–0.35 g, 0.08–0.51 g, 0.06–0.36 g, 0.07–0.36 g, 0.07–0.45 g, 0.07–0.48 g, and 0.06–0.38 g for the PBS1-PBS10 slip models, respectively. Similarly, the obtained average PGA was 0.13 g, 0.13 g, 0.11 g, 0.11 g, 0.14 g, 0.11 g, 0.12 g, 0.12 g, 0.15 g, and 0.11 g in the case of PBS1-PBS10 slip models, respectively. Similarly, a range of variations in the

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PGV, PGD, and the Arias intensity was obtained with the change of slip pattern. The obtained average PGA in the different slip models is either comparable or somewhat larger than that obtained using NGA West2 GMPEs (0.11 g). This finding reflects that slip pattern plays an important role in the spatial variation of ground motion characteristics and the engineering parameters.

References 1. Day, S.M.: Three dimensional simulation of spontaneous rupture: the effect of non-uniform prestress. Bull. Seismol. Soc. Am. 72(6), 1881–1902 (1982) 2. Oglesby, D.D., Day, S.M.: Stochastic fault stress: implications for fault dynamics and ground motion. Bull. Seismol. Soc. Am. 92(8), 3006–3021 (2002). https://doi.org/10.1785/012001 0249 3. Ripperger, S., Mai, P.M., Ampuero, J.P.: Variability of near-field ground motion from dynamic earthquake rupture simulations. Bull. Seismol. Soc. Am. 98(3), 1207–1228 (2008). https://doi. org/10.1785/0120070076 4. Haskell, A.: Total energy and energy spectral density of elastic wave radiation from propagating faults. Bull. Seismol. Soc. Am. 54(6), 1811–1841 (1964) 5. Brune, J.N.: Tectonic stress and the spectra of seismic shear waves from earthquakes. J. Geophys. Res. 75(26), 4997–5009 (1970) 6. Guatteri, M., Mai, P.M., Beroza, G.C.: A pseudo-dynamic approximation to dynamic rupture models for strong ground motion prediction. Bull. Seismol. Soc. Am. 94(6), 2051–2063 (2004). https://doi.org/10.1785/0120040037 7. Schmedes, J., Archuleta, R.J., Lavallée, D.: Correlation of earthquake source parameters inferred from dynamic rupture simulations. J. Geophys. Res. Solid Earth 115(3), 1–12 (2010). https://doi.org/10.1029/2009JB006689 8. Song, S.G., Somerville, P.: Physics-based earthquake source characterization and modeling with geostatistics. Bull. Seismol. Soc. Am. 100(2), 482–496 (2010). https://doi.org/10.1785/ 0120090134 9. Graves, R.W., Pitarka, A.: Broadband ground-motion simulation using a hybrid approach. Bull. Seismol. Soc. Am. 100(5A), 2095–2123 (2010). https://doi.org/10.1785/0120100057 10. Graves, R., Pitarka, A.: Refinements to the Graves and Pitarka (2010) broadband ground-motion simulation method. Seismol. Res. Lett. 86(1), 75–80 (2015). https://doi.org/10.1785/022014 0101 11. Mena, B., Dalguer, L.A., Mai, P.M.: Pseudodynamic source characterization for strike-slip faulting including stress heterogeneity and super-shear ruptures. Bull. Seismol. Soc. Am. 102(4), 1654–1680 (2012). https://doi.org/10.1785/0120110111 12. Graves, R., Pitarka, A.: Kinematic ground-motion simulations on rough faults including effects of 3D stochastic velocity perturbations. Bull. Seismol. Soc. Am. 106(5), 2136–2153 (2016). https://doi.org/10.1785/0120160088 13. Cochran, E.S., Li, Y.G., Shearer, P.M., Barbot, S., Fialko, Y., Vidale, J.E.: Seismic and geodetic evidence for extensive, long-lived fault damage zones. Geology 37(4), 315–318 (2009). https:// doi.org/10.1130/G25306A.1 14. Ben-Zion, Y., et al.: Basic data features and results from a spatially dense seismic array on the San Jacinto fault zone. Geophys. J. Int. 202(1), 370–380 (2015). https://doi.org/10.1093/gji/ ggv142 15. Mai, P.M., Galis, M., Thingbaijam, K.K.S., Vyas, J.C., Dunham, E.M.: Accounting for fault roughness in pseudo-dynamic ground-motion simulations. Pure Appl. Geophys. 174(9), 3419– 3450 (2017). https://doi.org/10.1007/s00024-017-1536-8

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16. Pitarka, A., Akinci, A., De Gori, P., Buttinelli, M.: Deterministic 3D ground-motion simulations (0–5 Hz) and surface topography effects of the 30 October 2016 Mw 6.5 Norcia, Italy, Earthquake. Bull. Seismol. Soc. Am. 112(1), 262–286 (2022). https://doi.org/10.1785/012021 0133 17. Narayan, J.P., Sahar, D.: Three-dimensional viscoelastic finite-difference code and modelling of basement focusing effects on ground motion characteristics. Comput. Geosci. 18(6), 1023–1047 (2014). https://doi.org/10.1007/s10596-014-9442-y 18. Joshi, L.: Quanti cation of Site City Interaction Effects on Responses of Buildings and Basin Under Realistic Earthquake Loading for Development of Economic Smart City (2021) 19. Liu, P., Archuleta, R.J., Hartzell, S.H.: Prediction of broadband ground-motion time histories: hybrid low/high-frequency method with correlated random source parameters. Bull. Seismol. Soc. Am. 96(6), 2118–2130 (2006). https://doi.org/10.1785/0120060036 20. Thingbaijam, K.K.S., Mai, P.M., Goda, K.: New empirical earthquake source-scaling laws. Bull. Seismol. Soc. Am. 107(5), 2225–2246 (2017). https://doi.org/10.1785/0120170017 21. Mai, P.M., Beroza, G.C.: A spatial random field model to characterize complexity in earthquake slip. J. Geophys. Res. Solid Earth 107(B11), ESE 10-1–ESE 10-21 (2002). https://doi.org/10. 1029/2001jb000588 22. Afnimar, Koketsu, K.: Finite difference traveltime calculation for head waves travelling along an irregular interface. Geophys. J. Int. 143(3), 729–734 (2000). https://doi.org/10.1046/j.1365246X.2000.00269.x 23. Hartzell, S.H., Heaton, T.H.: Waveform data for the fault rupture history of the 1979. Bull. Seismol. Soc. Am. 73(6), 1553–1583 (1983) 24. Kumar, S., Narayan, J.P.: Absorbing boundary conditions in a fourth-order accurate SH-wave staggered grid finite difference algorithm. Acta Geophys. 56(4), 1090–1108 (2008). https:// doi.org/10.2478/s11600-008-0043-9 25. Narayan, J.P., Kumar, S.: A fourth order accurate SH-wave staggered grid finite-difference algorithm with variable grid size and VGR-stress imaging technique. Pure Appl. Geophys. 165(2), 271–294 (2008). https://doi.org/10.1007/s00024-008-0298-8 26. Boore, D.M., Stewart, J.P., Seyhan, E., Atkinson, G.M.: NGA-West2 equations for predicting PGA, PGV, and 5% damped PSA for shallow crustal earthquakes. Earthq. Spectra 30(3), 1057–1085 (2014). https://doi.org/10.1193/070113EQS184M 27. Campbell, K.W., Bozorgnia, Y.: Campbell-bozorgnia NGA-West2 horizontal ground motion model for active tectonic domains. Earthq. Spectra 30, 1087–1115 (2014). https://doi.org/10. 4231/D3MS3K235 28. Chiou, B.S.J., Youngs, R.R.: Update of the Chiou and Youngs NGA model for the average horizontal component of peak ground motion and response spectra. Earthq. Spectra 30(3), 1117–1153 (2014). https://doi.org/10.1193/072813EQS219M 29. Idrissa, I.M.: An NGA-West2 empirical model for estimating the horizontal spectral values generated by shallow crustal earthquakes. Earthq. Spectra 30(3), 1155–1177 (2014). https:// doi.org/10.1193/070613EQS195M 30. Boore, D.M.: Orientation-independent, nongeometric-mean measures of seismic intensity from two horizontal components of motion. Bull. Seismol. Soc. Am. 100(4), 1830–1835 (2010). https://doi.org/10.1785/0120090400

Quantification of Ridge-Weathering Effects on the Simulated Ground Motion Characteristics Across 2D and 3D Topography Models Vishal and J. P. Narayan

Abstract This paper quantifies the impact of ridge-weathering on the simulation of ground motion across 2D and 3D topographical models. The models are excited with plane wave-fronts of Gabor wavelet. The seismic responses of a 3D ellipsoidal ridge topography having shape ratio 1.0 and one of its cross-Sect. (2D model) are simulated for different thickness and velocity of weathered layer. The SH- and SVwaves responses of a 2D cross-section of 3D topography are computed to understand the effect of dimensionality on the amplification pattern. The analysis of simulated responses reveals very large spectral amplifications and ASA in the case of 3D topography as compared to 2D. An increase of weathering effect on ground motion is inferred with an increase of weathering-thickness and a decrease of weathering velocity. A considerable variation of topography amplification with elevation is obtained. Finally, it is concluded that the shape of ridge topography and rheological parameters of the weathering layer should be taken in to account for the quantification of topographical effects on the ground motion characteristics. Keywords 3D topography effect · Finite-difference method · Azimuth effect · Weathering effect

1 Introduction Increasing constructions on higher topographies have prompted the focus of seismologists and earthquake engineers on the measurement of impacts of surface topography on the ground motion characteristics. In addition, essential infrastructure design must explicitly account for the amount of variation in ground motion characteristics caused by topography. Records of ground motion, studies of damage patterns from earlier earthquakes, and numerical modeling have all shed light on the topography amplifications. For example, exceptionally high accelerations of 1.25 g at the Pacoima Dam [1] Vishal (B) · J. P. Narayan Department of Earthquake Engineering, IIT Roorkee, Roorkee, Uttarakhand, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_27

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and 1.78 g at Tarzana hill were reported during the 1971 San Fernando earthquake and the 1994 Northridge earthquake, respectively [2]. In addition during the 1985 Chile Earthquake [3], the 1987 Superstition Hill Earthquake [4], the 1987 Whitter Narrows Earthquake [5], 2015 Gorkha Nepal Earthquake [6], and 2016 Norcia Italy M w 6.5 Earthquake [7], considerable damages were reported on the elevated topographies. It has been noticed that in the frequency range of earthquake engineering interest, numerically anticipated topographic amplifications rarely exceeded 3.0 [8–10]. In addition, earthquake data show that topographic amplifications of more than 10 have occurred [8, 10–12]. Possible reasons for this difference include using 2D models instead of 3D models, homogeneous ridges instead of heterogeneous ridges, ground motion records near the base of the hill as a reference site for calculating spectral amplifications, and the simulations not taking into account the topography of the area. Also, it has been observed that there are no thick soil deposits in the hilly region. But, there is a thin weathered layer. This layer of weathered rock acts as a low velocity layer and induces variation in ground motion as of topography-induced amplification and de-amplification and also the fundamental frequency of topography [11–14]. The primary objective of this study is to contribute to a better understanding of the impact of thickness and velocity of the weathering layer on 2D and 3D topography on the earthquake ground motion. The simulation of 2D (a cross-section of the same 3D topography) and 3D seismic responses of topography with various thickness and velocity of weathered layer has been carried out. Attempts have been made to evaluate the effect of model dimension by comparing 2D and 3D topographic amplification patterns.

2 Methodology Adopted The 2D and 3D seismic responses of the topographical models have been simulated using fourth-order staggered-grid viscoelastic SH-wave, P-SV-wave, and 3D finite-difference (FD) programs written by Narayan and his co-researchers [15– 17], respectively. These programs are effective enough to provide frequency and phase-dependent damping in the time-domain simulations utilizing the GMB-EK rheological model [18, 19]. Material independent anelastic function was employed because it is more appropriate for the air-rock discontinuities in the FD grid [17, 19]. Table 1 provides the P- and S-wave velocities and quality factors at reference frequency 1.0 Hz and density (ρ) for viscoelastic homogeneous rock and air. As a free surface boundary condition, an enhanced vacuum formulation proposed by [20] is adopted. In this improved vacuum formulation, the harmonic and arithmetic means of unrelaxed moduli and density, respectively, are employed. The sponge absorbing boundary condition was put on the model’s bottom, side edges, and faces to stop edge reflections [21, 22].

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Table 1 Rheological characteristics for the viscoelastic rock and air Materials

Velocities and quality factors at F R = 1.0 Hz V P (m/s)

V S (m/s)

QP

Density QS

ρ (kg/m3 )

Air

320

0

32

0

1.29

Rock

2600

1500

260

150

2000

3 Geometry of Models In order to study the impacts of weathering on seismic responses of 2D and 3D ridge topography, 10 different models of 3D ellipsoidal topography have been considered. The SH- and SV-wave responses of a 2D plane passing through the NS-axis of 3D topography have also been computed and analyzed. Figure 1 depicts the sketches for 3D ellipsoidal ridge models with base width 1000 m and height 500 m (shape ratio = 1). The velocity of the weathered layer has been varied between 500 m/s to 1100 m/s, with an interval of 150 m/s. The thickness of the weathered layer is taken as 5 m (WERM1-WERM5) and 10 m (WERM6-WERM10) for the 3D ellipsoidal topography. The details of these models are given in Tables 2 and 3. The thickness of the weathering layer at the base is assumed to be twice the thickness at the top of the topography. The effect of 3D topography model without weathering is also estimated and named as ERM model.

Fig. 1 Sketch for ellipsoidal ridge model along with NS- and EW-receiver arrays and point sources at a depth of 1535 m to generate a plane S-wave front propagating vertically upward

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Table 2 Velocity, density, and thickness of considered weathered ridge models WERM1-WERM5 Name of models V s (weathering) (m/s) Density

(kg/m3 )

Thickness (m)

WERM1

WERM2

WERM3

WERM4

WERM5

500

650

800

950

1100

1600

1660

1720

1780

1840

5

5

5

5

5

Table 3 Velocity, density, and thickness of considered weathered ridge models WERM6-WERM10 Name of models V s (weathering) (m/s) Density (kg/m3 ) Thickness (m)

WERM6

WERM7

WERM8

WERM9

WERM10

500

650

800

950

1100

1600

1660

1720

1780

1840

10

10

10

10

10

The axis of the ridge is used as a reference point for measuring horizontal distances. The vertical distances are measured with respect to the free surface level. In both, the horizontal directions grid spacing was taken as 5 m. However, in the vertical direction, grid spacing was taken 5 m from the top of model to a depth of 45 m below the free surface and 10 m thereafter. To prevent stability problems, a time step of 0.0005 s was chosen. A plane wave front of S-wave was generated in the FD grid employing 1000 point sources placed 5 m apart in a horizontal plane at a depth of 1535 m. A particular point source for a particular polarization of the S-wave was generated using shear stress in the form of Gabor wavelet. For example, to generate a S-wave with NS-polarization, shear stress σ X Z was used to generate all the 1000 point sources. The dominant frequency of Gabor wavelet was taken as 4.0 Hz with a frequency bandwidth of 0.1–10 Hz. All the three components of the seismic response on the NS- and EW-arrays extending from 850 m left to 850 m right of the ridge axis along the flanks of the topography and with equidistant 341 receiver points (5 m horizontally apart) were simulated (Fig. 1). The seismic response of homogeneous half-space model on a rectangular array with 5 m spacing between two consecutive receiver points was also simulated to use it as reference ground motion to quantify the topography effects.

4 Seismic Response of 2D Cross-Section of 3D Topography 4.1 SH-Wave Response In this section, the effect of 2D elliptical cross-section of the 3D ellipsoidal ridge models on the SH-wave characteristics has been calculated. Figure 2a, b shows the change of average spectral amplification (ASA) with varying velocity and thickness

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Fig. 2 Diagram showing variation of ASA in the case of 2D equivalent to a cross-section of a WERM1-WERM5, b WERM6-WERM10 models in low-frequency range (0.1–1.5 Hz) (left) and high-frequency range (1.5–10.0 Hz) (right) with SH-wave as input motion

of the weathered layer in low-frequency (0.1–1.5) and high-frequency ranges (1.5– 10 Hz). It is clear that in both low- and high-frequency range, ASA is increasing with decrease in the velocity and an increase in thickness of the weathered layer. The effect of weathering is more in the high-frequency range where the wavelength of wave is comparable or less than the width of base of topography.

4.2 SV-Wave Response The variation of ASA for the horizontal and vertical components of the SV-wave has been shown in Figs. 3 and 4, respectively. It has been observed that in the high-frequency range, there is an increase in ASA with increase in thickness and decrease in velocity of weathering. However, in the low-frequency range, there is

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Fig. 3 Variation of ASA of the horizontal component for the 2D equivalent of a WERM1-WERM5, b WERM6-WERM10 models in low-frequency range (0.1–1.5 Hz) and high-frequency range (1.5– 0.0 Hz) for SV-wave as input motion

no substantial changes in ASA. It is interesting to note that the amplification in the vertical component is more than the horizontal component in high-frequency range near the base of the 2D ridge.

5 Effect of 3D Topography In this section, the effect of 3D weathered ellipsoidal topography on the ground motion characteristics has been calculated. The WERM1-WERM10 models with specification as shown in Table 3 have been considered. The models have been excited with the plane S-wave front having NS-polarity. Figure 5 shows the variation of ASA for the NS-component of EW-array of in the case of WERM1-WERM10 models. This is very much similar to that of the SH-wave response. ASA is showing an

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Fig. 4 Variation of ASA of the vertical component of the 2D equivalent of a WERM1-WERM5, b WERM6-WERM10 models in low-frequency range (0.1–1.5 Hz) and high-frequency range (1.5– 0.0 Hz) for SV-wave as input motion

increase with decrease in velocity of weathered layer along the slope of topography. Further decrease in ASA for both low- and high-frequency range has been observed up to 200 m from top of topography. Overall, a correlation between the thickness of the weathering layer and the ASA has been detected. Figures 6 and 7 show the variation of ASA for the NS-component and vertical component of receivers along the NS-array of on the WERM1-WERM10 models. This is very much similar to that of the SV-wave response. The same pattern as in the NS-component of the EW-array has been observed. But, the amplitude is less in this case. The increase in ASA with decrease in velocity of weathered layer has been observed along slope of topography. In addition, there is a significant rise in ASA in the vertical component on the NS-array receivers, especially around the base of the ridges. A comparison of Figs. 5 and 6 shows the notable variation in ASA from top toward the base. Although, it is same at the top of ridge in both the cases, but it is somewhat larger in Fig. 6.

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Fig. 5 Variation of ASA for the NS-component of the EW-array of a WERM1-WERM5, b WERM6-WERM10 models in low-frequency range (0.1–1.5 Hz) and high-frequency range (1.5–10.0 Hz) for plane S-wave with NS-polarity

6 Comparison of 2D and 3D Weathering Effect In this section, a comparison of 2D and 3D average weathering effect (AWE) due to weathering effect in high-frequency range is presented. AWE is obtained by dividing the ASA obtained for weathered models (WERM1-WERM10) to without weathered model (ERM). Figure 8a, b show the obtained AWE for the SH-wave response of an elliptical ridge and the NS-component of an elliptical ridge response along the EW-array, respectively. The weathering is aggravating the topographic effects from the base to the top of the 2D ridge. In contrast to this, it is aggravating topography effects only near the base of the 3D topography. Figure 9a, b show the AWE for the horizontal components of the SV-wave response of a 2D ridge and the NS-component of the response of the 3D ridge along the NS-array, respectively. Similarly, in Fig. 10a, b the AWE for the vertical component of the SV-wave response of the 2D topography

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Fig. 6 Variation of ASA for the NS-component of the NS-array of a WERM1-WERM5, b WERM6-WERM10 models in low-frequency range (0.1–1.5 Hz) and high-frequency range (1.5–10.0 Hz) for plane S-wave with NS-polarity

and the vertical component of the response of the 3D topography along the NSarray. The analysis of these figures also reveals that weathering is aggravating the topography effects from the base to the top of the 2D ridge, and in contrast to this, it is aggravating 3D topography effects only near the base. Further, the AWE increases with the decrease in velocity of the weathering layer and increases with the increase in thickness. Furthermore, in the case of 3D topography, a de-amplification near the top of the ridge can be inferred. It is important to note that the AWE is greater in vertical component than in the horizontal component.

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Fig. 7 ASA obtained for the vertical component of NS-array of a WERM1-WERM5, b WERM6WERM10 models in low-frequency range (0.1–1.5 Hz) and high-frequency range (1.5–10.0 Hz) for plane S-wave with NS-polarity

7 Conclusions The analysis of the simulated results reveals that the dimensionality of the topography plays an important role in the amplitude amplification and de-amplification patterns along the flanks of the ridge. The obtained ASA in the case of 3D ellipsoidal topography is more as compared to 2D elliptical topography. Further, the increase of thickness and decrease of velocity of the weathered layer increases the topography

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Fig. 8 Comparison of AWE obtained for a SH-wave response, b NS-component of 3D response along EW-array for the WERM1-WERM5 (left) and WERM6-WERM10 (right) models

Fig. 9 Comparison of AWE obtained for a horizontal component of SV-wave response, b NScomponent of 3D response along NS-array for the WERM1-WERM5 (left) and WERM6-WERM10 (right) models

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Fig. 9 (continued)

Fig. 10 Diagram showing the comparison of AWE for a vertical component of SV-wave response, b vertical component of 3D response along NS-array for the WERM1-WERM5 (left) and WERM6WERM10 (right) models

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effects. In the low-frequency range, where the wavelength of the wave exceeds the width of the topography, the effect of weathering is not very prominent. While, in the case of high frequency range, the effect of weathering is significant. SH-wave horizontal component amplification is greater than SV-wave horizontal component amplification due to mode conversion in SV-wave response. The computed AWE reflects that weathering aggravates the topographic amplification throughout the 2D topography. But, it aggravates the topographic amplification only near the base of the 3D topography. An increase of AWE with a decrease of weathering velocity is obtained. It is concluded that the consideration of weathering effects is very important for an accurate estimation of the topographic amplification.

References 1. Trifunac, M.: Scattering of plane SH waves by a SemiCylindrical rigid foundation seismic energy distribution during soil-structure interaction view project. Earthq. Eng. Struct. Dyn. 1, 267–281 (1973) 2. Spudich, P., Hellweg, M., Lee, W. H. K.: Directional topographic site response at Tarzana observed in aftershocks of the 1994 Northridge, California, earthquake: implications for mainshock motions. Bull. Seismol. Soc. Am. 86(1 SUPPL. B) (1996) 3. Celebi, M.: Topographical and geological amplifications determined from strong-motion and aftershock records of the 3 march 1985 chile earthquake (1987) 4. Çelebi, M.: Topographical and geological amplification: case studies and engineering implications. Struct. Saf. 10(1–3), 199–217 (1991). https://doi.org/10.1016/0167-4730(91)900 15-2 5. Kawase, H., Aki, K.: Topography effect at the critical SV-wave incidence: possible explanation of damage pattern by the Whittier narrows, California, earthquake of 1 October 1987. Bull. Seismol. Soc. Am. 80(1) (1990) 6. Wang, F., et al.: Effects of topographic and geological features on building damage caused by 2015.4.25 Mw7.8 Gorkha earthquake in Nepal: a preliminary investigation report. Geoenviron. Disasters 3(1) (2016). https://doi.org/10.1186/s40677-016-0040-2 7. Pitarka, A., Akinci, A., De Gori, P., Buttinelli, M.: Deterministic 3D ground-motion simulations (0–5 Hz) and surface topography effects of the 30 October 2016 Mw 6.5 Norcia, Italy, Earthquake. Bull. Seismol. Soc. Am. 112(1), 262–286 (2022). https://doi.org/10.1785/012021 0133 8. Geli, L., Bard, P.-Y.: The effect of topography on earthquake ground motion: a review and new results (1988) 9. Poursartip, B.: Topographic Amplification of Seismic Motion. University of Texas at Austin (2017) 10. Massa, M., Barani, S., Lovati, S.: Overview of topographic effects based on experimental observations: meaning, causes and possible interpretations. Geophys. J. Int. 197(3), 1537–1550 (2014). https://doi.org/10.1093/gji/ggt341 11. Lee, S.J., Komatitsch, D., Huang, B.S., Tromp, J.: Effects of topography on seismic-wave propagation: an example from Northern Taiwan. Bull. Seismol. Soc. Am. 99(1), 314–325 (2009). https://doi.org/10.1785/0120080020 12. Grelle, G., et al.: Topographic effects observed at Amatrice hill during the 2016–2017 Central Italy seismic sequence. Earthq. Eng. Eng. Vib. 20(1), 63–78 (2021). https://doi.org/10.1007/ s11803-021-2005-z 13. Narayan, J.P., Prasad Rao, P.V.: Two and half dimensional simulation of ridge effects on the ground motion characteristics. Pure Appl. Geophys. 160(8), 1557–1571 (2003). https://doi. org/10.1007/s00024-003-2360-x

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14. Narayan, J.P., Kumar, V.: A numerical study of effects of ridge-weathering and ridge-shaperatio on the ground motion characteristics. J. Seismol. 19(1), 83–104 (2014). https://doi.org/ 10.1007/s10950-014-9452-1 15. Narayan, J.P., Kumar, V.: A fourth-order accurate finite-difference program for the simulation of SH-wave propagation in heterogeneous viscoelastic medium. Geofizika 30(2) (2013) 16. Narayan, J.P., Kumar, V.: Study of combined effects of sediment rheology and basement focusing in an unbounded viscoelastic medium using P-SV-wave finite-difference modelling. Acta Geophys. 62(6), 1214–1245 (2014). https://doi.org/10.2478/s11600-013-0199-9 17. Narayan, J.P., Sahar, D.: Three-dimensional viscoelastic finite-difference code and modelling of basement focusing effects on ground motion characteristics. Comput. Geosci. 18(6), 1023–1047 (2014). https://doi.org/10.1007/s10596-014-9442-y 18. Emmerich, H., Korn, M.: Incorporation of attenuation into time-domain computations of seismic wave fields. Geophysics 52(9), 1252–1264 (1987). https://doi.org/10.1190/1.1442386 19. Kristek, J., Moczo, P.: Seismic-wave propagation in viscoelastic media with material discontinuities: a 3D fourth-order staggered-grid finite-difference modeling. Bull. Seismol. Soc. Am. 93(5), 2273–2280 (2003). https://doi.org/10.1785/0120030023 20. Zeng, C., Xia, J., Miller, R.D., Tsoflias, G.P.: An improved vacuum formulation for 2D finite-difference modeling of Rayleigh waves including surface topography and internal discontinuities. Geophysics 77(1) (2012). https://doi.org/10.1190/geo2011-0067.1 21. Israeli, M., Orszag, S.A.: Approximation of radiation boundary conditions. J. Comput. Phys. 41(1), 115–135 (1981). https://doi.org/10.1016/0021-9991(81)90082-6 22. Narayan, J.P., Kumar, S.: A fourth order accurate SH-wave staggered grid finite-difference algorithm with variable grid size and VGR-stress imaging technique. Pure Appl. Geophys. 165(2), 271–294 (2008). https://doi.org/10.1007/s00024-008-0298-8

Developing a Comprehensive Historical Tsunami Database for the Indian Ocean Nazeel Sabah and Daya Shanker

Abstract The Indian Ocean is one of the most active tsunamigenic zones on the earth. The destructive tsunami of 26th December, 2004 and 28th September, 2018 are standing testimonies of the tsunamicity in the region. The occurrence of two strong tsunami in consecutive decades is a driving force behind the compilation of a comprehensive historical catalog for the region. Unlike the other tsunamigenic regions of the earth, there exist no historical tsunami catalog in a concretized form for the Indian Ocean. The importance of this study is that it gives impetus toward developing a probabilistic tsunami hazard analysis methodology for long-term risk reduction against tsunami. For this study, the geographic area between 45° S–27° N and 24° E–120° E has been considered as the extent of the Indian Ocean. All the significant tsunami events from 416 AD till 2021 have been collected from various online catalogs globally. A few regional catalogs have also been referred to. All magnitudes of tsunamigenic earthquakes are homogenized into moment magnitude scale (M W ) using global correlation equations. Statistical regression tools like standard linear regression (SLR), inverse standard linear regression (ISR), and orthogonal standard regression (OSR) are used to developed correlation between tsunami magnitude (M t ) and earthquake magnitude (M w ). The missing data on tsunami intensity of several events are calculated on the Soloviev scale using already existing relations. An updated catalog for tsunami events of the Indian Ocean is hence developed which fills several missing values in the already existing catalog. The catalog so compiled is presented on a webpage. Keywords Tsunami catalog · Magnitude homogenization · Tsunami intensity · Tsunami magnitude

N. Sabah (B) · D. Shanker Indian Institute of Technology, Roorkee, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_28

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1 Introduction The Indian Ocean region has experienced around 200 notable tsunami from 416 A.D. till date killing several thousands [1]. The Indian Ocean region bound between 45° S–27° N and 24° E–120° E [2] is one of the most active tsunamigenic zones on earth contributing to about 5% of global tsunamicity. There exists no well documented catalog for the Indian Ocean region unlike the other tsunamigenic zones. Also, the existing global catalogs of [1, 3] have a number of missing entries for the Indian Ocean region. This paper deals with improving the quality of the existing catalogs by completing the missing values wherever possible. 194 tsunami events were fetched from [1, 3, 4] for the Indian Ocean region between 416 A.D. till date for the Indian Ocean region. Figure 1 presents a color-coded plot epicentral locations of tsunamigenic earthquakes between 416 A.D. till 2021. The magnitudes of tsunamigenic earthquakes are homogenized using the existing global correlation equations of [5, 6]. There exists no correlation equations between real magnitude-type tsunami scale (M t ) as proposed by [7, 8] and moment magnitude (M W ) of tsunamigenic earthquakes. Here, we develop a correlation between M t and M W using statistical regression tools, viz. standard linear regression (SLR), inverse standard linear regression (ISR), and orthogonal standard regression (OSR). All these models are compared, and the best suited regression model is chosen based on coefficient of determination (R2 ).

Fig. 1 Historical locations of tsunamigenic earthquakes in the Indian Ocean between 416 A.D. till 2021 with magnitudes ranging from 4.5 to 9.2 MW. Developed using [9] and data obtained from [1, 3]

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There exist several intensity-magnitude relationships for earthquake events. But there is only a little work done to link the tsunami intensity with earthquake magnitude. Tsunami magnitude is expressed generally on Soloviev-Imamura scale [10]. In this study, we find the missing Soloviev-Imamura tsunami intensity of tsunami events in the existing global catalog. The formula suggested by [11] is used to predict tsunami intensity from the moment magnitude of earthquake (M W ). In this study, the existing catalog is improved by filling in the missing values either using the developed conversion equations or the existing predictive relations. The updated catalog so compiled is updated on a webpage for public access. The catalog can be viewed on the following link: https://sites.google.com/eq.iitr.ac.in/comprehensive-tsunami-io.

2 Magnitude Homogenisation There are several global and regional equations to convert magnitude from one scale to another. Different historical tsunami catalog presents the magnitude data in different units. Since, moment magnitude (M W ) is the most scientific measure of earthquake magnitude which depends on the seismic moment generated at the source, all the magnitudes of tsunamigenic earthquakes are converted into moment magnitude. The two most widely used magnitude homogenization equations are the ones suggested by [5, 12]. In this study, we use the relations suggested by [12] for magnitude homogenization since it is more recent one and is developed by incorporating a larger number of data into regression models (Eq. 1). Also, it is found to have a better accuracy than other homogenization equations. MW = 1.06(±0.0002)M S − 0.38(±0.006) 6.2 ≤ M S ≤ 8.4, R 2 = 0.89

(1)

Of all the 194, tsunami events extracted from various catalogs for the Indian Ocean, only 63 events had magnitudes presented on MW scale. Using Eq. 1, the missing moment magnitude data for 112 events have been calculated and updated on the Webpage mentioned in the introduction.

3 Calculation of Tsunami Magnitude (Mt ) Abe [7, 8] had developed a realistic scale for tsunami magnitude, which depends on the instrumental records of tsunami height. The readings of coastal tide-gages were used to read the maximum and average amplitudes of tsunami waves [10]. The moment magnitude of tsunami is calculated mathematically as per Eq. 2. Mt = a log H + b log R + D

(2)

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where, H

Highest amplitude of tsunami wave (in meters) as obtained from tide gage. R Distance to epicenter (in km). a, b, and D Constants determined to make M t scale closely related to M W scale. It may not be practically possible to ascertain these parameters, particularly “H” due to the absence of tide gage readings at site. Due to this limitation, out of 194 events obtained from various catalogs, only about 4 events had magnitudes expressed on M t scale. Here, we develop correlation equations to determine the tsunami magnitude (M t ) from earthquake moment magnitude (M W ). For this purpose, we use statistical regression tools SLR, ISR, and OSR as already mentioned in the introduction. The detailed mechanism and procedure used for this procedure have been adopted from [13]. The detailed procedure for standard linear regression (SLR) has been referenced from [14–17]. Draper and Smith [17] explains the detailed procedure for carrying out inverse standard regression (ISR). The procedure for orthogonal standard regression (OSR) has been adopted from the work done by [18–21]. To successfully develop a correlation between two parameters, one dependent and the other independent, we need a good amount of statistically reliable data, but as per the available online catalogs, only four events have their tsunami magnitude presented as Mt . Hence, it is difficult to develop a good correlation between M t and M W by considering just the data for the Indian Ocean. To get rid of this practical difficulty, we collect the worldwide data of tsunami events from [1, 3, 4] and develop a correlation between M t and M W . We develop this global correlation using 173 values of M t and M W extracted from the global database. The Indian Ocean region considered in this study, being a subset of the global database, the developed correlation equations fit well for the Indian Ocean region as well. Figures 2, 3, and 4 show the correlation equations developed between M t and M W for the Indian Ocean region using SLR, ISR, and OSR regression methods. The correlation equations are presented in terms of the slope and intercepts, with the errors within brackets. The developed correlation equations using three regression methods are compared based on their coefficients of determination (R2 ) since it is the best statistical metric for the goodness of any correlation for any scientific domain [22]. In this study, we assume a linear correlation between M t and M W, and hence, no attempt has been made to develop a bilinear correlation between the variables. Table 1 presents the summary of the correlation equations developed for the study area using 3 correlation methods along with the errors. It also compares the coefficients of determinations of the developed correlation equations. From Table 1, it can be inferred that the correlation equations developed using orthogonal standard regression have the highest value of coefficient of determination. Hence, OSR is the best regression tool for correlation between M t and M W . The missing values of tsunami magnitude (M t ) of tsunami events in the Indian ocean using the relation M t = 0.96 (± 0.0299) M W + 0.37 (± 0.2262) are completed and added to the updated catalog as mentioned in the link presented in the introduction. This way, the tsunami magnitude (M t ) of 112 events have been estimated.

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Mt = 0.89 (± 0.0274) M + 0.87 (± 0.207) W

R2 = 0.862

Fig. 2 M t versus M W correlation developed using standard linear regression (SLR)

Mt = 1.04 (± 0.0296) M - 0.21 (± 0.207) W

R2 = 0.862

Fig. 3 M t versus M W correlation developed using standard linear regression (ISR)

Figure 5 presents a comparison of the correlations developed between M t and M W for the Indian Ocean using SLR, ISR, and OSR.

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Mt = 0.96 (± 0.0299) M + 0.37 (± 0.2262) W

R2 = 0.9283

Fig. 4 M t versus M W correlation developed using standard linear regression (OSR)

Table 1 Summary of correlation equations developed Indian ocean region Regression type

Equation

R2

SLR

M t = 0.89 (± 0.0274) M W + 0.87 (± 0.207)

0.862

ISR

M t = 1.04 (± 0.0296) M W − 0.21 (± 0.207)

0.862

OSR

M t = 0.96 (± 0.0299) M W + 0.37 (± 0.2262)

0.928

4 Tsunami Intensity-Magnitude Relationship With the advent of instrumental means of earthquake and tsunami observation, some researchers tried means to link the tsunami run-up heights to the source magnitude of the causative earthquake. The tsunami intensity scale adopted for most studies is the Soloviev-Imamura tsunami scale which is developed using the average values of runup at the coastal region nearest to the site. The source magnitude scale of the causative earthquake event is either M S or M W . Chubarov and Gusiakov [11] developed a formula to deduce tsunami magnitude from the earthquake source magnitude. The formula is presented as Eq. 3. I = 3.55MW − 27.1

(3)

Figure 6 presents the predicted tsunami intensity of 684 tsunami events between 416 A.D. till date using the formula suggested by [11].

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Fig. 5 Comparison of the developed correlation equations between M t and M W

Fig. 6 Tsunami intensity versus moment magnitude of 684 tsunami worldwide from 416 A.D. till date

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Figure 6 shows that the tsunami intensity M t and moment magnitude M W of the source earthquake show a very little dependency. But one can note a general trend of an increasing tsunami intensity with an increase in moment magnitude of the source earthquake. Relying on this trend, we use the tsunami prediction equation of [11] as expressed in Eq. 3 to roughly calculate the values of the missing tsunami events (112 tsunami events) in the existing online catalogs for the Indian Ocean region. The tsunami magnitude is computed and uploaded on the Webpage as mentioned in the introduction part.

5 Conclusions The present work has attempted to update the existing tsunami catalog for the Indian Ocean region by filling in the missing values or by deducing a relationship to estimate/predict the same. Wherever possible, existing relationships have been used to predict the unknown value of parameters (like M w ) for historical tsunami of the Indian Ocean region. 112 missing values of moment magnitude (M W ) which were missing in the existing catalogs were filled by using the existing global relations to convert surface-wave magnitude (M S ) to moment magnitude (M W ). This way magnitude homogenization has been performed on the tsunami catalog of the Indian Ocean. A relationship has been developed between tsunami magnitude (M t ) and moment magnitude (M W ) of the earthquake source for the Indian Ocean region by regressing 173 globally observed values of M t and M W , obtained from the existing historical tsunami catalogs. This way 112 missing values of tsunami magnitude were added to the updated tsunami catalog of the Indian Ocean. It is also observed that orthogonal standard regression (OSR) is the best regression tool for developing a relationship between tsunami and earthquake magnitude for the Indian Ocean region as OSR provided the highest value of coefficient of determination (R2 ). Also, 112 missing values of tsunami intensity were added to the updated catalog using the existing tsunami intensity prediction equation suggested by [11]. Figure 7 shows the intensity of tsunami across the years as per the update tsunami catalog. Figure 8 presents a histogram of tsunami intensity as per the update catalog for time intervals of 50 years. The updated tsunami catalog for the Indian Ocean region can be found on the following link: https://sites.google.com/eq.iitr.ac.in/comprehensive-tsunami-io.

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Fig. 7 Tsunami intensity across years as per the updated catalog. The size of circle is in proportion to the earthquake magnitude in M w scale

Fig. 8 Histogram of tsunami occurrence in the Indian Ocean between 1600 and 2020 at intervals of 50 years as per the updated tsunami catalog. Dark tone indicates the total number of tsunami events. Whereas light tone shows the number of strong and damaging tsunami with I > 1

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References 1. National Geophysical Data Center/World Data Service (NGDC/WDS): Global Historical Tsunami Database. In: National Geophysical Data Center, NOAA. p. Boulder, CO, USA, 2015. https://doi.org/10.7289/V5PN93H7 2. Gusiakov, V.K.: Historical tsunami catalog for the Indian ocean region. In: Proceedings of the 22nd International Tsunami Symposium, 2005, p. 268 3. Gusiakov, V.K.: Historical tsunami database for the pacific, 47 B.C to present. In: Novosibirsk Tsunami Laboratory (NTL), 2005. Accessed 08 Nov 2018 [online]. Available http://tsun.sscc. ru/htdbpac/ 4. Ekström, G., Nettles, M., Dziewo´nski, A.M.: The global CMT project 2004–2010: centroidmoment tensors for 13,017 earthquakes. Phys. Earth Planet. Inter. 200–201, 1–9 (2012). https:// doi.org/10.1016/J.PEPI.2012.04.002 5. Scordilis, E.M.: Empirical global relations converting MS and mb to moment magnitude. J. Seismol. 10(2), 225–236 (2006). https://doi.org/10.1007/s10950-006-9012-4 6. Das, R., Wason, H.R., Sharma, M.L.: Magnitude conversion to unified moment magnitude using orthogonal regression relation. J. Asian Earth Sci. 50, 44–51 (2012). https://doi.org/10. 1016/j.jseaes.2012.01.014 7. Abe, K.: Size of great earthquakes of 1837–1974 inferred from tsunami data. J. Geophys. Res. Solid Earth 84(B4), 1561–1568 (1979). https://doi.org/10.1029/JB084iB04p01561 8. Abe, K.: Physical size of tsunamigenic earthquakes of the northwestern Pacific. Phys. Earth Planet. Inter. 27(3), 194–205 (1981). https://doi.org/10.1016/0031-9201(81)90016-9 9. Wessel, P., et al.: The generic mapping tools version 6. Geochem. Geophys. Geosyst. 20(11), 5556–5564 (2019). https://doi.org/10.1029/2019GC008515 10. Gusiakov, V.K.: Tsunami history—recorded V. K. Gusiakov. Most, November, pp. 1–30 (2007) 11. Chubarov, L.B., Gusiakov, V.K.: Tsunamis and earthquake mechanism in the island arc region. Sci. Tsunami Hazards 3(1), 3–21 (1985) 12. Das, R., Wason, H.R., Sharma, M.L.: Global regression relations for conversion of surface wave and body wave magnitudes to moment magnitude. Nat. Hazards 59(2), 801–810 (2011). https://doi.org/10.1007/s11069-011-9796-6 13. Davis, J.C.: Statistics and Data Analysis in Geology, 3rd edn. John Wiley and Sons, Kansas (2002) 14. Bindi, D., Spallarossa, D., Eva, C., Cattaneo, M.: Local and duration magnitudes in Northwestern Italy, and seismic moment versus magnitude relationships. Bull. Seismol. Soc. Am. 95, 592–604 (2005) 15. Chen, K.-P., Ben Tsai, Y.: A catalog of Taiwan earthquakes (190P_2006) with homogenized Mw magnitudes. Bull. Seismol. Soc. Am. 98, 483–489 (2008) 16. Yadav, R.B.S., Bormann, P., Rastogi, B.K., Das, M.C., Chopra, S.: A Homogeneous and complete earthquake catalog for northeast India and the adjoining region. Seismol. Res. Lett. 80(4), 609–627 (2009). https://doi.org/10.1785/gssrl.80.4.609 17. Draper, N.R., Smith, H.: Applied Regression Analysis, vol. 326. John Wiley & Sons (1998) 18. Carroll, R.J., Ruppert, D.: The use and misuse of orthogonal regression in linear errors-invariables models. Am. Stat. 50(1), 1–6 (1996). https://doi.org/10.1080/00031305.1996.104 73533 19. Fuller, W.A.: Measurement Error Models. New York (1987) 20. Kendall, M.G., Stuart, A.: The Advanced Theory of Statistics, vol. 2, no. 1. New York (1961) 21. Stefanski, L.A.: Measurement error models. J. Am. Stat. Assoc. 95(452), 1353–1358 (2000). https://doi.org/10.1080/01621459.2000.10474347 22. Chicco, D., Warrens, M.J., Jurman, G.: The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput. Sci. 7, 1–24 (2021). https://doi.org/10.7717/PEERJ-CS.623

Seismically Induced Landslide Hazard Analyses for a Road Corridor in the Lower Himalayas A. Tyagi , R. R. Nath , M. L. Sharma , and J. Das

Abstract Seismically induced landslide hazard assessment for important road networks in the hilly regions constitutes a significant component of planning and management of road infrastructures. Such multi-hazard assessment, though complicated, carries even more significance for seismically active mountains like the Himalayas. However, traditional landslide hazard zonation (LHZ) practice generally lacks in incorporating seismic factors. An endeavour has been made in the present study to carry out seismically induced LHZ mapping for 85 km stretches of KalkaShimla highway (NH-5) in the lower Himalayan belt under a scenario earthquake condition. Eight different landslide preparatory factors, viz. lithology, slope angle, aspect, elevation profile, distance form fault, distance from drainage, distance from road, and land-use-land-cover patterns are integrated with the landslide-triggering seismic factors in a Geographical Information System (GIS). The seismic factor is included in terms of peak ground acceleration (PGA) parameters generated for a 10% exceedance probability in 50 years using probabilistic seismic hazard assessment (PSHA). The study develops a heuristic model of landslide hazard assessment coalescing semi-quantitative and bivariate frameworks to generate the seismically The presentation of material and details in maps used in this chapter does not imply the expression of any opinion whatsoever on the part of the Publishers or Author concerning the legal status of any country, area or territory or of its authorities, or concerning the delimitation of its borders. The depiction and use of boundaries, geographic names and related data shown on maps and included in lists, tables, documents, and databases in this chapter are not warranted to be error free nor do they necessarily imply official endorsement or acceptance by the Publisher or Author. A. Tyagi · M. L. Sharma · J. Das Indian Institute of Technology, Roorkee 247667, India e-mail: [email protected] M. L. Sharma e-mail: [email protected] J. Das e-mail: [email protected] R. R. Nath (B) Adani Institute of Infrastructure Engineering, Ahmedabad, Gujarat 382421, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_29

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induced LHZ map. The prepared LHZ map demarcates more than 40% of the study area as the zones of high to very high landslide hazard under the scenario earthquake. The prediction accuracy of the proposed method is estimated as 80% for the study. Based on the findings, the study recommends that the seismic element be taken into consideration for a more realistic evaluation of the current landslide threats in seismically active mountain ranges like the Himalayas. Keywords Seismically induced landslide hazard zonation · Earthquakes · Probabilistic seismic hazard assessment · Analytical hierarchy process (AHP) · Frequency ratio (FR) method

1 Introduction Past experiences imply earthquakes as one of the major landslide-triggering factors in the Himalayas [1–3]. Seismically induced landslides in mountainous terrain have reported to cause over 70% of all earthquakes-related fatalities that were not directly brought on by ground shaking [4], and more than forty thousand seismically induced landslide fatalities were reported between 2004 and 2010 [5]. Data showed that road corridors in general have higher propensity to failure from landslides due to toe cutting [6–8] and require a robust assessment of the prevailing as well as prospective landslide hazard for proper planning, maintenance, and management of road infrastructures [9, 10]. In this context, inclusion of seismic factor(s) in landslide hazard zonation (LHZ) models is important for seismically active Himalayas as the government has now invested significant resources in developing the road infrastructure in the trans-Himalayan belt. Yet, only few studies [1–3, 11] considered seismic indicators in LHZ mapping of the Himalayas, and there is a requirement of even more studies for the lower Himalayas considering the increased volume of rapid development and road widening projects in this region. An endeavour has been made in this study to integrate landslide-triggering seismic factor with the landslide controlling factors using a heuristic approach for an important road corridor in the lower Himalayas. The resultant seismically induced LHZ maps for this important highwaycorridor will be of practical importance for disaster preparedness and sustainable development. One of the major challenges in creating seismically generated landslide hazard zonation (LHZ) maps is the selection of a well-known technique. Different statistical techniques that are frequently used to determine the susceptibility of landslides typically fall short in incorporating seismic indicators [1, 12]. This is mainly due to scarcity of records of seismically induced landslides, which may further be attributed to the rare occurrence of strong ground motions. A statistical technique produced from an inventory of earthquake-induced landslides (developed for a single seismic event) is also often insufficient for a diverse tectonic environment due to regional and distinctive predispositions. These issues make studying seismically generated landslide hazard zones more difficult. Different researchers have been trying to

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address these issues by including peak ground acceleration (PGA) parameter of a scenario earthquake generated through probabilistic seismic hazard assessment (PSHA) as a landslide-triggering seismic factor in the traditional bivariate models of LHZ mapping [1, 2, 11, 13, 14]. In this context, this paper presents a case study on the use of probabilistically generated PGA (for a scenario earthquake with a return period of 475 years) as a seismic factor in preparing seismically induced LHZ map for a part of National Highway-5 (NH-5) India (connecting two important establishments of lower Himalayas: Kalka and Shimla in Himachal Pradesh) using bivariate framework of LHZ mapping.

2 Study Area A stretch of about 85 km of the NH-5 starting at Kalka and ending at Shimla, India with a buffer of 1 km, is selected as the study area (Fig. 1). The development processes such as urbanization, road construction, and other civil infrastructures have rapidly been established in the study area in the last two decades. About 64 landslides have been identified for the study area using Google Earth historical images and field reconnaissance surveys.

Fig. 1 Study area map displaying the map of India and the study area buffer for a part of NH-5

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3 Methodology 3.1 Probabilistic Seismic Hazard Assessment For the study area, seven seismogenic sources are identified based on the heterogeneity of their seismicity and geological attributes. The widely used Gutenberg– Richter (G-R) relationship is used to define the annual rate of seismicity. Compilation and treatment of the earthquake catalogue is carried out as per the standard practice of probabilistic seismic hazard assessment (PSHA). The NGA-WEST-1 attenuation relationship [15] is observed to be the most appropriate attenuation relationship, which is used to generate the PGA parameter of a scenario earthquake with 10% exceedance probability in 50 years (corresponding to the return period of 475 years). It is to be noted that a detailed discussion on the PSHA methodology is not provided here considering the lack of both space and scope.

3.2 Generation of Landslide Potential Index (LPI) For the present study, eight landslide preparatory factors are identified, which are then integrated with the PGA parameter generated through PSHA in a GIS environment. As the study envisages to integrate two sets of temporarily variable parameters, it adopts a heuristic approach of LSZ modelling. For that purpose, the widely accepted map combination method [1, 16] is used where all the landslide causative parameters (both preparatory and triggering factors) are included as thematic layers. Each layer is further categorized into subclasses for establishing intra-class heterogeneity. Each thematic class is assigned a rank (Ri ), and its subclasses are assigned a weight (W i ) based on the perceived influence on the overall landslide susceptibility, terrain characteristics, and the professional judgement of the investigator. Availability of the pertinent dataset also plays a major role. The landslide potential index (LPI) for each pixel is produced by numerically integrating the specified rankings and weights in the GIS environment. LPI [1, 16] indicates the likelihood of landslide occurrence based on the prevalent causative factors in an area. Greater landslide hazard susceptibility is indicated by a higher LPI rating and vice versa. Mathematically, it may be defined as, LPI =

n  m 

Ri × Wi, j

(1)

i=1 j=1

where Ri denotes the rank of the ith thematic layer of landslide causative factor and W i,j denotes the weight of the jth thematic subclass of the ith layer. The LPI values are then consolidated and classified in GIS environment to generate the final LSZ map of the study area.

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The subjective nature of how different thematic layers and their subclasses are ranked and weighted is the main drawback of the map combination approach. The study adopts the semi-quantitative analytical hierarchy process (AHP) to reduce the subjectivity of the rank assignment process [1]. By generating the consistency ratio (CR) of the resulting decision matrix, AHP employs pair-wise comparisons to assess the consistency of the decision made (ranks assigned in this scenario). CR is defined as the ratio of the consistency index (CI) to the random consistency index (RI). Mathematically, CI =

λmax − N N −1

CR = CI/RI

(2) (3)

where λmax is the largest normalized principal eigenvector and N is order of the decision matrix. Values of RI are predefined for different matrices orders [17]. If CR ≤ 10%, the decisions made (i.e. the ranks assigned) are consistent, and if it not so, then the process should be repeated. The AHP scale is carefully chosen from 1 to 9 where 1 signifies equal importance; 3 signifies moderate importance; 5 signifies strong importance; 7 signifies very strong importance, and 9 signifies extreme importance between a pair of thematic layers. The values of 2, 4, 6, and 8 signify intermediate importance. The same scale is further extended for assignment of weights to the subclasses of various thematic layers. The weights of different subclasses of all the thematic layers are assigned based on the frequency ratio (FR) analyses with respect to the prepared landslide database. The FR is a bivariate statistical model of LHZ mapping which correlates the ratio of the landslide numbers in a particular subclass to the total landslide numbers in a thematic layer with the ratio of the area of that subclass to the total thematic area (in terms of pixel counts). It is mathematically expressed as FR =

Number of landslides in subclass/Total landslides in the thematic layer No. of pixels in the subclass/Total no. of pixels in the thematic layer (4)

Based on the computed FR values, the weights of the subclasses are assigned in the same AHP scale which is used for assignment of ranks of thematic layers. Therefore, the assigned ranks and weights are not based on the investigators’ perception or experience but are derived objectively. Thus, the present study reduces the subjectivity of the map combination method which can be considered as a distinct advantage of the study.

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Table 1 Details about the data used in the preparation of thematic maps Data

Format

Source

Landslide points

Vector

Filed surveys/Google Earth platform

DEM & its Derivatives (Slope, aspect etc.)

Raster

ALOS PALSAR (https://search.asf.ala ska.edu/)

LULC

Raster

10-m resolution Global land cover 2017 (https://doi.org/10.1016/j.scib.2019. 03.002.)

Faults

Vector

Seismotectonic Atlas of India and its environ/GSI/Bhukosh

Lithology

Vector

Ancillary data/Field surveys

Roads

Vector

Ancillary data

4 Preparation of Thematic Layers of Landslide Controlling Parameters In this study, eight different landslide preparatory factors, viz. lithology, slope angle, aspect, elevation profile, distance form fault, distance from drainage, distance from road, and land-use-land-cover patterns are integrated with the landslide-triggering seismic factors in a GIS environment. The seismic factor is included in terms of peak ground acceleration (PGA) parameters generated for a 10% exceedance probability in 50 years using PSHA. The data required for this study have been obtained from the satellite imageries, Survey of India (SOI) toposheets, field surveys, and Geological Survey of India (GSI) practical sheets. A 12.5-m ALOS PALSAR high-resolution digital elevation model (DEM) was used in this study. Thematic maps for all the DEM derivatives were prepared in GIS environment. All data used for the study are resampled at a scale of 1:12,500 corresponding to the DEM resolution. The format and sources of data collected is enumerated in Table 1. The prepared thematic maps of parameters and their subclasses are shown in Fig. 2. .

5 Seismically Induced Landslide Hazard Zonation: Results and Discussion 5.1 Assignment of Ranks (Ri ) to Landslide Thematic Layers In this study, the impact of the slope angle and geological formations on the overall landslide susceptibility is observed to be high to very high. Therefore, the thematic

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Fig. 2 a–i Thematic maps prepared for all the considered controlling parameters

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layers of slope angle and geological formations are assigned ranks of 9 and 8, respectively. The PGA thematic layer is assigned a rank of 7 considering the active seismicity of the study area and the past incidences of major earthquake (most notably the 1905 Kangra earthquake, M s = 7.8) in the region. Widening of the existing NH-5 in the study area has rendered additional instability due to toe cutting, and therefore, the road Euclidean distance thematic layer is assigned a rank of 7. All these causative parameters are perceived to have extreme to very strong influence on the overall landslide hazard of the study area. The slope aspect thematic layer, the LULC thematic layer, and the fault Euclidean distance thematic layers are assigned ranks of 6, 5, and 5, respectively, considering their strong influence on the landslide hazard. The elevation thematic layer is assigned a relatively moderate rank of 3 in this present study. Although the effects of drainage density on the landslide susceptibility are well recognized for the Himalayas, the drainage Euclidean distance thematic layer is assigned a low rank of 1 mainly due to the lack of major rivers/drainage in the study area. The consistency of the rank assignment process is checked by formulating the decision matrix using AHP which is shown in Table 2. 36 pair-wise comparisons are made for the formulated decision matrix shown in Table 2, with 5 Eigen vector solutions (λmax equals to 9.338) and an error of 4.9E-8. The consistency ratio (CR) for the computed decision matrix is 2.98%. Since CR < 10%, the decision made, i.e. the ranks assigned are found to be consistent.

5.2 Assignment of Weights (Wi ) to Subclasses of Landslide Thematic Layers The frequency ratios (FRs) of the subclasses of various thematic layers excluding the PGA thematic layer are computed as discussed in Sect. 3. The computed FR values range from a minimum of 0 to a maximum of 6.76 (discarding one value of 26.19 as a statistical outlier), implicating the significant variations of the effects of different thematic subclasses on the overall landslide susceptibility of the study area. For example, in the slope angle thematic layer, two subclasses have significant variations in their computed FR values. The subclass of slopes with less than 15° angle has a very low FR of 0.08 indicating that these slopes have very low susceptibility for landslides. Only 1 landslide is observed in 171,351 pixels for this subclass which constitute almost 21% of the total pixels in the study area. On the other hand, the slopes with greater than 45° angle have extremely high landslide susceptibility as 10% of total the identified landslides occur in this subclass which constitutes only 1.6% of the total pixels of this thematic layer. Based on their perceived influence on the overall landslide susceptibility of the study area, the FR values are categorized into 5 classes of [0–0.39], [0.40–0.69], [0. 70–1.09], [1.10–1.49], and [1.50–6.76], which are then assigned a weight of 1, 3, 5, 7, and 9, respectively. This allows for the extrapolation of the bivariate model into the heuristic model in the same AHP scale which is used for assignment of ranks (Ri ) to the landslide thematic layers. This is

0.50

0.25

0.20

7

7

6

5

5

3

1

PGA

Road ED

Slope Aspect

LULC

Fault ED

Elevation

Drainage ED

0.11

0.14

0.20

0.33

0.33

1

8

Slope angle

0.12

0.17

0.25

0.25

0.33

0.50

0.50

1

Lithology

AHP decision matrix

Lithology

Assigned ranks (Ri )

Slope Angle 9

Landslide controlling parameters

0.14

0.20

0.33

0.33

0.50

1.00

1

PGA

0.14

0.20

0.33

0.33

0.50

1

Road ED

0.17

0.25

0.50

0.50

1

Slope aspect

0.20

0.33

1.00

1

LULC

0.20

0.33

1

Fault ED

0.33

1

Elevation

1

Drainage ED

0.017

0.028

0.056

0.056

0.085

0.132

0.132

0.202

0.293

Normalized eigen values

Table 2 Assigned ranks (Ri ) to the landslide thematic layers and formation of the AHP decision matrix for the evaluation of the consistency of the rank assignment process

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Table 3 Computation of FR values for thematic subclasses and their weights (W i,j ) in AHP scale Slope angle subclass

No. of landslides

Total

Ratio of the no. of landslides

Pixels

Total pixels

Ratio

Frequency ratio

Final Weight

< 15°

1

64

0.015

171,351

827,001

0.2071

0.08

1

15°–25°

16

0.250

293,324

0.3547

1.42

7

25°–35°

20

0.313

266,336

0.3221

0.97

5

35°–45°

20

0.313

82,608

0.0999

3.13

9

>45°

7

0.109

13,382

0.0162

6.76

9

a crucial task in multi-hazard integration as it provides with the scope to fit in both the preparatory and triggering causative factors in the same scale. The same scale is then used to assign the weights to the subclasses of the PGA thematic subclasses facilitating multi-hazard integration. Table 3 shows computation of FR values for slope angle subclasses and assignment of the final weights in the AHP scale. The same process is used to assign the weights of other subclasses of various thematic layers, but not shown here due to constrain of space. The PGA thematic class is categorized into 5 different subclasses, each of which represent a different level of seismic hazard for the same scenario earthquake. Higher values of PGA represent a higher level of seismic hazard, and therefore, their weights can also be assigned in a linear scale. For example, a PGA value of 0.147 is less severe than a PGA of 0.163, and therefore, the weight of the former should be less than the latter. Based on this simple understanding, the PGA subclasses are assigned the following weights in the same AHP scale which is shown in Table 4. It may be noted that computation of FRs for PGA subclasses seems irrational as the PGA thematic layer is derived based on a particular exceedance probability in a finite time. Thus, the study refrains from carrying out proximity analyses for the PGA thematic layer which is believed to be one of many probable scenarios of future earthquakes in the lower Himalayan region. Readers may further refer to Nath et al. [1] for a more elaborate discussion on the implications of scenario earthquakes on landslide hazard zonation for the Himalayas. Table 4 Assigned weights to the subclasses of the PGA thematic layer in AHP scale

Range of PGA values (in g)

Weight

Perceived hazard level

0.1477–0.1539

5

High

0.1539–0.1582

6

High to very high

0.1582–0.1618

7

Very high

0.1618–0.1638

8

Very high to extremely high

>0.1638

9

Extremely high

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373

5.3 Generation of the Seismically Induced Landslide Hazard Zonation (LHZ) Map of the Study Area Numerical integration of ranks (Ri ) and weights (W i,j ) of the thematic layers and their subclasses in the GIS environment can produce the LPI of every pixel, which is consolidated to generate the seismically induced landslide hazard zonation (LHZ) map of the study area. The LPI values range from a minimum of 101 to a maximum of 445. They are further classified using the “Jenks natural breaks” optimization technique for maximizing each class’s deviation from the means of other classes. The prepared seismically induced LHZ map is categorized into 5 separate zones of landslide susceptibility from very low to very high. Figure 3 shows the prepared seismically induced LHZ map of the study area. From Fig. 3, it is observed that almost 27% of the study area falls in the low to very low landslide hazard zones that contains only 3% of the total landslides observed. However, almost 43% of the study area is susceptible to high to very high seismically induced landslides, which contains approximately 85% of the observed landslides. The remaining 30% of the study area shows moderate susceptibility towards landslide hazard with 12% of observed landslides falling in this zone. Fig. 3 Prepared seismically induced landslide hazard zonation map of the study area showing five separate classes of landslide hazard

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Table 5 Frequency ratio analysis (spatial validation) of the prepared seismically induced landslide hazard zonation map of the study area Landslide hazard zone

Range of LPI values

No. of Pixels

Ratio of the class pixels to total pixels

Very Low

101–194

62,754

0.0763

Low

194–249

167,324

0.2034

Moderate

249–292

237,842

High

292–339

244,437

Very High

339–445

110,335

No. of landslides

Ratio of the no. of landslides in the class to total no. of landslides

Frequency ratio of the landslide hazard zone

0

0

0

2

0.0313

0.154

0.2891

8

0.1250

0.432

0.2971

24

0.3750

1.262

0.1341

30

0.4688

3.495

5.4 Validation of the Seismically Induced Landslide Hazard Map Frequency ratio analysis of the prepared LHZ map is used for spatial validation, results of which are shown in Table 5. The frequency of observed landslides gradually declines from very high level to very low landslide hazard zones with significant division. Thus, it can be stated that the computed and identified susceptibility zones are in good accord with the incidences of prior landslides. With a FR value of 3.495, the extremely high landslide hazard zone has the highest density of reported landslides (47% of all observed landslides), despite its short spatial extent (around 13% of the overall area). Similarly, an efficient technique for testing an LSZ map with regard to model prediction success is the examination of the success rate curve [18]. The success rate curve for the prepared LHZ map is presented in Fig. 4 that shows the model prediction accuracy as ~ 79%. The prepared seismically induced landslide hazard zonation map indicates that for the considered earthquake scenario, almost 43% of the study area has high to very high landslide susceptibility. This implies that there is a high probability of widespread landslides induced by the particular earthquake scenario. Similar observations were made by Pareek et al. [2], where pattern shift of landslide zones from lower to higher susceptibility occurred post the 1999 Chamoli earthquake and Nath et al. [1], where 51% of the study area in the lower Himalayas was demarcated as very highly susceptible zone for a scenario earthquake with same exceedance probability. Such scenarios, though extremely rare (10% exceedance probability in 50 years), would invariably induce slope failure of extreme multitude, considering the fragile geology and other favourable conditions of the lower Himalayan belt. Therefore, it is important for planners to realize the actual risk associated with seismically induced landslides and incorporate seismic factor(s) in the traditional practice of LHZ mapping.

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4

(a) Frequency Ratio (FR) Analysis

3.5

Frequency Ratio

3

2

1.26 1 0.43 0.15 0 0 Very Low

Low

Moderate

High

Very High

Cumulative Percentage of Landslide Susceptibility Area

1

(b) Success rate curve

0.8

0.6

0.4

0.2

0 0

0.2

0.4

0.6

0.8

1

Landslides in Cumulative Percentage

Fig. 4 Spatial and statistical validation of the prepared seismically induced landslide hazard zonation map using a Frequency ratio (FR) analysis and b Success rate curve

6 Conclusion The present study proposes a heuristic approach that coalesces semi-quantitative and bivariate frameworks of landslide hazard zonation for assessment of seismically induced landslide hazard for an important road corridor in the Lower Himalayas.

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The study considers PGA of an earthquake scenario (return period of 475 years) as the landslide-triggering seismic factor. Using the proposed heuristic approach, the seismic factor is successfully integrated with eight landslide controlling parameters of the study area to generate a landslide hazard zonation map under the specific seismic condition. The results of the study demonstrate that application of probabilistically generated PGA factor can enhance the current practice of landslide hazard zonation in terms of evaluating the co- and post-seismic landslide hazard for an expected earthquake scenario. The findings of the study, interpreted in terms of hazard zones, show that the susceptibility of seismically induced landslides is high to very high for more than 40% of the study area and demonstrates the impact of major earthquakes on the overall landslide susceptibility of the lower Himalayan belt. Thus, the study recommends the inclusion of seismic factor for a more realistic assessment of the prevailing landslide hazards in the seismically active hilly regions like the Himalayas.

References 1. Nath, R.R., Pareek, N., Sharma, M.L.: Implications and inclusion of size-dependent scenario earthquakes on landslide hazard zonation: a case study of the Indian Himalayas. CATENA 212, 106027 (2022) 2. Pareek, N., Sharma, M.L., Arora, M.K.: Impact of seismic factors on landslide suscep-ibility zonation: a case study in part of Indian Himalayas. Landslides 7(2), 191–201 (2010) 3. Nath, R.R., Pal, S., Sharma, M.L.: Use of probabilistically generated scenario earthquakes in landslide hazard zonation: a semi-qualitative approach. In: Impact of Climate Change, Land Use and Land Cover, and Socio-economic Dynamics on Landslides. Springer, Singapore, pp. 247– 274 (2022) 4. Marano, K.D., Wald, D.J., Allen, T.I.: Global earthquake casualties due to secondary effects: a quantitative analysis for improving rapid loss analyses. Nat. Hazards 52(2), 319–328 (2010) 5. Kennedy, I.T., Petley, D.N., Williams, R., Murray, V.: A systematic review of the health impacts of mass earth movements (landslides). PLoS Curr. 7 (2015) 6. Bhambri, R., Mehta, M., Singh, S., Jayangondaperumal, R., Gupta, A.K., Srivastava, P.: Landslide inventory and damage assessment in the Bhagirathi Valley, Uttarakhand, during June 2013 flood. Himalayan Geol. 38(2), 193–205 (2017) 7. Singh, K., Kumar, V.: Hazard assessment of landslide disaster using information value method and analytical hierarchy process in highly tectonic Chamba region in bosom of Himalaya. J. Mt. Sci. 15(4), 808–824 (2018) 8. Panchal, S., Shrivastava, A. K.: Landslide hazard assessment using analytic hierarchy process (AHP): a case study of National Highway 5 in India. Ain Shams Eng. J. 13(3), 101626 (2022) 9. Ali, S., Biermanns, P., Haider, R., Reicherter, K.: Landslide susceptibility mapping by using GIS along the China–Pakistan economic corridor (Karakoram Highway), Pakistan. Nat. Hazards Earth Syst. Sci., 1–40 (2018) 10. Alsabhan, A.H., Singh, K., Sharma, A., Alam, S., Pandey, D.D., Rahman, S.A.S., Khursheed, A., Munshi, F.M.: Landslide susceptibility assessment in the Himalayan range based along Kasauli-Parwanoo road corridor using weight of evidence, information value, and frequency ratio. J. King Saud Univ. Sci. 34(2), 101759 (2022) 11. Tanya¸s, H., et al.: Presentation and analysis of a worldwide database of earthquake-induced landslide inventories. J. Geophys. Res. Earth Surf. 122(10), 1991–2015 (2017) 12. Nath, R.R., Sharma, M.L., Tyagi, A.: Review of the current practice on inclusion of seismicity in landslide susceptibility zonation: a case study for Garhwal Himalaya. Himalayan Geol. 41(2), 222–233 (2020)

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13. Nath, R.R., Sharma, M.L., Goswami, A., Sweta, K., Pareek, N.: Landslide susceptibility zonation with special emphasis on tectonic features for occurrence of landslidesin Lower Indian Himalaya. J. Indian Soc. Remote Sens. 49(5), 1221–1238 (2021) 14. Nadim, F., Kjekstad, O., Peduzzi, P., Herold, C., Jaedicke, C.: Global landslide and avalanche hotspots. Landslides 3(2), 159–173 (2006) 15. Boore, D.M., Atkinson, G.M.: Ground-motion prediction equations for the average horizontal component of PGA, PGV, and 5%-damped PSA at spectral periods between 0.01 s and 10.0 s. Earthq. Spectra 24(1), 99–138 (2008) 16. Sarkar, S., Kanungo, D.P.: An integrated approach for landslide susceptibility map-ping using remote sensing and GIS. Photogramm. Eng. Remote. Sens. 70(5), 617–625 (2004) 17. Saaty, T.L.: The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill Book Co, New York (1980) 18. Chung, C.J.F., Fabbri, A.G.: Probabilistic prediction models for landslide hazard mapping. Photogramm. Eng. Remote. Sens. 65(12), 1389–1399 (1999)

Assessment of Proxy-Based V s30 Estimation in Roorkee, Uttarakhand M. Srivastava and M. L. Sharma

Abstract A well-liked measure for site characterization and classification is the averaged seismic shear wave velocity with time for the top 30 m below the surface, abbreviated V s30 . It is associated with material property and thus has a strong influence on ground motions. V s30 is an important aspect of earthquake site effects and a crucial element for ground response analyzes (GRA) and localized site-specific effects. To examine shear wave velocity (V s ), standard penetration test and cone penetration test are a few in situ field tests in practice. Additionally, there are surface wave techniques like continuous surface wave systems, multichannel analysis of surface waves test, and spectral analysis of surface waves test (SASW) that provide a non-intrusive, non-invasive, economical, and expedient way to conduct site investigations. Complex geomorphology, geology, and lack of in situ measurements emphasize establishing a more suitable approach for V s30 based site classification. To deal with such issues, various proxy-based techniques have been proposed, such as topography slope, surface geology, and terrain-based approach. Among the three proxy-based approaches, the topography slope method is gaining importance for preparing site condition maps for seismic damage assessments. The goal of the current study is to carry out an MASW test at various locations, enumerate the shear wave velocity profile at each location, and compare those results to those from the proxy-based V s30 estimation. Both MASW and proxy-based V s30 estimations have emerged as an important tools. This paper presents important aspects of both techniques and their reliability for future site investigations and effectiveness in geotechnical seismic site characterization. Keywords V s30 · MASW · Slope proxy · Site characterization

M. Srivastava (B) · M. L. Sharma Department of Earthquake Engineering, IIT Roorkee, Roorkee, India e-mail: [email protected] M. L. Sharma e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_30

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1 Introduction Since ancient times, inquisitiveness to explore the subsurface to deliver the explanation for seismic activities has been a topic of interest among researchers. Seismic exploration and geotechnical investigation using passive sources (earthquake waves) and active sources (explosions) emerged as a progressive domain for the study. Modern portable equipment and various technologies have facilitated seismic studies even in the complex Himalayan regions. The behavior of the soil for a region under dynamic loading accounts for potential risk and damageability. The behavior of soil is determined by dynamic soil features, namely shear wave velocity (V s ), poison’s ratio, shear modulus (G), and damping characteristics [6]. Ground motions are precepted by V s , as it is associated with stiffness and material property of the soil profile. According to Anderson et al. [2] and statements from various researchers, features in the top 30 m are important for local site-effect analyzes of earthquake ground motions. As a result, V s30 came into the limelight for site characterization and profile description. To examine shear wave velocity (V s ), high strain tests such as standard penetration test (SPT-N), cross-hole test (CHT), cone penetration test (CPT), and down-hole test (DHT) are a few in situ field tests in practice. Additionally, there are surface wave techniques like continuous surface wave systems (CSWS), multichannel analysis of surface waves test (MASW), seismic reflection/refraction test, and spectral analysis of surface waves test (SASW) that can be used to investigate a site without being intrusive or invasive, as well as being inexpensive and quick. Standard penetration test (SPT) for 30 m depth provides numbers of blows (N), constructive details of the soil profile, relative density, and approximate shear strength but it is difficult to conduct the test in rocky structures. SPT tests are expensive and time-consuming. MASW consists of a three-step procedure in which Rayleigh surface wave data are acquired, phase velocity against frequency is plotted (dispersion curve), V s is inverted, and then, a shear wave velocity (V s ) profile is obtained after Rayleigh-type surface wave analysis on a multichannel record [12]. MASW is a widely used approach due to its non-invasive and time-effective application, but it is also a laborious and tough job in complex site conditions. Maheshwari et al. [8] conducted field tests at selected sites in the Himalayan region and provided an applicable relationship between V s and N. Reliable correlation between V s estimated from MASW and “N” from SPT test has been also presented by Muley et al. [7] for Roorkee. It can be inferred from Roy et al. [11] that the V s30 values may differ upon a random selection of V s -N correlation and can lead to pseudo site classification and therefore, pinned the need for site-specific correlations. Various studies have been made for the region for V s30 measurement, Sharma et al. [13], provided V s30 = 267 m/s and engineering bedrock around 300 m of depth for Roorkee. The amplification ratio of 7.0 was also observed, due to the presence thick alluvium deposit in the region. Considering geological studies and records, thick alluvial cover indicates the presence of three layers, i.e., soft, medium, and hard soil columns followed by hard rocks. Soil characterization using specific methodologies and information available, the area was divided into three: Solani River (V s30 = 190 m/s), Roorkee West &

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East (V s30 = 210 m/s & 240 m/s, respectively). It can be inferred that deep deposits should be included for more precise local site effects studies [14]. For such geologically challenging regions, proxy-based techniques such as topography slope, surface geology, and hybrid slope geology to determine V s30 have been introduced [1]. The topography slope proxy approach has proven to be a flexible tool for analyzing seismic site conditions in the first order to deliver V s30 . Since competent materials sustain a sharp slope and while on the contrary deep sediments are generally formed in conditions with extremely low gradients, shear velocity down to 30 m observations are tie-up against the topographic slope, and factors for V s30 predictions are created [18]. As part of a geology-based approach to site classification, Wills et al. [19] linked V s30 with geological parameters and created a site category map of California. The terrain-based classification was first introduced by Iwahashi and Pike [5] as a result of geomorphic categories based on the slope’s steepness/slope gradient, the roughness of the surface/surface texture, and surface curvature/local convexity. Stewart et al. [15] conducted a study in Greece that found that both the geology-based proxy and the terrain-based proxy assessment were equally useful. In California, the terrain-based method is ideally suited for site classification [20]. The National Earthquake Hazard Reduction Programme’s “Design criteria” are collated with the respective versus soil profiles of the various sites in this research generated from, MASW and the proxy-based method for site categorization [3].

2 Study Area Roorkee is a small city situated in the Gangetic belt close to the foothills of the Himalayas and lies in high seismic zone IV. It comprises a settlement of recent alluvium with a gentle slope. Roorkee city comes under the Haridwar district of the state Uttarakhand having 30 km from the district headquarters, 65 km from the state capital, Dehradun, and 184.3 km north of the national capital, New Delhi (Fig. 1).

3 Methodology 3.1 Geotechnical Method—Multichannel Analysis of Surface Waves (MASW) At three locations within the campus of IIT Roorkee, the Department of Earthquake Engineering DEQ, ABN ground, and convocation ground, respectively, MASW tests were carried out to evaluate the shear wave velocity (V s ) variation with depth, V s30 profile. The inversion of the dispersion curve yields the shear wave velocity (V s ) variation in a layered soil structure [4]. For shallower inquiry depth, the active MASW approach is used, and the passive Nakamura method/horizontal-to-vertical spectral

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Fig. 1 Site locations within IIT Roorkee Campus Roorkee, Uttarakhand

ratio method (HVSR) is used for deeper depth. Finding a comprehensive dispersion curve for the site is encouraged by active as well as passive data collected from the site location. Thus, by integrating dispersion curves and improving the general nature of extended frequencies and phase velocity ranges, more trustworthy results are obtained for the locations [9]. To derive V s30 , the velocity profile of the locations and joint inversion of HVSR with MASW have been used in the study (Fig. 2).

3.2 Proxy-Based Vs30 Estimation and Site Classification V s30 estimation based on topography slope as a proxy was extracted from the USGS V s30 map with grid resolution provided by the map of 30 arc seconds [17]. With the aid of geographic information system mapping tools, the analysis execution was performed (Fig. 3). The term “slope,” which is extracted from the digital elevation model (DEM) data, refers to, slope of vertical height to the horizontal distance ratio, also known as the gradient. Competent high-velocity materials maintain steep slopes and deep basin sediments with low gradients. Park and Elrick [10] place special emphasis on the correlation between lower shear wave velocity to the slope based on sediment

Assessment of Proxy-Based V s30 Estimation in Roorkee, Uttarakhand

(a)

(b)

383

(c)

Fig. 2 V s profile at test sites a DEQ, b ABN ground, and c convocation ground

Fig. 3 Site classification: a the topographic slope and b the geology-based proxy-based estimation

fineness as a proxy. Among the physical parameters, the void ratio affects the shear modulus, hence, more pertinent to shear velocity. Void ratio measurement can be well obtained by soil texture and relative grain size distribution, Fumal and Tinsley [16]. Shear velocity is found to be proportional to grain size explaining the support for good correlation (Table 1). Meghalayan and Holocene, Miocene-Pliocene, Pliocene–Pleistocene, and Middle-Late Pleistocene are the several geological eras that make up the region. V s30 was associated with geological units (Rock types/Age, Strata, Lithology) by Wills et al. in 2000 (Table 2). Site classification has been made in the current study for the area based on the geological/stratigraphical/chronological dataset and V s30 ’s established correlation. According to Yong et al. [20], the terrain-based strategy entails the creation of a collection of geomorphic categories based on surface texture, slope gradient, and local convexity generated from surface curvature (Table 3).

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Table 1 NEHRP’s summary of slope ranges and V s30 Site class

V s30 (m/s)

Slope ranges (m/m) Active region

E D

Stable region

0.025

Source Wald and Allen [18]

Table 2 Accordance between geological age and site class Geological age

Site class

Lithological description

Meghalayan/Holocene

D

Hard soil, sand, clay, and gravel

Pleistocene/Pliocene/Miocene

CD

Oligocene/Eocene/Paleocene

C

Hard to very hard soil, mostly gravel, and standard soft rock

Late Cretaceous/Early Cretaceous Late Jurassic/Middle Jurassic Early Jurassic/Late Triassic Middle Triassic/Early Triassic …

B

Rocks

Source Wills et al. [19]

4 Results According to S-wave velocity profiles derived from the MASW test, shear wave velocity (V s ) variation with depth is V s30 = 291 m/s at DEQ, V s30 = 302 m/s at ABN ground, and V s30 = 303 m/s at convocation ground, respectively. The study region is designated as site class D by the NEHRP criteria for site classification, with a V s30 value ranging 180–360 m/s. Site class D for DEQ as well as convocation ground is found in the region using the topographic slope-based technique, with V s30 values of 327 and 304 m/s, respectively. According to the NEHRP criteria, ABN ground V s30 , which was calculated using this method, is 368 m/s, placing it in site class C. The area is categorized as site class CD [Miocene-Pliocene, Pliocene– Pleistocene, Middle-Late Pleistocene] V s30 values ranging 360–760 m/s and site class D [Meghalayan and Holocene] V s30 values ranging 180–360 m/s from the geology-based proxy technique (Fig. 4).

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Table 3 Terrain types and correspondence mean V s30 Terrain types

Terrain description

Mean V s30 value

TT 1

Well-dissected alpine summits, mountains, etc.

519

TT 2

Large volcanos, high block plateaus, etc.

393

TT 3

Well dissected, low mountains, etc.

547

TT 4

Volcanic fans, foot slope of high block plateaus, etc.

459

TT 5

Dissected plateaus, etc.

402

TT 6

Basalt lava plains, glaciated plateaus, etc.

345

TT 7

Moderately eroded mountains, lava flow, etc.

388

TT 8

Desert alluvial slope, volcanic fans, etc.

374

TT 9

Well eroded plains of weak rocks, etc.

497

TT 10

Valleys, till plains, etc.

349

TT 11

Eroded plains of weak rocks, etc.

328

TT 12

Eroded alluvial fans, till plains, etc.

297

TT 13

Incised terraces, etc.

–

TT 14

Eroded alluvial fans, till plains, etc.

209

TT 15

Dunes, incised terraces, etc.

363

TT 16

Fluvial plains, alluvial fans, low-lying flat plains, etc.

246

Source Yong et al. [20]

Fig. 4 Terrain-based site classification

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Table 4 NHERP soil classification Soil class

Rock and soil description

Average shear wave velocity (V s30 ) m/s

A

Hard rocks

>1500

B

Rocks

760–1500

C

Hard, stiff to very stiff soil, most gravel 360–760

D

Sand, silt, stiff to very stiff clays, and some gravel

180–360

E

Soft clays

400 Gal) on the Central Weather Bureau (CWB) intensity scale. The region around northern Taiwan observed the shaking of intensity 5 (>80 Gal) [4]. This earthquake produced the highest intensity of 4 (>25 Gal) and duration of 10 s in Taipei city. The station TWD, which is located at epicentral distance of 6 km, recorded a peak ground acceleration of 110 Gal for the component in E-W direction and 167 Gal for the component in N-S direction. The Station ETM, located 12 km from the epicenter, recorded the highest peak ground acceleration with 379 Gal for the component in E-W direction and 515 Gal for the component in N-S direction [8]. CWB developed and maintain Geophysical Database Management System (GDMS) which provided the acceleration records utilized in this study. The preprocessing steps like baseline correction and scaling has been applied on the observed records according to the recording sensor. USGS and Global CMT have provided information regarding the magnitude, location and fault plane solution of this earthquake.

4 Methodology The Hualien earthquake was succeeded by many aftershocks between April 18, 2019, and May 3, 2019. During the 15 days after the mainshock, a total of 1906 aftershocks occurred along the eastern coast of Taiwan. The spatiotemporal distribution of aftershocks has been shown in Fig. 2. The network of accelerographs deployed by Central Weather Bureau (CWB) recording strong-motion reported the spatio-temporal aftershocks distribution of the 2019 Hualien earthquake between April 18, 2019, and May 3, 2019, which indicates the existence of SMGAs. The rupture plane of both identified SMGAs is shown by two small rectangles on the rupture plane of the mainshock. The spatio-temporal

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Fig. 2 Source model of Hualien Earthquake showing identified SMGAs within the plane of rupture. The epicenter and aftershocks of this earthquake are represented by black star and solid circles, respectively [8]

distribution of most of aftershocks are aligned along the nodal plane1, which has a dip of 46° and strike of 215° associated with the mainshock. According to Lee et al. [4], it was concluded that the plane of rupture of this earthquake dips towards west direction. The presence of SMGAs is also confirmed by the observed records on the surface. The acceleration records on the surface shown in Fig. 1 has been used to compute the displacement records for this earthquake. A double numerical integration has been applied to the observed accelerograms to get the displacement records on the surface. Figure 3 shows the displacement records which clearly confirms the presence of two SMGAs. The formula used to calculate the shear horizontal (SH component) of the acceleration record (ASH ) at the stations is given as: ASH = ANS cos δ + AEW sin δ

(1)

where NS and EW component of the acceleration record are represented by ANS and AEW . The angle δ denotes the anticlockwise between the ray path that joins the source and the station and the NS (North-South) direction. The graphical representation of numerical treatment for SH wave has been shown in Fig. 4. The station has been shown in the figure by red triangle, and the source is represented by the red star in Fig. 4.

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Fig. 3 NS component of observed displacement records at near-field stations. Red ellipse shows the envelope from SMGA1 and black ellipse shows the envelope from SMGA2 on the observed displacement records

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Fig. 4 Pictorial representation to compute shear horizontal component (ASH ) in the horizontal plane using NS and EW components of observed records

Suitable numerical integration schemes are applied to transform the particle motion due to propagation of SH wave to displacement records. Figure 5 shows the obtained displacement records of SH components.

5 Analysis of Strong Motion Generation Areas The clustering of aftershocks is observed in the spatiotemporal distribution that results from two SMGAs positioned within the plane of rupture of this earthquake which is shown in Fig. 2. Moreover, two distinct wave packets observed at a nearby station to the epicenter specifies the presence of two SMGAs within the plane of rupture that were accounted for the mainshock. Once the SMGAs have been identified both spatially as well as temporally, their properties in terms of seismic moment, corner frequency, and stress drop are computed from the identified phases in the observed records to know their role in the rupture process of the Hualien earthquake.

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Fig. 5 SH component obtained after numerical treatment of horizontal components of observed displacement records at near-field stations. Red ellipse shows the envelope from SMGA1 and black ellipse shows the envelope from SMGA2 on the SH component records

Visual inspection of observed acceleration records suggests that second envelope indicates SMGA having large dimensions than SMGA shown by first envelope. Source parameters have been assessed by analyzing the source displacement spectra (SDS) of the waveform that represents two SMGAs at different near-field stations. Figures 6 and 7 show the SDS obtained from the phase denoting the SMGA1 and SMGA2 of NS component of the observed records. The red line shows the theoretical spectra fit on SDS. The theoretical spectra fit on SDS helps to estimate the corner frequency (f c ) and low frequency spectral level (Ωo ) which are utilized to compute various source

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Fig. 6 Source displacement spectra (SDS) obtained from the phase denoting the SMGA1 of NS component of the observed records. The red line shows the theoretical spectra fit on SDS

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Fig. 7 Source displacement spectra (SDS) obtained from the phase denoting the SMGA2 of NS component of the observed records. The red line shows the theoretical spectra fit on SDS

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parameters like seismic moment (M o ), stress drop (Δσ ), radius (r), and moment magnitude (M w ) for both SMGAs as shown in Tables 1 and 2. The following equations are used to calculate the source parameters of both SMGAs:   Mo = 4πρβ 3 Ω0 R / F S × Rθϕ × PRIT

(2)

r = 2.34β / 2π f c

(3)

Δσ = 7Mo / 16r 3

(4)

The symbols in the equation are defined as follows: ρ represents the density of the medium, Rθϕ represents the radiation pattern coefficient of the S phase, β depicts the velocity of secondary wave of the medium, free-surface effect is denoted by FS, PRIT Table 1 Source parameters obtained from SDS from NS component of SMGA1 at various stations Parameters

M o (×1017 ) (Nm)

Δσ (×105 ) (N/m2 )

f c (Hz)

Radius (r) (km)

Mw

EAH

0.61

10.63

0.43

2.93

5.12

EHP

4.3

70.3

0.42

3

5.69

ETL

2.9

46.8

0.42

3

5.57

TWD

2.3

40

0.43

2.93

5.51

ETM

2.3

40.7

0.43

2.93

5.51

ESL

9.9

13.94

0.4

3.15

5.27

EHY

1.1

18.76

0.42

3

5.31

WHF

6

88.76

0.4

3.15

5.8

ETLH

4.7

107.61

0.47

2.68

5.72

SHUL

2.2

30.07

0.39

3.23

5.51

Table 2 Source parameters obtained from SDS from NS component of SMGA2 at various stations Parameters

M o (×1017 ) (Nm)

Δσ (×105 ) (N/m2 )

f c (Hz)

Radius (r) (km)

Mw

EAH

6.9

70.7

0.36

3.5

5.83

EHP

7.7

107.72

0.4

3.15

5.86

ETL

1.7

24.97

0.4

3.15

5.43

TWD

3.4

27

0.33

3.82

5.62

ETM

2.5

134.7

0.62

2.03

5.54

ESL

7.1

61

0.36

3.71

5.83

EHY

1.8

36.61

0.45

2.8

5.44

WHF

2.7

148.3

0.63

2

5.56

ETLH

6

97.6

0.42

3

5.79

SHUL

5.7

58.2

0.36

3.5

5.77

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is partitioning of energy into two components in horizontal direction, long-term flat level of SDS is represented by Ω 0 . The average seismic moment (M o ), moment magnitude (M w ), stress drop (Δσ ), radius and corner frequency (f c ) for SMGA1 are (3.60 ± 0.82) x 1017 Nm, 5.5 ± 0.06, and (46.7 ± 9.77) x 105 N/m2 , 3 ± 0.04 km and 0.42 ± 0.006 Hz, respectively. The average seismic moment (M o ), moment magnitude (M w ), stress drop (Δσ ), radius and corner frequency (f c ) for SMGA2 are (4.55 ± 0.7) x 1017 Nm, 5.6 ± 0.05, (76.68 ± 13.14) x 105 N/m2 , 3.06 ± 0.19 km and 0.43 ± 0.03 Hz respectively.

6 Discussions and Conclusions It is assumed that the Hualien earthquake is triggered by the Milun fault due to its focal mechanism and juxtaposition to the fault, which resulted in SGM along the eastern coast of Taiwan [5]. The spatiotemporal distribution of aftershocks and visual inspection of observed displacement records suggests the presence of two strong motion generation area within the rupture plane responsible for this earthquake. The numerical treatment of observed records produced the SH records at each nearfield station which confirms the presence of SMGAs. To investigate the rupture mechanism of this earthquake, it is necessary to compute the source parameters of the both identified SMGAs. The source displacement spectra of observed records at various near-field stations have been computed, and low-frequency spectral level (Ωo ) and corner frequency (fc ) have been estimated. The average seismic moment (Mo ), moment magnitude (Mw ), stress drop (Δσ), radius and corner frequency (fc ) for SMGA1 are (3.60 ± 0.82) x 1017 Nm, 5.5 ± 0.06, and (46.7 ± 9.77) x 105 N/m2 , 3 ± 0.04 km and 0.42 ± 0.006 Hz, respectively. The average seismic moment (Mo ), moment magnitude (Mw ), stress drop (Δσ), radius and corner frequency (fc ) for SMGA2 are (4.55 ± 0.7) x 1017 Nm, 5.6 ± 0.05, (76.68 ± 13.14) x 105 N/m2 , 3.06 ± 0.19 km and 0.43 ± 0.03 Hz respectively. Acknowledgements The acceleration waveform data and aftershocks data of the April 18, 2019 Hualien earthquake are provided by Geophysical Database Management System (GDMS). This is a Web-based data service platform in Taiwan that is constructed by Central Weather Bureau (CWB). The data provided by CWB for this work are highly acknowledged. The authors would like to offer special thanks to Indian Institute of Technology Roorkee for the support required for the research work shown in this paper. Project grant No. GITA/DST/TWN/P-75/2017 approved by Department of Science and Technology (DST), Government of India has been highly acknowledged.

References 1. Chan, C.H., Wang, Y., Wang, Y.J., Lee, Y.T.: Seismic-hazard assessment over time: modeling earthquakes in Taiwan. Bull. seism. Soc. Am. 107(5), 2342–2352 (2017)

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2. Chan, C.H., Ma, K.F., Lee, Y.T., Wang, Y.J.: Rethinking seismic source model of probabilistic hazard assessment in Taiwan after the 2018 Hualien, Taiwan, earthquake sequence. Seismol. Res. Lett. 90(1), 88–96 (2019) 3. Kuo-chen, H., Vu, Y.M., Chang, C.H., Hu, J.C., Chen, W.S.: Relocation of eastern Taiwan earthquakes and tectonic implications. Terr. Atmos. Ocean. Sci. 15, 647–666 (2004) 4. Lee, S.J., Wong, T.P., Liu, T.Y., Lin, T.C., Chen, C.T.: Strong ground motion over a large area in northern Taiwan caused by the northward rupture directivity of the 2019 Hualien earthquake. J. Asian Earth Sci. 192, 104095 (2020) 5. Mittal, H., Yang, B.M., Tseng, T.L., Wu, Y.M.: Importance of real-time PGV in terms of leadtime and shakemaps: results using 2018 ML 6.2 & 2019 ML 6.3 Hualien, Taiwan earthquakes. J. Asian Earth Sci. 220, 104936 (2021) 6. Miyake, H., Iwata, T., Irikura, K.: Source characterization for broadband ground-motion simulation: Kinematic heterogeneous source model and strong motion generation area. Bull. Seismol. Soc. Am. 93(6), 2531–2545 (2003) 7. Wang, Y.J., Chan, C.H., Lee, Y.T., Ma, K.F., Shyu, J.B.H., Rau, R.J., Cheng, C.T.: Probabilistic seismic hazard assessment for Taiwan. Terr. Atmos. Ocean. Sci. 27(3), 325–340 (2016) 8. Sharma, S., Joshi, A., Sandeep et al. Modeling of rupture using strong motion generation area: a case study of Hualien earthquake (Mw 6.1) occurred on April 18, 2019. Acta Geophys. 71, 1–28 (2023). 9. Smoczyk, G.M., Hayes, G.P., Hamburger, M.W., Benz, H.M., Villaseñor, A.H., Furlong, K.P.: Seismicity of the Earth 1900–2012 Philippine Sea Plate and Vicinity (No. 2010-1083-M). US Geological Survey (2013.

Role of SH Wave in the Mapping of Shallow Subsurface Jyoti Singh, A. Joshi, Saurabh Sharma, and Mohit Pandey

Abstract SH waves are directly related to soil matrix which makes SH wave refraction/reflection methods effective in site investigation. This paper presents the generation of purely SH wave by using a specially designed SH wave source; this generator plate is made of aluminum metals, and the wave is generated by giving impact on the generator plate in the horizontal direction, and it has nails on its base which keeps it fixed to the ground so that only horizontal motion is recorded. Since the SH wave propagates in a plane transverse to the ray path, this SH wave generator is kept fixed in a direction perpendicular to the geophone spread. The seismic waves have been recorded using geophones with a natural frequency of 10 and 4.5 Hz. The propagation of SH waves in shallow subsurface can be modeled by the finite-difference (FD) method based on a staggered algorithm. The developed software for FD modeling of the medium has been tested for SH wave propagation in a purely elastic medium in terms of numerical stability, dispersion, and boundary conditions. The obtained near-surface properties have been used as an input in the FD method to simulate SH waves and to compare with the real-data records acquired in the region surrounding North Almora Thrust (NAT). Keywords Finite-difference modeling · MASW · Ground motion simulation

1 Introduction Site effects can significantly affect the nature of strong ground motion. Local conditions can generate large amplifications and important spatial variations of seismic ground motion. These effects are of particular significance in the assessment of seismic risk, in studies of microzonation and planning, and in the seismic design of important facilities [4, 16]. The effect of soil conditions on the ground motion has J. Singh (B) · A. Joshi · S. Sharma · M. Pandey Department of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_33

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been observed in well-documented earthquakes [11, 19]; recent work has emphasized the physical understanding of site effects so that quantitative predictions can be made [2], but there is still a lack of numerical methods for dealing with the problem taking into account source, path, and local conditions. As SH waves are directly related to soil matrix, they can be analyzed separately from body waves and thus is very effective in site investigation studies. Finite-difference method (FDM) is one of the most popular methods used in seismic modeling (e.g., [1, 5, 9, 14, 23]) and migration [3]. The FDM is capable to handle a very complex seismic model. The fact that surface SH waves are observed in nature has been used to infer that the Earth’s crust is layered. Also, recent development in the oil industry, such as oil sand exploration and monitoring, involves more and more multi-component surveys [12]. The set of equations that is used for generating synthetic seismograms can be solved using finite-difference (FDM) methods on the numerical grid of nodal points both in space and time. Depending on the choice of nodal points, the grid scheme can be classified into two broad categories: staggered and non-staggered. Virieux [22, 23] successfully used a staggered finite grid scheme in wave modeling phenomena. The method is popularly known as explicit finite-difference time domain staggered grid. In the staggered FDM, stress and particle velocity are defined at different points of the grid. A staggered grid with the location of points defining particle velocity and stress is shown in Fig. 1. The finite-difference method has been used in this paper to simulate SH waves. The material through which the wave propagates is assumed to be perfectly elastic, isotropic, and homogeneous.

Fig. 1 Location of stress and particle velocity in the staggered grid used for SH wave modeling

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Fig. 2 Geological map of North Almora Thrust (NAT). Modified after [21]

2 Study Area The Almora klippe has apparently formed by the erosion of a large synclinal fold in the crystalline Munsiari sheet, tectonically underlying the Main Central Thrust sheet of the Higher Himalaya [20, 21]. The NAT marks the northern boundary of the Almora klippe; its southern margin is bound by the South Almora Thrust (SAT). The study area is shown in Fig. 2 which maps the north Almora Thrust.

3 Data Two sets of data have been acquired, one for calculating real-data records, and the second set of data has been acquired to calculate near-surface properties, i.e., shearwave velocity structure and density have been used as an input in the FD method to simulate the records. The source used was a specially designed SH wave generator, and a sledgehammer of 5 kg has been used for the survey. The specially designed source for SH wave is made up of an aluminum plate; the whole source generator is made up of an aluminum plate, and one piece makes the base of the plate with ground coupling spikes in it. On both sides, there is an impact plate, and this base plate with spikes is fixed to the ground so that no vertical motion is recorded. The source is effective in recording the horizontal motion of the ground, i.e., SH waves and thus this specially designed source is used to generate SH waves.

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Fig. 3 SH field setup by using specially designed SH wave source. Modified after [26]

MASW technique is employed for investigating shallow subsurface features based on variation in the seismic velocity of the subsurface lithological unit [6–8, 10, 13, 17, 18]. The technique involved in estimating the shear-wave velocity and delineation of horizontal and vertical variation of shallow surface material properties [15, 24]. A linear array of 24 receivers (4.5 Hz vertical geophones) is deployed in a horizontal 96 m, ground spread, and connected to a multichannel seismograph recorder. Two parameters source offset and receiver spacing are important for data acquisition, and a SH wave source and a sledgehammer of 5 kg are used for generating the seismic wave. The data were collected by using 24 geophones and a seismograph; this has been used to generate a 2D shear-wave velocity model. A field setup for generating and recording SH wave is used which is shown in Fig. 3. One more set of data has been finally collected for real-data records of SH wave using TROMINO as receiver and source as specially designed SH wave source on which hammer has made impact from both sides on impact plates by fixing the source at the middle of the profile. Data have been acquired by using TROMINO as receivers and keeping the source as fixed and recorded at different positions of the spread of the profile line by hitting on impact plates from both sides assuming one side as negative (West to East) and the other as positive (East to West) side.

4 Methodology In this study, 2D MASW has been used to collect the data by using OYO seismograph and 24 geophones of 4.5 Hz which were deployed at 4-m intervals and oriented in the perpendicular direction to wave propagation. Seismic waves have been recorded using geophones and horizontal geophones in the OYO seismograph. MASW processing involves the transformation of raw data from time to frequency

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Fig. 4 An example of processing of the 2D MASW method. a Multichannel raw field data. b Rayleigh wave phase velocities are extracted from field data in the F-K domain. c) Phase velocities are inverted for a shear-wave velocity profile (vs. depth). d 2D S-wave velocity map generated from geoplot. Modified after [25]

domain by Fourier analysis. The “energy accumulation pattern recognition technique” is employed to extract the fundamental mode Rayleigh wave for generating the dispersion curve, which represents the relationship between Rayleigh wave, phase velocity, and frequency. Finally, this extracted dispersion curve is used to calculate the S-wave velocity variations with depth. To calculate velocity variations with a depth, we match the theoretically developed dispersion curve and the field dispersion curve, the model for which it best fits is the velocity layer model. These three subsurface methods are used in combination to image and analyze the shallow subsurface fault and its extension through the width and depth of the fault line. The data processing steps for the data recorded in field are shown in Fig. 4 with an example of how data have been processed for 2D MASW method. This data have been processed in SeisImager software to calculate the 2D MASW velocity model and density which is further used in the FD method to simulate the SH waves. The SH waves are generated by hitting the wave generator plate in the E-W direction and vice-versa. As one shot was from one face and the other was from the reverse side, these two shots at the same position are added to cancel out any vertical component, and thus only the horizontal component is recorded.

5 2D Shear-Wave Velocity Model In this section, the two-dimensional velocity model obtained using the active source MASW method has been presented. The specially designed SH wave generator has been used to generate the SH wave. Figure 5 shows the results obtained after the inversion of dispersion curves obtained from experimental records. SeisImager software has been used to calculate the velocity model. The analysis of Fig. 5 shows

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Fig. 5 Shear-wave velocity obtained from 2D MASW survey

that the shear-wave velocity in 30 m is varying from 260 to 570 m/s near the north Almora Thrust.

6 Comparison of Experimental and Numerical Study In this section, the results obtained from the experimental and numerical study have been presented. To obtain the result using the MASW technique, a specially designed SH wave generator as described above has been used. In order to generate the purely SH wave, the shot has been taken from the impact from East–West and West–East direction. The results obtained from these two shots have been further averaged out to remove the effect of motion in the vertical direction. Figure 6a, b shows the record obtained from the impact in E–W direction and W–E direction, respectively. Figure 6c shows the record obtained after averaging the E–W and W–E recorded components. Figure 7 shows the comparison of results obtained from experimental and numerical studies. The results from experimental and numerical studies are presented in blue and black color, respectively. The analysis of Fig. 7 shows that the simulated

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Fig. 6 a Records obtained from impact in E–W direction. b Records obtained from impact in W–E direction. c Added E–W component of the profile

data are in good agreement with the recorded data. To further validate this argument, the first arrival time from simulated and experimental records has been computed and analyzed. Figure 8 shows the first arrival time with respect to offset for experimental and numerical records. The analysis of the results shows that the results from the numerical simulation are in good match with the experimental records.

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Fig. 7 Records obtained from observed data in McSEIS-SX and simulated from finite-difference method using 2D shear-wave velocity calculated from refraction survey

7 Conclusions In the present work, the subsurface velocity model near North Almora Thrust in Garhwal Himalaya has been presented. Further, the results obtained from the experimental and numerical studies have been matched and validated. In the numerical study, the velocity model obtained from the experimental McSEIS-SX technique has been used. An accurate second order in time and 12th order in space finite-difference code have been used to obtain the numerical results. The results obtained from the numerical study show a good match with the experimental study results. In order to further validate the results, the first arrival times of both the real data and the simulated data have been matched. It has been observed that the first arrival time obtained from the numerical study matches well with the experimental study results. In the conclusion of this work, it can be stated that the presented code can be efficiently used in simulating the active source results. Further, it can be stated that the developed instrument can be used in the generation of SH waves.

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Fig. 8 Observed and simulated first arrival time with respect to different channel numbers

References 1. Bansal, R., Sen, M.K.: Finite-difference modelling of S-wave splitting in anisotropic media. Geophys. Prospect. 56(3), 293–312 (2008) 2. Boore, D.M.: Stochastic simulation of high-frequency ground motions based on seismological models of the radiated spectra. Bull. Seismol. Soc. Am. 73(6A), 1865–1894 (1983) 3. Claerbout, J.F.: Imaging the Earth’s Interior, vol. 1. Blackwell scientific publications, Oxford (1985) 4. Esteva, L.: Microzoning: models and reality. In: Proceeding of 6th World Conference on Earthquake Engineering, pp. 27–43 (1977) 5. Etgen, J.T.: A tutorial on optimizing time domain finite-difference schemes: “Beyond Holberg.” Stanford Explor. Proj. Report 129, 33–43 (2007) 6. Guadagno, F.M., Nunziata, C.: Seismic velocities of fractured carbonate rocks (Southern Apennines, Italy). Geophys. J. Int. 113(3), 739–746 (1993) 7. Haberland, C., Maercklin, N., Kesten, D., Ryberg, T., Janssen, C., Agnon, A., Weber, M., Schulze, A., Qabbani, I., El-Kelani, R.: Shallow architecture of the Wadi Araba fault (Dead Sea Transform) from high-resolution seismic investigations. Tectonophysics 432(1–4), 37–50 (2007) 8. Improta, L., Zollo, A., Bruno, P.P., Herrero, A., Villani, F.: High-resolution seismic tomography across the 1980 (Ms 6.9) Southern Italy earthquake fault scarp. Geophys. Res. Lett. 30(10) (2003)

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9. Igel, H., Weber, M.: SH-wave propagation in the whole mantle using high-order finite differences. Geophys. Res. Lett. 22(6), 731–734 (1995) 10. Jeanne, P., Guglielmi, Y., Cappa, F.: Multiscale seismic signature of a small fault zone in a carbonate reservoir: relationships between VP imaging, fault zone architecture and cohesion. Tectonophysics 554, 185–201 (2012) 11. Jennings, P.C.: Engineering Features of the San Fernando earthquake of February 9, 1971 (1971) 12. Jiang, X.: A review of physical modelling and numerical simulation of long-term geological storage of CO2 . Appl. Energy 88(11), 3557–3566 (2011) 13. Karastathis, V.K., Ganas, A., Makris, J., Papoulia, J., Dafnis, P., Gerolymatou, E., Drakatos, G.: The application of shallow seismic techniques in the study of active faults: the Atalanti normal fault, central Greece. J. Appl. Geophys. 62(3), 215–233 (2007) 14. Kelly, K.R., Ward, R.W., Treitel, S., Alford, R.M.: Synthetic seismograms: a finite-difference approach. Geophysics 41(1), 2–27 (1976) 15. Park, C.B., Miller, R.D., Xia, J.: Multichannel analysis of surface waves. Geophysics 64(3), 800–808 (1999) 16. Ruiz, S.E.: Influencia de las condiciones locales en las características de los sismos. Universidad Nacional Autónoma de México, Instituto de Ingeniería (1977) 17. Sanny, T.A., Sassa, K.: Detection of fault structure under a near-surface low velocity layer by seismic tomography: synthetics studies. J. Appl. Geophys. 35(2–3), 117–131 (1996) 18. Sheley, D., Crosby, T., Zhou, M., Giacoma, J., Yu, J., He, R., Schuster, G.T.: 2-D seismic trenching of colluvial wedges and faults. Tectonophysics 368(1–4), 51–69 (2003) 19. Sozen, M.A. (ed.): Engineering Report on the Caracas Earthquake of 29 July 1967. National Academy of Sciences (1968) 20. Srivastava, P., Mitra, G.: Thrust geometries and deep structure of the outer and lesser Himalaya, Kumaon and Garhwal (India): implications for evolution of the Himalayan fold-and-thrust belt. Tectonics 13(1), 89–109 (1994) 21. Valdiya, K.S.: The two intracrustal boundary thrusts of the Himalaya. Tectonophysics 66(4), 323–348 (1980) 22. Virieux, J.: SH-wave propagation in heterogeneous media: velocity-stress finite-difference method. Geophysics 49(11), 1933–1942 (1984) 23. Virieux, J.: P-SV wave propagation in heterogeneous media: velocity-stress finite-difference method. Geophysics 51(4), 889–901 (1986) 24. Xia, J., Miller, R.D., Park, C.B.: Estimation of near-surface shear-wave velocity by inversion of Rayleigh waves. Geophysics 64(3), 691–700 (1999) 25. Xia, J., Miller, R.D., Park, C.B., Ivanov, J.: Construction of 2-D vertical shear-wave velocity field by the multichannel analysis of surface wave technique. In: 13th EEGS Symposium on the Application of Geophysics to Engineering and Environmental Problems, pp. cp–200. European Association of Geoscientists & Engineers (2000) 26. Yordkayhun, S., Sujitapan, C., Chalermyanont, T.: Joint analysis of shear wave velocity from SH-wave refraction and MASW techniques for SPT-N estimation. Songklanakarin J. Sci. Technol. 36(3) 2014

A Variational Mode Decomposition Approach for Modal Identification of Structures S. Gupta and S. Kaloni

Abstract Modal identification describes the behavior of the structure under dynamic loading conditions such as earthquake and wind. A newly developed variational mode decomposition (VMD) method identifies the modal parameter of structures based on dynamic responses. The VMD algorithm decomposes the acceleration response from the structure under the earthquake excitation into a series of a finite number of the mono-components. The decaying amplitude of the extracted modal responses is then utilized to find the natural frequencies by using a fast Fourier transform. Finally, the mode shape in the modal space for each decomposed modes in the structure is recognized from the obtained modal response data. This paper presents the applicability of the variational mode decomposition for modal identification purposes and its comparison with the earlier developed Hilbert spectral analysis (HSA) and empirical mode decomposition (EMD) method. To demonstrate the efficiency of VMD algorithm, two case studies were considered. First, modal parameters of a numerical model of a three-story shear-beam type building are computed using VMD technique followed by an experimental study on multistory building model. The results demonstrate that the proposed VMD approach can be easily identified the modal parameter of building with better accuracy. Keywords Variational mode decomposition · Empirical mode decomposition · Modal identification

S. Gupta (B) · S. Kaloni Dept of Civil Engineering, NIT Uttarakhand, Srinagar, India e-mail: [email protected] S. Kaloni e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_34

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1 Introduction In the fields of mechanical and aeronautical engineering, it is widely used, and there is also a great scope in the health monitoring of structures and identify the spot damage at the initial stage, and also, identifying the dynamic properties of civil structures from ground data captured during strong ground shaking has been a research topic in civil engineering for few decades. Techniques for modal identification are useful not only for assessing the present conditions of the building, but also for enhancing future performance, confirming retrofit processes, vibration serviceability assessment, and validating predictive models. Research on determining the dynamic properties of civil structures from the response observed during intense ground shaking has been ongoing for more than three decades. The basic idea for damage detection based on structural dynamics is variation in physical properties such as mass, damping, and stiffness due to damage that causes modal properties such as frequency, damping ratio, and mode shape would be detectable. Therefore, the variation in the modal parameter implies the variation of the structure state of health, that is, damage. Therefore, the modal identification is an experimental measurement and determination of the dynamic behavior of the structure. Since real-world structures may exhibit nonlinear behavior even in their healthy state, it is very difficult to identify the behavior of such structures by using traditional time domain and frequency domain method. Over the past few decades, many modal information-based damage detection time–frequency techniques such as Hilbert-Huang transform (HHT), wavelet transform (WT), short-time Fourier transform (STFT), synchro-squeezed wavelet transform (SSWT), empirical mode decomposition (EMD), and local mean decomposition (LMD) for structural damage identification have been developed. The WT and HHT approaches, which have been widely used to identify the dynamic behavior of structures subjected to earthquake excitation, may be the two most popular timefrequency analysis tools among these time-frequency techniques. A newly developed modal identification technique, namely, variational mode decomposition (VMD), has been successfully utilized for estimation of modal parameter of the civil structures under the dynamic loading condition in recent time.

2 Modal Identification Algorithm Variational mode decomposition (VMD) proposed by Dragomiretskiy et al. [7] which is non-recursive alternative to the EMD algorithm and applied to the modal identification of the structure systems. There are three main steps in the procedure of the VMD algorithm: (1) The VMD is used to extract the natural frequencies and modal response from a recorded acceleration response by applying fast Fourier transform (FFT), (2) For each modal response, a linear least-squares fitting function is suggested to determine the damping ratio from the response’s decaying amplitude of system, (3)

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Mode shape vectors are obtained after repeating the same procedure for all available sensors on the structure.

2.1 Extracting Modal Responses and Frequencies The VMD is used to decompose an acceleration signal S(t) into a series of discrete number of sub-signals (modes), sk (t), k = 1, 2, 3, …, K, each with a small spectral bandwidth. Each sub-signal is supposed to be compacted around a respective central frequency which can be determined along with the decomposition. The VMD decomposition process is a constrained optimization problem that minimizes the sum of the estimated mode bandwidths: ⎫ ⎧ k ⎬

k k   ⎨     j − j ωt  ∂t δ(t) + subjected to e ∗ s sk (t) = s(t) min α (t) k ⎭  {sk },{ω K }⎩ πt k=1

k=1

where * indicates the convolution, α represents the variance of the noise, δ and k indicate the Dirac function and number of sub-signal of response, respectively, {sk } = {s1 , s2 , . . . sk } and {ωk } = {ω1 , ω2 , . . . ωk } are the modes and center frequencies, respectively.

2.2 Identification of Modal Damping Ratios Since most of the civil engineering structures have low inherent damping, the damping ratios for each mode can be computed once the modal response has been specified. The damping ratio can be calculated from the modal response’s decaying amplitude. √ The modal damping ratios are calculated using the 1/ 2 Method, which has been modified to fit the Fourier √ spectrum. Figure 1 depicts the damping ratio evaluation method utilizing the 1/ 2 method on a Fourier spectrum of one data. The nth modal damping is calculated using f n as the nth modal natural frequency identified by the half power bandwidth approach and the damping ratio is computed as: Damping ratio(ξ ) =

1 δf 2 fn

√ where f is the bandwidth in frequency domain at X max / 2 method as half power bandwidth method. X max is the amplitude of the nth modal natural frequency f n on Fourier spectrum.

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Fig. 1 Evaluation method of each mode damping ratio

2.3 Identification of Mode Shapes Using VMD, we examine the decomposed measured response into its constituent parts. The mode shapes are then estimated using these separate parts. The ratio of recognized harmonic sources from the recorded data at different floor levels obtained by using VMD with the one corresponding to the higher energy level (generally roof level) can be used to determine the corresponding mode shape vector. Now, we considered the response of n-degree of freedom of structural system and its constituent parts, as determined by VMD: xk (t) = sk1 (t) + sk2 (t) + · · · + skm (t) where ski (t) signifies the ith VMD recognized component of the kth signal and xk (t) denotes the response of the kth degree of freedom of the system. We obtain the following equation using the modal expansion theorem (with mode truncation): xk (t) = ∅k1 q1 (t) + ∅k2 q2 (t) + · · · + ∅km qm (t) Dynamic response, x = ∅ki qi (t), where ∅i is mode shape matrix at the ith floor level and q represents the modal coordinate i = 1, 2, …, m  ∅i = mean

SK i (t) Sni (t)



SK i (t) denotes identified constitute component at the ith floor level and Sni = normalized constituent components at i = 1, 2, …, m.

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2.4 Modal Assurance Criteria (MAC) Modal assurance criteria (MAC) are used to assess the degree of correlation between the identified mode shape from analytical models and modes from the VMD method, and hence, mode shape vectors are evaluated for accuracy using the modal assurance criterion (MAC). It is easy to use, and there is no requirement of the formation of matrix for the estimation of mode shape. The range of MAC value lies between 0 and 1, where value of 1 indicates the good consistency of mode shape, and if the value is close to 0, the mode shapes are not consistent. It has been shown that the modal assurance criterion is a straightforward statistical idea that has the potential to be a very effective tool for experimental modal analysis. MAC (r, q) =

|{ϕ A }rT {ϕx }q |2 {(ϕ A }rT {ϕ A }r )({ϕx }qT qT{ϕx }q )

2.5 Effect of Measurement Noise Some simulated white Gaussian noises (i.e., 5 and 10% white noises) introduced to the response of the structural system in order to investigate the performance of VMD algorithm in noise signal processing. When VMD is employed for signal decomposition, the same procedure is performed. The input noise signal is scaled using simulated signals collected from experiments, and the signal is then decomposed into the number of IMFS using VMD, and the modal parameters from these noisy signals are found.

3 Numerical Study and Validation VMD method attempts to split an acceleration signal into a number of monocomponent with gradually changing amplitudes and compacted around their respective center frequencies, so that when their cumulative modes are combined, then responses reproduce the original acceleration response of the structural system. To validate the proposed algorithm, two examples were presented. In first example, three-story shear-beam type building model is to be considered. In the next example, vibration of six-story building recorded by an accelerometer was studied. Table 1 shows the consistency and accuracy of frequencies between acquired from the present algorithm and theoretical values. Additionally, the effectiveness of the suggested technique was examined at various signal-to-noise ratio levels (SNR) ranges from having low-to-high SNRs. The noisy signal in Fig. 2b was first introduced with white Gaussian noise and then proceed with the VMD algorithm and identified the

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Table 1 Natural frequencies and damping ratios of 3-DOF building model from the proposed method and HSA method Mode

1

Theoretical value

Identified value

HSA method [9]

Frequency (Hz)

Damping ratio (%)

Frequency (Hz)

Damping ratio (%)

Frequency (Hz)

Damping ratio (%)

2.22

2.0

2.20

2.2

2.22

2.1

2

6.22

5.6

6.20

5.4

6.22

5.7

3

8.98

8.1

8.90

7.3

8.94

7.8

(a)

(b)

Fig. 2 Acceleration impulse response of 3-DOF system: a response of third floor without noise, b response with noises (5%), and response with noise (10%) of third floor

desirable modal parameters results. Table 2 lists the identified frequencies for lowto-high SNRs, it is clear that all the natural frequencies were identified with efficient and better accuracy.

3.1 Identification of 3-DOF Shear-Beam Type Building First, a 3-DOF shear building structure was used to assess the capabilities of the suggested method in structural identification problem. The following properties of a three-story shear-beam building model are taken into consideration in which all stories have the same mass, stiffness, and viscous damping with m i = 1000 kg, ki = 980 KN/m, ci = 2.814 KN-s/m, respectively, (j = 1; 2; 3). Suppose an impact loading is applied to the second floor. The analytical modal frequencies of shear building were computed first, then compared to the experimental modal frequencies. For a three-degree of freedom structure, the mass matrix M, stiffness matrix K, and damping matrix C were formed. The acceleration responses of each story are collected using the accelerometers which are installed in the building and recorded the response. Now, recorded signals are processed to identify the modal properties of the structures. Finally, the VMD

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algorithm is used on the response in order to decompose the relevant IMFs and find out the modal parameters. Figure shows the derived IMFs from the vibration data without and with noise. After this, natural frequency of three-story building is calculated by fast Fourier transform. The acceleration response of the building is obtained with the help of time-stepped integration method (Newmark-beta method). The acceleration response of the top floor is selected for the signal decomposition which is shown in Figs. 2 and 4 (Tables 1 and 2). By computing the modal assurance criterion (MAC), consistency for the first three mode of the finite element model is checked and comparing them to those estimated by the VMD and determined to be in the range [1.0–0.90]. The uniformity of calculated mode shapes from vibration data is confirmed by large MAC values. The mode shapes identified by the VMD processes are extremely similar and confirming the accuracy of the mode shape of the structure system (Table 3; Fig. 5). (a)

(b)

Fig. 3 Decomposed IMFs using VMD corresponding to the response: a without noise, b with noises (5%), and with c noise (10%) of third floor

(a)

(b)

(c)

Fig. 4 Fourier transforms: a Fourier transform of third floor without noises, b Fourier transform with 5% noise and c Fourier transform with 10% noises to peak response ratio

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Table 2 Natural frequencies and damping ratios of 3-DOF building model with noise 5 and 10% Mode

Identified value with 5% noise

Identified value with 10% noise

HSA method with 5% noise [9]

Frequency (Hz)

Damping ratio

Frequency (Hz)

Damping ratio (%)

Frequency (Hz)

Damping ratio (%)

1

2.20

2.0

2.20

2.2

2.21

1.9

2

6.20

5.4

6.20

5.4

6.19

5.5

3

8.90

7.3

8.90

7.3

8.94

7.6

Table 3 MAC values for identified modes using the proposed method and the HSA method Mode

Identified values Noise = 0%

HSA method [9] Noise = 5%

Noise = 10%

Noise = 0%

Noise = 5%

1

0.99

0.91

0.91

1.00

1.00

2

0.96

0.96

0.96

1.00

0.99

3

0.91

0.91

0.91

0.99

0.99

2.5 2 1.5 1 0.5 0

2

4

-2

3.5

3.5

3

3

Height of floor (m)

Height of floor (m)

Height of floor (m)

3

0

Mode Shape 3

Mode Shape 2

Mode Shape 1

3.5

2.5 2 1.5 1 0.5 0

0

2

-5

2.5 2 1.5 1 0.5 0

0

5

Fig. 5 Identified mode shape of the 3-story building

3.2 Identification of Multistory Building A 6-story RC frame structure is considered with the following properties: Beam size = 350 × 500 mm, Column size = 500 × 500 mm, Clear cover for beams = 25 mm, Clear cover for columns = 40 mm, Shape of members = Rectangular, , Live load on terrace = 1 kN , Floor finish = Story Height = 3.3 m, Live loads = 4 kN m2 m2 kN N N 1 m2 , f ck = 25 mm2 , and f y = 415 mm2 . . An earthquake loading condition (PEER NGA strong ground data, Northridge-01, 1/17/1994, Tarzana—Cedar Hill A, 360) is used as the input force on the considered building model. Six accelerometer is employed to collect the response of each story of building under the earthquake excitation. Finally, the VMD algorithm is used to proceed on the response in order to produce the relevant IMFs. Fast Fourier transform (FFT) is

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(a)

(b)

429

(c)

Fig. 6 Acceleration impulse response of 6-DOF system: a response of top floor without noise pollution, b response with noises (5%), and response with noise (10%) of top floor

applied to each IMF of an acceleration response to transfer the modal response into frequency domain, and then, modal parameters are collected. Acceleration response of a building is obtained by time history analysis using finite element software which is shown in Figs. 6 and 7 (Tables 4 and 5). (a)

(b)

(c)

Fig. 7 Fourier transforms: a Fourier transform of top floor of 6-DOF system without noises, b Fourier transform with 5% noise and c Fourier transform with 10% noises to peak response ratio

Table 4 Natural frequencies and damping ratios of 6-DOF building Mode No.

Frequency (Hz) Theoretical

Without noise

Noise (5%)

Noise (10%)

1

0.823

0.825

0.825

0.825

2

2.66

2.65

2.65

2.65

3

4.99

5.35

5.35

5.35

4

6.01

6.9

6.9

6.9

5

7.88

8.00

8.00

8.00

6

9.38

9.625

9.625

9.625

430 Table 5 Damping ratios of 6-DOF building by propose method

S. Gupta and S. Kaloni Mode No.

Damping ratio (%) Without noise

Noise (5%)

Noise (10%)

1

4.6

4.6

4.2

2

1.46

1.46

1.46

3

1.40

1.40

1.40

4

0.81

0.81

0.81

5

0.78

0.78

0.78

6

0.77

0.77

0.77

4 Conclusions This study presents the variational mode decomposition technique as a potential approach for modal identification of civil structures. The method uses the measured free vibration time histories response and decomposed into the finite number of mono-component (IMFs), and then, fast Fourier transform is used to determine modal properties, such as natural frequencies and mode shapes of the decomposed modes. When the response of each story of a structure is measured, the proposed algorithm is efficient to identifying the mode shapes of the structure. The proposed method allows for the extraction of natural frequencies and damping ratios of all decomposed modes from one measurement only. The advantages of the proposed method for the modal identification have been illustrated through a numerical example, and these numerical simulations also considered the effect of different noise in the signal. The identification outcomes from the suggested method were compared with those from HSA method, and these results show the better performance of VMD method. The proposed VMD-based modal identification method avoids characterizing individual modes as signals with explicit IMFs, in contrast to existing decomposition methods such as the HSA and EMD. Therefore, the methodology proposed in this research provides a unique and effective tool for identifying linear MDOF structures’ dynamic features.

References 1. Liu, S., Zhao, R., Yu, K., Zheng, B., Liao, B.: Output-only modal identification based on the variational mode decomposition (VMD) framework. J. Sound Vib. 522, 116668 (2022) 2. Zhang, M., Xu, F.: Variational mode decomposition based modal parameter identification in civil engineering. Front. Struct. Civ. Eng. 13(5), 1082–1094 (2019) 3. Yang, K., Wang, G., Dong, Y., Zhang, Q., Sang, L.: Early chatter identification based on an optimized variational mode decomposition. Mech. Syst. Signal Process. 115, 238–254 (2019) 4. Bagheri, A., Ozbulut, O.E., Harris, D.K.: Structural system identification based on variational mode decomposition. J. Sound Vib. 417, 182–197 (2018). https://doi.org/10.1016/j.jsv.2017. 12.014

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5. Sadhu, A.: An integrated multivariate empirical mode decomposition method towards modal identification of structures. J. Vib. Control 23(17), 2727–2741 (2017) 6. Kaloni, S., Shrikhande, M.: ScienceDirect Output Output only only system system identification identification based based on on synchrosqueezed synchrosqueezed transform transform. Procedia Eng. 199, 1002–1007 (2017). https://doi.org/10.1016/j.proeng.2017.09.235 7. Dragomiretskiy, K., Zosso, D.: Variational mode decomposition. IEEE Trans. Signal Process. 62(3), 531–544 (2014) 8. Nagarajaiah, S., Basu, B.: Output only modal identification and structural damage detection using time frequency & wavelet techniques. Earthq. Eng. Eng. Vib. 8(4), 583–605 (2009) 9. Yang, J.N., Lei, Y., Pan, S., Huang, N.: System identification of linear structures based on Hilbert–Huang spectral analysis. Part 1: normal modes. Earthq. Eng. Struct. Dyn. 32(9), 1443– 1467 (2003) 10. Paultre, P.: Dynamics of structures. Wiley & Sons (2013) 11. Yang, Y., Nagarajaiah, S.: Time-frequency blind source separation using independent component analysis for output-only modal identification of highly damped structures. J. Struct. Eng. 139(10), 1780–1793 (2013) 12. Ni, P., Li, J., Hao, H., Xia, Y., Wang, X., Lee, J., Jung, K., Street, K., Hom, H., Kong, H. (n.d.). Time-Varying System Identification using Variational Mode Decomposition. Vmd, 0–2. https://doi.org/10.1002/stc.2175 13. Baker, J.W.: Measuring bias in structural response caused by ground motion scaling. In: Pacific Conference on Earthquake Engineering, vol 56, pp 1–6 (2007). https://doi.org/10.1002/eqe 14. Sun, M.M., Li, Q. S., Zhou, K., He, Y.H., Zhi, L.H.: Modal identification from non-stationary responses of high-rise buildings by variational mode decomposition and direct interpolation techniques. Int. J. Struct. Stab. Dyn. 20(11) (2020). https://doi.org/10.1142/S02194554205 01151 15. Pastor, M., Binda, M., Harˇcarik, T.: Modal assurance criterion. Procedia Eng. 48, 543–548 (2012). https://doi.org/10.1016/j.proeng.2012.09.551

Geo-Factor Inference Modelling with Empirical Susceptibility Weights Approach for GIS-Based Seismic Hazard Mapping of Thiruvananthapuram City Madhu Mohan Velapgy

and E. S. M. Suresh

Abstract Appropriate assessment of various site parameters influencing the spatial distribution of seismic hazard was investigated to critically identify the susceptible spots in the study area, Thiruvananthapuram city, a low to moderately active seismic zone in southern Peninsular India (PI). The Geo-Factor Inference (GFI) model developed in this study evaluates respective contribution level of local geofactors to estimate the peak ground acceleration (PGA), representing regional seismic hazard. Considering the complexity and limitations in the widely accepted analytic hierarchy process (AHP), mostly used in seismic studies, a simplified empirical susceptibility weights approach (ESWA) was proposed to assess the attribute weights for GFI model. The ESWA weights were mathematically calibrated by rank-order centroid (ROC) tool and subsequently, compared with AHP weights to deduce variations in scale ranges. ESWA values from GFI were finally assigned to respective geo-factor thematic layers for weighted overlay analysis in Geographical Information Systems (GISs) platform. The solution from GFI model, integrated with GISapproach, provides distinctive distribution of susceptibility levels ranging from very low to high in the seismic hazard mapping of the city. Keywords Seismic hazard · Geo-factor inference (GFI) · Empirical susceptibility weights approach (ESWA) · Rank-order centroid (ROC) · AHP The presentation of material and details in maps used in this chapter does not imply the expression of any opinion whatsoever on the part of the Publishers or Author concerning the legal status of any country, area or territory or of its authorities, or concerning the delimitation of its borders. The depiction and use of boundaries, geographic names and related data shown on maps and included in lists, tables, documents, and databases in this chapter are not warranted to be error free nor do they necessarily imply official endorsement or acceptance by the Publisher or Author. M. M. Velapgy (B) Department of Civil Engineering, National Institute of Technical Teachers’ Training and Research, Ministry of Education (MoE), Government of India, Chennai, India e-mail: [email protected] E. S. M. Suresh Civil Engineering and Head-Department of Engineering Education, National Institute of Technical Teachers’ Training and Research, MoE, Government of India, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_35

433

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1 Introduction The seismic hazard of a site is mainly influenced by three interacting factors: seismic wave characteristics of ground, local ground effects and built environment. However, in most of the studies, only the severity of ground motion at a particular site is commonly described by the seismic hazard without necessary consideration of consequent factors [11]. Generally, the geo-spatial uncertainties of geological and geotechnical variances influence the seismicity of a region, and hence, it is essential to characterise the site-specific geo-statistical assessments by trial-and-error, with a high degree of spatial variability [10]. The present study focuses on the mapping of seismic hazard of Thiruvananthapuram city, a low-seismicity area in the state of Kerala in southern part of PI. This region is classified as Zone-III [4], indicating earthquake susceptibility of this city where majority of the buildings are low-to-high-rise reinforced concrete construction. The seismic hazard map for this region is defined in terms of PGA which is based on the earthquake magnitudes, signifying the ground motion intensity [22] was selected as the single best alternative or target criterion. The seven key site-related geo-parameters: geology, lineaments/faults, geomorphology, elevation, slope, soil distribution and building density, influencing quantification of ground motion were considered as sub-criteria. The GFI model developed for generating hazard map of the city attempts for a reasonable assessment of weights and ranks of geo-factors to understand the respective contribution levels towards ground motion characteristics of the site. Although the pair-wise comparison method, AHP [18], is one of the well-known multi-criteria decision-making (MCDM) technique to obtain the weights of different criteria in seismic studies, many researchers have expressed their concerns on AHP, as a falsely precise weight elicitation methodology specifying its limitations, such as, ranking irregularities due to rank reversal impossibility with multiplicative variant [21], interpretation issue of criteria weights and difficulty in distinguishing amongst criteria due to artificial limitation of 9-point scale. Considering these limitations and uncertainty to obtain a realistic solution even after a lengthy AHP process, a simplified alternative method, empirical susceptibility weights approach (ESWA) is proposed for weight assessment of selected geo-factors, in this study. The ESWA weights of geo-factors were calibrated using an additive multi-attribute method, rank-order centroid (ROC) for validating the proposed GFI model, to ensure unbiased quantities and were further compared with corresponding AHP weights to understand the scale of variations in both. The ESWA values were finally assigned to respective thematic layers for weighted overlay analysis, using GIS platform. This integrated GFI model with GIS-based method has a reasonable analytical assessment capacity for providing distinctive distribution of susceptibility scenarios, between very low to high levels in the seismic hazard mapping of Thiruvananthapuram city.

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2 Study Area and Geo-Factor Data 2.1 Regional Geographical and Geological Settings Thiruvananthapuram (previously known as, Trivandrum) is a densely populated capital city of Kerala state in India, which is one of the most important tourist destinations in the world, with historical landmarks. The city is located at latitude 08° 30, N and longitude 76° 54, E covering an area of about 214.86 km2 , close to Western Ghats, limited by the west coast of southern PI (Fig. 1). The toposheet from Survey of India (SOI) was used for the base map and to locate built environment in the city, using ArcGIS software. The western coastal tracts of geological part form the city land profile, in the Southern Granulite Terrane (SGT) region of PI, occupied largely by lateritised, tertiary and quaternary sedimentary formations of Mio-Pliocene age. The SGT has three tectonic blocks separated by important shear zone systems [7] known as, Cauvery Suture Zone (CSZ) and Achankovil Shear Zone (AKSZ), separating Northern Granulite Block, Madurai Block and Trivandrum Block (Kerala Khondalite Belt). Two major segments of Trivandrum and Nagercoil blocks are located on the south of Achankovil fault (Fig. 2), revealing moderately active seismicity of this area. Only two fault lines [9], Achankovil (N 60° W) and Kallada (N 52° W) and five lineaments (medium and above) are close to the study area, which are amongst those considered to be sensitive to the West Coast fault systems, identified near the study area, having direct effect on regional seismicity. The geological datasets were based on Geology Map of Geological Survey of India (GSI), Chetty et al. [7] and Thiruvananthapuram District Resource Map. Bhuvan’s resourcesat-2 data from the Linear Imaging Self-Scanning Sensor (LISS-III) with 23.5 m spatial resolution were used for mapping the geology and lineament layers.

2.2 Regional Geomorphological, Geotechnical Aspects and Seismicity The geomorphological features of the city have a physiographically rugged topography with three distinctive topographic units as, lowland (coastal plain), midland and highland terrain, from west to east. Geomorphology map of GSI and Thiruvananthapuram District Resource Map, Bhuvan’s resourcesat-2 data, were referred. The ground terrain profile of this region largely constitutes rocks, with highly heterogeneous local soils varying from, red loams, coastal and riverside alluvium, lateritic soil, brown hydromorphic soil and forest loam. Soil map of SOI-School of Atlas and Geotechnical Investigation Reports of various locations in the city were used. Elevation and slope details were obtained from relief and slope map of SOI and Shuttle Radar Topographic Mission (SRTM)-DEM data with a resolution of about 30 m. The SOI, DEM and Google Maps were used, for sourcing building data. Many researchers have scientifically established that, the collision of Indian tectonic plate with Asia

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Fig. 1 Location map of study area [3]

Fig. 2 Geological features of SGT in PI (GSI & [7])

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plate had resulted in a compression effect on the Indian crust. In consequence to this collision effect, the PI shield experiences substantial earthquake hazards, due to the existence of many critically loaded faults [19]. The seismic hazard evaluated from the sizes (magnitudes) of potentially damaging earthquakes, occurring at multiple locations, is uncertain in most of the situations [13]. The catalogue of historical and instrumentally recorded earthquakes for the region was obtained from national and international organizations including, India Meteorological Department (IMD)previously, Bhuvan-National Remote Sensing Centre (NRSC), National Centre for Earth Science Studies (NCESS), Geological Survey of India (GSI), National Centre for Seismology (NCS), Amateur Seismic Centre (ASC), International Seismological Centre (ISC), International Seismological Summary (ISS) and United States Geological Survey (USGS). Several compilations from published literatures [6, 16] were used for historical earthquakes, which also include significant earthquakes of equivalent moment magnitudes (M w ≥ 4.0), after 1950. The experiences of past seismic studies showed that a site ground vibrates due to earthquakes originate anywhere around 300 km radius, about the site. However, considering the influence of global seismic activity rate on the regional seismic parameters and also due to the sparsely available data within this radius, the seismic datasets within the lager geographical influence zone of 500 km radius around Thiruvananthapuram city were collected [12] in this study, which were reasonably informative, after eliminating duplicate events. The maximum equivalent earthquake moment magnitude, M w = 5.8 (Coimbatore, 1900), within the influence zone had been considered for the target criterion in this investigation. Ganesha et al. [9], identified 22 events close to the 9 major lineaments/faults near this region, amongst 31 earthquakes (with equivalent moment magnitudes, 5 nos., M w > 5 and 26 nos., M w > 3) occurred in Kerala, since 1821. These fault lines with lengths more than 20 km indicate possible correlation between the fault systems and earthquakes in this zone.

3 Methodology and Data Analysis for GFI Model Hazard mapping of the study area involves subjective assignment of weight values for each of the influencing factors related to corresponding thematic layers and integrating multiple overlays of various criteria and analysed using ArcGIS software, to evaluate the target criteria. Based on the pilot survey and factor analysis, the main ground-associated parameters including geology (GL), lineament density/fault distribution (LD), geomorphology (GM), soil distribution (SD), elevation (EL), slope (SL) and building density (BD) which contribute to the quantification of ground motion were considered as sub-criteria set, for the study region. The decision about weight determination of respective contributing factors has a high influence in the final estimation of regional seismic hazard [17]. The most reasonable weights of identified geo-factors, specifically, GL, LD, GM, SD, EL, SL and BD, pertaining to the study area were assessed using a three-step procedure. ESWA weights were initially assigned, followed by testing and calibration of these values by ROC weighting

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method, which were subsequently compared with AHP weights, to ascertain the reasonably acceptable weights related to each of the selected GIS layers’ criterion. The degree of belief in evolving GFI model will mainly depend on how well they can represent the weight scenarios of ESWA values, which were validated by ROC weights, with the similar constraints. The ESWA weights were assigned according to the seismic sensitivity of respective layers, and the classes indicate the associated seismic risk level attributed to each layer, regarded from very low to high, the GIS layers were ranked, analysed, reclassified into a common scale of influence by the importance of ESWA weights and finally, overlaid using weighted overlay tool in ArcGIS software to generate the seismic hazard map of city.

3.1 Empirical Susceptibility Weights Approach (ESWA) in GIS Layers The ESWA methodology is a simplified, qualitative, non-iterative, mathematically tested and calibrated weight assessment model developed for estimating the rational contribution levels of seven identified key geo-factors (thematic layers) or sub-criteria towards the evaluation of PGA (target criterion) proposed for the hazard assessment for the study area, considering the limited number of attributes within the smaller area extent of low-seismicity site. The parameters influencing the seismic risk were studied for different level of susceptibility in the seismic hazard zone [8]. Normally, the higher weight is assigned to the thematic layer which contributes more to the hazard. Many previous investigations established the geology and soil attributes, as the influencing factors for the earthquake events [23]. Based on the earlier studies, the highest weights were assigned to the local geology and associated lineament density factors, which are recognised as the main contributing attributes to the target criterion representing regional seismicity. These identified geo-factors were weighed sequentially, on percentage-basis with reasonable judgements on the damage levels of these attributes, based on the understanding from the various available data sources, including site-related datasets, literatures from earlier research studies, reliable internet information and expert opinions. A questionnaire along with a query for comparison of each geo-factor (to get the geometric mean for the final solution) was formulated and conducted to twelve expert opinions from varied engineering disciplines, including civil, geotechnical, geology, geoinformatics, seismology and geophysics for the pilot survey. Accordingly, the ESWA weights of the selected geo-factors were empirically assigned as, geology (35%), lineament distribution (20%), geomorphology (15%), soil distribution (10%), elevation (10%), slope (5%) and building distribution (5%), according to the respective influence levels on PGA, aggregated to 100% of cumulative for the whole sub-criteria set.

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3.2 Testing and Calibration of ESWA Weights with ROC Weighting The ESWA geo-factor weights were validated using ROC weighting method, which is a simple and reliable additive MAV weighting procedure for eliminating the possibility of biased values and to ensure the appropriateness of these values in the conceptualisation of GFI model. It geometrically characterises the centroid of feasible weight-space for each of the sub-criteria elements, providing reasonably accurate and acceptable quantities. Understandably, the biases exist for several weight judgments, and precise weight elicitation is subject to inconsistencies, which can be resolved by the reconciliation method to get the final weights, based on ranking of attribute ranges with an effective rank-based formula [5]. The additive multi-attribute ROC weights, a surrogate weighting method [2] is a more systematic analysis of information implicit in the ranks for computing the ROC weights, which are derived from the simplex vertices (extreme points), w1 ≥ w2 ≥ … ≥ wn ≥ 0, limited to Σ n i=1 wi = 1 and indicated by the feasible weight-space, δ n . The vertices of the simplex are defined as, v1 = (1, 0, …, 0), v2 = (1/2,1/2, 0, …, 0), vn = (1/n, 1/n, …, 1/n). The respective coordinates of defining vertices are averaged to obtain the coordinates of centroid and hence, the centroid weight for the most imporΣ(weights), n 1/i. The centroid weight corresponding to the ith most tant element is, 1/n i=1 important attribute, w(i,(ROC)) , is calculated using Eq. (2), given below: w(i, (ROC)) = 1/n

n Σ

1/j , where, i = 1, . . . , n

(1)

j=i

Generally, the ROC weights order the alternatives according to average or expected MAV, which is the largest. In this study, the allotted ranks and computed weights for the ROC weights and corresponding ESWA weights, for each of the attributes in the sub-criteria set, are given in Table 1, and the calibration graph Fig. 3a illustrates the validity of the accuracy and consistency levels of ESWA weights.

3.3 Comparison of ESWA Weights with AHP The calibrated ESWA weights were further compared with the corresponding AHP values (scale 1–9) of geo-factor layers to understand the variations. By comparing the alternatives with respect to a subjective criterion, a pair-wise comparison matrix is formed first in AHP at each level of hierarchy (from second to the last level), followed by the computation of relative weight for each parameter. After estimating AHP weights, the consistency ratio (CR) was checked for each theme, which is equal to 0.072, satisfying the required consistency (CR < 0.1), as shown in Table 2. It is to be noted that the weights for the respective parameters, both in the proposed ESWA

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Table 1 Comparison of ESWA weights for testing with corresponding ROC weights Sub-criteria

Weights by ESWA (%)

ROC weights Allotted ranks

Weights

Weights (%)

Geology

35

1

0.37

37.04

Lineament distribution

20

2

0.23

22.76

Geomorphology

15

3

0.16

15.61 10.85

Soil distribution

10

4

0.11

Elevation

10

5

0.07

7.28

Slope

5

6

0.04

4.42

Building distribution

5

7

Total

0.02

2.04

1.00

100.00

Fig. 3 a Calibration of ESWA geo-factor weights by ROC weighting; b Comparison of geo-factor weights between ESWA and AHP methods

and in the well-accepted AHP methods, are evaluated based only on the subjective judgements. Figure 3b shows the deviations in the resulting weights between ESWA weights and corresponding normalised weight of each layer in AHP.

4 GIS Data for Spatial Analysis The ESWA weights were assigned based on the tangible influence levels of respective layers towards the PGA, and the classes specify the related seismic susceptibility of each layer. After determining the value ranges for geo-factors, the ranking of class values is assigned from 7 to 9 based on the regulations and assumptions of relevant datasets from various disciplines of the attributes, using the standard equal interval classification to emphasise the amount of an attribute value relative to other values, class breaks and to set appropriate ranges of classes. Accordingly, seven classes were considered for GL, five for GM and nine for LD, SD, EL, SL and BD layers

0.195

0.159

Geomorphology 0.112

Soil distribution 0.081

Elevation 0.039

Slope

0.029

Building distribution

1

Total

7.582 0.097 15.038 1.339 0.072

Largest or principal eigenvalue of the matrix, λmax

Consistency index, CI = (λmax − n)/(n − 1)

Mean principal eigenvalue of the matrix, λmax = 2.7699n − 4.3513

Average random consistency index, RI = (λmax − n)/(n − 1)

Consistency ratio, CR = CI/RI

7

0.385

Normalised weight (wi )

Lineament distribution

Order of the comparison matrix, n

Geology

Sub-Criteria Geo-Factors

Table 2 Summary of pair-wise comparison matrix of layers in AHP

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in GIS analysis. The GL, GM, SD and BD geo-factor layers were digitised based on symbols and visual interpretation from related input maps. The lineament/fault distances were determined for LD, using Euclidean distance tool, and the lineament density distribution per unit area is calculated using density command in the ArcGIS spatial analyst extension. The areas of high risk for SL were having the slope values above 5%, and the scale of elevation ranges vary from IM∗ |MW , R = 1 − FIM IM∗

(4)

where FIM (IM∗ ) is the cumulative distribution function (CDF) of IM at MW and R, obtained from the attenuation relations given by various researchers [21–23] considering a set of strong motion parameters.

2.5 Seismic Hazard Curves and Mean Annual Rate of Occurrences The seismic hazard rate in the Poisson model is used to evaluate the probability of exceedance of IM for specific time periods, or the exposed period of the system (also called the structural design life) and the mathematical relationship is given by:   P IM > IM∗ |MW , R = 1 − exp(−λIM∗ t)

(5)

where λIM∗ is the exceedance rate (or, probability) of IM∗ for the region of interest and t is the exposed period of the structure.   λIM∗ = ν ∫ ∫ P I M > IM∗ |MW , R f M (MW ) f R (R)dr dMW MW R

(6)

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ν = 10(a−bM0 ) where M0 is the minimum earthquake magnitude for a particular source location and ν is the mean exceedance rate of M0 .

3 Regional Seismicity and Seismic Hazard Curves There are four different seismicity zones on the seismic zoning map of the Indian seismic code [24], namely zones II, III, IV, and V. In the revision of 2002, seismic zones I and II were combined into a single zone II. These seismic zones do not even explicitly take into account the regional seismicity because they are mostly based on the iso-seismal contours of prior large earthquakes [25]. Only zones IV and V are taken into account in this study due to their higher probability of severe earthquakes. In zone IV, the regions of Delhi, Patna, and Darjeeling and Guwahati and Mandi for zone V are selected. These sites are spread out quite far from one another throughout the Himalayan and sub-Himalayan region in order to cover a large geographic area. A large database is compiled from numerous sources, and the majority of the events are taken from the United States Geological Survey (USGS) earthquake catalogue. These catalogues are used to establish seismicity parameters for these areas by taking into account all earthquake events that occurred within a 300 km radius of the center latitude and longitude for Delhi (28.7041 °N, 77.1025 °E), Patna (25.5941 °N, 85.1376 °E), and Darjeeling (27.0360 °N, 88.2627 °E) for zone IV, and Guwahati (26.1445 °N, 91.7362 °E), and Mandi (31.5892 °N, 76.9182 °E) for zone V. Since USGS earthquake catalogue gives moment magnitude for each event thus no requirement for homogenization catalogue treatment. For declustering and completeness check of earthquake catalogue, the window method [26] and slope method [27] are used, respectively. The map of complete catalogue and corresponding regions for different magnitude ranges are shown in Fig. 1. The results of the completeness study shown in Table 1, and these values are used to estimate the earthquake occurrence rates for G-R parameters. Seismicity parameters (‘a,’ ‘b’) of specified regions of interest are calculated by G-R recurrence relationship given in Eq. (1) and shown in Table 2 and Fig. 2. The number of catalogue entries after their treatment within the radius of 300 km is also given in Table 1. Table 1 shows, ‘a’ and ‘b’ values for Delhi and Darjeeling (zone IV regions) are alike to the Mandi (zone V region). The frequency and magnitude of earthquakes in Darjeeling and Mandi are substantially different from those in Delhi. As shown in Fig. 2, the G-R plots for the Darjeeling and Mandi regions nearly coincide, while the G-R line for the Delhi region is rather off-centered. This is due to the significantly lower ‘a’ value for the Delhi region, which was calculated from a very limited number of minor earthquake events. The lower ‘a’ and ‘b’ values for Guwahati region indicate

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Fig. 1 Earthquake events and different regions chosen in India. Map Source Basemap from ArcGIS online

Table 1 Completion years for magnitude ranges Sites

Magnitude ranges 4 ≥ MW < 5

5 ≥ MW < 6

MW ≥ 6

Delhi

58

58

118

Patna

58

58

118

Darjeeling

58

68

83

Guwahati

58

68

118

Mandi

58

68

118

Table 2 Seismicity parameters for all the selected regions Delhi

Patna

Darjeeling

Guwahati

Mandi

(85 events)

(76 events)

(269 events)

(456 events)

(296 events)

a

5.48

4.28

5.58

4.60

5.89

b

1.24

0.99

1.14

0.89

1.20

relatively smaller seismicity, but a higher likelihood of experiencing large earthquake events in the North-eastern part of India. An earlier study that used data collected from the seismicity catalogues of the Indian Society of Earthquake Technology and United States Geological Survey reported similar values for the G-R parameters for the Guwahati region (a ~ 4.60, b ~ 0.84) [28]. Therefore, the seismicity of the

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Fig. 2 G-R relation curve fitting

Darjeeling region, which is categorized in IS-1893 zone IV, is miscalculated, causing a higher risk to the structures designed and constructed in this location. With only 85 and 76 (relatively low magnitude) events in the catalogue, respectively, in Delhi and Patna region, both indicate a lower level of seismicity than Darjeeling region in zone IV. The ground motion prediction equation by Boore and Atkinson [20] is used here for ground motion intensity measure (IM). In the following, the expected peak ground acceleration (PGA) is used as an intensity measure of an earthquake. Figure 3 estimates and shows the mean yearly rate of exceeding the intensity measure (IM) for all regions. Earthquake hazard in Darjeeling region is found to be significantly higher than that of other locations in the same seismic zone (Zone IV). The estimated probability of exceeding IM, taking into account the exposure time of 50 years, is shown in Fig. 4.

(a) Zone IV

Fig. 3 Mean annual exceedance rate of IM for selected locations

(b) Zone V

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(a) Zone IV

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(b) Zone V

Fig. 4 Exceedance probability of IM (PGA) for all the regions for the design life of 50 year

4 Conclusions In this study, the GNU-Octave platform is used to perform the probabilistic seismic hazard analysis. This platform is easy to use, quick running time, and also allowing users to adjust their codes to meet their requirements. To estimate seismic hazard, three regions in zone IV (Delhi, Patna and Darjeeling) and two in zone V (Guwahati and Mandi) of IS-1893 are taken as the study area. The earthquake catalogues are compiled from multiple sources, followed the catalogue treatments such as homogenization, declustering, and completeness check. The seismicity of all the regions is evaluated using G-R recurrence relationships, and it is found that the seismicity of the Darjeeling region, which is categorized in zone IV of IS-1893, is miscalculated, causing a higher risk to the structures that are designed and built in that region. This study also estimates the mean rate of exceedance of intensity measure considering all the uncertainties in earthquake magnitude, source-site distance, and possible occurrence of future earthquake. Further, it estimates the probability of earthquake occurrence for a 50 year exposed period of structure. These estimates may also be used in the computation of structural reliability.

References 1. Gaxiola Camacho, J.R., Azizsoltani, H., Villegas Mercado, F.J., Haldar, A.: A novel reliability technique for implementation of performance-based seismic design of structures. Eng. Struct. 142, 137–147 (2017) 2. Ghobarah, A.: Performance-based design in earthquake engineering: state of development. Eng. Struct. 23(8), 878–884 (2001) 3. Haukaas, T.: Unified reliability and design optimization for earthquake engineering. Probabilist. Eng. Mech. 23(4), 471–481 (2008) 4. Collins, K.R., Wen, Y.K., Foutch, D.A.: Dual-level seismic design: a reliability-based methodology. Earthq. Eng. Struct. Dyn. 25(12), 1433–1467 (1996) 5. Ghosh, S., Collins, K.R.: Merging energy-based design criteria and reliability-based methods: exploring a new concept. Earthq. Eng. Struct. Dyn. 35(13), 1677–1698 (2006)

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6. Lu DG, Song PY, Yu XH, Wang, G.Y.: Global seismic reliability analysis of building structures based on system-level limit states. In: Proceedings of 14th World Conference on Earthquake Engineering. Beijing, China (2008) 7. Ditlevsen, O., Madsen, H.O.: Structural Reliability Methods. Wiley and Sons Ltd (1996) 8. Majhi, D.R., Shrikhande, M.: Residual life of earthquake damaged structures. Soil Dyn. Earthq. Eng. 145, 106694 (2021) 9. GNU-Octave: https://octave.org/download. Last accessed 1 Aug 2022 10. United States geological Survey (USGS): https://earthquake.usgs.gov/earthquakes/search/. Last accessed 1 Aug 2022 11. International Seismological Centre (ISC): http://www.isc.ac.uk/. Last accessed 1 Aug 2022 12. India Meteorological Department (IMD): https://www.imdtvm.gov.in/index.php?option=com_ content&task=view&id=30&Itemid=44. Last accessed 1 Aug 2022 13. National Geophysical Research Institute (NGRI): https://www.ngri.res.in/. Last accessed 1 Aug 2022 14. Wadia Institute of Himalayan Geology (WIHG): https://waics.wihg.res.in/seismology. Last accessed 1 Aug 2022 15. Institute of Seismological Research (ISR): https://isr.gujarat.gov.in/. Last accessed 1 Aug 2022 16. Indian Institute of Technology (IIT), Roorkee: https://pesmos.org/. Last accessed 1 Aug 2022 17. Molchan, G.M., Dmitrieva, O.E.: Aftershock identification: methods and new approaches. Geophys. J. Int. 109, 501–516 (1992) 18. Gutenberg, B., Richter, C.F.: Frequency of earthquakes in California. Bull. Seismol. Soc. Am. 34(4), 185–188 (1944) 19. Weichert, D.H.: Estimation of the earthquake recurrence parameters for unequal observation periods for different magnitudes. Bull. Seismol. Soc. Am. 70(4), 1337–1346 (1980) 20. Baker JW.: An Introduction to Probabilistic Seismic Hazard Analysis (PSHA), White Paper, Version 1.3. https://web.stanford.edu/~bakerjw/Publications/Baker_(2008)_Intro_to_ PSHA_v1_3.pdf. Last accessed 1 Aug 2022 21. Boore, D.M., Atkinson, G.M.: Boore-Atkinson NGA Ground Motion Relations for the Geometric Mean Horizontal Component of Peak and Spectral Ground Motion Parameters, PEER Report 2007/01. Pacific Earthquake Engineering Research Center, Berkeley, California (2007) 22. Campbell, K.W., Bozorgnia, Y.: A ground motion prediction equation for the horizontal component of cumulative absolute velocity (CAV) based on the PEER-NGA strong motion database. Earthq. Spectra 26(3), 635–650 (2010) 23. Abrahamson, N., Silva, W.: Summary of the Abrahamson & Silva NGA ground-motion relations. Earthq. Spectra 24(1), 67–97 (2008) 24. IS 1893. Part 1: Indian Standard Criteria for Earthquake Resistant Design of Structures, part 1 General Provisions and Buildings. Bureau of Indian Standards, New Delhi (2016) 25. Agarwal, P., Shrikhande, M.: Earthquake resistant design of structures. PHI Learning Pvt. Ltd., New Delhi (2006) 26. Uhrhammer, R.A.: Characteristics of northern and central California seismicity. Earthq Notes 57(1), 21 (1986) 27. Mulargia, F., Tinti, S.: Seismic sample areas defined from incomplete catalogs: an application to the Eastern Sicily. Tectonophysics 116, 335–364 (1985) 28. Das, S., Gupta, V.K., Gupta, I.D..: Codal provisions of seismic hazard in Northeast India. Curr Sci 2004–2008 (2005)

Site Amplification Study Using Strong Motion Data Recorded at Various Stations in India from Far-Field Earthquakes Sireesha Jaladi, Babita Sharma, Himanshu Mittal, and O. P. Mishra

Abstract Given the importance of site effects, the site response characteristics in the present work are evaluated at different locations of India using recorded data of three earthquakes that occurred in different parts of the country. The used earthquakes include the 2015 Afghanistan earthquake (Mw 7.5); 2016 Hindukush earthquake (Mw 6.7) and 2016 Manipur earthquake of (Mw 6.7). The results are presented in form of peak ground acceleration (PGA), peak ground velocity (PGV), amplification and predominant frequency. For both Afghanistan and Hindukush earthquakes, the highest and lowest PGA values are observed at SMLA and HYB stations, respectively. For the Manipur earthquake, the highest PGA value of the order of 103 cm/s2 was observed at the SHL station. In the case of the Afghanistan earthquake, the PGV values did not show much variation, as observed in the Manipur earthquake. The local soil effects at horizontal components of ground motion are strongly amplified compared to the vertical components. It is found that three sites, namely SMLA, SHL and KOHMA, are amplified in the frequency range 8–10 Hz, exhibiting less amplification and may be treated as rock sites, or stiff-soil sites. The other sites, namely BHPL, HYB, JORHT and LEKHA, show amplification at a lower frequency range of 0.1–2.9 Hz with amplification a little higher than the previous three sites. The shift of peak amplification from higher to lower frequency range indicates that these sites lie in soft soil regions. The estimated predominant frequency and amplification values in the present work may be very important for the valuation of seismic hazard assessment of the region of study. The site amplification characteristics play an important role in altering the ground motions recorded at different locations, and because of this, the damage may vary widely. The presentation of material and details in maps used in this chapter does not imply the expression of any opinion whatsoever on the part of the Publishers or Author concerning the legal status of any country, area or territory or of its authorities, or concerning the delimitation of its borders. The depiction and use of boundaries, geographic names and related data shown on maps and included in lists, tables, documents, and databases in this chapter are not warranted to be error free nor do they necessarily imply official endorsement or acceptance by the Publisher or Author. S. Jaladi (B) · B. Sharma · H. Mittal · O. P. Mishra National Center for Seismology, Ministry of Earth Sciences, New Delhi 110003, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_37

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Keywords Site amplification · Seismic hazard assessment · H/V ratio

1 Introduction The Indian subcontinent has a history of devastating earthquakes. The major reason for the high frequency and intensity of the earthquakes is that the Indian plate is driving into Asia at a rate of approximately 47 mm/year [1]. Geographical statistics of India show that almost 54% of the land is vulnerable to earthquakes. The latest version of the earthquake zoning map of India [2] divides India into 4 seismic zones (Zones 2, 3, 4 and 5) unlike its previous version, which consisted of five or six zones for the country. According to the present zoning map, Zone 5 expects the highest level of seismicity whereas Zone 2 is associated with the lowest level of seismicity. The Indian plate is continuously moving in the NE direction and colliding with the Eurasian plate, and as a result of the collision, the state of stress in the Indian plate is high, which in turn increases the earthquake hazard, particularly in northern India along the Himalaya collision zone. This process has given rise to three major thrust planes, the Main Central Thrust (MCT), the Main Boundary Thrust (MBT) and the Main Frontal Thrust (MFT) [4]. The region has experienced several great earthquakes in the past hundred years or so (1897 Assam,1905 Kangra; 1934 Bihar-Nepal; 1950 Assam) [1]. During the last episode of strain release, a 750-km-long segment, which lies between the eastern edge of the 1905 rupture zone and the western edge of the 1934 earthquake, remained unbroken. This segment, called the central seismic gap, continues to be under high strain (Fig. 1).

Fig. 1 Himalayan Arc region and the devastating past earthquakes in this zone [7]

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Large earthquakes occurred in the seismic gap in 1803 and 1833, but the magnitudes of these earthquakes were less than 8, and hence, they were not gap-filling events. Based on these considerations and a shortening rate of 20 mm/year across the Himalayas, Khattri [7] has estimated the probability of occurrence of a great Mw 8.5 earthquake in the gap in the next 100 year to be 0.59. The north-eastern region of India is also regarded as one of the most seismically active regions worldwide. A seismic gap called the northeast seismic gap exists between the source zone of the 1950 Assam earthquake and the 1934 Bihar-Nepal earthquake where no major earthquake occurred in recent time. The past seismicity data from 1897 shows that the north-eastern region has experienced two great earthquakes with magnitudes above 8.0 and about 20 large earthquakes with magnitudes varying between 8.0 and 7.0 [6]. Taking it into consideration the Indian subcontinent is at risk due to hazardous earthquakes from the Indian-Eurasian collision. So, it is important to study the site amplification using recorded earthquakes at various sites in India. It is evident that the collision of Indian and Eurasian plates created Hindu Kush subduction zone and it is consisting of metamorphic rocks such as schist, gneiss and marble, as well as intrusive such as granite and diorite of different ages and sizes. The Hindu Kush range remains geologically active and is still rising, and it is prone to earthquakes. Afghanistan is underlain by Precambrian metamorphic rocks which form high regions in the canter of the country and in the Hindu Kush. The swell is connected to the Hindu Kush by Precambrian rocks. The geological, topographic and structural characteristics have been evaluated for Manipur, a state of India. It lies in the north-eastern part of the country. It is surrounded by Myanmar (a neighbouring country) in the east and south-east, and by other adjacent states of India, namely Assam in the west, Nagaland in the North and Mizoram in the south-west. The site amplification characteristics play an important role in altering the ground motions recorded at different locations, and because of this, the damage may vary widely. The assessment of site effects is also needed in ground motion simulation techniques, as the simulation is essential to know about the probable characteristics of strong ground motion from future earthquakes, especially in a region having not many recorded ground waveforms. The present study aims to estimate the site amplification using recorded data of three earthquakes that occurred in the northeast and northwest parts of the country. The used earthquakes include the 2015 Afghanistan earthquake (Mw 7.5); the 2016 Hindukush earthquake (Mw 6.7) and the 2016 Manipur earthquake of (Mw 6.7). The detail about the used earthquakes is given in Fig. 2 and Table 1. The site amplification functions are important for determining the site-specific seismic hazard assessment of a region. The locations of all stations are shown in Fig. 2. Results of this investigation reveal the significance of site response studies. The surface ground motion may be strongly amplified if the geological conditions are dominated by the accumulation of sediments. The geology of all stations varies, and so the amplitude and frequency content of the bedrock motion get modified due to the effect of local soil conditions. The difference in predominant frequency due to various earthquakes may be attributed to path effect, local site conditions are considered independent of the distance between the source and the site, so that the

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Fig. 2 Map showing the different parts of the India region and seismological network deployed in the region. Three circles show major earthquakes in the region, and triangles represent the stations

predominant frequency is more in the third earthquake comparatively earthquake 1 and earthquake 2.

2 Data Processing and Methodology India Meteorological Department (IMD) maintains a 17-station Real-Time Seismic Monitoring Network (RTSMN). The ground motion data recorded at all 17 field stations is transmitted in real time through VSAT communication systems to the Central Receiving Station located at IMD, New Delhi, for processing. In the present, the amplitude time histories and the frequency response curves were analysed using

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Table 1 Geology of the recording stations S. no.

Date of earthquake

Name of the earthquake

Station

Geology of the stations

1

26-10-2015

Afghanistan earthquake

BHPL

Deccan trap, alluvium, sandstone

DDI

Siwalik (boulder conglomerate exposures), sandstone, claystone and mudstone

GOA

The rocks of Dharwar super group of pre-Cambrian age

HYB

Granites, granite gneisses and alluvium

SHL

Precambrian rocks of Gneissic composition are the oldest rocks

SMLA

Tertiary and pre-tertiary rocks

BHPL

Deccan trap, alluvium, sandstone

GOA

The rocks of Dharwar super group of pre-Cambrian age

HYB

Granites, granite gneisses and alluvium

SMLA

Tertiary and pre-tertiary rocks

BHPL

Deccan trap, alluvium, sandstone

GOA

The rocks of Dharwar super group of pre-Cambrian age

HYB

Granites, granite gneisses and alluvium

ITANA

Siwalik sediments and unconsolidated quaternary deposits

JORHT

Precambrian gneisses, schists and quartzites

KOHMA

Alluvium and terrace deposit, tertiary sedimentary rocks

SHL

Precambrian rocks of Gneissic composition are the oldest rocks

2

3

10-4-2016

3-1-2016

Hindu Kush earthquake

Manipur earthquake

(continued)

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Table 1 (continued) S. no.

Date of earthquake

Name of the earthquake

Station

Geology of the stations

SMLA

Tertiary and pre-tertiary rocks

LEKHA

Proterozoic gneissic complex, Shillong group of meso-paleo proterozoic age, granite plutons of neo-proterozoic-lower paleozoic age, lower gondwana sedimentary rocks of permo-carboniferous age

TADON

Precambrian, tertiary rocks

every station of all three earthquakes. The latest accelerographs are capable of providing digital records of ground acceleration with frequency content from DC to even up to 100 Hz with a sampling frequency of 200 sps or more. Few stations installed in northeast region of India are also used in the present study. For each of the case studies, the data ground motion data reported from each station was further analysed using seismic analysis software (SEISAN) to obtain the NS-component, EW-component and Z-component. ViewWave is the software used for viewing strong motion records and also estimating the velocity and acceleration response spectra. The H/V [9] spectral ratios were also derived using the Nakamura technique to obtain the predominant frequency and related amplification. The Fourier transform (FFT) of all the components is estimated, and then, mean of the horizontal components is divided by the vertical components in order to calculate the H/V for all the data considered for the present study.

3 Results and Discussions The accelerograms for each station were derived from the ViewWave software. The Fourier spectra and Pseudo response spectra have also been plotted for each station of each case (Figs. 3, 4 and 5). The H/V [9] spectral ratios and related predominant frequency were derived (Table 2) and plotted against the frequency for two respective stations for each earthquake considered (Figs. 6, 7 and 8). For both Afghanistan and Hindukush earthquake, the highest and lowest peak ground acceleration (PGA) values were observed at SMLA and HYB. For the Manipur earthquake, the highest value of the order of 83.9 cm/s/s was observed at SHL site. A couple of other stations in northeast region also have very high PGA values. A sharp decrease in PGA values is observed at sites away from the source. During Afghanistan earthquake, the peak ground velocity (PGV) values did not show much range, as observed in the Manipur

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earthquake. The highest PGV values observed did not coincide with the highest PGA values. However, PGV is supposed to be better indicator of disaster scenario as compared to PGA [8]. In general, nearby stations are found to experience more amplification compared far-off stations. However, some far stations are also amplified due to ground characteristics or local soil conditions, involving several factors such as: • the composition of soil layers • S-wave velocities • soil densities (internal damping of the individual layers). It has been observed that local soil effects are observed strongly on horizontal components of ground motion but weakly on the vertical components. So, the H/V

Fig. 3 Fourier spectra and velocity response spectra at HYB (upper panels) and SMLA site from Afghanistan earthquake (lower panels)

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Fig. 4 Fourier spectra and velocity response spectra at HYB (upper panels) and SMLA (lower panels) sites from Hindukush earthquake

ratio is considered to be a rough estimate of site amplification. The H/V ratio is plotted in the frequency range 0.1 to 10 for some of the stations for Afghanistan Earthquake, Hindukush Earthquake and Manipur Earthquake and is shown in Figs. 6, 7 and 8. The peak PGA and PGV values at each station are depicted in Table 2. It can be seen that site amplification for SMLA is highest at frequency range 10 Hz and for SML and KOHMA frequency range is 8–9 Hz. For BHPL, HYB, JORHT, and LEKHA regions, the frequency range is 0.1–2.9 Hz and shows the amplification ranging from 8 to 10. The shift to corresponding peak amplification to lower frequency range indicates that the consistency of soil layer in BHPL, HYB, JORHT and LEKHA region is soft as compared to BHPL, JORTH, etc. The results obtained here can be used by structural engineers to plan their constructions such that its natural frequency will not be close to the range of prominent frequency of the site considered.

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Fig. 5 Fourier spectra and velocity response spectra at HYB (upper panels) and SHL (lower panels) sites from Manipur earthquake

4 Conclusions In the present study, an attempt has been made to characterize the sites and to study the seismic hazard analysis considering the amplification and predominant frequency. The predominant frequency for soil sites is found to be less as compared to stiff and rock sites. To testify to the exposed geology, the endeavour is also made to classify sites in various classes based on predominant frequency. It is well known that the soil layers at a given site influence the final surface ground motion. We estimated amplification at various sites from three different earthquakes occurring in different parts of the country. The results show significant changes in predominant frequency and amplification factor from one place to another, which is caused due to the variation in soil type or thickness of overlying soil cover. The higher amplification

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Table 2 The observed PGA, PGV, amplification and predominant frequency at different stations during three different earthquakes S. no.

Name of the earthquake

1

Afghanistan earthquake

2

3

Hindu Kush earthquake

Manipur earthquake

Stations

PGA (cm/s/s)

PGV (cm/s)

H/V ratio

Predominant frequency

BHPL

0.7

0.22

1.8

8.3

DDI

8.0

1.10

2.0

5.6

GOA

0.4

0.11

3.8

4.3

HYB

0.3

0.17

1.8

2.7

SHL

1.5

0.08

1.9

2.9

SMLA

8.5

1.04

2.6

7.8

BHPL

0.6

0.05

2.3

0.1

GOA

0.3

0.01

3.8

4.4

HYB

0.1

0.03

1.9

5.7

SMLA

3.8

0.43

2.6

7.3

BHPL

0.2

0.04

2.2

0.1

GOA

0.1

0.02

3.6

4.5

HYB

0.1

0.02

1.8

5.3

ITANA

36.5

2.47

3.0

4.4

JORHT

10.9

1.56

1.9

2.0

KOHMA

54.1

5.01

2.5

8.1

SHL

7.4

0.35

2.1

9.9

83.9

2.94

3.1

10.0

LEKHA

0.2

0.03

2.6

2.1

TADON

2.9

0.14

2.5

6.3

SMLA

Fig. 6 H/V ratio at SMLA (left) and HYB (right) due to Afghanistan earthquake

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Fig. 7 H/V ratio at HYB (left) and SMLA (right) sites due to Hindu Kush earthquake

Fig. 8 H/V ratio at HYB (left) and SHL (right) sites due to Manipur earthquake

at low predominant frequency is at some of the sites which may be treated as soil sites, and the sites having less amplification at high frequency are regarded as stiff-soil sites. The shift of corresponding peak amplification from higher to lower frequency range indicates that the soil layer in BHPL, HYB, JORHT and LEKHA region is soft as compared to the far stations. The study is conducted using only three earthquakes. In future, the variations in site effects due to variation in distance will be attempted using the larger dataset. This study is helpful for construction for building at particular sites. Acknowledgements Authors are grateful to the National Centre for Seismology (NCS) for providing the necessary data to carry out this research work.

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References 1. Bilham, R.: Earthquakes in India and the Himalaya: tectonics, geodesy and history. Ann. Geophys. 47(2/3), 839–858 (2004) 2. BIS IS 1893 (2002) (Part 1). Indian Standard Criteria for Earthquake Resistant Design of Structures, Part 1—General Provisions and Buildings. Bureau of Indian Standards, New Delhi (2002) 3. Bora, D., Baruah, S.: Mapping the crustal thickness in Shillong–Mikir Hills Plateau and its adjoining region of northeastern India using Moho reflected waves. J. Asian Earth. Sci. 48, 83–92 (2012). [Web of Science ®] 4. Chen, W., Molnar, P.: Focal depths of intracontinental and intraplate earthquakes and their implications for the thermal and mechanical properties of the lithosphere. J. Geophys. Res. 88, 4183–4214 (1983) 5. Guha, S.K., Bhattacharya, U.: Studies on prediction of seismotectonics of northeastern India. In: Presented at the Eighth World Conference on Earthquake Engineering, San Francisco (CA), pp. 21–27 (1984) 6. Kayal, J.R.: Some Useful Definitions of Earthquake Seismology and Seismotectonic of Northeast India (2008) 7. Khattri, K., Wyss, M.: Precursory variation of seismicity rate in Assam area, India. Geology 6, 685–688 (1987). [Web of Science ®] 8. Mittal, H., Yang, B.M., Tseng, T.L., Wu, Y.M.: Importance of real-time PGV in terms of leadtime and shakemaps: results using 2018 ML 6.2 & 2019 ML 6.3 Hualien, Taiwan earthquakes. J. Asian Earth Sci. 220, 104936 (2021) 9. Nakamura, Y.: A method for dynamic characteristics estimations of subsurface using microtremors on the ground surface. QR RTRI 30, 25–33 (1989) 10. Studies on prediction of seismotectonics of northeastern India. Presented at the Eighth World Conference on Earthquake Engineering, San Francisco (CA), pp. 21–27

ROSERS—A Deep Learning Framework for Earthquake Early Warning and Its Interpretation Jawad Fayaz

Abstract Earthquake early warning (EEW) systems are swiftly evolving from standalone physics-inferred methods (requiring computationally expensive inversions) to data-driven strategies for efficient earthquake hazard mitigation in real time. Besides being speedy in prediction, data-driven approaches such as artificial neural networks usually require minimal assumptions in the training and execution processes. This study discusses and attempts to interpret the data-driven EEW framework: ROSERS (Real-Time On-Site Estimation of Response Spectra) proposed by Fayaz and Galasso (2022). ROSERS aims to utilize the early non-damaging p-waves and the recordingsite characteristics to predict the acceleration response spectrum (Sa (T )) of the anticipated on-site ground motion waveform. The framework’s efficacy is analyzed using an extensive database of ground motions, and it is observed that ROSERS leads to exceptional prediction power when implemented in a real-time backdrop. To provide a better interpretation of the framework, this study utilizes the concepts of explainable artificial intelligence (i.e., Shapley additive explanation, SHAP) to obtain insights into the decision-making process of the trained neural networks. Particularly, the cause-effect relationship of the computed latent variables and Sa (T ) is explored. The analyses showcase that the two latent variables of the framework complement each other in capturing stiff short-period and flexible long-period Sa (T ) thereby leading to excellent reconstruction power. Keywords Variational autoencoders · Neural networks · Earthquake early warning · Explainable artificial Intelligence · Model-agnostic Interpretability

J. Fayaz (B) School of Computing, Engineering, and Digital Technologies, Teesside University, Middlesbrough, UK e-mail: [email protected]; [email protected] Department of Civil, Environmental, and Geomatic Engineering, University College London, London, UK © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_38

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1 Introduction The overall goal of earthquake early warning (EEW) frameworks is to alert communities during earthquake-induced ground shaking to prevent casualty and monetary losses. Due to the constraints of available time during an earthquake event (i.e., time elapsed between the fault rupture and the end of ground shaking at the target site), the efficiency of the EEW frameworks plays a vital role in making sure that the warnings are rapid, accurate, and structurally useful. For the real-time setting, EEW frameworks are developed to obtain characteristics of the fast-traveling p-waves (usually possess low amplitude and do not lead to major damages) of the incoming seismic waveforms. This information is then utilized to predict characteristics of the incoming high-amplitude destructive transverse s-waves and appropriate alerts are triggered [1]. Among the various ground shaking parameters of interest [2], the acceleration response spectrum (Sa (T )) has been one of the most thoroughly used IMs that can effectively integrate the features of the ground motion waveform (such as amplitude and frequency content) with the dynamic characteristics of a structural system which is represented as a single-degree-of-freedom system (SDOF) [3]. Over the years, Sa (T ) has grown in its utility for structural and earthquake engineering purposes, including hazard analysis [4, 5], ground motion simulation validation [2, 6], ground motion selection and scaling [7], etc. Hence, an early and accurate estimation of Sa (T ) of the arriving ground motion during an earthquake can assist stakeholders in performing risk-informed decision-making [8, 9]. The study presented in this paper discusses the performance of the on-site EEW framework: ROSERS (real-time on-site estimation of response spectra), on Next Generation Attenuation (NGA) West 2 database [10] and attempts to interpret the trained neural networks using the concepts of explainable artificial intelligence (XAI) and game theory based Shapley additive explanations (SHAP) [11]. The ROSERS framework is developed using the key components of deep neural network (DNN) and variational autoencoder (VAE). The proposed framework utilizes a vector of recording-site characteristics (denoted as SC) and an IM vector (representing amplitude, energy, duration, and frequency content) of the initial three seconds of the ground motion waveform (denoted as IM3s ) computed after detection of p-waves, to predict a vector of PGA (also referred to Sa (T = 0)) and 95-period Sa (T ) spectrum of the anticipated complete on-site ground motion waveform in real time. The VAE is utilized to conduct dimensionality reduction of the Sa (T ) spectrum into two sufficient and efficient latent variables, which are then estimated in real-time using the pre-trained DNN with SC and IM3s as the inputs [12]. The estimated latent variables are then used to project the complete Sa (T ) spectrum using the pre-trained VAE decoder. Hence, ROSERS broadens the goals of EEW frameworks to be accurate, timely, and both structurally and ground motion informed. The proposed framework is analyzed on a comprehensive dataset and based on the findings; the proposed framework can be highly beneficial to advancing EEW research.

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Due to the “black-box” nature of the neural networks, understanding the decisionmaking process of the framework is imperative. This study provides insights into the framework by interpreting the cause-effect relationships using the concepts of XAI. In particular, SHAP is used to conduct a post hoc model interpretation of the two neural networks, including the DNN for estimation of latent variables using SC and IM3s , and the VAE decoder that projects the latent variables into the complete 96-point Sa (T ) spectrum (denoted as Sa ). The interpretation procedure basically quantifies each feature’s effect on the neural networks’ output, thereby attempting to provide necessary transparency to understand the framework. In addition, this process allows the users and stakeholders to obtain a physical interpretation of the two latent variables utilized by the framework.

2 ROSERS Framework Figure 1 illustrates the proposed framework. For a given location, a digital recording station constantly monitors the ground shaking. The site characteristics, i.e., SC of the recording station are known apriori. As the p-waves of the incoming ground motion are picked by the sensor [13, 14], the ROSERS framework waits to receive three seconds of the waveform. The ground motion shaking at a site is typically known to last for 1 to 2 min [15], and hence, using early three seconds of the arriving ground motion still allows ample time for on-site early warning and rapid response. Using the recorded three seconds of the waveform, a vector of seven IMs (i.e., IM3s ) is computed. The computed IM3s represent the amplitude, energy, frequency, and significant duration of the initial three seconds of the ground motion waveform after detecting the p-waves. IM3s and SC are used as the inputs to the pre-trained feedforward DNN which is trained to estimate the two statistically derived surrogate mean latent variables (i.e., μz1 and μz2 ). The mean latent variables in conjunction with the pre-trained VAE decoder are projected into Sa of the expected complete waveform at the end of the shaking. The estimated Sa can then be utilized by the stakeholders to alert a community informatively. The mathematical basis of the framework relies on four models/algorithms, including (1) automatic p-phase arrival-time picker (which detects the onset of pwaves in real-time), (2) VAE (which provides regularized surrogate parameters; whose encoder projects Sa into two sufficient and efficient latent variable space and decoder reconstructs Sa using the latent variables), (3) DNN (which utilizes IM3s and SC to compute the two mean latent variables μz1 and μz2 ), and 4) mixedeffects hierarchical regression (which provides the residuals a hierarchical structure and implicitly considers spatial correlation by dividing the residuals of the trained VAE + DNN into between-event and within-event variabilities). Further details of the components of the proposed framework can be obtained from Fayaz and Galasso [12]. The IM3s include seven IMs: Arias intensity (Ia ), significant duration (D5−95 ), mean period (Tm ), peak ground acceleration (PGA), peak ground velocity (PGV),

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Fig. 1 Illustration of the ROSERS framework. The implementation of the ROSERS framework, on average, takes ~ 0.75 s on a personal computer

peak ground displacement (PGD), and cumulative absolute velocity (CAV) described in Eqs. 1–7 where a(t) represents the acceleration time history of the ground motion, T i represents the time instance, and Ci is the Fourier amplitude spectrum at linearly spaced frequencies, f i , ranging between 0.25 ≤ f i ≤ 20 Hz. Ia =



π 2g

a(t)2 dt

D5−95 = T i (Ia@95% ) − T i (Ia@5% )  Tm =

  Ci2 1 f i  2 Ci

(1) (2)

(3)

PGA = max(|a(t)|)

(4)

     PGV = max  a(t)dt 

(5)

     a(t)dtdt  PGD = max 

(6)

 CAV =

 |

a(t)dt|dt

(7)

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3 Implementation on NGA-West2 Database The framework is developed and trained using a subset of recorded ground motions available in the comprehensive state-of-the-art database of the Next-Generation Attenuation West version 2 (NGA-West2) project [10]. An initial database of 13,916 ground motion components recorded from 277 mainshock seismic events was utilized. However, due to the asymmetry in the number of records available for low-magnitude and high-magnitude events, the database was statistically altered to obtain a conforming number of events from both ranges of magnitudes. This was done by undersampling the events with a moment magnitude (M) less than 5.5 until the distribution and number of the events with M ≤ 5.5 were similar to M > 5.5. The sampling is carefully conducted by maintaining the distributions of the closest rupture distance (Rrup ) and the site’s average shear-wave velocity of the topmost 30 m of the soil (Vs30 ). This results in 6392 undersampled ground motions with 3 ≤ M ≤ 8 and 0.1 km ≤ Rrup ≤ 90 km. The process details can be obtained from Fayaz and Galasso [12]. Finally, to obtain the p-wave arrival times of the ground motion components, the automated p-phase picker (PPHASE PICKER ) algorithm proposed by Kalkan [13] is utilized.

3.1 Computation of Latent Variables Using VAE The undersampled 6392 ground motion components are used to compute the Sa for 96 periods, and the Sa is used to train the VAE [16]. The VAE uses Sa as inputs and trains a neural network-based encoder to reduce Sa into two probabilistic latent variable representations while simultaneously training a neural network-based decoder that reverses the process and projects the latent variables back to Sa . The VAE provides a probabilistic approach to describing the Sa in their latent variable space, which is coerced to possess smooth and continuous latent variable representations. In this study, latent variable space of two dimensions is observed to be as a good trade-off between accuracy and explainability. The two latent variables are assumed to be independent and normally distributed (denoted as z 1 and z 2 with means μz1 and μz2 and variances σz21 and σz22 , , respectively) in the sampling layer. The VAE is trained with k-fold cross-validation using randomly selected 80% of the event dataset (10% of which is used as the validation set). The remaining 20% is used for testing, as showcased in [12]. The M and Rr up details of the train and test datasets are provided in Fig. 2a and b, respectively. The μz1 and μz2 are presented in Fig. 3a and b, where the marker colors characterize the M of the earthquake (Fig. 3a) and Rr up of the station site (Fig. 3b). In general, it is observed that an increase in M leads to decrease in μz1 and μz2 and increase in Rrup leads to increase in μz1 and μz2 . Hence, it is observed that μz1 and μz2 have an inverted attenuation relation with the M and direct proportionality with the Rrup . Furthermore, Table 1 presents the correlation coefficients of μz1 and μz2

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Fig. 2 M and Rrup details of: a train, and b test, datasets

with various parameters of IM3s and SC. It is observed from Table 1 that both SC and IM3s vectors are well correlated with μ Z 1 and μ Z 2 highlighting their significance in real-time prediction of μ Z 1 and μ Z 2 . In nutshell, μz1 and μz2 are in positive correlation with each other and are negatively correlated with the IM3s and SC. The results of the training VAE are presented in Fig. 4a and b. Figure 4a presents the coefficient of determination R2 for both train and test sets for the Sa (T ) at 96 periods. It is observed that for both train and test sets, the R2 for all periods is above

Fig. 3 Exploration of μz 1 and μz 2 against a M, b Rrup

Table 1 Correlations of latent variables with the selected SC and IM3s (NGA dataset) Vs30

Z 2.5

Ia3s

CAV3s

PGA3s

PGV3s

PGD3s

Tm3s

D5−953s

μZ1

μZ1

−0.27

−0.21

−0.83

−0.91

−0.82

−0.89

−0.91

−0.57

0.19

1

μZ2

−0.19

−0.07

−0.93

−0.91

−0.92

−0.92

−0.90

−0.36

0.27

0.88

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Fig. 4 NGA-West2 selected ground motions: a R2 of reconstructed Sa (T ) at various periods and b true versus reconstructed Sa (T )

0.98, thereby demonstrating excellent reconstruction power of the developed VAE with minimal bias and variance. Furthermore, Fig. 4b shows the true vs. reconstructed Sa (T ) for six periods of: 0.25 s, 0.75 s, 1.5 s, 3 s, 5 s, and 0 s (PGA). For all the cases, it is observed that the true vs. reconstructed Sa (T ) closely follows the purity (1:1) line for all ranges of values. Based on the observations made from Fig. 4a and 4b, it is clear that the trained VAE is sufficient and efficient to adequately reconstruct μz1 and μz2 .

3.2 Estimation of Latent Variables Using DNNs Based on the sufficiency and efficiency of the latent variables and VAE observed in the previous section, it is vital to accurately estimate the latent variables for constructing the Sa of the expected ground motion in a real-time setting. Hence, a DNN with nine input nodes and two output nodes is trained to estimate the μz1 and μz2 using the SC and IM3s vectors. The DNN is trained with cross-validation using randomly selected 80% of the dataset, and the remaining 20% is used for testing. The mean predictions of the trained DNN led to R2 of 0.91 and 0.93 for μz1 and μz2 , respectively, for the test dataset. Apart from a high prediction power, the DNN provides a joint estimation of the latent variables, thereby maintaining their internal correlation. The predictions of μz1 and μz2 from the DNN inherit a correlation coefficient of 0.91 compared to the true correlation coefficient of 0.88 (as observed in Table 1). This means that the trained DNN is highly successful in ensuring that the estimations are implicitly associated, i.e., properly reflecting their internal correlation. Finally, using mixed-effects regression [17], the residuals of Sa from the DNN + VAE predictions are divided into inter-event and within-event variabilities to maintain the hierarchical nature of the ground motions arising from multiple recordings from the same event

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and recordings from different events. Hence, the corresponding inter-event T2 and within-event 2 variance matrices are computed for the residuals of the 96 periods.

4 Interpretation of Neural Networks of ROSERS Due to the versatility of the deep learning models, they have been widely used in engineering applications. However, due to the “black-box” nature of these models, there is a general reluctance in the research community to propose and utilize such models. Hence, it is critical to provide sufficient analytics for model interpretability and its response in terms of predictions based on variability in the input features. With the onset of XAI, various algorithms have been developed that provide different methods that allow interpretability of these “black-box” models and step toward “gray-box” and even “white-box” nature (such as linear regression and decision trees). This study uses SHAP to analyze and interpret the nature of the developed framework for the NGA-West2 database [18]. SHAP is a post hoc model-agnostic procedure that provides insights to explain individual predictions of the model based on the game’s theoretically optimal Shapley values. Shapley values are a widely used approach from cooperative game theory with desirable characteristics [11]. The Shapley value is the average marginal contribution of a feature value across all possible coalitions where players of a coalition are acted by the feature values of a data instance. Shapley values are the only solution that satisfies the properties of efficiency, symmetry, dummy, and additivity, which form the basis of the most reliable explanation methods [18]. SHAP belongs to the class of models called “additive feature attribution methods,” where the explanation is expressed as a linear function of features, as described by Eq. 8. In this equation, g(z) represents a local surrogate model of the original model f (x) (in this case, f (x) is the trained DNN or the VAE decoder), K is the number of input features of the model f (x) (i.e., Sa (T1 ) and S F), θ0 denotes the bias term, which represents the base value of the predictions made by the model f (x), θi is the contribution of the ith feature toward the final output (i.e., SHAP value), and z i is a binary variable that takes a value of 1 for the feature corresponding to θi contribution and 0 otherwise. θi evaluates the difference to the final predictions made by the model f (x) by including the ith feature for all combinations of features other than i. This is expressed in Eq. 9, where S represents a subset of features among all N features except the ith feature (denoted as S ⊆ N \{i}), [ f x (S ∪ {i}) − f x (S)] is the is the weighing difference in the outputs made by the ith feature, and |S|!(M−|S|−1)! M! factor counting the number of permutations of the subset S. f x (S) in the different part of the equation represents the expected output given the subset of features S, which is similar to the marginal average on all other features other than the subset S. Hence, in a nutshell, SHAP values explain the contribution of the features to the respective outputs in a quantitative manner, thereby allowing interpretation. These are analogically similar to the coefficients of a regression model, which provide the impact of the corresponding feature on the target variable. Due to the computational

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complexity of computing Eq. 10, SHAP values are approximated using various types of explainers such as kernel-explainer, tree-explainer, and deep-explainer [19]. In this study, kernel-explainer uses a special weighted linear regression to compute the importance of each feature. The computed importance values are Shapley values from game theory and coefficients from a local linear regression [19]. SHAP analysis is applied to interpret the cause-effect relationship between the predictors and targets of the trained VAE decoder and DNN in the following sections. g(z) = θ0 +

M

θi z i

(8)

i=1

θi =

|S|!(K − |S| − 1)! [ f x (S ∪ {i}) − f x (S)] K! S⊆N \{i} f x (S) = E[ f (x)|x S ]

(9) (10)

4.1 Interpretation of the VAE Decoder The decoder of the trained VAE is analyzed using SHAP analysis by using μz1 and μz2 of the 6392 ground motions as the inputs (features) and the corresponding predictions of Sa vector as the outputs (targets). For each input–output combination, respective SHAP values are computed. Hence, a total of 6392 SHAP values are computed for each combination of the 96 outputs (Sa ) and two inputs (μz1 and μz2 ). The SHAP values are presented for PGA, Sa (T = 0.5s), Sa (T = 1s), and Sa (T = 2.5s) in Fig. 5, where the color of the data points represents the magnitude of the feature values. The low refers to values close to -0.4, and the high refers to values close to +0.4 for μz1 and μz2 (based on Fig. 3). It can be observed that, in general, with an increase in the value of both features, their corresponding SHAP values tend to move from positive values to negative values. This means as the values of the latent variables increase, they tend to lower (negatively contribute) the predicted Sa (T ) values obtained from the decoder. Also, the SHAP values tend to be symmetric on both sides of zero for both latent variables and all four targets. This means that for both extreme values of the latent variables (i.e., −0.4 and +0.4), their impact on the four target variables is similar (since the absolute SHAP value is similar). Furthermore, it is observed that μz1 leads to a similar range of SHAP values as μz2 for PGA indicating a similar level of importance of both variables to estimate PGA. However, comparing the SHAP values across the four target variables, it is noticed that the contribution of the μz1 increases while the contribution of μz2 diminishes with an increase in the period of the SDOF. Thus, the contribution of μz1 to estimate the Sa is observed to be higher than the μz1 for longer periods SDOFs. This diminishing effect of μz2 s SHAP values is compensated by increasing variance in μz1 s SHAP values.

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Fig. 5 SHAP of μz 1 and μz 2 for: a PGA, b Sa (T = 0.5s), c Sa (T = 1s), and d Sa (T = 2.5s)

To present the details more concisely, Fig. 6 illustrates the relative feature importance of μz1 and μz2 on the VAE decoder predictions, in terms of their mean absolute SHAP values (| SHAP|). This is done by computing the mean | SHAP | values for the 6392 samples for both μz1 and μz2 and then dividing them by the sum of the mean | SHAP | for each target Sa (T ). Since the SHAP values represent the contribution of the features to the model output, computing their relative sum for the two features in an absolute sense signifies the importance of the respective feature in predicting Sa (T ). It can be observed from the figure that the relative importance of μz1 and μz2 is similar for Sa (T ) with shorter periods (close to PGA) and the importance of μz1 increases as the period increases with a plateau effect around Sa (T = 1). Thus, in general, it is observed that μz2 inherits the capabilities to capture the acceleration effects on stiffer SDOFs, and with an increase in flexibility of SDOF, μz2 is not as effective in the prediction of Sa (T ) and μz1 leads to higher contribution. Hence, this process of interpreting the decoder provides insights into the cause-effect relationship between μz1 and μz2 and Sa , and also provides details about the latent variables in capturing the behavior of SDOF with different periods.

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Fig. 6 Relative mean absolute SHAP values of μz 1 and μz 2

4.2 Interpretation of the DNN Similar to the interpretation process of the VAE decoder, the trained DNN is analyzed using SHAP analysis by using the SC and IM3s features of the 6392 ground motions as the inputs and the corresponding mean latent variables μz1 and μz2 as the outputs. For each case, the respective SHAP values are computed for the trained DNN. Hence, a total of 6392 SHAP values are calculated for each combination of the nine inputs (SC and IM3s ) and two outputs (μz1 and μz2 ). Figure 7 presents the SHAP values for the nine features of SC, and IM3s corresponding to the two target mean latent variables μz1 and μz2 in descending order of contribution. The color of the data points represents the magnitude of the corresponding feature values. It can be observed from the sub-figures that C AV , P GV , and Ia lead to the highest SHAP values for μz1 while for μz2 P GV is replaced by P G A as one of the top three contributors, and the SHAP values of P GV get reduced significantly. This bolsters the observations made in the previous section about μz2 containing information related to PGA and stiffer period Sa (T ) and signifies the importance of μz2 in capturing the PGA and shortperiod Sa (T ) of the ground motion waveform. On the other hand, μz1 is observed to be connected to energy-based IMs, thereby leading to a higher contribution to capturing the long-period Sa (T ). Furthermore, it is observed from Fig. 11 that the DNN is less affected by the SC for the prediction of μz2 while the SC contributes to a higher degree for the prediction of μz1 . Also, the DNN is observed to be more impacted by the significant duration of the 3-s ground motion for the prediction of μz2 as compared to μz1 . Unlike the previous section, the SHAP values of the features are not observed to be symmetric around zero, thereby indicating that the value of different features leads to different contributions to the DNN predictions. In general, it is noticed that the lower values of the features lead to a positive contribution to the DNN predictions. In comparison, the higher values of the features lead to contribution in a negative sense. It should be noted here negative sense does not mean lower contribution but rather specifies that the corresponding feature value lowers the prediction from the average prediction value of the DNN.

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Fig. 7 SHAP values of SC and IM3s for: a μz 1 and b μz 2

To further present the SHAP details more concisely, Fig. 8 illustrates the relative feature importance of SC and IM3s on the DNN predictions of μz1 and μz2 , in terms of their mean absolute SHAP values (| SHAP|). This is done by computing the mean | SHAP | values for the 6392 samples for all features in SC and IM3s and then dividing them by the sum of the two mean | SHAP | for the two target latent variables. Since the SHAP values represent the contribution of the features to the model output, computing their relative sum for the features in an absolute sense signifies the importance of the respective feature in predicting μz1 and μz2 . It can be observed from the figure that the DNN is affected similarly by CAV to predict μz1 and μz2 . Among the top contributors to the DNN, Ia is observed to have the highest mean impact to predict μz2 and PGV is observed to have the highest mean impact to predict μz1 . Also, the prediction of μz1 is relatively affected higher by SC and prediction of μz2 is impacted more by D5−95 and Tm . In a nutshell, this process of interpreting the DNN helps in understanding the cause-effect relationship between the SC and IM3s and predictions of μz1 and μz2 and thereby provides insights about the DNN and how the predictions are affected by the changes in the features (similar to classical regression analysis).

5 Conclusions This study discusses and interprets a deep learning-based on-site earthquake early warning (EEW) framework called ROSERS (real-time on-site estimation of response spectra). The framework utilizes a pre-trained deep neural network (DNN) which uses the site characteristics and initial three seconds of the on-site ground motion after p-wave detection to estimate two latent variables that are statistical surrogates

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Fig. 8 Relative mean absolute SHAP values of SC and IM3s

of the Sa (T ) spectrum. The estimated two latent variables are then used in a trained decoder of variational autoencoder (VAE) to predict the peak ground acceleration (PGA) and 95-period spectral acceleration (Sa (T )) response spectrum of the anticipated complete on-site ground motion. While many other research studies have tried to use machine learning and deep learning frameworks for EEW purposes, they mainly concentrate on predicting ground acceleration, which can be significantly different from the acceleration caused in the structures (due to dynamic amplification). The prediction of the complete Sa (T ) spectrum (while inherently maintaining the cross-correlations) with only three seconds of the initial ground motion waveform in a highly accurate manner coupled with an average prediction time of less than one second makes this framework unique. However, due to the black-box nature of the neural networks, methods like these often lack explainability and interpretability. Hence to overcome this, a detailed explanation of the trained VAE and DNN is provided using the XAI concept of SHAP analysis. This study is an attempt to illuminate the black-box nature of the neural networks used in the ROSERS framework and try to understand the physical essense of the surrogate latent variables. Results indicate that μz2 captures the acceleration effects on stiff short-period SDOFs, while μz1 leads to higher contribution for estimating acceleration of flexible long-period SDOFs.

References 1. Kramer, S.L.: Geotechnical Earthquake Engineering. Prentice Hall (1996) 2. Fayaz, J., Azar, S., Dabaghi, M., Zareian, F.: Methodology for validation of simulated ground motions for seismic response assessment: application to cybershake source-based ground motions. Bull. Seismol. Soc. Am. 111(1), 226–241 (2021). https://doi.org/10.1785/012020 0240 3. Bazzurro, P., Cornell, C.A., Shome, N., Carballo, J.E.: Three proposals for characterizing MDOF nonlinear seismic response. J. Struct. Eng. 124(11), 1281–1289 (1998). https://doi.org/ 10.1061/(asce)0733-9445(1998)124:11(1281)

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Early Warning System: An Efficient Earthquake Disaster Mitigation Tool P. Kumar, Govind Rathore, Kamal, M. L. Sharma, R. S. Jakka, Pratibha, and A. Kumar

Abstract The earthquake risk mitigation goals are achieved by applying a number of approaches to reduce the vulnerability of the built environment. The most widely known long-term risk mitigation approaches are earthquake-resistant design and construction, reviewing building use regulations and codes, and retrofitting of the existing vulnerable infrastructure. Construction experts, earthquake, and civil engineers must emphasize on structural and non-structural elements to avoid potential damages and human loss. Accurate predictions of earthquakes would effectively reduce the damage caused by the earthquakes. Unfortunately, seismic stress buildup inside the earth’s crust and its release, starting from the nucleation to the end of the rupture, is a complex phenomenon. Therefore, making accurate predictions of earthquakes is quite tricky. Revolution in digital seismology, telecommunication, and high-performance computing has provided strong support for risk mitigation measures in the last four decades. The recent advancement in real-time processing and transmission of seismic data has enabled the evolution of a real-time risk mitigation system like an earthquake early warning system (EEWS). The EEWS is a real-time system for earthquake risk reduction. Keywords Earthquake · Early warning system · Lead-time · Risk mitigation

P. Kumar (B) · G. Rathore Centre of Excellence in Disaster Mitigation & Management, IIT Roorkee, Roorkee, India e-mail: [email protected] Kamal Department of Earth Sciences, IIT Roorkee, Roorkee, India M. L. Sharma · R. S. Jakka · A. Kumar Department of Earthquake Engineering, IIT Roorkee, Roorkee, India Pratibha Department of Mathematics, IIT Roorkee, Roorkee, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_39

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1 Introduction Natural disasters have always been frightening and devastating to humans due to their unpredictable nature. Earthquake is one of them, and it has always been very disastrous to humanity. Tectonic and volcanic activities cause earthquakes naturally, while some human activities like an explosion, mine collapse, and reservoirs induce them [1]. Tectonic earthquakes are the most common and can be explained by plate tectonic theory [2]. The world seismicity map clearly shows that the seismicity distribution is not random but follows a trend along the major plate boundary zones. Earthquakes occur along tectonic plate boundaries and faults in the Earth’s crust, along with fractures where friction has built up over time [3]. The whole fault does not move at once. It starts at an epicenter, and the rupture moves down the fault. This release of energy moves in two parts. Primary waves (p-waves) emit out first and fastest. They are followed by slower secondary waves, or s-waves, which cause the ground to ripple up and down—the shaking people experience during a quake. Earthquakes do not kill people; buildings do. Therefore, earthquake-proof buildings need to be constructed to mitigate earthquake hazards. Buildings made of local materials like bamboo or timber are highly resistant to earthquake jolts. To overcome the effects of seismic hazards in the buildings, seismic hazard study of the sites should be carried out and the building models should be tested on the shake table to check their resilience by applying jerks of different earthquake scenarios [4, 5]. Earthquakes cannot be predicted as of now [6] but their effects can be mitigated through proper planning and preparation. The municipality should be proactive in the verification of building plans and should examine the construction compliance with building guidelines that have been followed. Old buildings need to be retrofitted so that they can bear the shocks during earthquakes. Public awareness is essential and crucial; people must be aware of the consequences of earthquakes. Earthquake mitigation measures are typically intended to reduce both casualties and damage from future earthquakes. Special attention to early warning systems was given in the Hyogo framework for action (HFA) 2005–2015: Building the resilience of nations and communities to disasters. Risk assessment and early warning systems are essential investments that protect and save lives, property and livelihoods. They contribute to the sustainability of development and are far more cost-effective in strengthening coping mechanisms than primary reliance on post-disaster response and recovery. Early warning systems are an adaptive measure used to help communities to prepare for hazardous events by taking advantage of integrated communication systems. Similarly, the Sendai Framework for Disaster Risk Reduction 2015–2030 outlines seven clear targets and four priorities for action to prevent new and reduce existing disaster risks. The seventh target “G” clearly advocates for substantially increasing availability of and access to multi-hazard early warning systems and disaster risk information and assessments by 2030 [7]. It aims to reduce disaster risk and losses in lives and livelihoods substantially. The existing EEWS take advantage of the speed of the primary wave; they detect them and, on the basis of applied approaches in the EEWS dissemination setup, issue

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the warning to the public. An EEW system alerts the residents before the arrival of the impending strong ground motion as shaking hits their dwellings and causes damage. The lead time for EEW applications depends on the epicentral distance. It varies from a few seconds to tens of seconds. Despite having such a short lead time, a risk mitigation strategy and certain precautionary measures can be followed to save the loss of lives and property. The lead time of few tens of seconds can be enough for pre-programmed emergency measures for critical infrastructures. Several approaches like warning sirens, TV and radio broadcasts, cell phone applications, or dedicated alerting devices, have evolved to receive earthquake warnings. These alerts can also be integrated into the critical facilities systems to perform specific actions automatically upon receiving the alerts. The EEWS is one among the many effective risk mitigation measures to prevent new and reduce existing disaster risks.

2 EEWS Set up Around the Globe The term “earthquake early warning” (EEW) is used to describe a real-time earthquake information system that has the capability and potential to provide a warning before significant ground shaking [8]. Warning time ranges from few seconds to a little more than a minute and is primarily a function of the distance from the user to the earthquake’s epicenter. An early warning system is mainly required to issue an alarm to have a time margin for evacuating people, shutting down critical facilities, and not determining exact earthquake parameters. The first EEW system was developed for the Tohoku Shinkansen railway system in Japan in 1982 using the front detection technique [9]. A similar approach was also applied in Mexico’s seismic alert system in 1991 [10]. The first three seconds of P-wave motion after P-wave detection was used in the improved front detection techniques called UrEDAS and started operating for the Tokaido Shinkansen line in 1992 [9]. After the success of EEW system in Mexico and Japan, other countries also started to work on the development of EEW systems for their regions, and enormous improvements have been seen in this field in the last three-four decades. The EEW systems are fully functional in a few countries like Japan [11, 12], Taiwan [13–15], Mexico [16–18], and South Korea [19], to issue warnings nationwide, while some countries issue warnings region-wise, like Anatolia-Turkey [20, 21], Southwest Iberia [22–24], Southern Italy [25, 26], Vrancea-Romania [27–29], China [30, 31], Chili [32], Costa Rica [33, 34], Switzerland [35], Nicaragua [36], Israel [37, 38], and United States of America [39, 40]. While the development of the EEW system is crucial for seismic risk reduction, increasing capacity to provide more lead time is equally essential [41]. In India, Uttarakhand State Earthquake Early Warning System (UEEWS) has been developed. Under this project, 167 sensors have been installed in the seismogenic areas of Uttarakhand [42–44]. This project is being operated and maintained by the EEWS laboratory, Centre of Excellence in Disaster Mitigation & Management, Indian Institute of Technology Roorkee. The server has been set up in this laboratory

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to fetch the data from sensors to the server using the dedicated private network. The warning of the earthquake is issued on the mobile app and installed sirens in public places and all thirteen district emergency operation centers of Uttarakhand.

3 Scenario Earthquake Warnings, Mock Drills, and Awareness As per the 2011 census, the total population of Uttarakhand was 10,096 thousands. The projected population for the years 2021, 2025, 2030, and 2036 is expected as 11,399 thousands, 11,993 thousands, 12,524 thousands, and 12,974 thousands, respectively [45]. The trend shows that the population is increasing and it is our combined responsibility to provide earthquake awareness in the state. Therefore, mock drills of earthquakes are conducted in Uttarakhand at 12:00 PM on the first day of every month. In this continuous process, a mock drill was conducted at 12:00 PM on 1 June, 2022 and ringed the installed sirens in Dehradun, Haldwani and District Emergency Operation Centers. Alert messages were also received on the Uttarakhand Bhookamp Alert mobile app installed by the users. The received feedback from the users was analyzed, and the system was updated accordingly. The pamphlets, banners, posters (Fig. 1), stickers (Fig. 2), and booklets have been prepared to sensitize the public about earthquakes. These ways and means are pasted and distributed at various places by the technical teams who visit Uttarakhand during the installation and maintenance of the sensors and sirens. The mock drills are very useful to test the preparedness and planned response activities followed by disaster management authority and all the line departments involved in the aftermath operations.

4 Benefit-Cost Study of Earthquake Mitigation Approaches In India, large earthquakes are considered to be low probability but high-consequence events. One measure of the economic impact of earthquake damage is the ratio of total loss to insured loss (L/I). It is very high in developing nations due to the lack of home insurance practices. In a broad sense, the economic impact of earthquakes can be determined concerning the annual GDP of the country. Most of the urban areas have large informal settlements and slums. The poor practices of building standards, land use planning, overcrowding, and location in significantly hazard-prone areas make them vulnerable to seismic risks. Earthquake mitigation measures can be applied to existing infrastructure to reduce casualties and damage in future earthquakes. Standard earthquake mitigation measures include improving the capacity of a building to resist seismic forces. The retrofitting of a building improves the building elements that hold up a building and resist lateral forces from earthquakes, including foundations, columns, load-bearing walls, floor diaphragms, roof diaphragms, and

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Fig. 1 Posters to create awareness among the students while in hostels and classrooms

Fig. 2 Pamphlet for information dissemination regarding EEW mock drill

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connections between these structural elements. Replacement of an entire building is typically more expensive than retrofitting. Still, it may be appropriate if retrofit costs are high, especially if the existing building is in overall poor condition, needs non-seismic repairs, is near the end of its useful life, is functionally obsolescent, or has other deficiencies such as not being energy efficient. The facilities that may serve as emergency shelters should be designed and constructed with the higher than the minimum seismic standards required by building codes. Retrofitting a building is a costly affair. Certain studies demonstrate the cost of retrofitting school buildings and its benefit. In seismically active zones, small age group students are most vulnerable to earthquakes as in their routine; they stay in the schools for around eight hours on working days. In the benefit-cost analysis, two points play an important role in retrofitting schools against earthquake damage. First, if one considers only the physical damage to the building, the benefit-cost ratio will be considerably less than one, and the measure will be deemed economically unattractive. While in the second point, when one adds the reduction in fatalities by retrofitting the structure, then this measure is likely to yield a benefit-cost ratio greater than one. It is reasonable to consider the values of lives in determining the benefitcost ratio. Retrofitting of all school buildings in India would require a significant investment, i.e., $65 billion [46] (As per the study carried out in 2012). Now, it is the year 2022, so the cost could be hundreds of billions of dollars. The low-cost mitigation solutions can be opted. The EEWS can play a part in comprehensive earthquake risk reduction. The cost to set up an EEWS is not very high, on the contrary it, is affordable, and can be implemented with low investment. Infrastructure improvements are a selective approach while EEWS caters the service to all members of the population equally. India has diverse geographical aspects, consisting of densely populated cities in natural hazard-prone areas. Investments are required to mitigate hazards and make society disaster resilient. The disaster management act—2005, section 43, clearly states need for the creation of the National Disaster Mitigation Fund, State Disaster Mitigation Fund, and District Disaster Mitigation Funds to be used in hazard mitigation works in the society. In finance commission II–VIII, the money margin scheme was created to provide funds for disaster relief operations. The IX Finance commission recommended calamity relief fund scheme and more autonomy was given to states to use the funds in relief operations. In X finance commission recommended that along with the calamity relief fund, a national fund for calamity relief fund should be created. The XI finance commission recommended that the national calamity relief fund should be abolished and create a national climate contingency fund. The XII finance commission maintained the calamity relief fund and national climate contingency fund. Finance commission XIII recommended the merger of the calamity relief fund with the state disaster response fund. The XIV finance commission recommended a national disaster response fund, state disaster response fund and district disaster response fund. The finance commission XV has recommended setting up of national disaster mitigation fund at the national level and state disaster mitigation fund at state level. Instead of creating funds for them individually, these were merged with response fund in the head of the national disaster response and mitigation fund,

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and state disaster response and mitigation fund, and in the allotted funds, 20% would be used for mitigation measures and 80% for response operations [47]. These funds are available for the states to be used in the mitigation measure from the financial year 2021–22 to 2025–26 [48].

5 Losses Due to Earthquakes On 4 April 1905, Kangra earthquake of Ms 8.0 was one of the most devastating earthquakes in northern India. This earthquake killed around 20,000 people and around 53,000 domestic animals were also lost. The total economic cost of recovering from the effects of this earthquake was 2.9 (1905) Indian rupees [49]. On 15 January 1934, Bihar-Nepal earthquake of Mw 8.1 caused 10,700 deaths and maximum intensity X was observed in this event. On 15 August 1950, Assam–Tibet border earthquake of Mw 8.6 caused 1926 death and extreme loss of around $25 million and maximum intensity IX, were observed during this earthquake [50]. On 20 October 1991, Uttarkashi earthquake of Mw 6.8, killed 768 people, injured 5,066 people, and damaged around 42,400 houses [51]. On 29 March 1999, an earthquake of Mw 6.6 struck in Chamoli region of Uttarakhand. It killed about 100 people and injured several hundred. The maximum intensity of VII was observed during this earthquake [52]. On 25 April 2015, the most recent Nepal earthquake of Mw 7.6 and its aftershocks caused a lot of losses in the Himalayan country. It killed 9000 people and injured 23,000, rendering 785,000 people homeless. Due to this earthquake, about 2.8 million people were displaced. This earthquake caused a lot of economic loss and its cost was around $7 billion, one-third of Nepal’s GDP. Figure 3 shows some damaging earthquake and human and economic loss due to them. The trends in Fig. 3b clearly show that the economic loss will be more due to large earthquakes. Therefore, an EEWS can be an effective tool for alerting people so that they can follow precautionary measures to save themselves and their near and dear ones.

6 Maps A light earthquake of Mw 4.7 struck in Chamoli region of Uttarakhand, and an alert was issued to the public by UEEW System. A peak ground acceleration map is prepared based on the acceleration data recorded by the sensors. Figure 4 shows a map of the variation of acceleration in the region. The acceleration maps are a very important tool for disaster management authorities in carrying out post-earthquake operations. A large earthquake is overdue in the Uttarakhand region, therefore having an early warning system and its secondary products like acceleration maps. These maps can expedite post-earthquake operations like search and rescue, identification of severely affected areas, and rehabilitation and planning activities required to cope with the effects of earthquake damage.

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Fig. 3 a The fatalities during some disastrous earthquakes triggered in mainland India and the Himalayas. b The economic loss is due to these earthquakes

Fig. 4 The PGA map of 11 September 2021, Mw—4.7 Chamoli earthquake

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7 Tentative Lead Time The lead time for the user is important. It is the remaining time for the S-waves to reach at the target site. Lead time varies depending on the location of the user from the epicenter of the earthquake. A user at a far distance would get more lead time than the users at the nearest locations. The existing system is developed based on the regional EEW concept therefore it takes few seconds to process and then issue warnings. These few seconds are elapsed time to get at least 3 s of data from at least four sensors and time needed for transmission, processing, magnitude, and hypocenter estimation, and then sending the decision to the warning server. The lead time can be calculated as per Eq. (1) [53], where reporting time (T r ) is the time needed (T d ) to trigger and record a sufficient length of the waveforms and the time (T pr ) required to process the waveforms for the hypocenter and magnitude determination. The earthquake early warning time (T w ) is given by: Tw = Ts − Tr

(1)

Tr = Td + Tpr

(2)

and

where T s is the destructive S-wave travel time. For the advanced warning, we must have T w > 0. Clearly, this requires T s > (T d + T pr ). The locations for which T w < 0 are considered as blind zone. Figure 5 shows a diagram that describes the expected lead time for the few major cities of Uttarakhand and nearby states if a large earthquake hits the Uttarkashi region.

8 Conclusion The target G of the Sendai framework clearly mentions the importance of a multihazard early warning system. The retrofitting measures to strengthen the building capacity is costly, and the EEWS is less expensive with a wider reach to all sections of the society. A successful EEWS saves lives and jobs, land, and infrastructures and supports long-term sustainability. It can help government officials in their planning, saving money in the long run, and protecting the economy. The government of India has allocated funds for the mitigation measure. Some share of it can be used to develop pan-India EEWS. The awareness of the earthquake in mass is very important. Mock drills can help in this to follow a set of rules which are followed in the real scenario. The practice of drop, cover and hold on drills may reduce injuries from falling objects.

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Fig. 5 The expected lead time for the major cities of Uttarakhand if a large earthquake hits the Uttarkashi region

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Application of Regression Techniques for Preparing a Homogeneous Earthquake Catalog—An Overview Ranjit Das, H. R. Wason, and Claudio Meneses

Abstract Standard least squares regression (SLR) and general orthogonal regression (GOR) address different questions and make different assumptions about measurement errors in one or both of the variables. SLR minimizes the sum of squares of the vertical deviations and provides estimation of the dependent variable (Y ). It assumes that the independent variable (X) is an observed value which is known without error, and only the dependent variable (Y ) suffers from measurement error. GOR, on the other hand, yields a linear relationship (Yt = β0 + β1 X t ) between the dependent (Y t ) and the independent (X t ) variables based on observed data (X, Y ) having measurement errors in both the variables involved. Therefore, it is mathematically incorrect to use observed value X in place of X t in the equation Yt = β0 + β1 X t, and thus, if done so (as in the conventional GOR method), this procedure will produce biased estimates. The present study is an overview of different methodologies used for preparing a homogeneous earthquake catalog for different seismic environments. The error variance ratio (η) used in GOR has not been addressed in seismological literature, and this overview will address this critical issue as well. This study will also suggest a guideline for the use of regression methods for preparing a homogeneous earthquake database which is an important input to obtain improved seismic hazard assessment.

1 Introduction Seismic catalogs generally encompass occurrence time, hypocentral coordinates, earthquake size, and various others seismic parameters related to earthquake. Earthquake catalogs give useful information regarding seismicity study, seismotectonics, R. Das (B) · C. Meneses Computer Science and Engineering Department, Universidad Catolica del Norte, Antofagasta, Chile e-mail: [email protected] H. R. Wason Indian Institute of Technology Roorkee, Roorkee, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_40

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and earthquake hazard and risk of an earthquake-prone area. A homogeneous earthquake catalog is of crucial importance for understanding earthquake patterns in space and time, land use planning, seismic risk, and other earthquake-related studies. All these studies need well defined and consistent earthquake magnitudes. Historical earthquakes mainly before 1976 don’t fulfill this criterion. The first earthquake size measurement scale was proposed by [1] considering seismic body wave recordings from standard Wood–Anderson torsion seismometers using Southern Californian seismicity. Seismologists had felt a need for expansion of the Richter magnitude scale to produce data in different environments using different seismic wave types. These efforts culminated in formulation of additional magnitude scales, namely body wave magnitude (mb /mB ), surface wave magnitude (M s ), moment magnitude (M w ), Das magnitude (M wg ). Most of these earthquake size estimates use earthquake source process and, therefore, are contained with observation errors. In seismic applications dealing with earthquake catalogs including seismic hazard and risk assessment of a seismic region, it is required to define different magnitude types into a preferred magnitude type. Standard linear regression (SLR) is the most used regression methodology for homogeneous earthquake catalog preparation considering one of the variables is either error-free, or the order of its error is negligibly small compared to the measurement error of the other variable. However, SLR is not appropriate considering the measurement error inherent in the independent variable. As an alternative, it is better to use general orthogonal regression (GOR) relation, which considers the errors on both the dependent and the independent variables ([2–5]). More recent work for earthquake magnitude conversion, however, considers the fact that both the earthquake magnitudes involved are affected by errors. Related research papers in statistical seismology, listed chronologically, have been published by [4–14]. In these publications, the approach used is called GOR. In statistical literature, this approach is also called orthogonal regression ([15]). Many statisticians (e.g., [15]) asserted that GOR is applied incorrectly in statistics and hence provide overestimated slope. [15] suggested to modify the error variance ratio (ŋ) by incorporating the equation error. Recently, Das and co-workers pointed out the limitations of GOR and suggested a procedure to remove the limitations of GOR. GOR yields a linear relationship between the dependent (Y t ) and the independent variable (X t ) based on observed data (X obs , Y obs ) in the form of Y t = β0 + β1 X t which is incorrectly used in GOR2 application as Y t = β0 + β1 X obs . Inappropriateness of using equation Y t = β0 + β1 X obs instead of equation Y t = β0 + β1 X t produces bias in GOR2 (conventional GOR). An exception is the paper of [4] who use the theoretical true points of GOR line given by [16].

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2 Regression Methodologies 2.1 SLR As SLR is the most used regression technique, it details are skipped here for brevity. This well-known linear regression procedure applies when the variance of independent variable tends to zero and only error is in the dependent variable. Under these assumptions, the best fitting line is achieved by minimizing the square sum of the vertical distances between the true points/experimental (X i , yi ) on the line and their equivalents observed points (X i ,Y i ), that is, n 

(Yi − βslr X i − β1slr )2

(1)

i=1

where β0 slr andβ1 slr represent the slope and intercept of SLR line.

2.2 GOR The general orthogonal regression (GOR) methodology is very popular among researchers in different areas since the work of [16]. Earlier studies focused on GOR estimation, often in different ways. The contributors included [16–20], and many others. Methodology of GOR has been defined and re-defined by many investigators (e.g., [4, 5, 10, 12, 15, 21]). GOR takes place when (1) two procedures measure the same quantity, or (2) when dependent and independent variables are related to each other by following the same physical laws ([15]). The basic methodology of GOR is discussed in different studies (e.g., [8, 13, 15, 16, 20]). General orthogonal regression (GOR) is based on the minimization of the squares of the statistical Euclidean distances ([22]), and minimization distance is given below n  i=1



Yi − β0 − β1 xi σu2

2

(X i − xi )2 + σe2

 (2)

In Eq. (2), (X i , Y i ) and (x i , yi ) denote the observed and the true (on GOR line) values, respectively, of the n data pairs of the independent and the dependent variables. The slope and intercept of the GOR line are β0 and β 1 , respectively. σe2 and σu2 represent the errors of X i and Y i , respectively. GOR relationships among different earthquake size estimates have been discussed in many studies (e.g., [3, 4, 7, 8, 10, 12, 13]). It has been observed in several publications of Das and co-authors (e.g., [5]) that Eq. (2) is inappropriately presented in some research papers (e.g., Eq. (8) of [7]). In fact, a GOR relation for magnitude pairs (Xt , Yt ) provides the following form:

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yt = βˆ1 xt + βˆ0

(3)

where x t (unlike X t ) is the abscissa of a theoretical true point (x t , yt ) on the GOR line and βˆ1 , βˆ0 represent the slope and the intercept of the GOR line, respectively. The theoretical true point does not indicate the natural true value; it indicates theoretically achieving the best unbiased value (x t , yt ) on the GOR line. Users cannot use Eq. (3) for estimation because users have only the observed value (X t ), not the theoretical true value (x t ) ([5]). Note that in the estimation stage, the measurement error in X t is ignored and the following equation yt = βˆ1 X t + βˆ0

(4)

with X t with the same slope and intercept as in Eq. (3) above is incorrectly used. This form of GOR is referred to as GOR2 (as discussed above). This incorrect way of using GOR introduces serious bias in the dependent variable estimations that lead to biased estimations of seismicity parameters and eventually to seismic hazard results (e.g., [5]).

2.3 A Graphical Representation of GOR The graphical representation of GOR is provided below for easy understanding of the limitations involved in the GOR method. It is observed that the problem of GOR is not well addressed in the existing literature; therefore, to provide a clear and complete view of GOR, two different cases are discussed below ([5]). Case I: A SLR line is obtained considering three observed data pairs (X 1 , Y 1 ), (X 2 , Y 2 ), and (X 3 , Y 3 ), and the equivalent true points/experimental points on the SLR line are (X t1 = X 1 , Y t1 ), (X t2 = X 2 , Y t2 ), and (X t3 = X 3 , Y t3 ), respectively. These experimental/true points on the line are used in deriving the best fitting SLR line by minimizing the square sum of the vertical residuals. After substituting the independent variables X 1 , X 2 , X 3 in SLR line, the true points/experimental points can be obtained that had been used in the preparing the best fitting SLR line (see Fig. 1a). Case II: A GOR line is obtained considering three observed data pairs (X 1 , Y 1 ), (X 2 , Y 2 ), and (X 3 , Y 3 ) having dependent and independent variable errors. The theoretical true points/experimental points of these observed data pairs have been obtained considering statistical Euclidean distance and are (X t1 , Y t1 ), (X t2 , Y t2 ), and (X t3 , Y t3 ). It is not possible to achieve theoretical true points/experimental points on substitution of observed dependent variables (i.e., X 1 , X 2 , X 3 ) in the GOR line just like case I; hence, conventional GOR procedure doesn’t follow the basic norms of mathematics and produces biased results. Rather than achieving the theoretical true

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Fig. 1 Schematic diagram showing true points/experimental points on the fitted regression line for a set of three observed points (X 1 , Y 1 ), (X 2 , Y 2 ), and (X 3 , Y 3 ), a SLR line, b GOR line

points/experimental point, a totally different points is achieved in the conventional GOR (see Fig. 1b).

2.4 Chi-Squared Regression The chi-square regression methodology considered in seismic study mainly originated from [6], and as per authors, the chi-square regression procedure is given as below. n (Msi − am bi − b)2 (5) Chi-square = i=1 2 σ (Msi ) + a 2 σ 2 (m bi ) In Eq. (5), the terms M Si represents the dependent variable and mbi represents the independent variable. The constant terms ‘a’ represents slope and ‘b’ represents intercept. The denominator of Eq. (5) is the equivalent of Var (M si − ambi − b) from Gaussian error propagation law ([23]): 

δ(Msi − am bi − b)2 δ(Msi )  δ(Msi − am bi − b)2 + Var (Mbi ) δ(Mbi )

Var (MSi − am bi − b) ≈ Var (Msi )

= σ 2 (Msi ) + a 2 σ 2 (m bi )

(6) (7)

GOR given by [16] and other statisticians are the same with the chi-squared regression of [6] for the linear case. Stromeyer et al. [6] used the case of uncorrelated errors

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in variables. Therefore, there is no difference between [6] and [16]; the only difference is the name. Some literature incorrectly stating that chi-square methodology of [6] and GOR methods are two different methodologies, but in fact, they are the same.

3 Homogenization of Earthquake Catalogs in Seismic Literature Homogenized earthquake catalogs have been prepared all over the world using regression techniques. Reliable past earthquake catalog for the Indian region exits for about past 100 years. A general compilation of the earthquake catalog for the Indian region has been made by [24], a detailed catalog for earthquakes occurring in India from historical times to 1869 has been made by [25]. Later several researchers made significant compilations of earthquake catalog for the Indian region [26–31]. Srivastava and Ramachandran [32] has prepared earthquake catalog using events data from 1594 to 1975 covering latitudes 5° N–28° N and longitudes 67.5° E–90° E. The Indian Society of Earthquake Technology prepared an earthquake catalog [30] for India and its adjoining region up to 1979. Bapat et al. [33] prepared a catalog for Northeast India and neighborhood area. Several other researchers who complied earthquake catalogs for India and neighboring region include [34–44]. All these seismic records use different earthquake size and are heterogeneous in nature. Recently, unified homogeneous earthquake catalogs have been made by several researchers for the Indian region or specific areas like Northeast India region (e.g., [2, 13, 42, 45–48]). To understand the effect of magnitude conversion in seismicity parameters, we used homogenized earthquake catalog of CIGIDEN (Centro National de investigation Para la Gestion Intergrade de desastres Naturale) of 39,976 events for Northern Chile and Southern Peru for the period 151,302,016 using GOR1, GOR2, and SLR approaches. The unified catalog has been declustered and estimate magnitude of completeness Mc and Gutenberg–Richter parameters (‘b’, ‘a’) [5]. Our analysis finds a biased in the Gutenberg–Richter parameter b around 5–42%. The difference between magnitude of completeness Mc given by GOR1 and SLR vary in the range 0–9%. The biased introduced in the total activity rate ‘a’ due to incorrect use of regression vary in the range 4–33%

4 Conclusions GOR is the best method for encountering errors of both the variables involved only when it appropriately considers the equation error. Many statisticians reported that due to incorrect use of error variance ratio (ŋ), GOR gives overestimated slope. It is not correct to use ŋ = σσue [16] because ŋ value does not consider the equation error. The suggested method for estimation of ŋ by [15] is not so simple but important to

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address the equation error for GOR to get the better accuracy. A correct form of GOR has been suggested by [5] and their earlier publications. Improved GOR technique of [5] is the best GOR technique because (1) It is correctly expressed in terms of observed variables; (2) It incorporates equation error; (3) It provides improved values of uncertainties in terms of slope and intercept; (4) It removes the overestimation problem of conventional GOR, and (5) It provides improved correlation coefficient Rxy and root mean squared error (RMSE) values. Significant variations in the seismicity parameters have been observed due to incorrect use of regression analysis; therefore, it is important to use the proper regression methodology for seismicity and seismic hazard assessment study. Chi-square regression of [6] and GOR in statistical literature are the same. There is no added advantage of using Chi-square regression in place of GOR method. Homogeneous earthquake catalog using GOR technique helps us to address the biased in seismicity parameters. GOR technique further improved the seismic hazard results by encountering the error in magnitude conversion. Acknowledgement The article has benefitted from FONDECYT Grant 11200618.

References 1. Richter, C. F.: An instrumental earthquake magnitude scale. Bull. Seismol. Soc. Am. 25, 1–32 (1935). ISSN 0037-1106 2. Thingbaijam, K.K.S., Nath, S.K., Yadav, A., Raj, A., Walling, M.Y., Mohanty, W.K.: Recent Seismicity in North–East India and Its Adjoining Region. J. Seismolog. 12, 107–123 (2008) 3. Ristau, J.: Comparison of magnitude estimates for New Zealand earthquakes: moment magnitude, local magnitude, and teleseismic body-wave magnitude. Bull. Seism. Soc Am. 99, 1841–1852 (2009) 4. Das, R., Wason, H.R., Sharma, M.L.: Unbiased estimation of moment magnitude from body and surface wave magnitudes. Bull. Seism. Soc. Am. 104, 1802–1811 (2014) 5. Das, R., Wason, H.R., Gonzalez, G., Sharma, M.L., Chodhury, D., Roy, N., Salazar, P.: Earthquake magnitude conversion problem. Bull. Seismol. Soc. Am. 108(4), 1995–2007 (2018) 6. Stromeyer, D., Gruünthal, G., Wahlström, R.: Chi-square regression for seismic strength 216 parameter relations, and their uncertainties, with applications to an Mw based earthquake catalog 217 for central, northern and north-western Europe. J. Seismol. 8(1), 143–153 (2004). 218 https://doi.org/10.1023/B:JOSE.0000009503.80673.51 7. Castellaro, S., Mulargia, F., Kagan, Y.Y.: Regression problems for magnitudes. Geophys. J. Int. 165, 913–930 (2006) 8. Das, R., Wason, H.R., Sharma, M.L.: Global regression relations for conversion of surface wave and body wave magnitudes to moment magnitude. Nat. Hazard 59, 801–810 (2011) 9. Gutdeutsch, R., Castellaro, S., Kaiser, D.: The magnitude conversion problem: further insights. Bull. Seis. Soc. Am. 101, 379–384 (2011) 10. Wason, H.R., Das, R., Sharma, M.L.: Magnitude conversion problem using general orthogonal regression. Geophys. J. Int. 190(2), 1091–1096 (2012) 11. Lolli, B., Gasperini, P.: A comparison among general orthogonal regression methods applied to earthquake magnitude conversions. Geophys. J. Int. 1135–1151, 190 (2012) 12. Das, R., Wason, H.R., Sharma, M.L.: Magnitude conversion to unified moment magnitude using orthogonal regression relation. J. Asian Earth Sci. 50, 44–51 (2012)

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13. Das, R., Wason, H.R., Sharma, M.L.: General orthogonal regression relations between body wave and moment magnitudes. Seismol. Res. Lett. 84, 219–224 (2013) 14. Gasperini, P., Lolli, B., Castellaro, S.: Comparative analysis of regression methods used 198 for seismic magnitude conversions. Bull. Seism. Soc. Am. 105(3), 1787–1791 (2015). https:// doi.org/10.1785/0120150018 15. Carroll, R.I., Ruppert, D.: The use and misuse of orthogonal regression in linear errors-invariables models. Am. Stat. 50(1), 1–6 (1996) 16. Fuller, W.A.: Measurement Error Models. Wiley, New York (1987) 17. Adcock, R.J.: A problem in least squares. Analyst 5, 53–54 (1978) 18. Kummell, C.: Reduction of observation equations which contain more than one observed quantity. Analyst 6, 97–105 (1879) 19. Pearson, K.: On lines and planes of closest fit to systems of points in space. Philos. Mag. 2, 559–572 (1901) 20. Lindley, D.: Regression lines and the linear functional relationship. J. Roy. Stat. Soc. Supp. 9, 218–229 (1947) 21. Madansky, A.: The fitting of straight lines when both variables are subject to error. Am. Stat. Assoc. J. 54, 173–205 (1959) 22. Anderson, T.W.: Estimating linear statistical relationships. Ann. Stat. 12, 1–45 (1984) 23. Williamson, J.: Least-square fitting of a straight line. Can. J. Phys. 46, 1845–1847 (1968) 24. Press, W.H., Teukolsky, S.A., Vetterling, W.T. and F.B.P.: Numerical Recipes in C The Art of Scientific Computing, University Press, Cambridge (1992) 25. Baird-Smith, R.: Memoirs of Indian earthquakes. J. Asiatic Soc. Bengal 13(2), 964–983 (1844) 26. Oldham, T.: A catalogue of Indian earthquakes from the earliest times to the end of AD 1869. Mem. Geol. Surv. India 19(3), 1–53 (1883) 27. Gubin, I.E.: Seismic zoning of the Indian Peninsula. Bull. Int. Inst. Seism. Earthq. Eng. 5, 109–139 (1968) 28. Kelkar, Y.N.: Earthquakes in Maharashtra in last 300 years. Kesari Daily (Marathi newspaper), Pune, January 7 (1968) 29. Guha, S.K., Gosavi, P.D., Nand, K., Padale, J.G. and Marwadi, S.C.: Koyna earthquakes (October 1963–December 1973), Central Water and Power Research Station (CWPRS), Pune, India (1974) 30. Chandra, U.: Earthquakes of Peninsular India: a seismotectonic study. Bull. Seism. Soc. Am. 67, 1387–1413 (1977) 31. Rao, B.R., Rao, P.S.: Historical seismicity of peninsular India. Bull. Seism. Soc. Am. 74(6), 2519–2533 (1984) 32. Srivastava, H.N., Ramachandran, K.: New catalogue of earthquake for peninsular India during 1839–1900. Mausam 36(3), 351–358 (1985) 33. Bapat, A., Kulkarni, R., Guha, S.: Catalogue of Earthquakes in India and Neighbourhood from Historical Period up to 1979. Indian Society of Earthquake Technology, Roorkee (1983) 34. Gupta, H.K., Rajendran, K., Singh, H.N.: Seismicity of the North–East India region, Part I: the database. Jour. Geol. Soc. India 28, 345–365 (1986) 35. Pendse, C.G.: Earthquakes in India and neighborhood: scientific notes. Ind. Met. Dept. 10(129), 177–220 (1949) 36. Banerji, S.K.: Earthquakes in the Himalayan Region. Indian Asoc. Cultiv. Sci., 64 (1957) 37. Tandon, A.N. and Srivastava, H.N.: Earthquake Occurrence in India. In: Arya, A.S., et al. (eds.), Earthquake Engineering (Jai Krishna Volume), Sarita Prakashan, Meerut, pp. 1–48 (1974) 38. Srivastava, H.N., Das, S.K.: Historical seismicity and earthquake catalogues for the Indian region. In: Lee, W.H.K., et al. (eds.) Historical Seismograms and Earthquakes of the World. Academic Press, pp. 335–348 (1988) 39. Ramachandran, K., Srivastava, H.N.: New catalogue of felt Indian earthquakes during 1901– 1971. Mausam 42(2), 171–182 (1991) 40. Iyengar, R.N.: Earthquakes in Ancient India. Curr. Sci. 77, 827–829 (1999) 41. Bhattacharya, S.N.: A perspective of historical earthquakes in India and its neighbored up to 1900. Mausam 49, 375–382 (1998)

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A Cluster-Based Seismic Risk Assessment: Economic Loss Using GIS for Jaipur Sub-Urban Area V. Anand , M. Mahatab, A. Sharma, D. Raj , M. K. Jat, R. Sarkar, and S. Pal

Abstract Economic risk is an integral part of seismic risk assessment, which is eventually required in seismic risk mitigation, response planning, and better resource allocation for adaptation and mitigation. Geospatial techniques like remote sensingbased image clustering and Geographical Information systems (GIS) can be used for seismic risk assessment at city level. In this paper, a GIS-based framework is proposed to assess the seismic risk of a part of the city in terms of economic loss using homogeneous clusters of different built forms, which are proxies to different building typologies. A random sampling strategy is adopted for geo-tagged proportionate building surveys as a function of homogenous built-form clusters and built-up density for Jaipur Sub-urban Area. An extensive survey was conducted using GNSS receiver to collect the geo-tagged information of different building typologies, and the GIS database was created in real-time. Further, an analysis is carried out using HAZUS methodology for the estimation of seismic risk in terms of economic loss spatially, which can also help policymakers in planning and mitigation. Keywords Geographic information system · Economic loss · Seismic hazard · Earthquake · Damage

1 Introduction Earthquakes are extremely sudden occurring natural disasters which can wreak havoc in cities [1]. The devastating effects of an earthquake may cause the city to an V. Anand · M. Mahatab · A. Sharma · D. Raj (B) · M. K. Jat Deptartment of Civil Engineering, MNIT Jaipur, Jaipur, Rajasthan 302017, India e-mail: [email protected] R. Sarkar Deptartment of Civil Engineering, IIT ISM, Dhanbad, Jharkhand 826004, India S. Pal Department of Civil Engineering, DTU, New Delhi, Delhi 110042, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_41

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instantaneous halt and disrupt the day-to-day activities related to economic, social, recreational, etc. Past studies revealed the consequences of potential earthquakes on various cities, where the damaging effects of destruction were a significant number of casualties and moderate to severe damage to infrastructure including building stocks, roads, railways, bridges, water supply systems, power distribution systems, and telecommunication systems [2, 3]. Sometimes, the destruction from the earthquake was so monumental that it was regarded as an ‘Act of God’, which is inevitable [4]. After the earthquake, the rescue or relief team could only assist the victims of the disaster by providing medical, shelter, and food facilities and help in resuming their day-to-day activities as soon as possible [5]. In the past 50 years, due to technological advancement in multiple disciplines related to engineering seismology, monitoring and collection of earthquake data, and development of new building materials and construction techniques, it is now possible that by using the proper earthquakeresistant method to build infrastructure, and buildings and technical tools to predict expected earthquakes at any site, the damages and casualties can significantly be reduced [4, 6, 7]. However, various newly developed techniques are still not applied in practice due to a lack of the necessary financial or human resources and also a lack of awareness at the managerial or bureaucratic level [8–14]. In past research, it has been found that the increase in seismic vulnerability of building stock, infrastructure, etc., and the associated risk was a major concern in metropolitan areas. Due to lacuna in the efforts for seismic disaster reduction procedure implementation and immediately available budget, scenario generation is essential to find out what will happen if an earthquake hits a particular city. Seismic damage estimation can work as an initial attempt for an effective Seismic risk reduction program [15]. It is well known that these procedures call for an assessment of the potential damages due to seismic events before making suggestions for mitigation, preparation, and response. Risk assessment is the main component of the estimation of probable disaster damages, among other things. As far as decision-making and emergency management purposes are concerned, seismic risk can be characterized in terms of possible economic, social, and environmental losses from a specific earthquake occurrence [16, 17]. The first substantial attempts to develop a seismic risk index were made in the United States of America (USA), which can compare the relative risk of several cities but not the risk of the urban fabric inside a metropolis. In addition to physical danger, the impact of social fragility and societal resilience were also taken into account while defining overall risk [18]. The disaster risk index has been defined globally using a variety of approaches that incorporate hazard and vulnerability analyses at various levels. Federal Emergency Management Agency (FEMA) has included the socio-economic factors related to urban earthquake risk in the development of HAZUS software [19]. The HAZUS technique is sophisticated for urban seismic risk assessment [20]. Generally, the output of the majority of the research conducted in Europe was in the form of software packages for estimating seismic risk and estimating earthquake losses as monetary and social damages [21, 22]. Meanwhile, there are several projects happening to provide approaches for estimating earthquake

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losses and assessing earthquake risk. Further, the fragility function-based vulnerability assessment method was incorporated in the HAZUS methodology, which was a probabilistic and more physically sound and exhaustive method as compared to qualitative methods [23–25]. The fragility functions give the probability of reaching or exceeding the particular damage states (like No Damage, Slight, Moderate, Extensive and Complete Damage) subjected to a particular level of seismic hazard defined in terms of Peak Ground Acceleration (PGA), Peak Ground Velocity (PGV), Spectral Acceleration (S a ), average spectral acceleration (S avg ) or Spectral Displacement (S d ) [26–28]. To implement the HAZUS methodology in the Indian scenario, an intensive literature review has been conducted to collect the fragility function for different building typologies existing in India [29]. These past studies have developed the fragility functions for a particular building typology, such as masonry buildings [29], RC Frame buildings [30, 31], RC buildings with infill walls [32] and steel buildings [33, 34]. Using these fragility and vulnerability functions, the economic loss for a particular building can be estimated. However, to estimate the loss at the city level, several authors have used the GIS framework in the past [19, 22, 35–37]. In this study, an attempt has been made to estimate the economic loss due to earthquakes considering the seismic hazard and vulnerability of building stock present in the Jaipur Sub-urban Area, using HAZUS methodology within the GIS framework.

2 Study Area Jaipur, also known as the pink city, is the capital city of the largest state Rajasthan (by area), located at the latitude of 26° 55, 19.4520,, N and a longitude of 75° 46, 43,, E having an area of 472 km2 [38]. The pink city enjoys heavy footfall of tourists throughout the year due to the fascinating monuments, markets offering culture and heritage-rich products, and wonderfully laid-out gardens [39]. It also makes the pink-walled city of Jaipur a UNESCO world heritage site. The present study focuses on the different types of clusters (formed by similar building typologies) surrounding the Malviya National Institute of Technology, Jaipur. Four different types of clusters have been identified, namely Malviya Nagar Commercial, Malviya Nagar Residential, Malviya Nagar Industrial Area, and Jhalana Gram. These four clusters comprise residential buildings, commercial buildings, and slums. The satellite image for this particular region has been used for the identification of different clusters. To capture the structural and architectural features of buildings present in the identified clusters, an intensive field survey has been conducted. For the field survey, the representative buildings have been selected using stratified random sampling to cover all the existing typologies. Global Navigation Satellite System (GNSS) data logger has been used to store the building information in the form of attributes which can be directly imported to the GIS platform.

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3 Methodology In this paper, the HAZUS methodology has been modified based on the fragility functions available for different building typologies present in Indian cities and adopted for Indian scenarios using GIS. Further, the economic loss has been estimated based on these vulnerability functions for the considered study area, as explained in the following section.

3.1 Study of City Urbanization Process Through Identifying the Homogenous Built-Form Regions Using Satellite Image Segregation of different homogenous built form-based clusters provides an aid in the prior planning of field survey and a further improvement in the identified clusters of the entire built-up area. These identified clusters have been used further to utilize for the selection of representative buildings of different typologies using stratified random sampling and also to estimate the cluster-wise economic loss. In order to form the clusters, different variables (e.g., texture, association, pattern, etc.) have been identified as the proxy for various built forms. Using these proxy variables, the study area has been categorized into different clusters, as shown in Fig. 1.

3.2 Field Survey The identification of building typology and its architectural and structural features are required for the selection of appropriate vulnerability functions used for the estimation of seismic risk of a particular building and further extended to the city level. GNSS-based survey automates the process of data collection, transfer of data to the GIS platform, and further processing along with the photographs. This process also reduces the possibility of any error which may occur during manual data entry. In this study, the survey forms used for data collection are in line with NDMA having some additional fields. As GNSS-based survey (shown in Fig. 2) was used, the entire collected building inventory data was geo-tagged, including the spatial locations with other attributes in digital format. Figure 2 shows a sample data collection screen in GNSS datalogger Trimble Juno T41/5. All the fields are filled in the GNSS datalogger, which generates the building information along with their spatial coordinates as ESRI point Shapefile format.

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Fig. 1 a Satellite image from Sentinel 2B platform of Jaipur City (sensed on 22-04-2022) and b segmented satellite image

Fig. 2 a GNSS receiver Trimble Juno T41/5, b sample screenshots during the field survey data collection through Juno

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Fig. 3 A sample of the GIS-based dataset prepared through the GNSS-based data collected from the field survey

3.3 Creation of GIS-Based Dataset The collected field survey data have been exported as ESRI shapefile format, and the corresponding attributes are shown in Fig. 3. The assessment of economic loss due to seismic hazard has been performed for four different visually homogeneouslooking clusters based on building typologies present in the study area, as shown in Fig. 4. These clusters have been classified using a satellite image of sentinel 2B with object-based image analysis. Cluster A, i.e., Malviya Nagar, is dominated by commercial areas adjacent to JLN Marg and residential buildings in a planned manner usually comprised of RC-framed structures. Cluster B is also similar in nature, with a relatively higher number of residential buildings as compared to cluster A. Cluster C is the industrial area of Malviya Nagar, which comprises relatively larger size buildings to incorporate industries. Steel-framed and RC-frame structures are the dominant building typologies in Cluster C. Cluster D, i.e., Jhalana Gram, is an unplanned settlement which has a drastic difference in terms of building typologies as compared to all the other clusters. It comprises Stonemasonry and Brick Masonry, with only a few houses having RC-frame structures. Figure 4 also shows the cluster along with the sample data point used for the study area. Further, the GIS-based dataset has been prepared directly through GNSS-based field survey data and used for the further processing of vulnerability and exposure estimation.

3.4 Hazard In the present study, the seismic hazard (expressed in terms of PGA) has been adopted from the NDMA Probabilistic Seismic Hazard Map of India database. This gridded data contains the PGA values with a 2% probability of exceedance in 50 years (Return

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Fig. 4 GIS map showing the clusters and points surveyed in the study area

Period ~ 2500 years) on A-type Sites. Further, the PGA value has been modified to include the effect of overlaying soil presented at the site. The gridded data of PGA has been converted into a raster map using geostatistical interpolation with a spatial resolution of 500 m. The hazard map generated in terms of PGA data on each cell has been used to estimate the spatial distribution of risk within the study area after the inclusion of exposure and vulnerability.

3.5 Vulnerability Estimation Vulnerability describes the degree of damageability of exposed entities of a city when subjected to a specific level of hazard. The vulnerability has been estimated from the fragility functions defined for the different building typologies. The fragility function for the building typologies present in the study area has been taken from past studies.

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Table 1 Cost of repair/replacement rates adopted in this study

Sr. no.

Building typology

Cost per square meter

1

RC building

Rs. 13550

2

Brick masonry

Rs. 9540

3

Stone masonry

Rs. 8500

3.6 Economic Loss Estimation Now, the economic loss has been calculated using the following relationship: Economic Loss = (PN × RCSN + PS × RCSS + PM × RCSM + PE × RCSE + PC × RCSM ) × Cri × Ns where the structure repair cost ratio for no damage state is RCSN , slight damage state is RCSS , moderate damage state is RCSM , extensive damage state is RCSE , and complete damage state is RCSC ; C ri is the location-based cost of repair/replacement per unit area, and N s is the number of stories. The location-based cost of repair/replacement per unit area has been taken from standing order No. X-3/2015 Public Work Department, Govt. of Rajasthan. The cost of repair/replacement rates adopted in this study are shown in Table 1.

4 Result and Discussion Through a selection of the adequate number of points for the field survey using GNSS data logger, a GIS database containing building structural and architectural features has been generated. Using this GIS database, all the required inputs for the estimation of economic loss have been calculated at each surveyed building location and then interpolated for the whole cluster. The probabilities of slight damage, moderate damage, extensive damage, and complete damage according to building typologies and at a given hazard level have been calculated and interpolated for the whole region. In the same manner, the repair cost ratio for slight damage, moderate damage, extensive damage, and complete damage according to occupancy class has been calculated and further converted into a raster layer. Further, the spatial inputs for economic loss have been prepared. After the generation of input layers, as discussed above, the economic loss has been fed into the raster calculator in ArcGIS software to delineate the Economic loss Map of the study region. The economic loss map of the study is depicted in Fig. 5. The economic loss map generated through this study highlights the regions with improper planning and building practices with relatively higher value of economic loss in those regions. In our current study, Jhalana Gram is showing the highest value

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Fig. 5 Economic loss map of the study area

of economic loss due to improper building practices when subjected to considered seismic hazard levels.

5 Conclusion In this study, HAZUS methodology based on the GIS framework has been used to assess seismic risk for Jaipur Sub-urban Area, in terms of economic loss. Various homogeneous clusters of similar built forms within the study area have been identified based on remotely sensed images. For the number of geo-tagged buildings to be surveyed within the identified clusters, a random sampling strategy has been adopted as a function of the homogenous built form and built-up density. An extensive survey has been conducted using GNSS receivers to collect the geo-tagged information of different building typologies and to create the GIS database. Further, spatially distributed seismic risk in terms of economic loss has been estimated using the HAZUS methodology in the GIS framework.

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The outcomes of this study provide a useful framework for quickly finding out the economic loss for every location of a city on a cell basis, which can further be updated according to the different levels of seismic hazard. The spatial distribution of economic loss can also help policymakers in planning and mitigation through funds allocation optimally using the micro-zonation city map based on the severity of the seismic risk. Thus, this methodology can be used for the whole city for the identification of seismically prone areas having more amount of economic losses, and by following the guidelines given in standards/codes, this probable economic loss can be reduced. Acknowledgements The work presented in this article is funded by the National Disaster Management Authority, Government of India. The support received from the funding agency and the institutes are gratefully acknowledged.

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Modelling of Empirical Accelerograms of 1999 Chamoli Earthquake (Himalaya) Using a Modified Hybrid Approach A. Sharma, D. Kumar, and A. Paul

Abstract In the present study, the empirical accelerograms of the 1999 Chamoli earthquake (M s 6.6) have been modelled using a modified hybrid approach. The earthquake occurred in the Central Seismic Region of the Himalayan region and was recorded at the seismic network installed and maintained by the Department of Earthquake Engineering, I.I.T.—Roorkee. The modified hybrid technique [1] has been used to model the empirical accelerograms of the 1999 Chamoli earthquake at 9 recording sites. The epicentral distance of various observing stations lies in a range of 10–110 km. The empirical accelerograms of the earthquakes are available at the sampling rate of 50 Hz. The hybrid technique includes the generation of envelope functions by summing the envelope functions of the randomly distributed subevents on the fault plane. In the present modified hybrid technique, the site response high-frequency decay parameter, i.e. kappa factor (κ), has also been evaluated and incorporated into the existing technique. The site response functions have been estimated using the HVSR technique. The high-frequency decay parameter ‘κ’ has been estimated to be in the range of 0.04–0.07. The simulated accelerograms have been compared with those of recorded ones in terms of Peak Ground Acceleration (PGA), duration, Response and Fourier spectra. The modelled value of PGA is 347 cm/s2 estimated at Gopeshwar is found to be close to the observed one (352 cm/s2 ). The essential parameters of simulated accelerograms, including PGA values, duration, Response and Fourier spectra, are well matched with those of the recorded accelerograms for most sites. Keywords Seismic Hazard · Simulation · Earthquake · Kappa · Site response function A. Sharma (B) · D. Kumar Department of Geophysics, Kurukshetra University, Kurukshetra, India e-mail: [email protected] A. Paul Wadia Institute of Himalayan Geology, Dehradun, India A. Sharma Ministry of Earth Sciences, Borehole Geophysics Research Laboratory, Karad 415105, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_42

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1 Introduction 50 Mya ago, the collision of the Indian-Eurasian plates led to the rise of the Himalayas. In the last two centuries, more than eight earthquakes have occurred in the Himalayas and no earthquakes of magnitude greater than or equal to 8 have hit since 1950 [2, 3], although a large part of the destructive boundary remains undisturbed. Based on the regional earthquake pattern, three earthquake zones near the belt of the Himalayas have been described by Khattri and Tyagi [4] and Khattri [5]. From the 1905 Kangra to the 1934 Bihar earthquake, the Central seismic gap (CSG) is the longest stretch. They have proposed that CSG may expect next future earthquakes. The Kumaun-Garhwal region of Western Himalaya was hit by an earthquake of magnitude (6.6) on 29 March 1999 at 00:35 h IST. The epicentre location and focal depth reported were 79.416 °E, 30.408 °N and 21 km, respectively. Earthquakeaffected region is not the same topographically. Population growth is found to be quite low in the area. The quake shook the twin cities of Gopeshwar and Chamoli along with the neighbouring region of Rudraprayag. The maximum affected districts are mainly the river valley and Alaknanda. The affected region is in the V seismic zone of India, which is the highest risk zone and needs to establish the earthquakeresistant structures. The earthquake studies give us the chance to learn about the nature and the earthquake processes. Many researchers have studied 1999, Chamoli earthquake such as Shrikhande et al. [6], Kumar et al. [7], Joshi [8], and Chopra et al. [9] for various research purposes. In the present study, the empirical accelerograms of 1999, Chamoli earthquake have been simulated using the modified Hybrid technique. In the present modified technique, the estimated site response and the high-frequency decay parameter, i.e. kappa (κ) factor, have been incorporated into the already existing technique.

2 Seismotectonics The Kumaun-Garhwal section is situated in the central seismic gap (CSG), which is a rupture zone between the historic 1905, Kangra and the famous 1934, BiharNepal earthquakes. The continuous movement of the Indian plate is moving towards the Tibetan segment of the Eurasian plate with a convergence rate of 0.05–0.06 m annually (Molnar and Tapponnir [10]. This movement not only distorts the rocks to developing the higher Himalayas but making the Garhwal region of Himalaya seismically active. In Garhwal and the adjacent province of the Himalayas, the three most important tectonic divisions are parted from one another by Main Central Thrust (MCT) and Main Boundary Thrust (MBT). Molnar [11] has given various models regarding the Himalaya’s evolution and the effect of the MBT and MCT in these mountain buildings. Most of the earthquakes in this region are situated near the north and south of MCT. The tertiary belt of sub-Himalayas is overthrust by the Proterozoic-Eocene

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Fig. 1 The location of the 1999 Chamoli earthquakes along with the recording sites of an installed seismic network

sequence of the lesser Himalayas. In the southern direction of it, the MFT, i.e. Main Frontal Thrust, separates the above belt from the Indo-Gangetic plains (IGP). The metamorphosed sediments (low grade) of lesser Himalayas are overridden sharply by high to medium-grade gneiss, migmatite and schist which belong to crystalline Higher Himalayas [12]. The location of the 1999 Chamoli earthquakes along with the recording sites of an installed seismic network has been shown in Fig. 1.

3 Methodology The Renowned hybrid technique suggested by [13] is an addition of two famous methods of simulation, i.e. composite source model (CSM) [14, 15], and envelope function (EF) [16, 17] methods. Firstly, the EF of the main event is estimated by the addition of EF’s corresponding to small size events. Small-size events or subevents are distributed non-uniformly in terms of size and their location on the fault plane. Multiplication of obtained EF for the main event with the band-limited white noise

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provides the final simulated accelerograms. Yadav [1] has modified the existing hybrid technique by introducing site amplification functions and κ factor. The EF, eij (t), calculated for individual (i, j)th small event on fault using Eq. (1) [18]: ei j (t) = (ai j t/di j ) ∗ exp((1 − t/di j ))

(1)

where a and d are the PGA value and the duration parameter of the EF. The EF of the main event e(t) is estimated by Eq. (2) [17]: ⎡

ΣΣ

e(t) = ⎣

i

⎤1/ 2   2 ⎦ ei j t − ti j

(2)

j

where tij is the delay in time caused due to travel and the rupture produced by the seismic waves. The number of randomly distributed subevents has been evaluated using Eq. (3) suggested by Zeng et al. [15]  −D 

−D / N (R) = P R − Rmax D

(3)

where D, N and Rmax define the fractal dimension, number of sub/small events having radii R, and largest sub-event’s radius, respectively, and parameter ‘p’ is calculated using Eqs. (4) and (5) by Zeng et al. [15]:

/ E p = 7M O (3 − D) [16Δσ (Rmax − Rmin )] when D /= 3 E/ p = 7M O [16Δσ ln(Rmax − Rmin )] when D = 3

(4) (5)

The sub-events size is estimated using Eq. (6):

−1/D / −D Ri = D Ni p + Rmax

(6)

where N i and Ri is the ith random number and ith subevent’s radius. Equation (7) helped in realizing the actual seismic moment by adjusting stress drop [15]: M OR =

Σ

/ Σ M O = 16 7 Δσ Ri3

(7)

Numerous fault models may be possible for the random distribution of these random-size subevents. To choose the best model for simulating the accelerograms renowned, the optimization technique, i.e. Genetic algorithm (GA) technique, has been used. This algorithm is based on the basic fittest existence standards.

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It begins with a generation of probable models. The artificial data produced from these probable models are then compared with the recorded ones, and a fitness score is estimated for every model of generation. The three operators of genetic algorithm (GA) are selection, crossover and mutation. These three of them are applied to the current generation to generate the models of the next generation. This procedure repeats it again till a constant fitness score is obtained or fitness score reaches a maximum saturation level. In the present study, we have used the PGA-attenuation relation [Eq. (8)] suggested by Abrahamson and Litehiser [19]:   log(PGA) = −0.62 + 0.177 MS − 0.982 log R + e0.248MS + 0.132F−0.0008 ER

(8)

In this expression, PGA (in g), M s is surface (S) wave magnitude and R denotes the hypocentral distance (in km). F values are either 1 (reverse fault) or 0 otherwise. The value of E is 0 or 1 for intraplate and interplate events, respectively. Midorikawa [17] has established relation [Eq. (9)] below to estimate the parameter of duration: d = 0.015 × 100.05m + 0.12R 0.75 (5 ≤ m ≥ 6)

(9)

where d = duration parameter m = earthquake magnitude. The site amplification functions and kappa factor have been calculated using the famous horizontal to vertical ration method (HVSR) using Eq. (10): H/ = V

/       abs H ( f )2ew + abs H ( f )2ns /2   abs V ( f )z

(10)

Kappa factor values have been estimated using the method suggested by Anderson and Hough [20]. According to this method, the decay of spectra A(f ), can be written as Eq. (11): A( f ) = AO e−πκ f ; f > f E

(11)

A0 depends on the distance and source, etc., f is frequency and f E is that frequency below which the shape of spectral shape is not linear on log-lin scale as Eq. (12): log[A( f )] = log(AO ) − π κ f. log(e)

(12)

This indicates that κ can be calculated with a linear least-square fit to observed log-lin spectra. The parameter kappa ‘κ’ is estimated using Eq. (13):

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Fig. 2 Step-by-step methodology of modified hybrid technique used in this study

/ κ = φ π. log(e)

(13)

φ is the slope of the linear fit. Figure 2 shows the step-by-step methodology of Modified Hybrid technique used in this study.

4 Results and Discussion Earthquakes have proven themselves very damaging for society but they also give the wealth of information that helps us to understand the earthquake process better. This understanding eventually helps us to better understanding of earthquake phenomenon and helps to diminish the hazard caused by any earthquake in a particular area. In the current study, the conformity of a modified hybrid method has been presented to model the empirical accelerograms of 1999 Chamoli (M 6.6) earthquake. The earthquake has been observed at strong motion array equipped with three component SMA-I instruments. The recording stations of 1999 Chamoli earthquake are located in epicentral distance range is 10–110 km. The digital empirical accelerograms of the earthquake are available at the sampling rate of 50 Hz. The earthquake parameters are given in Table 1. Recorded accelerograms of 1999 Chamoli earthquake at 9 stations have been modelled with the help of modified hybrid technique. The site response functions have been estimated using the famous horizontal to vertical ration method for each

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Table 1 Parameters used for modelling of empirical accelerograms of 1999 Chamoli earthquake Sr. no.

Parameter

Value

References

1

Fault orientation

Strike: 280° Dip: 7°

CMT

2

Focal depth (km)

15

CMT

3

Epicentre

30.38 N, 79.21 E

CMT

4

Seismic moment (dyne-cm)

7.8 × 1025

CMT

5

Fault dimension (km)

25 × 12

Wells and Coopersmith [21]

6

No. of subevents

11

This study This study

7

κ value

0.03–0.07

8

Fractal dimension

2

9

Shear wave velocity (km/s)

3.0

10

Rupture velocity (km/s)

2.7

11

Dynamic stress drop (bars)

100

12

Q-relation

126f 0.95

13

Duration parameter

d = 0.015 × 0.12R0.75

14

PGA-attenuation relation

Log(PGA) = −0.62 + 0.177Ms −0.982log(R + exp(0.248Ms)) + 0 132 F −0.0008 ER

Gupta et al. [22] 100.05 M ‘

+

Modified by Kumar et al. [23] Abrahamson and Litehiser [19]

station. The smooth site response curve has been used in the study. The least-square fitted line for the log-lin curves of Fourier spectra helped us to estimate the kappa factor. The kappa range estimated for this earthquake shows a range of kappa value from 0.04 to 0.07. Total 11 number of subevents having different magnitudes have been found on the fault plane. Their best location on the same has been decided using the renowned genetic algorithm. In this event, 30 generations of 200 models have been created and best model of the 30th generation has been chosen for further study. Table 2 shows the simulated and the observed PGA at various stations. The PGA values of modelled waveforms are found in good agreement with those of recorded PGA values for the 6 stations Almora, Barkot, Ghansali, Gopeshwar, Tehri and Uttarkashi. For rest of the 3 stations—Chinyalisaur, Joshimath and Ukhimath—the matching is not good. The radiation pattern or/and non-linear site effects along with the directivity effect may be the cause of such difference. The decay rate of both simulated and observed PGA values are found similar. Figure 3a–d depicts the comparison of simulated and recorded accelerograms with their corresponding Fourier and Response spectra at Gopeshwar, Ukhimath, Tehri and Ghansali recording stations. We have observed that the duration of simulated accelerograms is matching quite well with those of recorded ones at most of the stations. The matching of duration is satisfactory at Joshimath. The duration of simulated accelerograms has found longer as compared to recorded ones at the stations Almora and Uttarkashi.

UTTA

CHIN

ALMO

BARK

Uttarkashi

Chinyalisaur

Almora

Barkot

8

9

TEHR

6

Tehri

5

GHAN

JOSH

UKHI

GOPE

Station code

7

Joshimath

Ghansali

3

2

4

Gopeshwar

Ukhimath

1

Station name

Sr. no.

30.8

29.58

30.55

30.73

30.3

30.4

30.55

30.5

30.4

Latitude

78.2

79.65

78.33

78.45

78.58

78.65

79.56

79.1

79.33

Longitude

107.1775

98.3621

86.2826

82.3814

61.0023

53.6652

38.4371

16.9776

11.7024

Epicentral distance

108.2221

99.4993

87.5768

83.7359

62.8194

55.7221

41.2603

22.6548

19.0249

Hypocentral distance (km)

Table 2 Comparison of observed and synthetic PGA values for the 1999, Chamoli earthquake

17

26

51

52

53

71

66

89

195

PGA-L-Comp (cm/s2 )

22

27

44

62

61

82

62

95

353

PGA-T-Comp (cm/s2 )

14

21

28

67

63

82

107

141

347

PGA-simulated (cm/s2 )

530 A. Sharma et al.

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531

Fig. 3 Simulated accelerograms, observed accelerograms at various stations of 1999, Chamoli earthquake along with Fourier and Response spectra

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Fig. 3 (continued)

The Fourier spectra of simulated accelerograms are matching well with that of recorded ones at Chinyalisaur, Gopeshwar and Uttarkashi. For the rest 6 stations, the matching is in well agreement for higher frequencies as compared to the low frequencies.

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Fig. 4 Hypocentral distance versus PGA graph of observed and simulated accelerograms of 1999, Chamoli earthquake

The matching of Response spectra of recorded accelerogram with that of simulated spectra is well matched for the Uttarkashi station. At Almora station, the matching is good at lower and intermediate periods, and for station Barkot, it is good for the periods 0.3–0.5 s. The response spectrum of simulated accelerogram is good up to 0.9 s for the station Tehri. For Gopeshwar, it is good for lower and longer periods. The matching of Response spectra is satisfactory for the stations Ghansali for lower periods. The Response spectra of observed accelerogram is weak as compared to that of simulated one at the station Ukhimath. Figure 4 shows the simulated and observed PGA value versus hypocentral distance graph for different stations.

5 Conclusion The fidelity of the modified hybrid technique has been demonstrated successfully for the modelling of empirical accelerograms of 1999 Chamoli earthquake. Both frequency and time domain parameters of modelled accelerograms are found to be well matched with those of the recorded accelerograms for most of the sites. The lower frequency contents of modelled accelerograms are overestimated at some sites. This may be due to the limitation of the modified hybrid technique in its present form as envelop waveform of the large/target earthquake (Eq. 2) is only valid for high frequencies. The procedure of technique involves the product of envelope function with white Gaussian noise in time domain which has same effect as convolution in frequency domain. Overall the modified hybrid technique has been found to be successful for the modelling of empirical accelerograms of 1999 Chamoli earthquake. This technique can be further used to find out the seismic Hazard in the region also.

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References 1. Yadav, R: Modelling of Empirical Accelerograms of 2011 Sikkim Earthquake (M 6.9), Kappa (κ)- Model For NE Region (India) and Scenerio Seismic Hazard Maps for Sikkim Region of Himalaya. Ph.D. Thesis, Kurukshetra University, Kurukshetra, Haryana, India (2019) 2. Bilham, R.: Location and magnitude of the 1833 Nepal earthquake and its relation to the rupture zones of contiguous great Himalayan earthquakes. Curr. Sci. 69, 101–128 (1995) 3. Khattri, K. N: An evaluation of earthquakes hazard and risk in northern India, Himalayan Geol. 20, 1–46 (1999) 4. Khattri, K.N., Tyagi, A.K.: Seismicity patterns in the Himalayan plate boundary and identification of the areas of high seismic potential. Tectonophysics 96, 281–297 (1983) 5. Khattri, K.N.: Great earthquakes, seismicity gaps, and potential for earthquake disaster along the Himalaya plate boundary. Tectonophysics 138, 79–92 (1987) 6. Shrikhande, M., Rai, D.C., Naryan, J., Das, J.: The March 29, 1999 earthquake at Chamoli, India. In: Proceedings of the Twelfth World Conference on Earthquake Engineering, Upper Hutt, NZ: New Zealand Society for Earthquake Engineering, p. 8 (2000) 7. Kumar, D., Ram, V.S., Khattri, K.N.: A study of source parameters, site amplification functions and average effective shear wave quality factor Q seff from analysis of accelerograms of the 1999 Chamoli earthquake, Himalaya. Pure Appl. Geophys. 163(7), 1369–1398 (2006) 8. Joshi, A.: Analysis of strong motion data of the Uttarkashi earthquake of 20th October 1991 and the Chamoli earthquake of 28th March 1999 for determining the mid crustal Q value and source parameters. ISET J. Earthq. Technol. 468(43), 1–2 (2006) 9. Chopra, S., Kumar, D., Rastogi, B.K., Choudhury, P., Yadav, R.B.S.: Estimation of site amplification functions in Gujarat region, India. Nat. Hazard. 65(2), 1135–1155 (2013) 10. Molnar, P., Tapponnier, P.: Cenozoic tectonics of Asia: effect of a continental collision. Science 489, 419–426 (1975) 11. Molnar, P.: The structure of the mountain ranges. Sci. Am. 254, 70–79 (1986) 12. Valdiya, K.S.: Tectonics and evolution of the central sector of the Himalaya. Philosophical transactions of the Royal Society of London. Ser. A Math. Phys. Sci. 326(1589), 151–175 (1988) 13. Kumar, D., Teotia, S.S., Sriram, V.: Modelling of strong ground motions from 1991 Uttarkashi, India, earthquake using a hybrid technique. Pure Appl. Geophys. 168, 1621–1643 (2011). https://doi.org/10.1007/s00024-010-0236-4 14. Frankel, A.: High-frequency spectral fall off earthquakes, fractal dimension of complex rupture, b-value, and the scaling of strength on faults. J. Geophy. Res. 96, 6291–6302 (1991) 15. Zeng, Y., Anderson, J.G., Yu, G.: A composite source model for computing realistic synthetic strong ground motions. Geophys. Res. Lett. 21, 725–728 (1994) 16. Irikura, K.: Prediction of strong acceleration motion using empirical green’s function. In: Proceedings of the 7th Japan Earthquake Engineering Symposium, vol. 151, pp. 151–156 (1986) 17. Midorikawa, S.: Semi empirical estimation of peak ground acceleration from large earthquakes. Tectonophysics 218, 287–295 (1993) 18. Kameda, H., Sugito, M.: Prediction of strong earthquake motions by evolutionary process model. In: Proceedings of the Sixth Japan Earthquake Engineering Symposium, pp. 41–48 (1978) 19. Abrahamson, N.A., Litehiser, J.J.: Attenuation of vertical peak acceleration. Bull. Seism. Soc. Am. 79, 549–580 (1989)

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20. Anderson, J.G., Hough, S.E.: A model for the shape of the Fourier amplitude spectrum of acceleration at high frequencies. Bull. Seismol. Soc. Am. 74(5), 1969–1993 (1984) 21. Wells, D., Coppersmith, K.: New empirical relationships among magnitude, rupture length, rupture width, rupture area, and surface displacement. Bull. Seismol. Soc. Am. 84, 974–1002 (1994) 22. Gupta, S.C., Singh, V.N., Kumar, A.: Attenuation of Coda Waves in the Garhwal Himalaya, India. Phys. Earth Planet. Inter. 87(3), 247–253 (1995) 23. Kumar, D., Khattri, K.N., Teotia, S.S., Rai, S.S.: Modelling of accelerograms of two Himalayan earthquakes using a novel semi-empirical method and estimation of accelerogram for a hypothetical great earthquake in the Himalaya. Curr. Sci., 819–830 (1999) 24. Yadav, R.: Modelling of Empirical Accelerograms of 2011 Sikkim Earthquake (M 6.9), Kappa (κ)- Model For NE Region (India) and Scenerio Seismic Hazard Maps for Sikkim Region of Himalaya. Ph.D. Thesis, Kurukshetra University, Kurukshetra, Haryana, India (2019)

Development and Implementation of a Regional Earthquake Early Warning System in Northern India Govind Rathore, Pankaj Kumar, Mukat Lal Sharma, Kamal, Ravi Sankar Jakka, and Ashok Kumar

Abstract The Himalayan region has experienced many devastating earthquakes, due to the collision of the Indian Plate with the Eurasian plate. The movement of the Indian Plate is continuous but for a long time, this region has not released the accumulated energy. This accumulated energy makes this region more vulnerable to major to great earthquakes in near future. The urbanization and initialization in this region have magnified the effect of earthquakes by many folds. Earthquake Early Warning (EEW) systems are being used to mitigate the effects of earthquakes in many countries, by sending alerts of upcoming damaging waves to the users. The development of an EEW system in Uttarakhand was started in 2014 as a pilot project after validating its feasibility, which is now operational for the public. The operation EEW system includes (i) a central processing server at Roorkee; (ii) a warning server at Google cloud platform; (iii) around 160 sensors in Kumaun and Garhwal region of Uttarakhand; (iv) 80 public sirens at schools, hospitals, district emergency centers, etc., and (v) a smartphone for the public. This paper elaborates on the different components and architecture of the implemented EEW system. Further, this paper also includes information about the tasks carried out to aware the public about earthquake safety tips and the EEW system to utilize its potential at the user end. Keywords Earthquake early warning system · Warning dissemination system · Disaster risk reduction · EEW smartphone app G. Rathore (B) Regional Remote Sensing Centre North, National Remote Sensing Centre, Indian Space Research Organisation, Bengaluru, India e-mail: [email protected] P. Kumar EEW System Laboratory, IIT Roorkee, CoEDMM, Roorkee, India e-mail: [email protected] M. L. Sharma · R. S. Jakka · A. Kumar Department of Earthquake Engineering, IIT Roorkee, Roorkee, India Kamal Department of Earth Sciences, IIT Roorkee, Roorkee, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_43

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1 Introduction The Himalayan region has an area of around 2500 sq km and covers 13 Indian states Uttarakhand, Himachal Pradesh, Jammu and Kashmir, Ladakh, Arunachal Pradesh, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, Tripura, Assam, and West Bengal, which is vulnerable to large earthquakes as this region has not seen any major earthquake for a long time and storing the energy to generate one [21]. Earthquake Early Warning (EEW) systems are used to alert their end-users about the upcoming destructive seismic waves by detecting the primary seismic waves traveling at twice the speed of secondary waves. These systems have successfully shown their potential in saving lives and economic losses in many developed countries like Japan [4, 11, 12, 15–18, 20], Taiwan [6, 13, 22, 24, 25], Mexico [8–10], Turkey [1, 7, 26], and Romania [3, 14, 23]. So, the EEW system could be also used in the Himalayan region as a disaster risk reduction tool but it requires huge instrumentation to cover all vulnerable areas. An initiative for developing an EEW system in Northern India was started by the Ministry of Earth Sciences (MoES) and the Indian Institute of Technology Roorkee (IITR) in 2014 [5], which is further funded and extended by the Uttarakhand State Disaster Management Authority (USDMA), Uttarakhand Government.

2 Regional Eew System in Uttarakhand A regional EEW system is also being developed in Uttarakhand and has around 165 strong ground motion sensors that stream ground acceleration data over broadband leased lines to a central server at IIT Roorkee. The central server has been equipped with well-known software Earthworm, which is developed by USGS. The server has various modules attached to Earthworm for data receiving, storing, processing, and displaying. The central server is the main component of the developed system, as it is responsible for earthquake detections and parameter calculations. The central server uses Allen’s modified STA/LTA algorithm [2] for P-onset of the earthquake. It uses the Pd parameter of 3 s for the magnitude determination [25]. The central server sends all information about the detected earthquakes to the warning server, which is disseminated to the required devices based on the earthquake parameters.

3 Sensor Array The Uttarakhand EEW system uses low-cost accelerometer sensors, which are being imported from Taiwan and the locations of deployed sensors in Uttarakhand. These sensors stream real-time ground acceleration data to a central server situated at IIT Roorkee over TCP/IP protocol. Further, we have also developed a sensor similar to

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Fig. 1 Deployed sensors Palert (left) and Palert + (right) in the fields

the existing one to reduce the cost of the EEW system, as well as maintenance of these sensors, could be easily done. Further, a reduced cost of the sensors helps us to extend the sensor network for covering a large portion of the Himalayan region. The images of the deployed sensors are being shown in Fig. 1.

4 Warning Dissemination System The Warning Dissemination System (WDS) is a complicated system that includes dedicated warning devices, smartphone apps, desktop software, and a warning server for receiving the earthquake information from the central server and pushing it to all warning devices and software/apps. The warning server has an MQTT broker, which is used to distribute the warning message to all sirens and desktop/laptop apps. A database has been created to store all information related to public sirens, home sirens, desktop, and smartphone app. A program named Siren Manager has been developed for logging the status of all dedicated warning devices such as public and home sirens into the database. The database also keeps the information about smartphone app users and logs their conditions during the earthquake. A dedicated GIS-based web portal has been developed for providing assistance to the maintenance team. The maintenance team can log in to the web portal for viewing the real-time status of the sirens on the maps. The web portal could be accessed from anywhere to make field installations and maintenance easy. All newly installed sirens are being automatically added to the database with the help of a program named Siren Manager. The early warnings to the smartphone app are being disseminated through Firebase Cloud Messaging (FCM) service by the warning server as running background processes or sockets are not allowed in Android and iOS.

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Fig. 2 EEW public sirens installed in the fields

5 EEW Warning Dissemination Devices The dedicated EEW public sirens were developed for warning dissemination with the help of loudspeakers [19], and image of the developed siren is shown in Fig. 2. These sirens have three LEDs for giving information about power, system, and warning server connectivity and three pushbuttons for validating connections at the time of installation for easy testing of the siren. Further, these sirens have been equipped with different panel power connectors for making installation easy and avoiding misconnection. These sirens are being installed in the fields for more than 3 years and successfully tested and validated. Currently, these sirens have been installed in two major cities Dehradun and Haldwani of Uttarakhand and locations are shown in Fig. 2.

6 Warning Dissemination App: Uttarakhand Bhookamp Alert The installation and maintenance of public sirens are time-consuming and laborintensive works, which creates a need for the development of a smartphone app for warning dissemination to the public at large. So, a smartphone app for the public has been developed for warning dissemination and officially launched for the public on 4 August 2021. The app could be downloaded from Play Store or App Store. The app needs to be installed on the smartphone, and notification permission to the app should be granted for receiving early warnings for the earthquakes. The developed

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app could be used in two language options Hindi and English. The home screen of the app has three tabs named Home, Instructions, and Recent Earthquakes. The home tab is being used to provide instructional videos for users about safeguarding themselves during earthquakes. The instruction tab is being used to give textual instructions for safety in brief. The recent earthquakes tab gives details about the past earthquake in the region. The warning message is sent through notification with a loud human voice to alert users so that users should get alerted even the smartphone resides in the pocket or at some distance. On tapping the alert notification, the user gets information about the origin, magnitude, and time left for the impinging damaging earthquake waves. 1 min after when the timer stops, users are being asked for their conditions. If user presses “I need Help,” then this information is being shared with the disaster management authority, and if user presses “I am Safe,” then the user is marked as safe.

7 Mock Drills and Success Stories Apart from the development of the EEW system, it is equally important to spread awareness about the EEW system and dos/don’ts during the early warning in the public. For this reason, we are constantly conducting monthly mock drills, which encourage the public to install the app for their safety. We are sticking the poster about steps that need to be followed during the earthquake as well as distributing the pamphlets to the public about the app. Further, we are also spreading awareness through conferences and stalls. We are also taking the help of news media and radio channels. Some pictures related to these activities are being shown in Fig. 3. The developed EEW system was successfully validated for 3 real earthquakes in Uttarakhand. Both of these earthquakes were less than 5.0 magnitude, so users were informed only through the smartphone apps with an early notification without any alerting sound. The public sirens were also kept silent as earthquakes with a magnitude less than 5.0 are not damaging. The estimated magnitudes of the earthquakes were not accurate, but close to the actual ones. These types of notifications build confidence in the public and encourage people to install the app for their safety. 5 mock drills are also successfully conducted in public through public sirens and smartphone app.

8 Conclusion The first EEW system in Uttarakhand State has been successfully implemented with around 165 sensors in Kumaun and Garhwal region with a central data processing server at Roorkee for earthquake detection. Further, a warning dissemination server has been established that stores all information about the smartphone app users and public sirens and disseminates the early warnings to the desired users based on the

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Fig. 3 Activities performed for spreading awareness about EEWS in India

earthquake parameters received from the central processing server. The implemented EEW system has successfully delivered three earthquake early notifications to the public after the app launches. Further, the complete warning dissemination system is being tested publicly each month during the mock drills. The smartphone apps are being developed for IOS and Android platforms for alerting the public, which is also spreading the awareness about EEW system in public. The paucity of strong-motion data for the development of GMPEs for this region makes it difficult to predict the strong ground motion close to the accurate one. Therefore, the low-cost strong ground motion sensors and sirens with low-cost MEMS-based sensors have been developed to collect the data for such works in the near future.

9 Future Scope The strong ground motion records for the Himalayan region are less in number to get the regression analysis for magnitude estimation with various well-known EEW parameters, due to the lack of instrumentation, which could be increased by installing the low-cost sensors in this region. Further, a denser network of strong ground motion sensors will help in detecting earthquakes faster and avoiding the consequence of sensor/network failure. An array of low-cost sensors should be established for covering all state 13 states for earthquake early warning systems. The data collected by these sensors should be used for developing AI-based algorithms for earthquake detection and size estimation. The public should be trained and there should be some compulsory routine exercises and mock drills in the schools about safeguarding themselves during the various regional disasters. The density of the public siren should be increased to cover a large area for the safety of the public as well as CAP protocol should be used to alert the public. Acknowledgements The authors would like to acknowledge the Uttarakhand State Disaster Management Authority, Uttarakhand Government, and Ministry of Earth science, Government

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of India, for supporting and funding this project. This project is very important for the safety of people, and the authors are also thankful to USDMA for their support in conducting mock drills and spreading awareness.

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A Critical Review of Existing Building Regulations and Bye-Laws in Hilly Regions of India P. Das Choudhury

and D. Raj

Abstract In this article, an attempt has been made to understand and review the existing Building Regulations and Bye-laws available in the hilly region of India, primarily focusing on the structural configurations and construction provisions, like the number of stories and building height in seismically active hilly areas. This article also examines and compares the customization of individual-specific regulations, framed after considering guidance from Model Building Bye-Laws 2016. A comprehensive summary is presented herein, including most of the available ByeLaws containing the construction and architectural regulations in hilly regions, which will help classify different building typologies along with their associated constraints. Keywords Bye-laws · Seismicity · Hilly regions · Topography · Building regulation

1 Introduction Rapid urbanization, increasing population, tourist influx, and scarcity of flat land led to extensive building constructions in hilly regions. Consequently, these factors drove the enormous developmental activities beyond the region’s land capacity. The studies on the impact of past strong earthquakes, specifically on the structures in the seismically active hilly areas, affirmed the detrimental performance of the existing buildings. The combined action of potential seismic activity, traditional design, and non-engineered construction practices on sloping land has caused human lives and property devastation after strong seismic events like Sikkim Earthquake 2011 [1] and Nepal Earthquake 2015 [2]. To cater these issues related to buildings, central authorities have formulated specific standards called Bye-laws, at the municipal level. P. Das Choudhury · D. Raj (B) Department of Civil Engineering, MNIT Jaipur, Jaipur, Rajasthan 302017, India e-mail: [email protected] P. Das Choudhury Department of Civil Engineering, UEM Jaipur, Jaipur, Rajasthan 303807, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_44

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Building bye-laws are general guidelines, which is applicable to a particular municipal area; used to control the building’s coverage, height, design, and construction; and to execute resilient and orderly development of that area. These laws protect buildings against structural failures, fire, earthquake, noise, and other hazards. However, haphazard coverage, development, and encroachment are witnessed in various small to medium-sized towns, which do not fall under the jurisdiction of building bye-laws. The second revision of model building Bye-laws, “Model Building Bye-Laws 2016,” [3] was released by the Ministry of Urban Development, India, to incorporate modern and advanced provisions for construction in a hazardprone area and with many other conditions (like, regulation for, conservation of heritage sites including heritage buildings, Rooftop Solar PV Installation, etc.) and integrate the same to the existing Bye-laws of different regions. Many researchers studied existing building regulations and their application to specific major hilly towns/cities to assess the extent of unprecedented development [4, 5]. Along with controlling haphazard development, building regulations play a critical role in deciding the general building typologies witnessed in a city or town, concerning structural configuration based on foundation level and slope retaining system [6]. In this paper, an attempt is made to catalog and inspect the various critical parameters concerning hilly regions of India in existing Building Regulations and Bye-laws available in the hilly area of India. The selection of parameters depended on the structural and construction provisions, like the maximum height of buildings, the number of stories, slope in hilly areas, and others. Based on this review, the conclusions have been drawn and presented herein.

2 Overview This paper concisely articulates Building regulations and Bye-laws for 13 states and Union Territories encompassing north, northeast, east, and south India, based on the altitude and severity of seismic zonation. The states covered in this study are Himachal Pradesh, Uttarakhand, Jammu and Kashmir, Sikkim, Meghalaya, Arunachal Pradesh, Manipur, Mizoram, Nagaland, Assam, West Bengal, Tamil Nadu, and Kerala. The Building regulations, selected for this review, are taken from the latest existing building bye-laws and development plans available on the corresponding websites of local/state governing authorities. The general information of the region, like the population, seismic zone, range, total geographical area, and proportionate hill area, is listed in Table 1.

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Table 1 Hilly regions/states with their characteristics State

Population*

Seismic zone

Ranges/region

GA geographical area (km2 ) [7]

Geographical area in hill districts (km2 ) [7]

Himachal Pradesh

6,864,602

IV, V

Western Himalayas

55,673

55,673

Uttarakhand

10,086,292

IV, V

Western Himalayas

53,483

53,483

J&K

12,541,302

IV, V

Western Himalayas

222,236

222,236

Sikkim

610,577

IV

Eastern Himalayas

7096

7096

Meghalaya

2,966,889

V

Khasi and Jaintia Hills

22,429

22,429

Arunachal Pradesh

1,383,727

V

Eastern Highlands

83,743

83,743

Manipur

2,855,794

V

Purvanchal

22,327

22,327

Mizoram

1,097,206

V

Purvanchal

21,081

21,081

Nagaland

1,978,502

V

Purvanchal

16,579

16,579

Assam

31,205,576

V

Barail Range

78,438

19,153

West Bengal

91,276,115

III, IV

Eastern Himalayas

88,752

3149

Tamil Nadu

72,147,030

II

Eastern Ghats

130,058

22,789

Kerala

33,406,061

III

Western Ghats

38,863

29,572

*

According to 2011 Census of India

3 Building Regulations for Residential Buildings Several critical parameters have been identified in this study, and based on these parameters, a detailed review of the building regulations or bye-laws is presented here and summarized in Tables 2 and 3: 1. Plot Size: Depending on the housing types, different hilly regions have different plot size limits, which is necessary for orderly development of municipal area. 2. Setbacks: Setbacks are the vacant area around a building to ensure optimum daylight and ventilation, which later can be modified into landscaping, as shown in Fig. 1a. Setbacks also depend on building typology. The plot size’s dimension decides the setbacks’ size, as smaller plots demand lesser setback requirements than bigger plots. 3. Maximum Ground Coverage: Ground coverage represents the percentage of the total plot area used by the building footprint and varies with the size of the plot area. In northern hilly states, the ground coverage ranges from 30 to 80%.

Housing type1 /plot size (m2 )

90–120

121–150

R

SD

State

(HP) Himachal Pradesh [8–18]

2.0–3.0/2.0/2.0/2.0

3.0/-/-/2.0

F/L/R/Re2

Setbacks (m)

Max. F.A.R.3

60 (Bilaspur, Dalhousie, Hamirpur, Mandi, Parwanoo), 75 (Chamba, Nahan, Solan)

63 (Bilaspur), 1.50–2.00 65 (Dalhousie, Hamirpur, Manali, Mandi, Una), 75 (Chamba, Kasauli, Nadaun, Nahan, Parwanoo, Solan)

Max. ground coverage

Table 2 Summary of existing building regulations/bye-laws in hill states (part 1)

Bilaspur, Manali, Manikaran, Palampur

Amb Gagret, Dharamshala, Ghumarwin, Kandaghat, Kullu Bhuntar, Nadaun, Paonta Sahib, Parwanoo, Rampur Bushahr, Reckong Peo, Rohru, Sundernagar, Theog, Trilokpur 18.80

21.00

Max. height of building (m)

(continued)

4.0 to 5.0, except Chamba and Nahan (3.0)

No. of Stories

548 P. Das Choudhury and D. Raj

State

D

3.0/2.0–3.0/2.0–3.0 45 (Kasauli, Nadaun), 50 (Bilaspur, Chamba, Dalhousie, Hamirpur, Mandi, Nahan, Parwanoo, Solan, Una), 55 (Manali)

251–500

50 (Bilaspur, Kasauli, Nadaun), 55 (Dalhousie, Hamirpur, Parwanoo), 60 (Chamba, Nahan, Solan, Una)

2.0–3.0/2.0/2.0/2.0

151–250

55 (Mandi), 60 (Manali)

50 (Nadaun), 60 (Kasauli, Manali, Una)

Max. ground coverage

3.0/2.0/2.0/2.0

F/L/R/Re2

Setbacks (m)

121–250

121–250

Housing type1 /plot size (m2 )

Table 2 (continued) Max. F.A.R.3 18.00

Kasauli

Hamirpur

20.00

15.50

Chamba, Nahan 10.70

Bir-Billing

Max. height of building (m)

(continued)

No. of Stories

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RS

PSD

5.0/3.5/3.5/3.5

6.0/4.0/4.0/4.0

751–1000

1001 and above

276–350

4.0/2.0/-/2.0

3.00/-/-/2.00

4.0/2.5/2.0/2.5

501–750

2.50/-/-/1.50

3.0/1.50/-/2.00

301–500

126–275

2.00/-/-/1.50

151–300

76–125

1.50/-/-1.20

76–150

1.50/-/-/-

1.50/-/-/-

46–75

Up to 75

1.20/-/-/-

3.0–5.0/3.0/3.0, 2.0–3.0

F/L/R/Re2

Setbacks (m)

30–45

501 and above

Housing type1 /plot size (m2 )

PR (J) Jammu region of J&K [20, 21]

(UK) Uttarakhand [19]

State

Table 2 (continued)

55

65

75

75

45

50

55–50

65–55

70–65

75

80

80

40 (except for Manali and Shimla, which is 50)

Max. ground coverage

1.10

1.20

1.30

1.40

1.50

1.60

1.80

1.80

Max. F.A.R.3

12.00

NA

Char Dham towns

Nainital Municipal Area

Mussoorie

Hilly regions (general)

Mandi, Shimla

NA

6.50

7.50

11.00

12.00

13.50

Max. height of building (m)

3.0

NA

2.0

2.0

4.0

4.0

(continued)

No. of Stories

550 P. Das Choudhury and D. Raj

G

PD

10.0/3.0/2.0/3.0

1001 and above

35

40

45

50

Max. ground coverage

10/10/10/10

10/10/10/10

10/10/10/10

P(Cat. D)

P(Cat. D)

50

50

50

55

10/10/10/10

P(Cat. C)

60

10/10/10/10

4000–8000 1/3rd of height of 30 building or 7.5 m or 30 building line of the abutting road whichever is more

7.0/3.0/2.0/3.0

601–1000

8001 & above

6.0/3.0/2.0/3.0

5.0/2.0/-/2.0

F/L/R/Re2

Setbacks (m)

451–600

351–450

Housing type1 /plot size (m2 )

(K) Kashmir P(Cat. NA region of J&K A) [22, 23] P(Cat. B)

State

Table 2 (continued)

2.00

2.00

2.20

2.40

2.25

1.75

Max. F.A.R.3

15.24

15.24

15.24

15.24

16.76

40.00

40.00

Max. height of building (m)

NA

NA

NA

(continued)

No. of Stories

A Critical Review of Existing Building Regulations and Bye-Laws … 551

(SK) Sikkim [24]

State

1/4th of height of building or 4.6 m whichever is more

F(Cat. A)

40

F(Cat. D)

2.00

2.40

2.40

2.80

1.00

Max. F.A.R.3

No. of Stories

To be determined on the basis of maximum ground coverage and maximum FAR/FSI

9.14

Max. height of building (m)

RS 40

Above 920

70 50

3.0/1.5/1.5/1.5

501–930

251–500

NA

Accordingly restricted as per stability of the area as identified by the mines and geology department and the land profile. The stability of area is divided into categories 1, 2, 3, 4, 5, and 6

16.76, 4.57, 10.67, 7.62, 4.57, and no construction is allowed for categories 1, 2, 3, 4, 5, and 6, respectively

(continued)

5.5, 1.5, 3.5, 2.5, 1.5, and no construction allowed for categories 1, 2, 3, 4, 5, and 6, respectively

Note The towns/cities are categorized into A, B, C, D, and E on the basis of Physiography/Terrain of this region. The elevation progressively increases from A to E

F(Cat. Not permitted in this category of towns/areas E)

40

40

40

50

Max. ground coverage

F(Cat. C)

F(Cat. B)

10/10/10/10

F/L/R/Re2

Setbacks (m)

P(Cat. E)

Housing type1 /plot size (m2 )

Table 2 (continued)

552 P. Das Choudhury and D. Raj

4.0/3.0/2.4/3.0

698–1858

RS

3.0/2.1/1.8/2.4

465–697

(AR) Arunachal Pradesh [26]

3.0/1.2/1.2/1.8

186–464

50 50 45

3.0/1.2/1.2/1.2

501–1000

1000–1500 3.0/1.2/1.2/1.2

1501–3000 3.0/1.2/1.2/1.2

65 60

3.0/1.2/1.2/1.2

3.0/1.2/1.2/1.2

65

101–250

3.0/1.2/1.2/1.2

61–100

75

75

50 (for residential bungalows and apartments)

Max. ground coverage

251–500

2.0/1.0/1.0/1.0

2.0/1.0/1.0/1.0

48

6.0/6.0/6.0/6.0

49–60

Above 4645

3252–4645 6.0/5.0/5.0/6.0

1859–3251 5.0/4.5/3.0/3.0

3.0/1.0/1.0/1.8

RS

(ML) Meghalaya [25]

F/L/R/Re2

Setbacks (m)

Less than 185

Housing type1 /plot size (m2 )

State

Table 2 (continued)

2.25

2.50

2.50

2.00

1.80

1.80

1.50

1.50

2.00

Max. F.A.R.3

17.40

17.40

17.40

17.40

14.40

14.40

8.40

8.40

Residential Apartments

Residential Bungalows 19.00

13.00

Max. height of building (m)

NA

6.0

4.0

(continued)

No. of Stories

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RS

(NL) RS Nagaland [29]

(MZ) Mizoram [28, 29]

2.0/-/-/1.8

2.0/-/-/2.0

3.0/-/-/3.0

125

125–500

501–1000

2.0/1.2/1.2/1.2

Above 130

2000–2500 8.0/4.5/4.0/4.5

NA

68

68

71

1500–2000 7.0/4.5/3.5/4.5

1.5/0.9/0.9/0.9

2.50

1000–1500 6.0/4.5/3.0/4.50

1.20/0.6/0.6/0.6

2.50

5.00/4.2/2.5/4.5

750–1000

94–130

2.50

4.0/2.4/1.2/3.0

46.45–93

2.00

3.0/1.8/1.2/2.0

500–750

NA

3.00 for residential A (sleeping accommodation < 150m2), 2.50 for residential B (sleeping accommodation > 150m2)

2.00

1.80

1.80

300–500

1.80

2.0/1.2/1.2/1.2

NA

Max. F.A.R.3

1.50/0.9/0.9/0.9

Max. ground coverage

150–300

F/L/R/Re2

Setbacks (m)

90–150

Housing type1 /plot size (m2 )

(MN) Manipur RS [27]

State

Table 2 (continued)

6.0

5.0

4.0

8.0

7.0

6.0

5.0

4.0

4.0

3.0

3.0

No. of Stories

(continued)

Depend on building plans accompanied by relevant structural designs and drawings depending on soil conditions, adoption of foundations duly certified by the structural engineer/geo-technical expert

19.00

16.00

12.85

25.00

22.00

19.00

16.00

13.00

13.00

11.00

11.00

Max. height of building (m)

554 P. Das Choudhury and D. Raj

RS

RS

RS

(WB) West Bengal [31, 32]

(TN) Tamil Nadu [33–35]

(KR) Kerala [36, 37]

3

2

3.0/1.8/1.8/3.0

402

NA

3.0/1.0/1.0/1.5

1.50/–/–/– 65

NA

50

Over 1200

NA

60

70

50 (For both RCC and Assam type)

50

Max. ground coverage

400–1200

1.5/1.5/1.5/1.5

3.0/1.8/1.8/3.0

200

100–400

3.0/1.5/1.5/3.0

134

3.0/-/-/5.0

F/L/R/Re2

Setbacks (m)

3.00

NA

For 3, 3–5, 5–7.5, and above 7.5, width (means for access + Open space), FAR are 1.00, 2.00, 2.50 and 2.75

1.25

1.00

Max. F.A.R.3

3.0

No. of Stories

2.0 Shall not exceed twice the width NA of the street abutting the plot plus twice the setback from the abutting road. If the building is abutted by streets having varying width, consider the street having greater width

7.00

For 3, 3–5, 5–7.5, and above 7.5, NA width (means for access + Open space), heights are 4.50, 6.50, 11.50, and 13.50

11.50/shall not exceed 1.5 times of the width of the road plus front setback

Max. height of building (m)

row, SD semi-detached, D detached, PR plotted row, PSD plotted semi-detached, PD plotted detached, G group housing, P plotted, F flatted, RS residential F/L/R/Re front/left/right/rear, F.A.R floor area ratio

1R

RS

Above 1000

Housing type1 /plot size (m2 )

(AS) Assam [30]

State

Table 2 (continued)

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Table 3 Summary of existing building regulations/bye-laws in hill states (part 2) State

Min. floor height (m)

Max. floor height (m)

Plinth level (m)

Max. acceptable slope (deg.) 45 (30 for Mandi)

HP [8–18]

2.70–3.00

3.5

0.45–4.00

UK [19]

NA

NA

Not less than 0.45 30 (in case of Nainital Municipal Area, it shall not be more than 26.5)

J [20, 21]

2.75

3

Not less than 0.45 30

K [22, 23]

2.75

3

0.45–1.5

30

SK [24]

2.75 (above NA 1372 m altitude), 3.00 (below 1372 m)

NA

NA

ML [25]

3.00

Not less than 0.45 NA

3.25

AR [26]

NA

NA

Not less than 0.3

30

MN [27]

2.75

NA

NA

NA

MZ [28, 29]

2.40

NA

Not less than 0.45 NA NA

NL [29]

2.75

NA

AS [30]

2.40

3.0 (4.8 for ground 0.5–0.75 story)

45

WB [31, 32] 2.75

3.6

NA

30 (in case of land vulnerable to debris slide)

TN [33–35]

NA

NA

Not less than 0.45 33

KR [36, 37]

3

NA

NA

NA

45

Whereas, in Northeastern hilly states and the hilly region of West Bengal, it ranges from 40 to 75% and from 50 to 70%, respectively. 4. Maximum FAR: FAR is the ratio of total covered area on all floors to the plot area. It represents the extent of development (combined covered area/plinth area of all floors) permissible on a plot area. Hence, FAR also varies according to plot sizes, as shown in Fig. 1b. In hilly towns, smaller plots have lesser FAR than larger plots, as the built-up area increases for smaller plot sizes. In northern hilly states, especially in Himachal Pradesh and Uttarakhand, FAR ranges from 1.1 to 2.0, whereas in Jammu and Kashmir, it ranges from 1.0 to 2.8. In northeastern hilly states, the range is from 1.0 to 3.0. In West Bengal, an eastern hilly state FAR ranges from 1.00 to 2.75. In southern states, where hills regions are prominent, like Kerala, FAR is limited to 3.0. 5. Maximum height of the building: The maximum height of the building and number of stories depend on the elevation profile and seismic zone. In case of building on slope, the maximum height is measured from the uppermost foundation level to the roof of the building, as per Fig. 1c. In the North, the maximum

A Critical Review of Existing Building Regulations and Bye-Laws … Fig. 1 a Setbacks b variation of FAR with plot size c building on a sloping ground

557

(a)

(b)

(c)

building height ranges from 10.7 to 21.0 m. In the Northeast, having a comparatively lesser peak and abundance of flat lands, the height of building varies from 8.4 to 25.0 m. In hilly region of West Bengal, the height of a building varies from 4.5 to 13.5 m, whereas in hilly region of Tamil Nadu, the height of a building is limited to 7.0 m.

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6. Number of stories: In Himachal Pradesh, the number of story ranges from 4 to 5, except for Chamba and Nahan which lie in seismic zone V. Rest of the considered northern hilly states, the number of story ranges from 2 to 5, whereas in the Northeast, this parameter ranges from 1.5 to 6. The variation of no. of stories with respect to states is shown in Fig. 2a. 7. Floor height: The minimum and maximum floor height generally vary from 2.4 to 3.00 m and 3.60 m throughout the hilly states of India. 8. Plinth level: The plinth level remains in the same range from 0.30 m to 0.45 m except in Assam, which has 0.70 m. 9. Maximum acceptable slope angle: Across the hilly regions, the maximum acceptable slope angle is regulated and maintained between 30° and 45°, based on topographical features and soil types, which also influence seismic activity. The variation of this parameter with respect to states is shown in Fig. 2b. 6

4 3

NA

NA

NA

1

NA

2

NA

No. of Stories

5

0 HP

UK

J

K

SK

ML

AR

MN

MZ

NL

AS

WB

TN

KL

States

(a)

40

30

J

K

SK

ML

AR

NA

UK

NA

HP

NA

10

NA

20

NA

Max. acceptable slope (deg.)

50

MN

MZ

NL

AS

WB

TN

KL

States

(b) Fig. 2 a Variation of no. of stories (average) with the states considered. b Variation of max/acceptable slope with the states considered

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4 Summary and Conclusions In this paper, a summary of the existing Building Regulations and Bye-Laws available for various states has been presented. The building regulations and bye-laws in hilly regions are found significantly different from the regulations in flat terrain, regarding some critical parameters, like structural configurations. The most critical and differing parameter is the capping on maximum height of buildings constructed in hilly regions compared to their flat-terrain counterparts. The capping on maximum height of buildings in hilly terrain holds for both conventionally designed and earthquake-resistant structures. The intensity of a region’s seismicity also influences the building’s height limitation. For example, in Himachal Pradesh, Chamba, and Nahan, being in seismic zone V, have a height limitation of 10.3 m or two stories. Whereas, Manali being in seismic zone IV has a height limitation of 18.8 m or five stories. Due to the scarcity of flat terrains in hilly states and increasing demand of rapid population growth and urbanization, the buildings are often constructed to fit the natural slope of the ground. This reality encouraged constituting the acceptable limit of ground slope angle as another building regulation parameter. The limitation of the ground slope angle of a particular region depends on the seismicity and type of soil. The available studies based on the after-effects of past earthquakes have exposed the vulnerability of existing buildings in seismically active hilly terrain. These studies have also shown that even though governing bodies made concrete stipulations of regulation as the developmental rule in the hilly areas, disparities between the provision and enforcement of these mandatory regulations are prevalent. Frequent and non-engineered construction/development activity on slopes beyond the permissible slope angle and building height lead to several consequential hazards like landslides, slope instability, and more seismically vulnerable building. Considering the complex nature of hilly terrain, seismicity, increasing population, and haphazard constructional practices, a critical review of the existing building regulations and bye-laws of the significant Indian hilly states is presented in this study. This study will aid in developing building typologies for seismic vulnerability analysis and risk management of buildings specific to hilly regions. Acknowledgements The authors are grateful to the Department of Civil Engineering, MNIT Jaipur, for providing all the necessary facilities for this investigation.

References 1. Sharma M.L., Sinvhal A., Singh Y., Maheshwari B.K.: Damage Survey Report for Sikkim Earthquake of 18 September 2011. Seismol. Res. Lett. 84(1), 49–56 (2013) 2. Lizundia B., Davidson R.A., Hashash Y.M.A., Olshansky R.: Overview of the 2015 Gorkha, Nepal, Earthquake and the Earthquake Spectra Special Issue. Earthq. Spectra. 33(1_suppl), 1–20 (2017)

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3. Model Building Bye-Laws, 2016. Town and Country Planning Organization, Ministry of Urban Development (2016) 4. Kumar, A.: Pushplata: building regulations for hill towns of India. HBRC J. 11(2), 275–284 (2015) 5. Kumar, A.: Impact of building regulations on Indian hill towns. HBRC J. 12(3), 316–326 (2016) 6. Surana, M., Singh, Y., Lang, D.H.: Seismic characterization and vulnerability of building stock in hilly regions. Nat. Haz. Rev. ASCE 19(1), 04017024 (2018) 7. The Committee to Study Development in Hill States Arising From Management of Forest Lands with Special Focus on Creation of Infrastructure, Livelihood and Human Development. Planning Commission, New Delhi (2013) 8. Draft Development Plan Shimla Planning Area 2041. Town and Country Planning Department, Himachal Pradesh (2022) 9. Development Plan for Amb-Gagret Planning Area 2035. Town and Country Planning Department, Himachal Pradesh (2016) 10. Development Plan Baijnath-Paprola Planning Area. Town and Country Planning Department, Himachal Pradesh (2016) 11. Development Plan for Bilaspur Planning Area. Town and Country Planning Department, Himachal Pradesh (2005) 12. Development Plan for Chamba Planning Area. Town and Country Planning Department, Himachal Pradesh (2007) 13. Development Plan for Dalhousie Planning Area. Town and Country Planning Department, Himachal Pradesh (2004) 14. Development Plan Dharamshala Planning Area 2035. Town and Country Planning Department, Himachal Pradesh (2016) 15. Development Plan for Kasauli Planning Area. Town and Country Planning Department, Himachal Pradesh (2008) 16. Draft Development Plan for Manali Planning area 2021. Town and Country Planning Department, Himachal Pradesh (2005) 17. Development Plan Manikaran Special Area 2035. Town and Country Planning Department, Himachal Pradesh (2016) 18. Development Plan Nadaun Planning Area 2035. Town and Country Planning Department, Himachal Pradesh (2016) 19. The Uttarakhand Building Construction and Development Bye Laws/Regulations, 2011 (Amendment 2017). Mussoorie Dehradun Development Authority, Uttarakhand (2011) 20. Jammu and Kashmir Unified Building Bye-Laws. Housing and Urban Development Department, Jammu and Kashmir (2021) 21. The Jammu Municipal Corporation (Building) bye-laws 2011. Jammu Municipal Corporation, Jammu and Kashmir (2011) 22. Pre-Draft Uniform Building Code-2019 for Kashmir Region. Town Planning Organisation, Kashmir, J&K (2020) 23. Building Regulations and Bye-Laws (Kashmir Division). Srinagar Municipal Corporation, Jammu and Kashmir (2010) 24. The Sikkim Building Construction (Amendment) Regulations. Urban Development and Housing Department Gangtok, Sikkim (2001) 25. Meghalaya Building Bye-Laws, 2021. Urban Affairs Department, Meghalaya (2021) 26. Arunachal Pradesh Building Bye-Laws—2019. Department of Town Planning and ULBs, Arunchal Pradesh (2019) 27. The Imphal Municipal Council Building Bye-laws (First Amendment), 2019. Imphal Municipal Corporation, Manipur (2019) 28. The Aizawl Municipal Council Building Regulations, 2012. Urban Development and Poverty Alleviation Department, Mizoram (2013) 29. Aizawl Municipal Council Building Regulations (Amendment) 2019. Urban Development and Poverty Alleviation Department, Mizoram (2019) 30. The Nagaland Building Bye-Laws 2012. Urban Development Department (2012)

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31. Assam Notified Urban Areas (Other than Guwahati) Building Rules, 2014. Urban Development (T) Department, Assam (2014) 32. The West Bengal Municipal (Building) Rules, 2007. Department of Municipal Affairs, West Bengal (2007) 33. The West Bengal Municipal (Building) Rules, 2007 (Amendment). Department of Municipal Affairs, West Bengal (2016) 34. Tamil Nadu Combined Development and Building Rules, 2019. Municipal Administration and Water supply Department (2019) 35. Tamil Nadu District Municipalities (Hill Stations) Building Rules, 1993. Municipal Administration and Water Supply Department, Tamil Nadu (1993) 36. The Tamil Nadu Hilly Areas Special Building Rules, 1981. Rural Development and Local Administration, Tamil Nadu (1982) 37. Kerala Municipality Building Rules, 2019. Local Self Government (RD) Department, Kerala (2019) 38. Kerala Municipality Building (Amendment) Rules, 2020. Local Self Government (RD) Department, (2020)

Microseismic Analysis Using Event Count and Potency Displacement for Stability Evaluation of an Underground Cavern Vikalp Kumar , V. R. Balasubramaniam , and K. S. Divyalakshmi

Abstract The long-term stability of an underground hydroelectric powerhouse cavern, which depends on the rockmass deformation rate, is paramount for tunnel engineers. The underground powerhouse cavern of the Tala Hydroelectric Project (THP), Bhutan, had faced several strata instabilities issues during its construction and even post-construction. So, a Microseismic Monitoring System, which is continuous real-time monitoring, was installed at this underground powerhouse cavern to assess the rockmass strata status. Seismic source parameter event count and potency displacement revealed the zone of maximum inelastic damage zone in both the major tunnels, namely machine hall and transformer hall, while Gutenberg-Richter relationship forecasted the maximum magnitude in and around the powerhouse cavern. This helped to augment timely support measures to enhance the life of this underground structure. Keywords Microseismic monitoring · Underground cavern · Hydropower · THP · Seismic potency

1 Introduction The life of a mega hydroelectric project may extend even beyond 50 years [1], and it is required to keep the underground powerhouse safe and stable for this long lifespan, which is a vital component of the hydroelectric project. The underground powerhouse has many vital installations and 365 * 7 * 24 working personnel. Thus, it requires continuous monitoring to save the lives and machinery from rockmass collapses that may occur as violent rockbolt failure, rockburst, wall heaving, etc., and due to deep micro-cracks, fault, high stress, etc. [2, 3] To arrest the rockmass damage/collapse timely, support measures are needed. Conventional instruments are useful to monitor the rockmass movements, but they V. Kumar (B) · V. R. Balasubramaniam · K. S. Divyalakshmi National Institute of Rock Mechanics, Bengaluru, Karnataka, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_45

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Fig. 1 Microseismic monitoring system layout

are of limited use to monitor the deep micro-cracks in the rock mass before a macroscopic rockmass failure. The formation of micro-cracks deep inside the rockmass and their build-up with time before a macroscopic failure releases energy in the form of stress waves, i.e. seismic signals [4, 5]. These energies are basically Microseismic signals that can be recorded with a network of suitable sensors installed in the cavern rockmass (Fig. 1). Data from the sensor arrive at the recording workstation server through the data acquisition unit and communication equipment via armoured and network cable. It is a passive method that indicates the inelastic damage zones of the underground structure by using spatiotemporal analysis of the microseismic events. The seismic signals can be recorded to gain insight into the origin time, microcrack location, event count, seismic potency, longitudinal and transverse wave energy, dynamic and static stress drop, apparent stress, corner frequency and various other seismological parameters that are derived after processing the seismogram. Seismic event clustering may help to provide information about the rockmass stability status in and around a particular zone. Seismic potency P represents the rockmass volume, of whatever shape, related to co-seismic inelastic deformation at the seismic source. The seismic potency will further help for the identification of zones of likely rockmass deformation. The underground powerhouse of THP located in Chukka Bhutan has encountered several rockmass engineering issues since its construction to today. Rockbolts have failed and ejected irregularly, which increases the threat to the life of working personnel and damage to the machinery at this underground powerhouse cavern. The wall of the machine hall cavern is converging at a slow rate [6, 7]. So, THP underground cavern has a continuous induced threat. Therefore, National Institute of Rock Mechanics (NIRM) has installed a Microseismic Monitoring System of the Institute of Mines Seismology (IMS), Tasmania, Australia, at the THP powerhouse cavern to monitor and evaluate the rockmass stability status. This Microseismic Monitoring method in recent years has been developed as an early warning method for underground rockmass safety monitoring in several underground structures (during and post-excavation) and mines. Various countries are using this method as an engineering practice to timely assess the rockmass stability issues during and post-construction [8–10].

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2 Microseismic Monitoring System at the Underground Powerhouse of Tala Hydropower Plant The Microseismic Monitoring System at THP mainly consists of the following components: 15 triaxial geophones, 15 uniaxial geophones, eight data acquisition units (DAQ), data transmission equipment and a workstation (Figs. 2 and 3). All the geophones were installed in the boreholes of various depths and are connected to the seismic DAQ having DSL modem, UPS, netSP and ADC. The underground laboratory at this powerhouse mainly consists of a workstation (for data recording, processing and analysis) and data transmission equipment. The seismic DAQ is further connected to the data transmission equipment in the underground laboratory using an armoured shield cable. IMS data recording, processing and analysis software are installed on the workstation which receives data as a seismic signal is generated due to the micro-crack that occurred in and around the underground powerhouse at THP. Using the web, the recorded data at the underground laboratory can be transferred anywhere in the world. Using the reconnaissance method, longitudinal (P) and transverse (S) wave velocities were obtained [11] and other parameters were set up after three months of the calibration of the entire microseismic monitoring system at THP.

Fig. 2 THP underground Powerhouse showing major tunnel and thirty installed geophones

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Fig. 3 Microseismic monitoring system at THP

3 Four Major Wall Stability Status Using Microseismic Data Processing and Analysis For the period of one year from 01 January to 31 December 2020, seismograms were recorded on the workstation of the underground laboratory. During microseismic data processing, signals (an example Fig. 4) and noises (Fig. 5) were separated. During this one-year time period, 20,007 seismograms were recorded and in those only 109 sets of seismograms were considered microseismic events and the rest were rejected as noise over a monitoring volume of 410 * 380 * 132 m3 . Microseismic events were further analysed to understand the stability status of the walls (upstream and downstream) of the machine and transformer hall using event count and potency displacement contours (Fig. 6).

3.1 Upstream Wall of the Machine Hall In the upstream wall of the machine hall, the maximum event count contour between RD 70–100 m and elevation 509–517 m with a maximum value at (10,772,514) (Fig. 7). But the maximum seismic potency displacement observed was not in the same zone as the maximum number of events (Fig. 8). The cluster of events is more in and around 514 m elevation and RD 70–100 m. As events having co-seismic

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Fig. 4 Microseismic signal recorded by six sensors on 08 April 2020 at 17:49:49 h

Fig. 5 Noise recorded by microseismic monitoring system

inelastic deformation more in and around 490 m, this induces maximum potency displacement at this elevation.

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Fig. 6 Microseismic event in and around the THP underground powerhouse over a monitoring volume (410 m * 380 m * 132 m)

Fig. 7 Event count (upstream wall: machine hall)

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Fig. 8 Potency displacement (upstream wall: machine hall)

3.2 Downstream Wall of the Machine Hall The event count and displacement contour of the downstream wall of the machine hall are shown in Figs. 9 and 10, respectively. Most of the events occurred below bus duct-1 but due to the occurrences of high local magnitudes at a lower elevation, maximum potency displacement has occurred below 491 m elevation.

Fig. 9 Event count (downstream wall: machine hall)

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Fig. 10 Displacement (downstream wall: machine hall)

3.3 Upstream Wall of Transformer Hall Maximum event count in the upstream wall of the transformer hall occurred in and around the location: X-10736 m, Z-503 m and RD: 73 m (Fig. 11). Maximum potency displacement is around the location having co-ordinate X-10758 m, Z-493 m and RD 110 m (Fig. 12). The difference associated with the location in event count and potency displacement contour is due to the occurrences of high-magnitude events in and around X-10758 m and Z-493 m.

3.4 Downstream Wall of Transformer Hall If one considers only the downstream wall of the transformer hall, more event count below EL 449 m and RD 78 m appear in the zone opposite bus duct-1 (Fig. 13). Maximum displacement is also more in the below EL 493 m and RD 103 m. (Fig. 14). The maximum deformation at EL 493 m and RD 103 m is because of high-magnitude events in and around this location. The stability of the powerhouse cavern, especially the four major walls, was studied separately. Microseismic event count contour in these four major walls requires future monitoring as the addition of more microseismic events in the zone of maximum event count contour may increase the threat to the wall in those zones.

Microseismic Analysis Using Event Count and Potency Displacement …

Fig. 11 Event count (upstream wall: transformer hall)

Fig. 12 Potency displacement (upstream wall: transformer hall)

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Fig. 13 Event count (downstream wall: transformer hall)

Fig. 14 Potency displacement (downstream wall: transformer hall)

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Maximum potency displacement was in and around the locations (RD: 10,797, EL: 492), (RD: 10,783, EL: 491) in the respective upstream and downstream walls of the machine hall, while (RD: 10758, EL: 493) and (RD: 10,751, EL: 493) in the respective upstream and downstream walls of the transformer hall of the underground powerhouse cavern. These four locations need immediate ground support systems for the long-term stability of the underground cavern.

4 Statistical Hazard Assessment of THP Underground Powerhouse Cavern The occurrences of seismic events are not strictly an arbitrary process. A statistical approach to the seismic event analysis in an underground cavern provides a reasonable basis for seismic hazard assessment. The seismic events in and around the underground cavern follow the same laws as those followed by natural earthquakes. A prominent analysis of both types of seismicity obeys the Gutenberg-Richter law (Number of events N-magnitude M relation) that on a logarithmic scale takes the following equation: log10 N = a − bM

(1)

In this relationship, “a” and “b” are constant that depend on space and time. The b-value is a measure of the seismic environment for a given region and defines the possible seismicity pattern of that region like stress build-up pattern and material conditions in the focal region [12]. The value of parameter “b” normally varies from 0.8 to 1.1 with a notional value of 1. A higher b-value characterizes more occurrences of microseismic events of low magnitude, whereas a lower b-value shows the dominance of higher-magnitude microseismic events. For this THP powerhouse during the period of one year of monitoring, the values of parameters “a” and “b” are 1.02 and 0.49, respectively. As local magnitude drops by 1.02, there is an increase of approximately ten times in the number of microseismic events. As the value of parameter “b” is low, it indicates there is a dominance of higher-magnitude microseismic events. One important parameter that can be derived from this semi-log plot is the mean recurrence time (Table 1). It appears that internal constant driving forces due to the composition of rockmass are acting continuously on the walls of the powerhouse. The values of the parameters a and b are 1.02 and 0.49, so the local magnitude of the largest seismic event mmax to be expected in one year is about 2.08. So, the occurrence of such high seismic magnitude in and around the powerhouse may damage the structure. So, advanced safety precautions are required to handle such high-magnitude seismic events at THP powerhouse cavern (Fig. 15). As local magnitude increases, the mean recurrence time increases, and the probability of the corresponding events decreases. In this THP underground powerhouse cavern, a microseismic event of local magnitude of 0.6 takes 68 days to recur and

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Table 1 Probability table and recurrence times for various local magnitude Local magnitude

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

Mean recurrence days

18

22

28

35

43

54

68

Pr (2 weeks)

0.54

0.46

0.39

0.32

0.27

0.22

0.18

Pr (1 month)

0.81

0.73

0.65

0.57

0.49

0.41

0.35

Pr (3 months)

0.99

0.98

0.96

0.92

0.86

0.80

0.72

Pr (6 months)

1.00

1.00

0.99

0.99

0.98

0.96

0.92

Pr (1 year)

1.00

1.00

1.00

1.00

1.00

0.99

0.99

Fig. 15 Gutenberg-Richter relationship for THP powerhouse

its probability to occur in two weeks is 0.18, while an event having a local magnitude of −0.6 will return in 18 days and its probability to occur in two weeks’ time period is 0.54. It is evident that for a microseismic event of definite local magnitude, higher is the probability as time period increases. For example, the probability of occurrence of a seismic event of local magnitude −0.2 increases from 0.39 in two weeks to 0.99 in six months. Therefore, microseismic events of lower magnitude recur more frequently than the higher local magnitude events and their probability also increases for events of the same local magnitude, and the probability of the occurrence increases as time duration increases. The occurrence probability of a seismic event of maximum magnitude 0.6 is 0.994 in one year.

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5 Conclusions Rockmass performance assessment of an underground cavern to ensure its long-term stability is a challenging task. Damage in the form of micro-cracks and its growth with time is a dynamic process. The development of micro-crack linkage with real-time monitoring helps to plan the appropriate restorative steps in a timely manner. Microseismic Monitoring analysis provides information in real time about the stability status of the underground powerhouse rockmass cavern of THP, Bhutan. Microseismic monitoring at THP provides a 3-D picture of micro-cracking in the spatiotemporal domain over a defined monitoring volume. Of all the recorded waveforms, only 109 waveforms are genuine microseismic events. The walls of the machine and transformer hall of this underground powerhouse were investigated using seismic events and potency displacement contours. The seismic event count contour indicates the probable failure zone in the future, while the seismic potency demarcates the zone that requires immediate attention with ground support systems. The parameter b of the Gutenberg-Richter law indicates that there are dominances of higher-magnitude seismic events and forecasts the maximum local magnitude, which will help to take the necessary steps in advance to minimize the damage due to future higher-magnitude events. This methodology could be extended to assess the condition of rockmass not only in underground hydropower projects, but also in underground hydrocarbons, LPG storage caverns and a variety of other underground rockmass structures. Acknowledgements The authors thankfully acknowledge the facility and support extended by Dr. H. S. Venkatesh, Director, National Institute of Rock Mechanics, to publish this research work. We are thankful to the management of THP, DGPC Ltd., for providing the required funds for this research work.

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References 1. Dincer, I., Ishaq, H.: Hydro energy-based hydrogen production. In: Renewable Hydrogen Production. Elsevier, pp. 191–218 (2022) 2. Wang, M., Zhou, J.W., Shi, A.C., Han, J.Q., Li, H.B.: Key factors affecting the deformation and failure of surrounding rock masses in large-scale underground powerhouses. Adv. Civ. Eng. (2020). https://doi.org/10.1155/2020/8843466 3. Backstram, A.: Rock Damage Caused by Underground Excavation and Meteorite Impacts (2008) 4. Yuan-hui, L., Gang, L., Shi-da, X., Da-wei, W.: The spatial-temporal evolution law of microseismic activities in the failure process of deep rock masses. J. Appl. Geophys. 154(2017), 1–10 (2018). https://doi.org/10.1016/j.jappgeo.2018.04.024 5. Lai, X., Jia, C., Cui, F., Chen, J., Zhou, Y., Feng, G.: Microseismic energy distribution and impact risk analysis of complex heterogeneous spatial evolution of extra—thick layered strata. Nat. Publ. Group UK (2022). https://doi.org/10.1038/s41598-022-14538-7 6. Bhasin, R., Pabst, T.: Case study of a large underground cavern in the Himalayas. J. Rock Mech. Tunn. Technol. 20(1), 5–20 (2014) 7. Bhasin, R., Pabst, T.: Finite element and distinct element analysis of the stability of a large underground hydropower machine hall in the Himalayas. KSCE J. Civ. Eng. 19(3), 725–732 (2015). https://doi.org/10.1007/s12205-013-1351-4 8. Guanglia, F., Ma, Q., Zhang, X., Qu, D., Wang, G., Liu, J.: Developments of microseismic monitoring technology in deep tunnels in China. In: Proceedings of the 8th International Conference on Civil Engineering, pp. 541–548 (2022) 9. Hudyma, M., Brown, L., Cortolezzis, D.: Seismic risk in Canadian Mines. In: CIM MEMO— Maintenance Engineering and Mine Operations (2016) 10. Alexander, J., Trifu, C.: Monitoring mine seismicity in Canada. In: Proceedings of the Sixth International Symposium on Rockburst and Seismicity in Mines Proceedings, pp. 353–358 (2005). https://doi.org/10.36487/acg_repo/574_34 11. Kumar, V., Jha, P.C., Singh, N.P., Cherukuri, S.: Dynamic stability evaluation of underground powerhouse cavern using microseismic monitoring. Geotech. Geol. Eng. (2020). https://doi. org/10.1007/s10706-020-01588-9 12. Kulhanek, O.: Seminar on b-value (2005) [Online]. Available: https://citeseerx.ist.psu.edu/vie wdoc/download?doi=10.1.1.627.514&rep=rep1&type=pdf

Characteristics of Strong Ground Motions for Delhi National Capital Region (NCR) Using Small to Moderate Size Earthquakes Abhishek, Manisha Sandhu, and Babita Sharma

Abstract The characteristics of strong ground motions such as spectral acceleration and their behaviour on the structural design code are rigorously studied using small to moderate earthquakes occurred in the Delhi NCR region and recorded at local seismic network installed by IIT, Roorkee under the sponsorship of the Ministry of Earth Sciences. Seven earthquakes with magnitude range 3.3 to 4.9 occurred at depths of 5 to 22 km have been used in the present study. Total 159 earthquake waveforms from 27 seismic stations are processed for this purpose. The observed peak ground acceleration (PGA) associated with 5 March 2012 (M 4.9) lies in range of 2.4 to 30.8 cm/s2 and the peak ground velocity (PGV) lies in between 0.07 and 0.88 cm/s. The maximum PGA (30.8 cm/s2 ) is observed at Jaffarpur station and minimum PGA (2.6 cm/s2 ) is observed at ridge observatory of New Delhi. The maximum horizontal spectral amplification occurred at 0.054 s in Holocene age formation, 0.109 s in Pleistocene age formation and 0.068 s in Proterozoic age formation. The maximum vertical spectral amplification is at 0.045 s in Holocene age formation, 0.084 s in Pleistocene age formation and 0.060 s in Proterozoic age formation. The normalized acceleration response spectra for Holocene, middle to late Pleistocene and Proterozoic age group is overestimated when compared with that of the Bureau of Indian standard code particularly in short period range, whereas it is underestimated for the longer periods associated with the available data. The present analysis is beneficial for the seismic hazard studies in the Delhi NCR and may be utilized to improve the design code by characterizing the strong motion scenarios in the region. Keywords Strong ground motion · Amplification · Normalized response spectra

Abhishek (B) · B. Sharma National Centre for Seismology, Ministry of Earth Science, New Delhi, India e-mail: [email protected] M. Sandhu Department of Geophysics, Kurukshetra University, Kurukshetra, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_46

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1 Introduction National capital region (NCR) is well known seismically active region of India which experienced small to moderate size earthquakes in the past. This region lies in the Indo-Yamuna plain situated at the foot of the Himalayas. Many seismotectonic features like faults, sub-surface faults, lineaments pass through this region. According to the Bureau of Indian Standards [1], Delhi NCR region falls in seismic zone IV which is associated with the seismic intensity scale of VIII (MMI scale). On 5 March 2012 at 07:41:05 IST, an earthquake of magnitude 4.9 occurred in Delhi NCR region (Delhi-Haryana Boarder) at the depth of 14 km, which created panic among peoples in the early morning. This earthquake was well recorded at 20 seismic stations installed in this region. There was another similar earthquake six months ago on 9 September 2011 with magnitude of 4.3 hit this region near Sonipat, Haryana. Due to its proximity to the central Himalayan seismic gap, its own intense seismicity, many fold increase of population in metropolitan city Delhi, and lower standard of multi-storey buildings, there is an urgent need for the seismic hazard assessment for this region in an appropriate manner. The importance of response spectra is well known among the seismologist and civil engineers. It is used to design the engineering structures that can bear the forces generated by earthquakes as civil structures experienced three-dimensional earthquake ground motion [2]. It is very simplest method to estimate the response of engineering structures to the ground motion produced by earthquakes [3]. The concept of response spectra was first coined by Biot [4, 5] and Housner [6]. The response spectrum is the graph between maximum response value of single degree of freedom system within the period range of interest and collection of them at various damping level known as response spectra. Housner [7] estimate the response spectra using number of earthquakes and developed an average acceleration response spectrum using different level of damping. Hayashi et al. [8] studied the response spectra at various soil conditions in Japan. Chopra and Choudhary [3] studied the response spectra at different geological age units in Gujarat region. Sharma et al. [9] studied the characteristic of strong ground motion in Nepal India boarder region and calculated the response spectra according to geological exposure of the region. In the present study, we are trying to characterize the strong ground motion for national capital region using small to moderate size earthquakes. Different seismic hazard attributes like peak ground acceleration, peak ground velocity, peak ground displacement and acceleration response spectra are estimated from accelerograms. Peak ground acceleration is very important ground motion parameter which is used to build earthquake resistant structures. This kind of study is needed for the Delhi NCR region as this region associated with seismic activity not only in the local regime but also from the adjacent Himalayan region.

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2 Geology and Seismotectonic The Delhi NCR is basically flat region except central Delhi and northern part of Rajasthan due to the presence of Delhi-Haridwar ridge and Aravalli Mountain belt [10]. The Yamuna River passes through the north-eastern side of the region which deposits sediments derived from the Himalayas into the study region. The upper layer is mostly composed of alluvium deposits. These alluvium deposits are divided into younger and older alluvium deposits. The younger alluvium composed of slit and clay, whereas sand silt clay kankar are present in older alluvium. The important rock formation is Delhi super group which consists of quartzite of Alwar series. The whole region is divided into three geological age groups, namely Proterozoic (Older), Pleistocene and Holocene (Younger) as shown in Fig. 1. Proterozoic age group mainly composed of phyllite, slate, limestone, quartzite and schist. Pleistocene age group composed of oxide slit-clay with kankar and micaceous sand, whereas Holocene age group consist of grey micaceous sand, slit and clay. Tectonically, this region is rich in form of various faults, ridges and lineaments. The Mahendragarh-Dehradun subsurface fault (MDF), Delhi-Haridwar Ridge (DHR), Delhi-Sagodha Ridge (DSR), Sohna fault and Mathura fault are major tectonic features. The Rajasthan Great Boundary Fault (RGBF) zone is another well-defined fault, which runs for about 400 km NNE–SSW to NE–SW as a major dislocation zone in Rajasthan. The intersection of these faults with Delhi axis of folding is found to be the reason for the associated seismicity of this region as shown in Fig. 1.

Fig. 1 Geological set-up and seismotectonic set-up of the study area. Red stars are the earthquake events and blue triangle are seismic stations used in present analysis

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3 Strong Ground Motion Data and Analysis The strong ground motion data are available from accelerographs installed by Indian Institute of Technology, Roorkee IIT-R and sponsored by Ministry of Earth Science, New Delhi. The data is downloaded from PESMOS website (https://pesmos.org/dow nload-ground-motion-records-2005-2017). The data from accelerometers installed in Delhi NCR region are used for the present analysis. These accelerometers (strong ground motion sensor) are Kinemetrics K-2 (K-2S) type with model episensor (Internal accelerometer) having digitizer of 18 bit [11]. All the instruments within Delhi NCR region are set at sampling frequency of 200 samples per second. The threshold level 0.001 was set for all the instruments. The recording is done on a 256 MB GeoSIG or 1 GB Kinemetrics compact flash card [11]. The earthquakes of magnitude range 3.3 to 4.9 occurring at the depth of 5.0 to 22.0 km are used in present analysis. Figure 1 shows the analysed earthquakes, seismic stations along with seismotectonic present in the region. Total 159 accelerographs recorded at 27 seismic stations are used to generated the acceleration response spectra and strong ground motion characteristics. Out of total 27 stations 19 stations are lie on middle-late Pleistocene formation; 4 stations lie on Holocene formation, whereas 4 stations lie on Proterozoic formations as given in Table 1. We observed the majority of seismic stations lies on the middle-late Pleistocene age site. All waveform data are analysed carefully to obtained the accurate and precise results. The accelerographs are band pass filtered in frequency range 0.01 to 20 Hz and then the baseline correction is applied. The filtered and the baseline corrected waveforms are then used to estimate the peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). The maximum value obtained from acceleration waveform, velocity waveform and displacement waveform are used as PGA, PGV and PGD, respectively. The acceleration response spectra are determined from each station and then grouped together according to the same geologically site conditions. The acceleration response spectra are estimated using fast Fourier transformation (FFT) integration method. The acceleration response spectra are evaluated from the geometric mean of two horizontal components and vertical component at 5% damping value. The obtained acceleration response spectra are then normalized with the first value of ground acceleration to get normalized acceleration response spectra (NARS). The normalization of acceleration spectra is applied to both geometric mean of horizontal components and vertical component. The mean and standard deviation are also calculated to check the deviation from average acceleration response. The seismic stations with the same geological site are grouped together. The grouped seismic stations normalized acceleration response spectra are then undergoing weighted average to get common normalized acceleration response spectra for specific geological site. The final obtained acceleration response spectra from the geometric mean of two horizontal components and vertical component are also compared with the BIS code for both hard and soft sites.

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Table 1 Different accelerometers stations with site geology, number of recorded earthquakes at each station and recorded magnitude range Stations

Longitude

Latitude

Geology

Site condition

Middle to late pleistocene

Soft

3

3.4–4.1

Soft

1

4.9

Soft

3

4.2–4.9

ROH

76.593

28.896

SON

77.01

29.00

JAFR

76.908

28.594

Number of earthquake recorded

Magnitude

NSIT

77.041

28.608

Soft

2

4.2–4.9

GUR

77.029

28.449

Soft

2

4.3–4.9

LDR

77.217

28.583

Soft

1

4.3

RGD

77.123

28.665

Soft

2

3.3–4.9

NTPC

77.304

28.508

Soft

2

3.3–4.9

DU

77.212

28.691

Soft

2

3.3–4.9

ZAKI

77.23

28.64

Soft

2

4.2–4.9

GGI

77.232

28.665

Soft

2

3.3–4.9

PAL

77.33

28.13

Soft

1

4.3

NOI

77.479

28.507

Soft

2

4.2–4.9

IIT

77.192

28.545

Soft

2

3.3–4.9

IMD

77.22

28.588

Soft

3

3.3–4.9

FAR

77.325

28.383

Soft

1

4.2

BAL

77.321

28.342

Soft

1

4.3

BAR

77.255

29.097

Soft

1

4.9

HGR

77.321

28.342

REW

76.61

28.184

MAC

77.295

28.603

Holocene

Soft

1

3.3

Soft

1

4.3

Soft

1

4.3

ALIP

77.141

28.795

Soft

3

3.3–4.9

DCE

77.118

28.795

Soft

2

3.3–4.9

JNU

77.165

28.542

Hard

2

3.3–4.9

ANC

77.264

28.539

Hard

2

4.2–4.9

NDI

77.217

28.683

Hard

1

4.3

DJB

77.188

28.652

Hard

2

4.2–4.9

Proterozoic

4 Results and Discussion In the present study, the strong ground motion characteristics of Delhi and national capital region are evaluated using 159 accelerographs recorded at well azimuthally covered seismic sites located in Delhi, Haryana and Uttar Pradesh states. The seismic stations are grouped into three categories according to geological aspects of region as

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Holocene group, middle-late Pleistocene group and Proterozoic group and acceleration response spectra are estimated for each group. Peak ground acceleration, peak ground velocity and peak ground displacement are evaluated associated with earthquake of magnitude 4.9 occurred on 05 march 2012 in Delhi-Haryana boarded region. It is observed that associated peak ground acceleration (PGA) varies from 2.4 to 30.8 cm/s2 . Peak ground velocity (PGV) varies from 0.07 to 0.88 cm/s, whereas peak ground distance (PGD) varies from 0.011 to 0.068 cm. Maximum PGA (30.8 cm/s2 ) is observed at JAFR station which is located 32 km from the epicentre, while minimum PGA (2.6 cm/s2 ) is observed at ridge observatory (IMD) which is 62 km from the earthquake epicentre. It is well known fact that peak ground acceleration is function of magnitude and distance. It follows the law of geometrical spreading which indicate the attenuation of seismic wave as the distance increase between epicentre and seismic station. In our study, we observed no such relation among many stations. The stations like SON and JAFR which lies close to Mahendragarh-Dehradun fault and Delhi-Haridwar ridge experienced maximum ground acceleration, whereas the station lies on far away from Delhi-Haridwar ridge and opposite side of Sohna fault experienced low ground motion. We can interpretate this as, the Delhi-Haridwar ridge and Sohna fault act as barrier for propagation of seismic wave which might be controlling factor of PGA, PGV and PGD. It is also fact that most of these stations lie on soft alluvium deposits and experienced high ground acceleration as compare to the stations with hard deposits which is in good agreement with general seismological observation. Sonipat station observed second highest PGA (26.6 cm/s2 ), maximum PGV (0.88 cm/s) and PGD (0.068 cm) as shown in Fig. 2a, b, c. The location of this station is along the Mahendragarh-Dehradun fault (MDF) and Delhi-Haridwar ridge (DHR). This can be interpreted as rupture directivity effect of the earthquake as MDF and DHR may be controlling factor of ground motion or act as barrier for propagation of strong ground motion beyond them. Bansal and Verma [12] observed the minimum PAG (2.50 cm/s2 ) at ridge observatory station and maximum PGA (39.4 cm/s2 ) at Jaffarpur station on transverse component. They observed the site amplification was not uniform and varies in 3 to 6 range for various sites. Babita Sharma et al. [9] also observed the rupture effect of 25 April 2015 Nepal earthquake and suggested the sub-surface material heterogeneity, seismotectonic feature and rupture directivity affect the variation of strong ground motion parameters. The effect of local geology on response spectra is also studied in the present work. The seismic stations are grouped according to their local geology via Holocene age group (4 stations), middle to late Pleistocene age group (19 stations) and Proterozoic age group (4 stations) as given in the Table 1. The normalized acceleration response spectra (NARS) and weighted average normalized acceleration response spectra for Holocene age group are shown in Fig. 3a. The average acceleration r– esponse spectra show maximum spectral amplification of 2.968 at 0.045 s on vertical component whereas maximum spectral amplification of 2.690 at 0.054 s on horizontal component. It is also observed that at short period up to 0.098 s, the vertical acceleration spectra are higher than horizontal acceleration response spectra, while for period greater than 0.098 s, it follows the reverse order (horizontal spectra are high

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Fig. 2 Distribution of a peak ground acceleration in cm/s2 , b peak ground velocity in cm/s and c peak ground displacement in cm associated with 5 March 2012 (M: 4.9) earthquake

as compares to vertical spectra). The similar kind of observation is found in middle to late Pleistocene age group. In this maximum spectral amplification is 2.760 at 0.085 s on vertical component and maximum spectral amplification 2.460 at 0.109 s is observed at horizontal component as shown in Fig. 3b. The vertical acceleration response spectra are high as compare to horizontal acceleration response spectra in the short period less than 0.1153 s. In case of Proterozoic age group Fig. 3c, the peak spectral amplification on vertical component is 3.28 at 0.060 s, whereas the peak spectral amplification on horizontal component is 3.54 at 0.068 s. From Fig. 3c, it is observed that vertical acceleration response spectra are small as compare to horizontal response spectra in period range 0.055–0.283 s. We also compared the weighted average normalized acceleration response spectra for Holocene, Pleistocene and Proterozoic age groups with the acceleration response spectra for soil and rock site suggest by Bureau of Indian standard code for entire country as shown in Fig. 3a–c, respectively. We observed the high amplification on vertical component as compare to horizontal component in Holocene and Pleistocene age group. On other hand, maximum amplification is observed in horizontal component on maximum period as compare to vertical component which is unison to the

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Fig. 3 Normalized acceleration response spectra for vertical component as well as for geometric mean of horizontal components and its comparison with Bureau of Standard Indian code 2002 for a Holocene age formation, b middle to late Pleistocene age formation and c Proterozoic age formation. (Light grey and red colour lines are acceleration response spectra of individual station)

standard norms. It is observed that at period less than 0.09 s the spectral amplification is overestimated when compared with Indian standard code whereas for period greater than 0.2, second the spectral amplification is underestimated than Indian standard code for both hard and soft rock for Holocene age group as shown in Fig. 3a. In case of Pleistocene age group, the SA is higher than the Indian standard code for period less than 0.08 s and lesser for period greater than 0.15 s as shown in Fig. 3b. A similar kind of observation in Proterozoic age group is shown in Fig. 3c. Hayashi et al. [8] estimated the average horizontal response spectra for various soil conditions in Japan and concluded that highest SA is 2.6, 2.5, 3.3 in loose soil, intermediate soil and stiff soil respectively. Mohraz [13] studied the earthquake response spectra for different geological conditions California region. He found the average SA is 2.6 on

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vertical component and 2.1 on horizontal component for rocky site and 2.3 on vertical and 2.1 on horizontal component for alluvium site. In Indian scenario, Raghuknath and Iyenger [14] estimate the seismic spectral acceleration for Peninsular India by matching the SA for different soil site classes as suggested by NEHRP (2001) with Indian standard code. Chopra and Choudhary [3] and Sharma et al. [9] also estimated the SA for Gujarat region and boarder area of India to Nepal, respectively. In present study, the vertical acceleration response spectra are comparatively high from horizontal spectra in case of Holocene and middle to late Pleistocene age group, whereas in case of Proterozoic group, the horizontal spectra are high as compared to vertical which is unison with general seismological observation. This can be interpreted as, the station on quaternary sediments experienced high vertical motions. Richter (1958) explained that the station near to faults or epicentre may experience larger vertical motion as compare to horizontal motion than elsewhere. In the present study, small to moderate size earthquakes only have been used as the large/major earthquakes data for the study region are not available. The obtained results may be modified by adding more recorded earthquake and simulated earthquake data for the analysis. The present research work will be helpful for construction of important structures and the risk due to earthquakes can be minimized as it is very important for Delhi NCR to build up a safer build environment as in recent years the study region have experienced tremendous demographic growth.

5 Conclusion The strong ground motion characteristic of Delhi and national capital region are estimated using well recorded strong ground motion data by local seismic network. The effect of local geological conditions on spectral acceleration are also studied. The estimated strong ground motion parameters associated with 5 March 2012 earthquake shows that the Peak ground acceleration (PGA) varies from 2.4 to 30.8 cm/s2 , peak ground velocity (PGV) varies from 0.07 to 0.88 cm/s and peak ground distance (PGD) varies from 0.011 to 0.068 cm. Maximum PGA 30.8 cm/s2 and 26.6 cm/s2 is observed at JAFR and Sonipat station, respectively, while minimum PGA (2.6 cm/s2 ) is observed at ridge observatory (IMD). We observed PGA distribution is uneven with respect to the epicentral distance. It is also observed that stations which are closed to Mahendragarh-Dehradun fault (MDF) and Delhi-Haridwar ridge (DHR) are experienced maximum ground motion and these discontinues may acts as barrier for future propagation of strong ground motions. The maximum amplitude of acceleration response spectra for Holocene, middle to late Pleistocene and Proterozoic age group formation shows peak 2.968 at 0.045 s (V), 2.690 at 0.054 s (H); 2.760 at 0.084 s (V), 2.460 at 0.109 s (H); 3.28 at 0.060 s (V), 3.54 at 0.068 s (H), respectively. The normalized acceleration vertical spectra are high as compared to horizontal spectra for the short period range and vice versa. The average SA for all three group (Holocene; middle to late Pleistocene; Proterozoic age) overestimating the Indian standard code for short period and underestimating for longer periods. In the present study, small

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to moderate size earthquakes only have been used as the large/major earthquakes data for the study region are not available. The obtained results may be modified by adding more recorded earthquake and simulated earthquake data for the analysis. The present analysis is very helpful for the seismic hazard studies in the Delhi NCR and may be utilised to improve the design code by characterizing the strong motion scenarios in the region.

References 1. BIS.: Criteria for earthquake resistant design of structures, part I—general provisions and buildings. Bureau of Indian Standards. IS 1893 [part I] (2002) 2. Shrestha, B.: Vertical ground motions and its effect on engineering structures: a state-of-theart review. In: International Seminar on Hazard Management for Sustainable Development. Kathmandu, Nepal. November 29–30, (2009) 3. Chopra, S., Choudhury, P.: A study of response spectra for different geological conditions in Gujarat India. Soil Dynam. Earthq. Eng. 31, 1551–1564 (2011) 4. Biot, M.A.: A mechanical analyzer for the prediction of earthquake stresses. Bull. Seismol. Soc. Am. 31, 151–171 (1941) 5. Biot, M.A.: Analytical and experimental methods in engineering seismology. Proc ASCE 68, 49–69 (1942) 6. Housner, G.W.: An investigation of the effects of earthquakes on buildings. Ph.D. thesis. California Institute of Technology, Pasadena, California (1941) 7. Housner, G.W.: Behavior of structures during earthquakes. J. Eng. Mech. Div., Proc ASCE 1959 1959; 85(EM4):109–29 8. Hayashi, S., Tsuchida, H., Kurata, E.: Average response spectra for various subsoil conditions. In: Proceedings of Third Joint Meeting. U.S. Japan Panel on Wind and Seismic Effects. UJNR, Tokyo (1971). 9. Sharma, B., Chingtham, P., Sharma, V., Kumar, V., Mandal, H.S., Mishra, O.P.: Characteristic ground motion of 25th April 2015 Nepal earthquake (Mw 7.9) and its implication for the structural design code for the border areas of India to Nepal. J. Asian Earth Sci. (133), 12–23 (2017) 10. Srivastava, A.K., Sinha, K.K., Dasgupta, G., Jalote, P.M., Srivastava, M.C., Gupta, S.K.: Report on Geotechnical project, Delhi. Unpub. Geol. Surv. India Report (1980) 11. Mittal, H., Kumar, A., Ramhmachhuani, R.: Indian national strong motion instrumentation network and site characterization of its Stations. International Journal of Geosciences (3), (2012). 12. Bansal, B. K., Verma, M.: The M 4.9 Delhi earthquake of 5 March 2012. Current Science (102), 12–25 (2012) 13. Mohraz, B.A.: study of earthquake response spectra for different geological conditions. Bull. Seismol. Soc. Am. 66, 915–935 (1976) 14. Raghukanth, S.T.G., Iyengar, R.N.: Estimation of seismic spectral acceleration in peninsular India. J. Earth Syst. Sci. 116, 199–214 (2007)

Detection of Liquefaction Phenomenon from the 2015 Nuweiba Earthquake Using Remote Sensing Data Hrik Chaudhury, Abhishek Kumar , and Rishikesh Bharti

Abstract Egypt is a country situated in the northeastern part of the African continent. It has great historical importance, making this country’s economy depend on tourism. Egypt faced significant earthquakes throughout history. One of the major earthquakes occurred in 1995, with a magnitude of 7.8. It left its marks on Nuweiba city, located at the banks of the Gulf of Aqaba, in the form of liquefaction. This disaster called for much economic loss in the country. Hence, it is essential to know that the area is still undergoing liquefaction. The estimation of liquefaction-related damages is possible using conventional field investigation approaches. However, conducting field research is difficult due to several factors, including cost, tool limitations, and accessibility issues. In this study, an attempt is made to detect water content changes which is a manifestation of soil liquefaction, using the 2015 Nuweiba earthquake of moment magnitude 5.5 in the vicinity of the epicenter. Band ratios and a statistical technique have been used to analyze pre- and post-earthquake optical satellite imageries to identify changes in water content. The algorithm has shown a change throughout the area of the study. Moreover, all the techniques produce a similar pattern for the immediate vicinity of Nuweiba city, proving the need for detailed geotechnical discussion. Keywords Liquefaction · Nuweiba earthquake · Remote sensing

1 Introduction Egypt has a history of earthquake occurrences. According to reports, thousands of earthquake incidents have occurred since 2200 BC [1]. USGS earthquake catalog reports 12 earthquakes having a moment magnitude greater than 5 in the Gulf of Aqaba region since 1900 AD [2]. Nuweiba city in Egypt experienced a major earthquake of moment magnitude 7.2 on November 22, 1995, in the Gulf of Aqaba [3]. H. Chaudhury (B) · A. Kumar · R. Bharti Department of Civil Engineering, IIT Guwahati, Guwahati 781039, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_47

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Fig. 1 Nuweiba earthquake of moment magnitude 5.5 (USGS earthquake catalog) [2]

This 1995 Gulf of Aqaba earthquake had left its mark in the vicinity through the ground and structural failures. Liquefaction caused ground deformation along the coastline near Nuweiba city, especially in the hotel areas. Ground fissures even as wide as 10 cm were reported [4]. These failures caused major economic drawbacks in the tourist area along the Gulf of Aqaba in the vicinity of Nuweiba city. To ensure such financial loss does not hinder the country’s growth, it is important to know if Nuweiba city and its vicinity still tend to undergo such damage. According to the USGS earthquake catalog [2], a significant earthquake having moment magnitude 5.5 with a focal depth of 22 km and coordinates of 29.040° N, 34.667° E occurred on Saturday, June 27, 2015, at 15:34:03 UTC in the Gulf of Aqaba along the Dead Sea fault zone near Nuweiba city [2] (see Fig. 1). This paper aims to find possible liquefied zones due to the 2015 Nuweiba earthquake by detecting soil moisture change through geospatial techniques.

2 Study Area Nuweiba city is located between the coast of the 180 km long Gulf of Aqaba (see Fig. 2) and the Sinai Peninsula. The Dead Sea fault system, which links the spreading of the Red Sea seabed with the development of continental rifts, is represented by the Gulf of Aqaba as its southern end. The Gulf of Aqaba is seismically active because of the Dead Sea Transform fault. The gulf formed when the Red Sea started rifting into the land. That displacement is maintained to date, forcing a transverse component of the Dead Sea Transform [5].

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Fig. 2 False color composite of the narrowed down study area having a buffer zone of 11 kms around the epicenter shown by the white hollow circle

3 Dataset and Methodology According to Papadopoulos (1993) [6], an earthquake of a certain magnitude has an influence area in terms of liquefaction. The empirical relation presented by Papadopoulos (1993) [6] is given in Eq. (1). Equation (1) shows the buffer zone for the 2015 Nuweiba earthquake to be 11 km. As the pre- and post-earthquake LANDSAT 8 images for the analysis covers a very large area compared to 11 km square, the study area had to be narrowed down, given in Fig. 2. Moreover, further closer inspection through Google Earth images reveals that most of our selected study area is covered in hilly terrain and has no permanent settlement. This narrows the study area to the coastal region in the vicinity of Nuweiba city (Fig. 3), which has significance in terms of population and economic aspects. Mw = −0.44 + 3 × 10−8 × Re + 0.98 × log Re

(1)

where Mw is the moment magnitude of the earthquake. Re is the epicentral distance from the earthquake. LANDSAT satellite has a temporal resolution of 16 days and acquires the image from a height of 705 km. The main goal of this research is to use pre- and postearthquake imageries to document moisture changes that occurred in the study area following the Nuweiba 2015 earthquake. For this purpose, LANDSAT 8 Operational Land Imager (OLI) data from June 24, 2015, and July 10, 2015, is used. The pre-

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Fig. 3 Selected study area from Google Earth images

and post-earthquake images were acquired at the same time on their respective days. Moreover, sun elevation and sun azimuth also do not differ significantly. Because the spectral reflectance values of soil and vegetation change with water content, the spectral reflectance of liquefied and non-liquefied surfaces will vary. On LANDSAT 8, spectral bands help identify changes in reflectance associated with the moisture content of the surface. On the other hand, weather factors may affect the surface’s water content and result in variations. The study area is one of the driest places on Earth. Reports from the Center for Hydrometeorology and Remote Sensing show that there was no rainfall between the period of pre- and post-earthquake image acquisitions [7]. Therefore, any moisture content change should result from the earthquake. Earlier studies have attempted to employ a geospatial approach to identify changes in soil moisture, like Ramakrishnan et al. [8], Oomen et al. [9], Baik et al. [10], Chaudhury et al. [11] and Mondal and Bharti [12]. The change in moisture is detected in this work using a variety of indices. These indices contain bands from LANDSAT 8 that are sensitive to the surface’s moisture content. Three types of Normalized Differential Water Index (NDWI) [13] (NDWI (green,NIR) , NDWI (green, SWIR1) , NDWI (green, SWIR2) ) and [12] Temporal Differential Liquefaction Index (TDLI) are used for the analysis. As shown in Eq. (2), NDWI (green, NIR) employs the green band, which is the 3rd band of LANDSAT 8, and the Near Infrared (NIR) band, which is the 4th band of LANDSAT 8. NDWI (green, SWIR1) uses the green band (3rd band of LANDSAT 8) having a wavelength of 550 nm and Short Wave Infrared (SWIR 1) band, which is the 6th band of LANDSAT 8 shown in Eq. (3). NDWI (green, SWIR2) uses the green band and SWIR 2 band, which is the 7th band of LANDSAT 8 shown in Eq. (4). Also,

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TDLI was computed as shown in Eq. (5). The image difference (Indexpre-earthquake and Indexpost-earthquake ) was calculated using LANDSAT 8 images for the two time periods before and after the earthquake shown in Eq. (6). An increase in water content after the earthquake is shown by a positive value, whereas a negative value indicates a decrease. Green550 nm − NIR860 nm Green550 nm + NIR860 nm

(2)

NDWIgreen, SWIR1 =

Green550 nm − SWIR1600 nm Green550 nm + SWIR1600 nm

(3)

NDWIgreen, SWIR2 =

Green550 nm − SWIR2200 nm Green550 nm + SWIR2200 nm

(4)

NDWI(green,NIR) =

TDLI =

Pre_earthquake_SWIR2200 nm − Post_earthquake_SWIR2200 nm Pre_earthquake_SWIR2200 nm + Post_earthquake_SWIR2200 nm Index = IndexPost_earthquake − IndexPreearthquake

(5) (6)

The tasseled cap transformation coefficients developed by Huang et al. [14] for LANDSAT ETM + reflectance were used. Based on numerous field observations of soil, impervious surfaces, dense vegetation and moisture content, Huang et al. [14] created this transformation. The field data were used to determine the rotation of the principal axes received from principal component analysis (PCA) while maintaining the orthogonality of the primary axes collected from PCA. Tasseled cap and PCA differ because the GramSchmidt technique enables the user to choose the calculation order based on a physical image interpretation. In contrast, PCA organizes the principal directions in the data a priori. Using the LANDSAT ETM + image tasseled cap transform wetness axes, the surface wetness/moisture content difference between pre- and post-earthquake coverages is assessed.

4 Results and Discussion The images show the change in the NDWI before and after the earthquake that occurred on June 27, 2015. A more significant difference in the index value indicates an increase in the water content (red and yellow pixels). Tasseled cap wetness resembles (see Fig. 8) the exact pattern of NDWI (green, NIR) (see Fig. 4). The water content has increased throughout the majority of the study area, as can be observed in the image difference of NDWI (green, NIR) (see Fig. 4) and tasseled cap wetness (see Fig. 8). Tasseled cap wetness gives more moisture change than NDWI (green, NIR). NDWI (Green, SWIR1) (see Fig. 5), NDWI (Green, SWIR2) (see Fig. 6) and TDLI (See Fig. 7) are showing more or less similar patterns for the study area with NDWI (Green, SWIR1) showing more moisture change followed by NDWI (Green, SWIR2) and TDLI. Three of

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Fig. 4 NDWI (Green, NIR) pre- and post-earthquake image differences of study area

the NDWIs and TDLI and tasseled wetness image differences give the same pattern for the pixels in the south near Nuweiba city (Fig. 8). .

5 Conclusion The results obtained from NDWI (Green, NIR) and wetness derived through tasseled cap transformation corroborate well for the moisture change distribution in the study area.

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Fig. 5 NDWI (green, SWIR1) pre- and post-earthquake image differences of study area

On the other hand, NDWI (Green, SWIR1) , NDWI (Green, SWIR2) and TDLI produce the same distribution pattern. Enhanced moisture content is observed near the southern region of Nuweiba city, as confirmed by all the geospatial techniques. According to Abuzied et al. [15], the groundwater potential in the vicinity of Nuweiba city is moderate to good. Therefore, though Nuweiba is a desert city, it has the scope of increased moisture content due to earthquakes because of the groundwater potential. Hence, further geotechnical study should be conducted to understand the liquefaction vulnerability of the area, and mitigation needs to be carried out.

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Fig. 6 NDWI (green, SWIR2) pre- and post-earthquake image differences of study area

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Fig. 7 TDLI pre- and post-earthquake image difference of study area

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Fig. 8 Tasseled cap wetness pre- and post-earthquake image differences of study area

References 1. Sawires, R., Peláez, J.A., Fat-Helbary, R.E., Ibrahim, H.A.: An earthquake catalogue (2200 BC to 2013) for seismotectonic and seismic hazard assessment studies in Egypt. In Earthquakes and Their Impact on Society, pp. 97–136. Springer, Cham (2016) 2. USGS Earthquake Catalogue 3. Baer, G., Sandwell, D., Williams, S., Bock, Y., Shamir, G.: Coseismic deformation associated with the November 1995, Mw= 7.1 Nuweiba earthquake, Gulf of Elat (Aqaba), detected by synthetic aperture radar interferometry. J. Geophy. Res. Solid Earth 104(B11), 25221–25232 (1999) 4. Abdel-Halim, M.A., Al-Tarazi, B.: Structural and geotechnical aspects of the 1995 Gulf of Aqaba earthquake. Doboku Gakkai Ronbunshu 2004(759), 15–23 (2004)

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5. Hamouda, A.Z.: Recent evaluation of the assessment seismic hazards for Nuweiba, Gulf of Aqaba. Arab. J. Geosci. 4(5), 775–783 (2011) 6. Papadopoulos, G.A., Lefkopoulos, G.: Magnitude-distance relations for liquefaction in soil from earthquakes. Bull. Seismol. Soc. Am. 83(3), 925–938 (1993) 7. CHRS data portal 8. Ramakrishnan, D., et al.: Mapping the liquefaction induced soil moisture changes using remote sensing technique: an attempt to map the earthquake induced liquefaction around Bhuj, Gujarat, India. Geotech. Geol. Eng. 24(6), 1581–1602 (2006) 9. Oommen, T. et al.: Documenting earthquake-induced liquefaction using satellite remote sensing image transformations. Environ. Eng. GeoSci. 19(4), 303–318 10. Baik, H., Son, Y.-S., Kim, K.-E.: Detection of liquefaction phenomena from the 2017 Pohang (Korea) earthquake using remote sensing data. Remote Sensing 11(18), 2184 (2019) 11. Chaudhury, H., Kumar, A., Bharti, R.: Detection of liquefaction phenomenon from the 2017 Tripura earthquake from remote sensing data. In: 2022 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE (2022) 12. Mondal, S.K., Bharti, R.: Seismic impact around himalayan snow-melt fed lake using InSAR: a case study for 20 March 2020 MW5.7 Tibet Earthquake. In: 2022 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE (2022) 13. Özelkan, E.: Water body detection analysis using NDWI indices derived from landsat-8 OLI. Pol. J. Environ. Stud. 29(2), 1759–1769 (2020) 14. Huang, C., Wylie, B., Yang, L, Homer, C., Zylstra, G.: Derivation of a tasselled cap transformation based on landsat 7 at satellite reflectance: Int. J. Remote Sens. 23(8), 1741–1748 15. Abuzied, S.M., Yuan, M., Ibrahim, S.K., Kaiser, M.F., Seleem, T.A.: Delineation of groundwater potential zones in Nuweiba area (Egypt) using remote sensing and GIS techniques. Int. J. Signal Process. Syst. 4(2), 109–117 (2016)

Estimation of Site Amplification Factor and Predominant Frequency in and Around Panchkula City, Haryana, India M. Sandhu, R. B. S. Yadav, D. Kumar, and Abhishek

Abstract In this study, we used the horizontal-to-vertical spectral ratio (HVSR) technique to estimate the site amplification factor and the fundamental/predominant frequency at some of the sites in the Panchkula city of Haryana state of India. This technique gained popularity when Nakamura [1] estimated the site amplification factor by considering the horizontal-to-vertical spectral ratio of microtremor noise data. This method helps in the seismic microzonation of an area with very less seismicity. It has been found that the site amplification factor and the fundamental frequencies at 19 sites in the city vary in the range of 1.9–3.1 and 0.4–1.0 Hz, respectively, depending upon the local geological conditions. The sites associated with the riverside show more site amplifications (> 2.8), while sites away from the river show less amplifications (< 2.1). Similarly, we observed that some sites show higher predominant frequencies (> 0.85), while some sites show less predominant frequencies (< 0.5). It has been also observed that some sites show clear H/V peaks, while some sites show broad peaks due to poor impedance contrast between bedrock and soil. The variation of site amplification factor and predominant frequency in the study region exhibits variable geological conditions and site characteristics. This study is highly useful for civil engineers in designing and constructing high-rise buildings in this region. Keywords Site amplification · Predominant frequency · Microzonation · Horizontal-to-vertical spectral ratio (HVSR)

M. Sandhu · R. B. S. Yadav (B) · D. Kumar · Abhishek Department of Geophysics, Kurukshetra University, Kurukshetra, India e-mail: [email protected] Abhishek National Centre for Seismology, Ministry of Earth Sciences, New Delhi, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_48

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1 Introduction It has now been well understood that the earthquake ground motion characteristics like amplitudes, frequency contents and duration are influenced significantly by the local site conditions. This further affects the degree and extent of damage patterns during an earthquake. The ground vibrations due to the same earthquake at one site have been observed to be stronger than those of another site at a similar distance. The topographic and local geological conditions termed as site effects are the reasons for such variations of earthquake ground motions. This depends on the softness of the rocks as well as the thickness of the sediments above the hard bedrock. The phenomenon of site effect can be explained by the lower velocity and density of unconsolidated sedimentary layers as compared to underlying hard bedrocks. The parameters like intensity and the angle of incidence of the upcoming seismic waves also play a vital role in modifying the ground motion and might introduce a nonlinear phenomenon [2] in combination with the local site effect. Site effect can be defined as the modifications by the surficial layers of soil formations and topography to the features (amplitude, frequency content and duration) of the seismic waves coming to the surface which results in the amplification or deamplification of the ground motion. The amplification factor and resonant frequency are generally measured as the qualitative and quantitative parameters of the site effects. The severe consequences of site effects have been observed during the 1985 Michoacan Mexico earthquake, 1988 Spitak, 1989 Lomaprieta and 2001 Bhuj earthquakes. It has been shown that a small, localized variation of the thickness of the very soft clay could originate in very significant differences in the surface ground motions over short distances, such as those observed during the 1985 Mexico earthquake [3]. It has been suggested that during the 1988 Spitak earthquake, the local site effects were a major factor in contributing to increased damage levels in the city located on a broad alluvial basin [3]. During the 1989 Loma Prieta earthquake, the combination of liquefaction and site amplification was the cause of the extensive damage in the Marina District of San Francisco [4]. Similarly, during the 2001 Bhuj earthquake, the site amplification was identified as the cause of heavy damage in Ahmedabad city which is 300 km away from the source zone [5]. The structures located on sediments suffered more damage during the 1997–1998 Umbria-Marche seismic sequence than those of similar type located within a few tens of meters on a rock site [6]. Thus, for determining the site-specific seismic hazard, the site amplification function plays a vital role and will be helpful for microzonation studies and simulation of strong ground motion at the surface of a region. In this study, the site amplification functions have been estimated at different sites in and around Panchkula city. Panchkula is a district of the state of Haryana, which lies in the foothills of the Himalayas or Shiwaliks along the main Frontal thrust (MFT) of the Himalayas. The geological and seismotectonic setting of the area makes it more vulnerable to the occurrences of earthquakes or other hazards in the region. Being in the vicinity of the capital of Haryana, Chandigarh and the administrative hub of Haryana, it becomes more important to assess the site characteristics for the region. Determination of ground

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Fig. 1 Geological map of the study area (https://www.sciencedirect.com/science/article/abs/pii/ S2352801X1830081X)

structure is important for seismic hazard analysis and earthquake design of structures (Fig. 1).

2 Geology and Seismotetonics of the Study Area Panchkula is the district of Haryana, which is located near the capital of Haryana (Chandigarh). Panchkula is located approximately between 76°51, East longitude and 30°41, North latitude. The study area, according to the geological setting, is located in the Himalayan foothills, i.e., Himalayan Frontal Thrust (HFT) or MFT or Shivalik Range and Sindhu Plain (Indo-Gangetic Plain) which lies in the seismic zone IV. Morni hills are the highest point in this area, whose elevation is 337 m above mean sea level. Due to geomorphological features, the study area has accumulated large boulders and pebbles. Alluvial plain deposits due to Ghaggar and Sirsa River, which lies on the Sindhu plain in Punjab. The district lies in the Himalayas boundary fault zones near to Main boundary thrust (MBT) and Main Central Thrust (MCT), where earthquakes of moderate (VII) to high (X) intensity have occurred in the past. Now, the principal tectonic displacement zone in the region is the Himalayan Frontal Fault system situated at the edge of the Indo-Gangetic plains [7, 8].

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3 Methodology Site amplification and predominant frequency at any site are the key parameters in designing the structure of a building, which can sustain during an earthquake due to the seismic ground motions that are amplified in the presence of loose soil sediments or deposits. The degree of shaking due to the occurrence of an earthquake depends upon the local geological conditions. There are various methods to evaluate the site effects based on theoretical and empirical approaches. The theoretical approach requires geotechnical characteristics of the site such as soil density, wave velocity, thickness and damping of sediments. The empirical approaches are different from theoretical approaches as they are based on empirical data. Most of these techniques are based on analyzing data in the frequency domain. The site amplification can be measured in two ways: one by using the earthquake waveform data and the other by using the ambient noise recording data. The influence of source and path effects has to be removed in order to estimate site effects precisely. These methods can be separated into two categories: one that uses a reference site (particular location with no site effects) and the other that uses only one site. These include (i) the standard spectral ratio (SSR) technique, (ii) generalized inversion scheme (GIS), (iii) the coda wave technique and (iv) the horizontal-to-vertical spectral ratio (HVSR) technique [9]. In the present study, we have used the HVSR technique using the ambient noise data measured by TROMINO 3G as it does not require the reference site for its estimation. The horizontal-to-vertical (H/V) spectral ratio method was first presented by Nakamura [1] to determine site amplification using ambient noise. This method assumes that the ground motions in the horizontal component at a site are amplified around the fundamental frequency of a site, while there is no amplification in the vertical component of ground motion. The H/V ratio technique assumes a half-space with a horizontal sedimentary layer on top. The vertical component of the transfer function, hv (f), is given as the ratio between the vertical component of motion on the surface, Vs(f), and that on the base of the sediments, V B (f), in the frequency domain [10]: Hv ( f ) = VS ( f )/VB ( f ) = SV ( f )P( f )Z V ( f )/SV ( f )P( f )

(1)

where Sv (f) represents the source effect, P(f) represents the path effect from the source to the base sediments and Zv (f) represents the amplification caused by the site sediments [9]. Similarly, the horizontal component of the transfer function Hu (f) can be defined as HU ( f ) = H s( f )/H B ( f ) = Su( f )Z u( f )P( f )/Su( f )P( f )

(2)

where Hs(f) and H B (f) are the horizontal components of the motion at the surface and the bedrock. Now from Eqs. (1) and (2), the ratio can be given as:

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Fig. 2 Estimated H/V curve at the site GHUMTHALA

HU ( f )/HV ( f ) = Z U ( f )/Z V ( f ) = HS ( f )VB ( f )/H B ( f )V s( f )

(3)

Now, Zv (f) = 1 as it is assumed that the vertical component of the ground motion is free from any influence of the sediments at the site. It can also be assumed that the spectral amplitude of both the components (H B (f) and V B (f )) at the bedrock, i.e., at the base of the sediments is the same and can be given as [10]. HB ( f ) = VB ( f )

(4)

From Eq. (3), the horizontal transfer function can be given as: Z u( f ) = H s( f )/V s( f )

(5)

The site amplification function at the given site can thus be obtained using Eq. (5). The Hamming and Tuckey Technique [11] is used for smoothing the H/V ratio obtained from the above process and will appear as shown in Fig. 2.

4 Results and Discussion The site response characteristics using the ambient noise H/V spectral ratio have been investigated in this study. Several measurements were made using the Tromino 3G instrument in the nearby areas of the Panchkula district of Haryana (Northern India). The frequency range from 0.2 to 20 Hz has been taken for the data analysis in the study region because most of the engineering structures have a resonant frequency of less than 10 Hz and hence the selected range will take care of all such components. In this work, for each site, the recording system operated continuously for 20 min with a sample rate of 128 Hz. We have acquired data at the 19 different sites in the Panchkula region with a maximum offset of about 2 km between each successive reading. The GRILLA software is used to archive, organize, view and analyze the recordings of

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TROMINO 3G. Figure 3a–c shows the obtained H/V curve along with the Fourier spectra of all three recorded components of the ambient noise with the module of noise filtering. Figure 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 and 15 shows the estimated site amplification functions with the predominant frequencies at the different sites. Table 1 gives the estimated predominant frequency and the corresponding site amplification functions at all 19 sites.

Fig. 3 a Estimated spectral H/V using the HVSR technique, b fourier spectra of all three components of recorded ambient noise, i.e., N-S, E-W, vertical c frequency-time graph to eliminate unwanted frequency

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Fig. 4 Estimated spectral H/V and the predominant frequency at Manka ITBP

Fig. 5 Estimated spectral H/V and the predominant frequency at Billa village

Fig. 6 Estimated spectral H/V and the predominant frequency at Panchkula extension

We note that the site amplification function lies in the range from 1.9 to 3.2 at the predominant frequency. The estimated site amplification function in the region is found to be consistent with the geology of the area. We have obtained the predominant frequency in the range of 0.42 ± 0.28 to 0.91 ± 0.17 Hz. The estimated predominant frequency is less than 1.0 Hz, which reflects the case of a deeper interface with the two-layer scenario. Furthermore, with more detailed surveying one can estimate the depth and velocity model of the region.

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Fig. 7 Estimated spectral H/V and the predominant frequency at Nadda Sahib Gurdwara

Fig. 8 Estimated spectral H/V and the predominant frequency at enclave

Fig. 9 Estimated spectral H/V and the predominant frequency at Chownki

Figure 16a shows the spatial distribution of the estimated predominant frequencies at various sites. The spatial distribution of the estimated amplification levels corresponding to the predominant frequency (0.42 to 0.91 Hz) which in turn corresponds to tall buildings to high-rise buildings is shown in Fig. 16b.

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Fig. 10 Estimated spectral H/V and the predominant frequency at Birghaghar

Fig. 11 Estimated spectral H/V and the predominant frequency at Lana Billa village

Fig. 12 Estimated spectral H/V and the predominant frequency at Manka village

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Fig. 13 Estimated spectral H/V and the predominant frequency at Paras hospital

Fig. 14 Estimated spectral H/V and the predominant frequency at sector 21

Fig. 15 Estimated spectral H/V and the predominant frequency at Upper Nada

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Table 1 Estimated parameters at 19 different stations S. No

Site name

Latitude (°N)

Longitude (°E)

Site amplification

Predominant frequency (Hz)

1

Mhtctay

30.3915

76.5325

2.6

0.91 ± 0.17

2

Mnkafct

30.3850

76.5343

2.1

0.42 ± 0.28

3

Biriaghr

30.2180

76.5247

2.0

0.50 ± 0.08

4

Gmthala

30.4350

76.5345

2.5

0.44 ± 0.08

5

Chownki

30.4246

76.5345

2.8

0.44 ± 0.07

6

Prshspt

30.4123

76.5254

2.0

0.94 ± 0.33

7

Sec-21

30.4015

76.5147

1.9

0.53 ± 0.14

8

Enclave

30.3915

76.5325

2.6

0.91 ± 0.09

9

Nrgudra

30.4142

76.5243

2.5

0.50 ± 0.34

30.634

10

Manka Itbp

76.9066

2.5

0.84 ± 0.06

11

Billa village 30.649

76.935

2.4

0.41 ± 0.30

12

Lana Billa village

30.628

76.9225

2.2

0.84 ± 0.17

13

Upper Nadda

30.6925

76.8972

2.6

0.50 ± 0.27

14

Panchkula extension

30.6451

76.8791

3.2

0.81 ± 0.06

15

Chandi Mandir

30.727

76.8927

2.2

0.50 ± 0.90

16

Jhiriwala

30.673

76.8830

2.8

0.53 ± 0.20

17

Manka village

30.635

76.9041

2.3

0.84 ± 0.05

18

Chiken village

30.821

77.006

2.4

0.68 ± 0.08

19

HMT

30.780

76.910

2.5

0.89 ± 0.12

5 Conclusions The site amplification level at 19 different sites in and around the Panchkula region has been computed in this study. The horizontal-to-vertical spectral ratio technique has been used for this purpose. The average value of the site amplifications ranges from 1.9 to 3.2 for the predominant frequency, which lies in the range of 0.42 to 0.91 Hz. The spatial distribution of the predominant frequencies and the site amplification levels at a predominant frequency corresponding to the natural frequency of the tall and high-rise story buildings have been shown. These maps are useful for the evaluation of seismic hazards to the different story buildings. The site amplification functions estimated in this study may be used for the simulation of the

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(a)

(b)

Fig. 16 Spatial distribution of a estimated predominant frequencies and b site amplification function functions corresponding to high-rise buildings at all the sites

site-specific ground motions in the region as well as for urban development planning. The estimated values in this study will also be useful for civil engineers in developing earthquake-resilient infrastructure in the region. Acknowledgements The authors are thankful to the reviewers for positive and constructive suggestions that improved the quality of the manuscript. The authors are also thankful to RUSA 2.0 for financial support.

References 1. Nakamura, Y.: A Method for dynamic characteristics estimations of subsurface using microtremors on the ground surface. QR RTRI 30, 25–33 (1989) 2. Pitilakis, K.: Site effects (recent advances in earthquake geotechnical engineering and microzonation). A. Ansal. Dordrecht, Kluwer Academic Publishers 1:139–197 (2004) 3. Papageorgiou, A.S., Kim, J.: Study of the propagation and amplification of seismic waves in Caracas valley with reference to the July 29, 1967 earthquake response: SH Waves. Bull. Seism. Soc. Am. 81(6), 2214–2233 (1991) 4. Bonilla, L.F., Steidl, J.H., Lindley, G.T., Tumarkin, A.G., Archuleta, R.J.: Site amplification in the San Fernando valley, California: variability of site-effect estimation using the S-wave, coda, and H/V methods. Bull. Seism. Soc. Am. 87(3), 710–730 (1997) 5. Mahajan, A.K., Kumar, S., Kamal.: Macroseismic field observations of January 26th, 2001 Kachchh earthquake and its Seismotectonics. J. Asian Earth Sci. 23(1), 17–23 (2004) 6. Rovelli, A., Scognamiglio, L., Marra, F., Caserta, A.: Edge-diffracted 1-sec surface waves observed in a small-size intramountain basin (Colfiorito, central Italy). Bull. Seism. Soc. Am. 91, 1851–1866 (2001) 7. Nakata, T.: Active faults of the Himalaya of India and Nepal. GSA Special Papers 232, 243–264 (1989)

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8. Yeats, R.S., Nakata, T., Farah, A., Fort, M., Mirza, M.A., Pandey, M.R., Stein, R.S.: The Himalayan frontal fault system, Annales Tectonicae, Special issue, supplement to 6, 85–98 (1992) 9. Sandhu, M., Kumar, D., Teotia, S.S.: Estimation of site amplification functions for the National Capital (Delhi) Region India. Nat Hazards 85(1), 171–195 (2017) 10. Castro, R.R., Mucciarelli, M.F., Petrungaro, C.: S-wave site response using horizontal to vertical spectral ratio. Bull. Seismol. Soc. Am. 87, 256–260 (1997) 11. Bath, M.: Spectral Analysis in Geophysics, vol. 563. Elsevier Scientific Publishing Company, Amsterdam (1974)

Use of GIS for Hypsometric Analysis for Determining Erosion Proneness of Mandakini Watershed, Lesser Himalaya, Uttarakhand, North India James Xavier Paul and Daya Shanker

Abstract An attempt has been made for the assessment of erosion proneness which is quite essential in tectonically active, highly fragile and environmentally sensitive hilly regions. The assessment will not only help in knowing erosion proneness but also supports in adopting the best practices for integrated watershed management. The hypsometric analysis was performed to know the geological stages of the development of erosional landscapes that reveal the health or condition of a watershed. The hypsometric integral was estimated from the graphical representation of the measured contour height and enclosed area, and using the empirical formula. The study was carried out to assess erosion susceptible areas of the Mandakini River Watershed, which forms a tributary of the Alaknanda River catchment of Lesser Himalaya, Uttarakhand. Six sub-watersheds were delineated from the Mandakini Watershed for performing the hypsometric analysis using contours generated from the DEM in a GIS environment. The hypsometric integral values were quantified by the elevation-relief method for all the sub-watersheds and are ranging between 0.46 (B3) and 0.50 (B4). Further, it was found out that almost all the sub-watersheds are comparatively matured and erosional processes are in the course of stabilization. The study reveals that the sub-watershed primarily B1 of the Mandakini Watershed is susceptible to erosion. Moreover, the past earthquakes occurred near by area can also lead to the instability of the zones. Therefore, suitable remedial measures such as structural and non-structural methods may be adopted to mitigate soil erosion and also in enhancing sustainable conservation and management practices. Keywords Hypsometric analysis · Lesser Himalaya · Erosion proneness · Mandakini watershed · GIS

J. X. Paul (B) · D. Shanker Earthquake Engineering Department, Indian Institute of Technology Roorkee, Roorkee, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_49

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1 Introduction Hypsometric analysis helps to understand the various driving factors that influence the topographical features of a drainage basin. It assesses the data especially related to water structures at different elevation levels within an area [1]. It is not only used for evaluating the smaller water bodies but also the larger bodies covering the whole Earth, and even other terrestrial bodies. Langebein [2] was the first who introduced that the hypsometric analysis to ascertain the overall slope and relative forms of a watershed. Hypsometry is a measure of the proportion of area covered at various elevations within an area of interest. Likewise, the hypsometric curve represents the spread over the surface of a particular area of its elevation [1]. It is the important tool to estimate the evolution of geomorphic landforms and to infer the various driving factors that are acting on landscape structures of a drainage basin (Horton [3]; Schumn 1956; Markose and Jayappa [4]). Hypsometry can be explained by a “hypsometric curve” (HC) or as an integral parameter known as a “hypsometric integral” (Hsi). The HC is related to the overall volume of soil mass concentration in a drainage basin and the total sum of erosion taken place in a drainage basin in comparison to the left-over mass [5]. The shape of the HC forms an important component which is used in measuring the evolutionary stages of a drainage basin. It is a plot of a continuous function of dimensionless distribution of relative area of drainage basin [1, 6]. The elevational distribution of an area has been widely used for topographical assessment since it is more advantageous in portraying three-dimensional data by a two-dimensional representation. The shape of the HC for different watersheds with comparably similar hydrologic conditions may provide information about past mass movement in the watersheds. Thus, the shape of the HC also describes the past fluctuations in the slope of the concentrated watershed. Ritter et al. [7] described that HC and Hsi are the two main components that capture the state of erosion and health of a watershed. Thus, the intensity of exposure is indicated by the Hsi value and shape of the HC [8]. Strahler [1] viewed that the HC shows the phases of the erosion cycle of landforms by studying different catchments, they were divided into different classes based on the shape of the HC as (i) peneplain or distorted for a drainage basin in which the curve is concave upward, (ii) S-shaped curve for the mature basin, where the curve is convex toward the bottom, indicating a low height and concave toward the top, indicating a high height and (iii) convex curve upwards for a basin in youth stage. It is noted that the shape of the curves is constantly changing in the initial stages of the geomorphological cycle and stabilizes after reaching the stage of maturity. The HC influences the erosion properties of a catchment and also indicates the erosion cycle. A suitable model for interpreting the HC is given here [7], (Fig. 1). Like the HC, the Hsi describes the “cycle of erosion” and the progress in the geologic stages of the watershed’s development [1]. And, useful for the following prioritization of achieving sustainable soil and water conservation practices [9, 10]. The Hsi values provide a comprehensive knowledge of the erosion condition and

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Fig. 1 Conceptual model representing hypsometric analysis and curves (modified after [7])

limits, as proposed by [1] are: (i) in-equilibrium (young) stage, if the Hsi ≥ 0.6, (ii) equilibrium (mature) stage, if the Hsi is between 0.3 and 0.6, and (iii) monadnock (old) stage, if the Hsi ≤ 0.3. The Hsi shows an inverse correlation with various morphometric parameters such as channel gradient, drainage density, total relief and slope [1]. Thus, Hsi is useful to explain the soil degradation that occurred in a watershed during the geologic past due to various hydrologic and erosive factors [11]. Until recently, hypsometry analysis was used in various parts of the world for different studies to estimate erosion and set priorities for watersheds [11–21]. The hilly watersheds of the Himalayan regions are liable to erosion due to persistent rainfall during the monsoon, pre-monsoon and post-monsoon seasons. In this context, the HC and Hsi are two important parameters useful for assessing the status of watersheds in ecologically sensitive and fragile ecosystems such as those of the Himalayas. During the literature review, it was found that few studies have been conducted in the Himalayan region using hypsometry to assess the condition of watersheds [9, 22–24]. However, estimating and collecting data in the rough terrain of the Himalayan region is very tedious. The advent of new technologies such as the Geographic Information System (GIS) and remote sensing has facilitated the estimation process with greater precision [10]. Based on the literature study and to fill a research gap, an attempt was made to conduct a hypsometric analysis in a watershed of the Lesser Himalaya that forms a major tributary of an ecologically highly

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sensitive area. The study is conducted for the determination of erosion behavior and health status of the Mandakini watershed and its six sub-watersheds.

2 Study Area The Mandakini River is a tributary of the Alaknanda River in the Indian state of Uttarakhand. The river located between the Rudraprayag and Sonprayag areas and originates from the Chorabari Glacier. The river joins the Songanga at Sonprayag and flows past the Madhyamaheshwar Hindu temple at Ukhimath. At the end of its course, it joins the Alaknanda, which flows into the Ganges. The study area lies between latitudes 30°26, 6,, N to 30°32, 10,, N and longitudes 79°2, 60,, E to 79°7, 40,, E (Fig. 2). The Mandakini basin lies between 3800 and 6090 m above sea level. The climate is generally cooler than mainland India, with maximum temperatures of 30°– 35 °C and a minimum of 0°–8 °C. Humidity is relatively high, especially during the monsoon season. The Mandakini region is seismically and environmentally sensitive, being located in a collision zone. Geologically, it consists of parts of the crystalline rock groups of the higher Himalaya and parts of the lesser Himalaya. These rocks are very susceptible to erosion and are therefore one of the main causes of the numerous landslides in the region. Sandy skeletal soils, coarse clay, fine clay and mesic clay soils predominate in the study area. Tectonically, the area is a very active zone, as evidenced by the presence of several overthrusts and several northeast to southwest trending faults/lineaments.

3 Materials and Methods The 30 m resolution Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) was downloaded from the USGS Earth Explorer portal (https://earthe xplorer.usgs.gov/). And the DEM was used to delineate the natural drainage layer and boundaries of the six sub-basins along with their stream numbers and stream orders listed in the figure, using hydrologic tools (Fig. 3). Based on the delineated sub-watersheds, the DEM for the Mandakini sub-watersheds was created using the “Extract Mask” tool in the ArcGIS environment (Fig. 4). The slope maps were prepared for the six sub-watersheds of Mandakini Watershed by using slope tool of the spatial analyst module in the ArcGIS platform (Fig. 5).

3.1 Hypsometric Curve (HC) Estimation Hypsometric analysis of six sub-watersheds of the Mandakini River (B1, B2, B3, B4, B5, and B6) was conducted to determine the different stages of the erosion cycle to

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Fig. 2 Study map of the Mandakini watershed with subdivision of its six sub-watersheds

Fig. 3 Stream numbers and stream orders of six sub-watersheds of Mandakini watershed

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Fig. 4 Digital elevation model (DEM) of the six sub-watersheds of Mandakini watershed

Fig. 5 Slope map of the six sub-watersheds of Mandakini watershed

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which the sub-watersheds were subjected. HCs for sub-watersheds were constructed by plotting relative area (the area above a given contour (a) relative to the total area of the catchment (A)) on the abscissa and relative height (height of a given contour (h) from the basal level to the maximum height of the catchment (H)) on the ordinate. The resulting HC provides information about the volume of land mass located below or above the reference basal plane (e.g., Markose and Jayappa [4]).

3.2 Hypsometric Integral (Hsi) Estimation The Hsi is generally calculated from the HC, where it is accurately demonstrated [25]. The elevation-relief ratio (E) proposed by them is demarcated as follows: E ≈ Hsi =

Elev(mean) − Elev(min) Elev(max) − Elev(min)

where E Elev(mean)

equivalent to Hsi weighted mean elevation of the delineated watershed, determined from the conspicuous contours derived from the DEM; and Elev(max) and Elev(min) are the maximum and minimum elevations in the watershed. The values derived from HC and Hsi are helpful in determining progress in the stages of exogenic geologic evolution or geological weathering. The criteria proposed by [1] for determining the stages of watershed development were used in determining the geologic stages of watershed development as a function of Hsi values. When the Hsi value is ≤ 0.35, it indicates the Monadnock stage of the landform of a watershed, where the HC is distorted and the area is represented by projected Monadnock hills, when the Hsi value is between 0.35 and 0.5, it indicates an equilibrium and mature fragmentation stage of landform within the watershed, indicating a stable state of development; and finally, when the Hsi value is ≥ 0.5, it indicates that the watershed is in the juvenile or equilibrium stage of development [26].

4 Results and Discussion The results of this study are discussed in the following subsections: hypsometric curve, hypsometric integral, prioritization, conservation and watershed management.

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Table 1 Hypsometric integral values of the six sub-watersheds of Mandakini watershed Sub-watersheds

Area (km2 )

Max elevation (m)

Min elevation (m)

Mean elevation (m)

Hypsometric integral (Hsi)

Geological stages

B1

63.0

2258

1016

1636.8

0.50

Late youthful

B2

38.0

2113

949

1530

0.49

Early mature

B3

61.3

2046

960

1462

0.46

Mature

B4

78.4

2729

920

1768

0.46

Mature

B5

49.7

2128

883

1476

0.47

Mature

B6

42.0

1854

849

1302

0.45

Mature

4.1 Hypsometric Curve For the six sub-watersheds of Mandakini Watershed, hypsometric curves had been prepared based on the hypsometric parameters (Table 1; Fig. 6). Later, the shoulders of the plotted curves were interpreted as an important source for the study of erosion progress in a catchment area [1]. The interpreted shoulders of most of the subwatersheds indicate that they have a concave shape and have passed from the youth stage to a mature stage. Thus, the results of the study are consistent with those of other watersheds in the western Himalayas, where most watersheds show a transition from the younger to the more mature stage [9]. From the interpretation of the curves, it is clear that the erosion progress varies a little in the different sub-watersheds of the Mandakini Watershed.

4.2 Hypsometric Integral Runoff and mass movement are the two main hydrological processes responsible for precipitation events in a watershed system. The Hsi value plays an important role in measuring erosion processes, even if it is used indirectly to estimate soil losses in a watershed system. In small watersheds, HCs are convex and values of Hsi are in the unity range compared to large watersheds, indicating active slope processes. In large watersheds, the integral approaches zero, HCs are concave and fluvial processes dominate [21]. As given in Table 1, the Hsi values of the sub-watersheds of Mandakini Watershed are in mature stage and are in the transformation phase of peneplanation or in the geological phase of deterioration. Strahler [1] observed that hypsometry describes the age of watershed. Moglen and Bras [16] found that the hypsometric distribution is influenced by the subsurface rock formation. From soil degradation, it can be inferred that most sub-watersheds were formed by incision of channel beds, downward movement of quantifiable topsoil and rock

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Fig. 6 Hypsometric curves of the six sub-watersheds of Mandakini watershed

masses from higher elevations and removal of soil masses and erosion of stream banks. Ritter et al. [7] examined that sub-watershed that are in the mature phase have slow erosional activity, except under certain circumstances with high storm intensity and peak runoff.

4.3 Relationship with Past Earthquakes Three earthquakes which have occurred in the last two decades in the neighboring area, viz. 4.5 magnitude with epicenter distance 22 km NE of Rudraprayag earthquake of November 19, 1998; 4.5 magnitude with epicenter distance 13 km NNE of Rudraprayag earthquake of March 12, 2001 and 4.6 magnitude with epicenter

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distance 20 km NE of Rudraprayag earthquake of September 05, 2005 (https://earthq uake.usgs.gov/earthquakes). Hence, earthquake density is frequent in the neighbor areas which leads to the instability of the watershed basin and also make it the more proneness for the erosion.

4.4 Prioritization, Conservation and Management of Watershed From the hypsometry analysis, it is clear that the study area is susceptible to high runoff. Therefore, it is imperative that the necessary measures be taken to regulate surface runoff. The Hsi values show that all six sub-watersheds are affected by maximum total surface runoff. Accordingly, the Hsi values can be used to prioritize the sub-watersheds. When sub-watersheds are identified, appropriate measures can be taken to contain and control surface runoff and the resulting erosion. It is very important that both structural and non-structural measures be taken to contain the process of sediment loss and conserve water by implementing integrated watershed management practices. Structural control measures include the construction of retention dams, tillage pods, contour walls and other structures to prevent flooding and promote water conservation measures. In addition, sustainable agricultural practices such as strip cropping, crop rotation and agroforestry will be introduced. Nonstructural measures include an early warning system, especially to predict flooding, and awareness programs to discourage traditional agricultural practices.

5 Conclusions Hypsometric analysis is useful to determine the complex nature of denudation activity and also to evaluate the change in morphological features in a watershed. It includes the HC and Hsi, which serve as indicators for evaluating runoff and associated erosion in a watershed, and particularly in a hilly area where much of the terrain is inaccessible. Therefore, it is beneficial to know the erosion status and prioritize the sub-watersheds to apply conservation strategies. The application in the present study showed that all sub-watersheds of Mandakini Watershed are comparatively mature and erosion processes are stabilizing. The results of Hsi values showed that sub-watershed B1 is at high risk of erosion unlike the other associated sub-basins of the Mandakini Watershed. Therefore, this sub-basin requires extreme care and appropriate remedial measures such as structural and non-structural methods can be initiated as a priority to minimize soil erosion and promote water conservation for integrated management of the basin.

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References 1. Strahler, A.N.: Hypsometric (area-altitude) analysis of erosional topography. Geol. Soc. Am. Bull. 63, 1117–1141 (1952) 2. Langebein, W.B.: Topographic characteristics of watersheds. USGSWater Supply Paper, 947-C, pp. 127–157 (1947) 3. Horton, R.E.: Erosive development of streams and their catchments, hydrophysical approach to quantitative morphology. Geol. Soc. Am. Bull. 56, 275–370 (1945) 4. Markose, V.J., Jayappa, K.S.: Hypsometric analysis of the Kali River Basin, Karnataka, India, using geographic information system. Geocarto Int 26(7), 553–568 (2011) 5. Hurtrez, J.E., Lucazean, F., Lave, J., Avouac, J.P.: Investigation of the relationship between basin morphology, tectonic uplift and denudation based on the study of an active fold belt in the Siwalik Mountains. (Central Nepal). J. Geophys. Res. 104, 779–796 (1999) 6. Chow, V.T.: Hand Book of Applied Hydrology. McGraw Hill Book Company, New York (1964) 7. Ritter, D.F., Kochel, R.C., Miller, J.R.: Process Geomorphology. McGraw Hill, Boston (2002) 8. Weissel, J.K., Pratson, L.F., Malinverno, A.: The length scaling properties of topography. Geophys. Res. 99, 13997–14012 (1994) 9. Singh, O., Sarangi, A., Sharma, C.M.: Methods for estimating hypsometric integral and its significance for erosion status of Northwestern lesser Himalayan watersheds. Water Resour. Manage. 22, 1545–1560 (2008) 10. Manjare, B.S., Pophare, A.M.: Identification of groundwater development zones in the Morna river sub-basin, central India. J. Geosci. Res. 52(2), 139–145 (2020) 11. Bishop, M.P., Shroder, J.F., Bonk, R., Olsenholler, J.: Geomorphic change in high mountains: a western Himalayan perspective. Glob. Planet. Change 32, 311–329 (2002) 12. Harsha, J., Ravikumar, A.S., Shivakumar, B.L.: Evaluation of morphometric parameters and hypsometric curve of Arkavathy river basin using RS and GIS techniques. Appl. Water Sci. 10(86). (2020). https://doi.org/10.1007/s13201-020-1164-9 13. Huang, X.J., Niemann, J.D.: Modelling the potential impact of groundwater hydrology on long-term watershed development. Earth Surf. Process. Landf. 31, 1802–1823 (2006) 14. Lamsoge, B.R., Vijesh, V.K., Pophare, A.M., Katpatal, Y.B.: Determination of erosion susceptibility of WR -2 watershed using hypsometric analysis. J. Geosci. Res. 3(1), 85–89 (2018) 15. Mishra, N.: Hypsometric integral—a basis for determining erosion status and priority number of unmeasured watersheds. J. Soil Water Conserv. 32, 38–45 (1988) 16. Moglen, G.E., Bras, R.L.: The effect of spatial heterogeneities on geomorphic expression in a model of basin evolution. Water. Resour. Res. 31, 2613–2623 (1995) 17. Ohmori, H.: Changes in the hypsometric curve due to mountain building resulting from simultaneous tectonics and denudation. Geomorphology 8, 263–277 (1993) 18. Pradhan, K., Senapati, P.C.: Hypsometric analysis of some selected watersheds of Hirakund catchment. J. Soil Water Conserv. 30, 183–185 (2002) 19. Shukla, D.P., Dubey, C.S., Ningreichon, A.S., Singh, R.P., Mishra, B.K., Singh, S.K.: GIS based morpho-tectonic studies of Alaknanda River basin: a precursor for hazard zonation. Nat. Hazards 71, 1433–1452 (2014). https://doi.org/10.1007/s11069-013-0953-y 20. Sivakumar, V., Biju, C., Deshmukh, B.: Hypsometric analysis of Varattaru River Basin of Harur Taluk, Dharmapuri Districts, Tamil Nadu, India using geomatics technology. Intl. J. Geomat. Geosci. 2, 241–247 (2011) 21. Willgoose, G., Hancock, G.: Revisiting the hypsometric curve as an indicator of form and process in transport-limited catchment. Earth Surf. Process. Landf. 23, 611–623. (1988) https:// earthexplorer.usgs.gov/ 22. Goel, A.K., Singh, J.K.: Hypsometric analysis for the foothills of the Shivalik. Indian J. Soil Conserv. 28, 84–85 (2000) 23. Sharma, S., Mahajan, A.K.: GIS-based sub-basin prioritization through morphometric analysis in the outer Himalayan region of India. Appl. Water Sci. 10, 1–11 (2020). https://doi.org/10. 1007/s13201-020-01243-x

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24. Singh, O.: Hypsometry and erosion susceptibility: a case study in the lesser Himalayan watersheds. J. Soil Water Conserv. 8(2), 53–59 (2009) 25. Pike, R.J., Wilson, S.E.: Elevation-relief ratio, hypsometric integral and geomorphic areaaltitude analysis. Geol. Soc. Am. Bull. 82, 1079–1084 (1971) 26. Kusre, B.C.: Hypsometric analysis and watershed management of the Diyung watershed in northeast India. J. Geol. Soc. India 82, 262–270 (2013). https://doi.org/10.1007/s12594-0130148-x

Understanding the Structure and Tectonic Configuration of Bengal Basin for Earthquake Magnitude Prediction Mir Fazlul Karim and Daya Shanker

Abstract The Bengal Basin is a living model of an active and complex geological entity consisting of dynamic deltaic depositional complex, heterogenous and multidimensional tectonic deformational settings from three tectonic plates. The earthquake risk of the basin is not rationally anticipated due to the absence of adequate seismic and geodetic data, subsurface geological maps, insufficient representative and instrumental data acquisition system. For a geometrical array and vector-mapping of tectonic deformation, intensive data search is done from published sources. An investigation on crustal configuration using available seismic data, velocity images, and travel time tomography along number of profiles is done. Based on these studies, the basin is classified into five geotechnical domains. An attempt is made to understand the scenario of sediment input, tectonic and gravitational stress distribution, and their pattern of deformation vectors for earthquake magnitude prediction. The crustal study indicates that the basin is severely fragmented, and topography of the sedimentary sequences is shaped and stressed by various sizes of graben and horsts. The basin received enormous volume of sediments, which resulted in deposition of very thick clastic sediments. A seismic zoning map is prepared using the five major geotechnical domains with maximum possible magnitude of earthquakes to be occurred. The 1918 Srimangal event of Mw ≈ 7.5 is the largest recorded earthquake and no seismogenic structure has been found to cause great earthquakes in Bangladesh. The presentation of material and details in maps used in this chapter does not imply the expression of any opinion whatsoever on the part of the Publishers or Author concerning the legal status of any country, area or territory or of its authorities, or concerning the delimitation of its borders. The depiction and use of boundaries, geographic names and related data shown on maps and included in lists, tables, documents, and databases in this chapter are not warranted to be error free nor do they necessarily imply official endorsement or acceptance by the Publisher or Author. M. F. Karim (B) Director (Ex), Geological Survey of Bangladesh and GeoEastern Inc. Inc., Massachusetts, USA e-mail: [email protected] D. Shanker Department of Earthquake Engineering, IIT, Roorkee, Uttarakhand 247667, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_50

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Keywords Bengal basin · Tectonics · Seismicity · Earthquake

1 Introduction Bangladesh and its surrounding regions, including West Bengal and the north-east India, occupy the most active tectonic regime of the world. Seismic risk in Bangladesh is one of the major socio-economic concerns due to very high population density, unplanned urbanization, and non-engineered construction practices. We emphasize a rational prediction of the state of seismic threats in the region and not to apprehend overscored earthquake magnitude. Overscored and uncertain earthquake prediction would increase unnecessary recurring costs in the engineering practices and earthquake risk management system for the citizens. The Bengal Basin is mostly built of the Ganges–Brahmaputra Meghna (GBM) delta and assumed to have the thickest sedimentary cover of the present world. The GBM delta is the largest prograding delta consisting of varied floodplain sediments from most dynamic, densely arrayed intricate river system, and older terraces. The delta occupies a major part of basin which is one of the largest and youngest sedimentary basins actively and tectonically stressed by the Indian, Tibetan, and Burmese plate motions [1]. The historical records of earthquakes in and around Bangladesh indicate that generally at every two decades of interval there are earthquakes of intensities greater than X in MM scale in the northern and north-eastern part of the country [2]. The damages due to these earthquakes are widespread. The seismicity of these events is deeply associated with tectonic behavior of the subducting Indian plate. It is believed that the country is neo-tectonically active for being located near the syntax of three tectonic plates and Himalayan Mountain Building System. The Himalayan range is seismically most active zone of Asia. Four great earthquakes of magnitude exceeding 8 during 1897, 1905, 1934, 1950 and another 10 earthquakes exceeding magnitude 7.5 have occurred in the Himalayan belt during the last 100 years. Previous studies indicate presence of active faults in and around the Bengal Basin [3, 4]. These faults are capable to cause moderate earthquakes. The Himalayan Arc to the north and northeast and Myanmar Arc to the east and southeast have generated number of devastating great earthquakes in the past and shaken the entire Indian subcontinent. Recurrence of similar shake in this region would cause unmanageable destruction. The region is placed in a high seismic zone in the Global Seismic Hazard Map and by Lamont–Doherty Earth Observatory (LDEO) [6]. The chronicle of studies of tectonics of the Bengal Basin System (BBS) is not that long and most of the studies were based on limited field and meager instrumental data and theoretical assumptions. Recently, several papers and instrumental data are available over the internet having a detailed quantitative database, mechanical, and geodynamic analysis of the BBS, consisting of methodical investigations. During the last few decades, an aggressive advancement has taken place for studies and understanding of geodynamic behavior of the basin system. The finding [5] of decollement structures and development of Neogene accretionary prism complex in an oblique

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subduction environment is a very important turning point in the understanding of tectonic mechanism of BBS. The Lamont–Doherty Earth Observatory (LDEO) scientists executed a revolutionary sophisticated study in the BBS for a decade for the first time using Global Positioning System (GPS) and computer models [6]. The scientists described a huge, locked subduction interface or megathrust, where the front is extended to the megacity of Dhaka from the tectonically folded sediments in the east. Though the physical attributes and geometrical dimensions and configurations are not well defined. The LDEO study concluded with likelihood of occurrence of great earthquakes from the accumulated elastic strain across the megathrust zone. The study concluded with a prediction of occurrence of a great earthquake (Mw 8.2 to 9) in Bangladesh and North-East Indian region at any time. This prediction has a lot of uncertainties which were not mentioned by the authors [6] in the publication and turned an issue that weighed heavily with Bangladesh geologists and geotechnical engineers. We believe that this is a subject of further studies for a coherent understanding and engineering practices in the region.

1.1 Data and Methods This work is mainly based on detailed field investigations throughout Bangladesh and neighboring states of India. As Bangladesh doesn’t have adequate instrumental geophysical and GPS database, we used required GPS data from Nevada Geodetic Laboratory [7] Map Browse GPS Stations and UNAVCO. Earthquake magnitude, locations, and hazard maps are retrieved from IRIS, USGS and Global Earthquake Model (GEM). The deep crustal and Moho configuration data are used from published sources including the seismic, geodetic data and subsurface geological maps.

2 Geology of Bangladesh A larger part of Bangladesh is occupied by the world’s largest delta formed by the Ganges–Brahmaputra Meghna River system. It is almost entirely formed of vast alluvial plain except some strips of asymmetrically and plunged folded hills on the north-east and eastern margins. The alluvial plains have the elevation from about 90 m in the north-western part of the country to ~ 0 m along the coastal plains. Apart from these, there are three tracts of Terraces in the country with a maximum elevation of 40 m, raised concurrently during the Himalayan mountains final uplift. About 80% of the country is covered by Holocene deposits; consisting of fluviodeltaic sediments, where upper top materials (10–20 m) are very sensitive to cyclic stresses and vulnerable to annual flooding. The 12% area is exposed with tertiary sedimentary rocks where seismic response is very much complex due to intricate geological structures and the 8% area are raised terraces and formed of stiff and hard clay residuum, Fig. 1.

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Fig. 1 Geological map of Bangladesh (Modified by Karim (2004) from Alam et al. (1990))

The oldest exposed rock is the Tura Sandstone of Paleocene age but older rocks like Mesozoic, Paleozoic, and Precambrian Basement have been encountered in the drill holes in the north-western part of the country [9], Karim 2004). Mainly sandstone, siltstone, shale, and claystone represent the tertiary rocks. The field investigation indicates that the highly oxidized Pleistocene sediments and weathered soils are exposed in three isolated uplifted blocks. The Holocene deposits, consisting of unconsolidated sand, silt, and clay of varying amounts, are the products of active alluvial, fluvio-deltaic, or coastal depositional environment. All these materials will behave differently during various degrees of ground shaking.

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3 Tectonics of Bengal Basin Bangladesh shares the Bengal Basin with West Bengal with equal degree of seismic risks. The evolution of the basin is associated with the movements of Indian, Eurasian and Myanmar platelets. Through a long geological time (Permian to Recent) the basement of Bengal Basin, below a thick sedimentary cover has been severely faulted and fractured. The remote sensing image analysis indicates that the modern-day topography very clearly reflects the gradient of deep basement or crustal configuration (Fig. 2). We have determined that many of the Bangladesh rivers are structurally controlled and follow the old faults or lineaments. We detected deep seated crustal low and high by investigating available published tomographic images, seismic section, (Figs. 2 and 3), and rheological characteristics of the Neogene sedimentary cover [10]. We expect presence of similar vertical detachments or staggered crustal blocks all along the thick sediments. These detachments cause frequent moderate to low magnitude earthquakes. We studied a swarm of small to moderate earthquakes in 2021 and found linear alignments of the epicenters which reflects the subsurface crustal structural trends. Following a prediction of 8.2–9 M earthquake in Dhaka Megacity by Steckler et al. [6], we performed a comparative review of the geodynamic and geothermal gradient and geo-tectonic structures. Our study indicates that the shallow thermal gradient is getting cooler toward east [15] or folded belt, whereas the deep crustal

Fig. 2 An illustrative tectonic map of Bengal Basin and adjoining regions showing important tectonic elements and crustal divisions. A Schematic tectonic map of Bengal Basin and surrounding terrains of Indian shield covering Bangladesh and part of India and Myanmar (political boundaries not shown). The terrain analysis portrayed a unique westward sinusoidal progression of the folds due to steady uniform compressional stresses from the east with a convergence vector (∼N27 °E) [11]. B The crustal low or Graben and crustal high or Horst in the Bengal Basin are identified, shown in a satellite image with their surficial scars [12]. C Based on seismic and earlier studies (by Geological Survey of Bangladesh), Bangladesh is divided into five major geotechnical domains: 1. Stable shelf, 2. Continental slope and Hinge Zone, 3. Deep central trough (Sylhet-Hatiya), 4. Eastern Fold Belt of Chittagong Tripura and 5. Dauki Fault Zone [13]

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Fig. 3 Three geological sections along A-A’, B-B’ and C-C’ are prepared from the velocity model cross sections of the Bengal Basin (modified after [18] and [19]. The section A-A’ (750 km) apparently does not show any vertical or horizontal lineation and indicate moderately isotropic shear wave velocity pattern at the used resolution. We compared and determined gradual vertical variation indicating 20 km thick upper crust and 20 km thick lower crust (after Karim et al. [12], Red arrow for Shillong popup mark). The section B-B’ shows a significant variation in shear wave velocities, both vertically and laterally. The basin profile clearly reflects the subsiding geometry of Bengal Basin. The high angle shear zone close to Chandina terrace shows trace of vertical lineation and flexures. Presence of low density sedimentary thick deposits indicates a rapid sedimentation below 3 km of depth. A drastic change in the depositional environment in the central region of the basin is remarkable. The velocity section C-C’ shows an extraordinary and heterogenous structural pattern associated with both depositional and tectonic environments. It appears that the buoyant mantle materials have pushed the Shillong massif up and crushed the neighbouring continental crust slabs into multiple horsts and grabens (?)

and mantle are cooler westward from the Indo-Myanmar subduction slope or under Bengal Basin [16]. The thermal variation possibly causing overall density and frictional variation of the tectonic slabs under heterogenous sedimentary sequences. We assume that the GPS velocity vectors require further calibration for geothermal and physico-mechanical of tectonic movement in the Burmese–Bangladesh folded zone. An extensive field search for any signatures of Subduction Zone was carried out and no such structures were found in the regions. This work did not find any geological evidence to justify earthquake greater than 7.5 magnitude within the periphery of Bengal Basin under huge gravitational load or vertical stress of average > 15 km of sediments. The detailed structural and tectonic morphological studies along with deep petroleum drilling data analysis and available geophysical data did not find

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presence of any such megastructures capable of generating great earthquakes. Hence considering the distal source structure, that caused the popularly known Assam earthquake of 1897 8.5 Magnitude earthquake, a test model was run at the USGS (pers. cmmun. John Whitney, former USGS). The analysis does not indicate any higher amplification factors in the existing ground conditions of Bangladesh. It is identified that the slowed down tectonic stresses helped to accommodate the thick tertiary folded sedimentary belt out of thrusts from the Burmese plate while the energy of the thrust slowly reduced and helped in keeping the petroleum deposits of Bengal Basin less disturbed. The gradually merging folded sediments started pushing the NE-SW trending hinge zone, tilted and raised the deeper basin deposits including Madhupur Residuum (Fig. 1). Similarly, the Shillong massif, a crustal slab of Bengal Basement thrusted up where the conjugate slabs slipped or sank down to the south, e.g., Sylhet Trough, [17]. We performed a comparison of the exhumation and uplift sequences of Shillong plateau for understanding of Bengal basin crustal topography. We identified that tectonic fragmentation or crustal disintegration has occurred throughout the basement [18] of Bengal Basin in the form of graben and horsts, since a very long geological time and deposition of enormous sediments. The heterogenous depositional and tectonic partition of undetermined set of graben, horst, and trenches in the basin (e.g., Surma basin or Surma graben, Fig. 2).

4 Crustal Configuration and Sediment Thickness For a detailed crustal investigation for the Bengal Basin, we used published reports and research papers and found that the Bengal Basin is severely fragmented, and topography of the sedimentary sequences is shaped by various sizes of grabens and horsts. We are very much convinced in identifying the Sylhet Trough as a tectonic graben and active subsiding basin. The received huge volumes of Neogene sediments, which resulted in deposition of measuring between 14 km and over 16 km in thickness. The tectonic deformation patterns of Bengal Basin are assembled in the sediments of various time series where both the thick-skinned and thin-skinned deformation are present and characterized by tomographic anomalies. In conjunction, we used Bouguer gravity anomaly and aeromagnetic anomalies of Bangladesh published by the Geological Survey of Bangladesh (GSB) and United States Geological Survey (USGS) to correlate recent available high-resolution images and deep geophysical data and found satisfactorily positive correlation among the maps and geological sections. We used an interpretation done by [19] and Center for Earthquake Research and Information at The University of Memphis, for ground motion characteristics of the Bengal Basin region in Bangladesh. A computer simulation was run by the authors for a series of historical seismic records and scenario earthquakes. We found this study on seismic velocity model for the region very appropriate for our understanding of true geometrical boundary condition. It presented the initial version of a three-dimensional model built upon a geology-based representation of the geometry

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of the Bengal Basin and simple approximations of the basin deposits, crustal, and background structures (Fig. 3). The results from a low-frequency ground motion simulation (f ≤ 0.5 Hz) from a Mw 5.1 Chandpur Earthquake compared with available basic qualitative characteristics, finite-element parallel code used by Huda and [19]. The results are very satisfactory with the known geometry of Bengal Basin crustal configuration and exposed bed rock in the Shillong Massif regime. The horizontal and vertical discontinuities clearly comparable with the known stratigraphic stackings and intensity of folding in the east. The extent and shape of all tectonic divisions are positively correlated.

5 The Geometry of Tectonic Motion Vector in and Around Bengal Basin Present study involves a detailed geological field mapping, remote sensing image processing and geotechnical analysis throughout the Bengal Basin and NE Indian region for understanding and identification of seismic source areas [12]. Most of the seismic sources and crustal (both clastic and crystalline) layers reflect that lateral and vertical discontinuities are at various locations subjected to multiple segmentation and torsional shear stress having complex tectonic motion vectors. The differential tectonic motion over the Bengal Basin segments is potentially influenced by the complex and multidirectional regional compression forces derived from various directions (Fig. 5 insets A-E). The oblique and transverse convergence of the Sunda and Indian plates and the intense compressive stress rotates from NE-SW to E-W near the Bengal Basin along the inner and northern arc. This rotation is consistent with the splitting deformation reflected in the rotation of the relative displacement vector from the SSW direction, where the Sunda-Burma motion is toward BurmaIndia subduction, i.e., NNW directional motion. As a result of this segmentation, the main belt-parallel fault zones show a differential movement along N-S arc with strike-slip and oblique reverse dextral slip displacement. This study finds a differential pattern of tectonic motion as monitored by various GPS measurement group. The multiple convergent and divergent vectors make a jargon of the stress environment (inset H in Fig. 4) in and around Bengal Basin. This would require further substantial studies before a prediction or superfluity of extraordinary earthquakes [6] in the region. The general relationships among geological structures, tectonic motion vector, earthquake dynamics, and deformation characteristics of rocks have complex interaction over an uncertain time and space and requires to well analyzed for a rational seismic risk management in Bangladesh (Fig. 5).

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Fig. 4 This diagram shows a clarification of the regional seismotectonic interaction over the Bengal Basin, the crustal and mantle dynamics, existing stress pattern. Multiple convergent and divergent vectors make a jargon of the stress environment centering deeper part of the Bengal Basin. Modern days GPS motion data can be contradictory to the major NNE-SSW India-Eurasian (inset A) and E-W Sunda-India convergent (inset B, E, F), while syntaxial torsional vector in the NE India at the northern tip (insets B, C, D) of Bengal Basin plays severe deformation causing shallow depth moderate size earthquakes. The N-S strike-slip faults (insets E, F, G, H) over the India-Bruma subduction splay generate deep earthquakes due to strike-slip partitioning in an oblique convergence zone between the India Plate and the West Myanmar Block (see also Fig. 5 for systematic clusters of earthquakes). [Ref. for Inset diagrams. A Socquet et al. [26], B Karim [11] C Schellart (2019) D Barber et al. [20], E and F Earnest [24] G Khin et al. [25]. The Inset H is prepared for this paper]

6 Exploring the State of Moho and Deep Crustal Configuration of the Bengal Basin Our study along the Dauki fault region and reviewing of published papers, data [10] and [21] and receiver function analysis indicate presence of continental crust under the Brahmaputra Valley and the Shillong Plateau, whereas the crust beneath Bengal Basin is identified as a complex of amalgamated continental to transitional type. Toward the north of the plateau, the crustal thickness increases by 8–10 km with Moho flexure of 30°. The southern section is downfaulted by12–13 km across the Dauki Fault. The plateau uplift is mediated by thrust faulting on the reactivated Dauki Fault and back thrusting on the north dipping Oldham Fault. We extensively studied and reviewed the deep geology and Bengal Basin crustal and mantle dimensions, and configuration from available published geophysical research papers, reports, maps, and data; and found out that most of the Bengal Basin has experienced severe fragmentation and sinking of extended Indian craton along the southern neighborhood of Shillong craton.

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Fig. 5 Versus structure along different profiles marked in the seismicity map of Bangladesh, NE India, and Myanmar (Burma). Showing Moho discontinuity, sedimentary sequences, and crustal boundaries. (Modified from Mitra et al. [21] and Kumar et al. [10]) Abbreviations—IBR Indo-Burma Ranges; SM Shillong Massif; ITSZ Indus-Tsangpo Suture Zone; STD Southern Tibetan Detachment; MCT Main Central Thrust; MBT Main Boundary Thrust; HFT Himalayan Frontal Thrust; JF Jamuna fault; SF Sylhet fault; DBF Dhubri fault; DKF Dhansiri-Kopili fault; DF Dudhnoi fault; EHZ Eocence Hinge Zone; OF Oldham fault; PCF Po Chu fault; PF Purlang Fault

The tomographic image of shear wave along six sections of Bengal Basin is clearly showing crustal and mantle geometry of Bengal Basin. Section B shows upheaval of mantle that pushed up a part of Indian craton to form the present-day Shillong Plateau and caused sinking of southern portion of craton to preserve the continental crust under thick sediments of Bengal Basin [10].

7 Seismic Zonation Maps A Seismic Zoning Map of Bangladesh (SZMB) is prepared (Fig. 6) using Global Earthquake Model [22] in conjunction with consideration of seismotectonic behavior, spatial distribution of earthquakes in the region, and potential seismic source zones and geological attributes. The maximum possible magnitude of earthquakes to occur within the Bengal Basin is Mw ≥ 7.5 considering an occurrence period of last 100 years in northeast India and its adjoining regions. It is found in this study that there are no seismogenic structures in the Bengal Basin that can generate earthquakes greater than 7.5 magnitude. We have identified a gross anomaly in the results of tectonic structures done by various researchers from different geographical locations and chose a rational

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Fig. 6 Proposed seismic zoning map A of Bangladesh considering tectonic setup, potential seismic source area, distribution of past earthquakes, crustal and basement geometry, and neotectonics progression in and around Bengal Basin B. Present SZMB as published with Bangladesh National Building Code, 2021

seismic source map of Bengal Basin and neighboring region (Seismic source map— after [2] and Panthi et al. [2]). The recently published SZMB as included in the Bangladesh National Building Code 2021 reasonably correlates with the proposed SZMB (modified from Global Earthquake Model and Global Seismic Hazard Map, version 2018.1, Pagani et al. [22]).

8 Discussion The seismic or earthquake risk management in Bangladesh is one of the major socioeconomic concerns owing to its high population density together with tectonic setting due to complex syntaxial stress–strain and multidimensional differential deformation conditions from Indian, Tibetan, and Burmese tectonic plates. Bengal Basin System is a unique and dynamic natural laboratory for studying an active deltaic depositional environment under a continuous influence of tectonic and gravitational stress pattern. The earthquake risks in Bangladesh are poorly understood due to absence of required instrumental seismic data acquisition facilities and institutional research and development systems. This study identifies seismic source areas, possible configuration, and crustal thickness of Bengal Basin. It is found that the crust is severely segmented during the rapid break up of Indian craton and rising of Shillong Plateau. The segmentation pattern is a matter of further studies for seismic hazard analysis. The basin is filled up with heterogeneous deposits in a unique deltaic environment. Possibly the crustal segmentation later facilitated to weaken the tectonic movements as the basin devoid of giant structures which would not be able to accommodate much strain to generate earthquakes larger than magnitude 7.5 in Bangladesh. This finding

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will provide an improved understanding and considerations in the geo-engineering practices and the earthquake risk of the country will be rationally anticipated.

References 1. Alam, M.: Tectonic classification of Bengal Basin. Geol. Soc. Am. Bull. 83(2), 519–522 (1972) 2. Shanker, D., Panthi, A., Singh, H.N.: Long-term seismic hazard analysis in northeast Himalaya and its adjoining regions. Geosciences 2(2), 25–32 (2012). https://doi.org/10.5923/j.geo.201 20202.04 3. Karim, M.F., Khan, S.I.: Report on Geological Investigation of Rangamati Earthquakes. Unpublished report of Geological Survey of Bangladesh, July August (2003) 4. Khan, A.A., Hoque, M.M., Shaharier, K.M., Hoque, M.: Convergent tectonics and sedimentation in the Eastern Margin of the Indian Plate with emphasis on the Bengal Basin Bangladesh. J Geol 21, 9–22 (2002) 5. Sikder, A.M.: Tectonic evolution of eastern folded belt of Bengal Basin. Unpubl. PhD Thesis, Dhaka Univ., p. 175 (1998) 6. Steckler, M., Mondal, D., Akhter, S., Seeber, L., Lujia F., Gale LJ., Emma M., Michael, H.: Locked and loading megathrust linked to active subduction beneath the Indo-Burman Ranges. Nature Geosci. 9. (2016). https://doi.org/10.1038/ngeo2760 7. Blewitt, G., Hammond, W.C., Kreemer, C.: Harnessing the GPS data explosion for interdisciplinary science, Eos, 99. 2018. https://doi.org/10.1029/2018EO104623 8. Alam, M.K., Hasan, A.K.M.S., Khan, M.R., Whitney, J.W. 1990. Geological map of Bangladesh. Geological Survey of Bangladesh, Dhaka. Digitally compiled by F.M. Persits, C.J. Wandrey, R.C. Milici, (USGS), A. Manwar, (DG, GSB), (2001) 9. Coleman, J.M.: Brahmaputra River: channel processes and sedimentation. Sediment Geol Special Issue 3(2/3), 131–237 (1969) 10. Kumar, A., Kumar, N., Mukhopadhyay, S., Klemperer, S.L.: Tomographic image of shear wave structure of NE India based on analysis of Rayleigh wave data. Front. Earth Sci. 9, 680361 (2021). https://doi.org/10.3389/feart.2021.680361 11. Karim M.F.: Country Paper: High-Level Expert Group Meeting on Technical Options for Disaster Management Systems: Tsunamis and others. 22–24 June 2005, UNESCO, Bangkok, Thailand. (2005) 12. Karim, M.F., Rahman, M.Z., Sikder, A.M., Shanker, D.: Tectonic and Geotechnical Review for Engineering Design and Seismic Risk Management in Bangladesh. Poster Presentation at 2021 SCEC Annual Meeting. 2021, 08. Poster #055, Earthquake Engineering Implementation Interface (EEII). https://www.scec.org/meetings/2021/am/poster/055 13. Karim, M.F., Kayal J.R., Shanker, D., Khandaker, N.I., Sikder, A.M., Rahman, M.Z., Hassan, M.Q.: Tectonic and geotechnical review of Bengal Basin for Seismic Risk assessment in Bangladesh. Geological Society of America. Abstracts. 53(6). https://doi.org/10.1130/abs/202 1AM-371261 15. Akbar, M.A.: An assessment of the geothermal potential of Bangladesh. Geothermal Training program Report 2011 Orkustofnun, Grensasvegur 9(5) IS-108 Reykjavik, Iceland. (2011) 16. Beardsmore G.R., Cull, J.P.: Crustal heat flow a guide to measurement and modelling. Cambridge University Press. (2001). https://doi.org/10.1017/CBO9780511606021 17. Johnson, S.Y., Alam, A.M.N.: Sedimentation and tectonics of the Sylhet Trough. Bangladesh. Geol. Soc. Am. Bull. 103, 1513–1527 (1991) 18. Biswas, S., Coutand, I., Grujic, D., Hager, C., Stockli, D.: Exhumation and uplift of the Shillong plateau and its influence on the eastern Himalayas: new constraints from apatite and zircon (U-Th-[Sm])/He and apatite fission track analyses. Tectonics, American Geophysical Union (AGU), (2007)

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19. Huda, M., Taborda, R.A.: Three-dimensional regional-scale earthquake ground motion simulation for the Bengal Basin. Center for Earthquake Research and Information. In: 16th World Conference on Earthquake Engineering Santiago Chile, January 9th to 13th, (2017) 20. Barber, A.J., Zaw, K., Crow, M.J. (eds.): Myanmar: geology, resources and tectonics. Geological Society, London, Memoirs, 48, 19–52. 2017. https://doi.org/10.1144/M48.2 © 2017 The Author(s). Published by The Geological Society of London. Publishing disclaimer: www.geo lsoc.org.uk/pub_ethics 21. Mitra, S., Priestley, K.F., Borah, K., Gaur, V.K.: Crustal structure and evolution of the Eastern Himalayan plate boundary system, Northeast India. J Geophys Res Solid Earth 123, 621–640 (2018). https://doi.org/10.1002/2017JB014714 22. Pagani, M., Garcia-Pelaez, J. Gee, R., Johnson, K. Poggi, V. Styron, R. Weatherill, G. Simionato, M. Viganò, D. Danciu, L. Monelli, D.: Global earthquake model (GEM) seismic hazard map (version 2018.1-December 2018). https://doi.org/10.13117/GEM-GLOBAL-SEISMIC-HAZ ARD-MAP-2018.1 24. Earnest, T.C.A., Sunilkumar, K.: Silpa sinking slab stress and seismo-tectonics of the IndoBurmese arc: a reappraisal. Tectonics (2021). https://doi.org/10.1029/2021TC006827 25. Khin, K., Moe, A. Myint. M, Aung. K.P.: Dextral transgressional shearing and strike-slip partitioning developments in the Central Myanmar Basin during the collision between the India Plate and West Myanmar Block. Journal of Asian Earth Sciences. (1998). https://doi.org/ 10.1016/j.jaesx.2021.100055 26. Socquet, C. Vigny, N. Chamot-Rooke, W. Simons, C. Rangin, B. Ambrosius. India and Sunda plates motion and deformation along their boundary in Myanmar determined by GPSA. (2006)

Exploring an Alternate Perspective of the Importance Factor for Seismic Design of Structures Narsiram Gurjar

and Dhiman Basu

Abstract Contingent to the risk to human life in the event of failure structural systems is designed with an additional factor, commonly known as the importance factor (Cl 7.2.3) (IS-1893 (2016) Indian standard criteria for earthquake resistant design of structures, Part-1: General provisions and buildings. In: Bureau of Indian Standards, New Delhi.). In other words, importance factor is aimed to augment the design seismic hazard to reduce the probability of failure of a structure contingent on the associated risk to human life. Most standards recommend a factor (such as 1.5, 1.2 and 1.0 by Indian standard) based on the importance of a structure. The important question that arises here is, given the varying level of epistemic uncertainty contingent on the fundamental period and regional seismicity, are we maintaining the underlying objective of uniform mean annual rate of exceedance while using a constant importance factor (such as that specified by most seismic standards)? This study offers an alternate perspective of increasing the design seismic hazard while maintaining the same underlying principle in an effort to comprehend the underlying problem. The ratio of fractile-to-weighted mean hazard is envisioned in this study as an alternate perspective of the importance factor used in seismic design, which appears to be dependent on the regional seismicity and fundamental period (but not on the soil sites). Besides, PSHA of the North-east Indian region is carried out using the logic tree approach and the results of few cities are presented here for the purpose of comparative illustrations. Finally, the importance factor is given by a linear function of the time period with contour maps of location-dependent constants. Keywords Importance factor · Weighted mean and fractile seismic hazard · Logic tree · Contour map

N. Gurjar (B) · D. Basu Department of Civil Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, India e-mail: [email protected] D. Basu e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_51

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1 Introduction Contingent to the risk to human life in the event of failure (or impaired functionality!), the structural systems in ASCE 7–16 [1] are categorized into four groups with Category-I and Category-IV indicating the lowest and highest risk levels, respectively (Cl 1.5) [1]. Conditional probability of failure given the design shaking, i.e., the target reliability is specified against each category along with an importance factor. Eurocode 8 [2] imposes a target reliability through an importance factor against each structure considering the risk of its failure or impaired performance (decided by the National Authorities). Along the same line, the design seismic hazard per Indian standard [3] considers the importance factor (Cl 7.2.3) [3] which has the interpretation of accounting for the risk associated with failure (or impaired performance) of the structure. In other words, importance factor is aimed to augment the design force and, alternatively, the design seismic hazard to reduce the probability of failure of a structure contingent on the associated risk to human life. Figure 1 explains a schematic illustration of the need for importance factor. While aiming to augment the design seismic force, most standards recommend a factor such as 1.5, 1.2 and 1.0 by Indian standard based on the importance of the structure, and this paper explores the alternate viewpoint of increasing the design seismic hazard. Assuming the ordinary structures are designed with mean hazard (that corresponds to importance factor 1.0), the important structures may be designed against ‘mean plus x times standard deviation (μ + xσ )’ hazard, e.g., 84 and 99 percentiles. Although the choice of percentile needs rigorous risk assessment, for the purpose of illustration, the ratio of 84.13th percentile to weighted mean and 99.86th percentile to weighted mean is considered as the two importance factors, I84 and I99 , respectively, in this paper. Probabilistic seismic hazard assessment for seven states of North-east Indian region is carried out using the logic tree approach for this purpose. Sample illustration of results is reported in terms of different fractile and weighted mean hazard (and their ratio) for some of the major cities of the region at National Earthquake Hazard Reduction Program (NEHRP) soil site class-C (average Vs30 = 550 m/s) with 2475 and 475 years of return period. Finally, it is recommended that the importance factor of a structure can be considered as time period and location dependent, in terms of a ratio of fractile (importance level) to weighted mean hazard from sitespecific probabilistic seismic hazard assessment (PSHA), instead of a time period and location-independent factor as per Indian standard recommendation. However, decision of fractile levels for differently important structures is beyond the scope of this study.

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Earthquake of certain magnitude?

Statement! The buildings should be designed for DBE level of hazard

NO!

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YES!

Intensity measure (IM) with x% PE in y years (or return period)

What is DBE?

Hazard level is fixed for a particular category of structures (say buildings) Do all the building have same importance (irrespective of their use)?

NO! Different buildings have different importance based on risk to human lives in the event of failure

What shall we do?

Design the important structures for higher forces Higher return period? NO! (Already decided)

Higher forces from where?

Why?

↓ Risk = ↓ Hazard (↑ Hazard level) x ↓ Exposure (change location, NO!) x ↓ Vulnerability (↓ probability of failure)

Present study (Alternate perspective) Consider higher epistemic uncertainty in the seismic hazard characterization

IS 1893 (and most other standards) Just use the importance factor accordingly

How do we get and ?

Interesting? See you next Fig. 1 Schematic illustration of the need for importance factor

How do we get this? Why 1.2 and 1.5 only? No information!

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2 Probabilistic Seismic Hazard Assessment Seismic hazard estimation is one of the important steps to quantify the possibility of hazard at any particular site. The assessment provides a basis for proper planning and safe design of important structures in a seismically exposed region. North-east Indian region is one of the extreme seismically active regions all around the world. Probabilistic seismic hazard assessment for seven states of North-east region is carried out. Seismic hazard is characterized in terms of weighted mean and fractile representation of hazard using the logic tree approach. Recurrence relation parameter, magnitude and distance probability distribution, maximum magnitude and ground motion predictive equations (GMPEs) are considered the source of epistemic uncertainty. Detailed description of the hazard computation with GMPE rule and logic tree for accounting epistemic uncertainty in seismic hazard characterization is explained by Gurjar and Basu (2022) [4, 5], and only a brief description is provided here for the ready reference.

2.1 Hazard Computation with GMPE Rule and Logic Tree Logic tree diagram used for construction of model hazard curves (MHCs) in PSHA is given in Gurjar and Basu [4]. The logic tree is mainly divided into two sections, i.e., Earthquake Rupture Forecast (ERF) and Sub-Earthquake Rupture Forecast (SERF). The ERF consists of four subsections: (a) distance probability distribution as magnitude dependent and independent, (b) recurrence relation parameters as straight-line fitting (linear regression) and log-likelihood, (c) magnitude distribution as given by Gutenberg-Richter, (1944) [6] and Main and Burton [7] and (d) maximum magnitude as maximum of five alternatives from Kijko and Singh [8]; Gupta [9]; and proportional to fault length. GMPE rule is defined in the SERF for the consideration of epistemic uncertainty due to selection of GMPEs. The rate corresponding to the intensity measure (IM) value is contributed by GMPE rule; i.e., all ruptures (m − r pair) in qth source zone will be governed by ONLY vth of the aq number of possible candidates for GMPE, whereas other ruptures in any other source zone (1, L but = q) will be contributed from the weighted attributes of all possible GMPEs in the respective source zone. Note that GMPE rule is defined for the SERFs and hence remains same in all ERFs.

3 Results and Discussion Weighted mean and fractile representation of hazards are estimated in this study using the logic tree approach of PSHA. Sample illustration of the PSHA results is reported in terms of the weighted mean and fractile (50, 84.13 or 99.86th percentile,

Exploring an Alternate Perspective of the Importance Factor for Seismic …

1.0E+00

1.0E-04

λ

λ

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643

Mean 50th P 84.13th P 99.86th P

0.1 Sa (g)

1

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1.0 0.5 0.0 0.01

0.1 1 Time Period (sec)

50th P 84.13th P 99.86th P Mean

1.0 0.5 0.0 0.01

0.1 1 Time Period (sec)

Fig. 3 Site-specific uniform hazard spectra (UHS) accounting for importance factor at site class-C, 10% PE in 50 years, for Imphal West (Manipur)

etc.) representation of hazard curve (Fig. 2) and site-specific UHS accounting for importance factor (Fig. 3) for one city (Imphal West district, Manipur) of the Northeast region. Although the choice of percentile needs rigorous risk assessment, for the purpose of illustration, the ratio of 84.13th percentile to weighted mean and 99.86th percentile to weighted mean is considered as the two importance factors, I84 and I99 , respectively, in this paper. Table 1 features the variation of 475-years I84 against time period at several locations for the site class-C. Also included are the similar data for I99 in Table 2. Similar exercise is also carried out for the 2475-year return period but not included in the table for brevity. The importance factor I84 varies in the range of 1.1 ~ 2.1 for 2475-years return period and 1.1 ~ 1.8 for 475 years return period over a period range of 0 ~ 3 s for the entire North-east region (2302 site locations considered at 0.1° grid spacing). Similarly, the I99 varies in the range of 1.2 ~ 3.8 and 1.2 ~ 3.1 for 2475 years and 475 years return period, respectively. Further, these ratios are noted as nearly insensitive to the site class. For example, a variation in the order of ± 0.2 is noted

22.88

23.45

25.76

Lunglei

Champhai

Dimapur

14

15

16

25.90

23.88

North Garo Hills

Aizawl

12

25.57

25.37

13

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11

24.64

Thoubal

East Khasi Hills

9

10

25.32

Senapati

8

26.14

24.82

Dhubri

Imphal West

26.30

6

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5

27.90

26.08

7

Lohit

Kamrup Metropolitan

3

4

27.76

27.17

Papum Pare

Changlang

1

2

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S. No.

93.78

93.18

92.70

92.90

90.49

90.22

91.75

94.00

94.15

93.90

89.93

92.70

91.64

96.17

96.62

93.70

Long (˚E)

1.19

1.15

1.16

1.17

1.30

1.34

1.20

1.11

1.12

1.11

1.22

1.18

1.23

1.15

1.12

1.19

T=0s

1.19

1.14

1.20

1.21

1.43

1.45

1.32

1.11

1.14

1.12

1.33

1.26

1.33

1.33

1.26

1.33

T = 0.1 s

1.16

1.15

1.19

1.17

1.41

1.42

1.23

1.12

1.14

1.13

1.34

1.33

1.26

1.19

1.17

1.26

T = 0.5 s

84.13th percentile/weighted mean

Table 1 84.13th percentile-to-weighted mean hazard ratio (I84 ) variation with time period and location

1.21

1.17

1.21

1.21

1.51

1.51

1.31

1.15

1.15

1.15

1.47

1.30

1.32

1.26

1.26

1.34

T=1s

1.27

1.22

1.25

1.26

1.45

1.49

1.40

1.18

1.24

1.21

1.41

1.33

1.38

1.30

1.29

1.32

T=2s

(continued)

1.37

1.27

1.27

1.30

1.69

1.74

1.50

1.26

1.34

1.29

1.71

1.41

1.49

1.37

1.35

1.44

T=3s

644 N. Gurjar and D. Basu

West Tripura

South Tripura

North Tripura

19

20

21

24.08

23.23

23.90

25.75

26.80

Kohima

Mon

17

18

Lat (˚N)

City

S. No.

Table 1 (continued)

92.26

91.56

91.40

94.98

94.11

Long (˚E)

1.16

1.20

1.16

1.13

1.13

T=0s

1.25

1.26

1.29

1.13

1.11

T = 0.1 s

1.21

1.27

1.25

1.15

1.14

T = 0.5 s

84.13th percentile/weighted mean

1.25

1.27

1.29

1.20

1.18

T=1s

1.29

1.31

1.34

1.27

1.24

T=2s

1.34

1.35

1.39

1.38

1.34

T=3s

Exploring an Alternate Perspective of the Importance Factor for Seismic … 645

22.88

23.45

25.76

Lunglei

Champhai

Dimapur

14

15

16

25.90

23.88

North Garo Hills

Aizawl

12

25.57

25.37

13

West Garo Hills

11

24.64

Thoubal

East Khasi Hills

9

10

25.32

Senapati

8

26.14

24.82

Dhubri

Imphal West

26.30

6

Nagaon

5

27.90

26.08

7

Lohit

Kamrup Metropolitan

3

4

27.76

27.17

Papum Pare

Changlang

1

2

Lat (˚N)

City

S. No.

93.78

93.18

92.70

92.90

90.49

90.22

91.75

94.00

94.15

93.90

89.93

92.70

91.64

96.17

96.62

93.70

Long (˚E)

1.41

1.35

1.37

1.43

1.56

1.62

1.64

1.26

1.28

1.25

1.53

1.45

1.64

1.34

1.31

1.50

T=0s

1.56

1.35

1.44

1.68

1.67

1.71

1.75

1.27

1.30

1.27

1.56

1.52

1.71

1.56

1.48

1.47

T = 0.1 s

1.40

1.43

1.49

1.43

1.82

1.85

1.75

1.31

1.36

1.33

1.87

1.74

1.83

1.49

1.44

1.88

T = 0.5 s

99.86th percentile/weighted mean

Table 2 99.86th percentile-to-weighted mean hazard ratio (I99 ) variation with time period and location

1.72

1.56

1.71

1.68

2.24

2.25

2.11

1.39

1.50

1.46

2.35

2.18

2.23

2.01

1.96

2.39

T=1s

1.83

1.60

1.68

1.69

2.31

2.36

2.25

1.51

1.64

1.57

2.40

2.16

2.33

2.15

2.10

2.39

T=2s

(continued)

2.41

1.91

2.03

2.07

2.90

2.99

2.88

1.86

2.13

2.00

2.99

2.66

2.94

2.76

2.70

2.93

T=3s

646 N. Gurjar and D. Basu

West Tripura

South Tripura

North Tripura

19

20

21

24.08

23.23

23.90

25.75

26.80

Kohima

Mon

17

18

Lat (˚N)

City

S. No.

Table 2 (continued)

92.26

91.56

91.40

94.98

94.11

Long (˚E)

1.60

1.47

1.67

1.32

1.28

T=0s

1.63

1.56

1.69

1.32

1.27

T = 0.1 s

1.66

1.63

1.76

1.33

1.37

T = 0.5 s

99.86th percentile/weighted mean

1.94

1.90

2.02

1.66

1.66

T=1s

1.88

1.84

1.98

1.86

1.79

T=2s

2.32

2.27

2.46

2.51

2.34

T=3s

Exploring an Alternate Perspective of the Importance Factor for Seismic … 647

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for both I84 and I99 , respectively. Hence, importance factor using this alternative perspective depends on the fundamental period, seismicity of the site but not on the site class. The location- and time period-dependent importance factor for 475 years return period can be represented as I84 = M84 T + C84 I99 = M99 T + C99

 f or T = 0 − 3 s

Here, M84 , C84 , M99 , and C99 are the location-dependent constants. The locationdependent constants (site specific) can be determined by fitting a linear relation as represented in Fig. 4 for East Khasi Hills district of Meghalaya. The contour maps of location-dependent constant (M84 , C84 , M99 , and C99 ) for the entire Northeast region are determined in a similar manner and shown in Fig. 5a–d. Figure 5a–d illustrates the spatial variability of location-dependent constants similar to the hazard map of a region. The observation presented above is somewhat startling with a chance of being misconstrued in absence of a separate discussion. As a sample illustration let us consider two buildings with fundamental period T = 1.0 s (~10 story), located at a site with hard soil (NEHRP site class-C) in the East Khasi Hills (Meghalaya) area of North-east India. Let both the structures be of different risk to human life in the event of failure (or impaired functionality!), e.g., a business community center and a hospital building. An importance factor of 1.2 and 1.5 should be considered for the business community center and hospital building, respectively, per the Indian 3.0 84.13th P/ Mean 99.86th P/ Mean Linear (84.13th P/ Mean) Linear (99.86th P/ Mean)

2.8

Percentile /Mean

2.6

I99 = 0.414T + 1.603 R² = 0.964

2.4 2.2 2.0

I84 = 0.109T + 1.203 R² = 0.910

1.8 1.6 1.4 1.2 1.0 0

0.5

1

1.5

2

2.5

3

3.5

Time Period (sec)

Fig. 4 Site-specific period-dependent importance factor for 475 years return period, Site class-C at East Khasi Hills (Meghalaya)

Exploring an Alternate Perspective of the Importance Factor for Seismic …

649

(a)

(b)

Fig. 5 Contour maps of location-dependent constants for 475 years return period, site class-C, a M84, b C84, c M99, d C99

standard regardless of the fundamental period and regional seismicity. However, defining the importance factor as I84 and I99 at the fundamental period, one may show 1.31 and 2.02 (Fig. 4) for the business community center and hospital building, respectively. Although at T = 0 s, I84 = 1.2 and I99 = 1.6 for East Khasi Hills are nearly same as Indian standard, a significant effect of this new perspective of importance factor on the design spectra (specially at higher time period) can be clearly witnessed from Fig. 6. Hence, the consideration of a constant importance factor may underestimate the design forces, especially for the high-rise buildings

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(c)

(d)

Fig. 5 (continued)

with higher fundamental period. The step-by-step procedure for the computation of alternate perspective of the importance factor can best be explained through a flowchart as presented in Fig. 7. Greater importance factor per this new perspective implies larger epistemic uncertainty in the seismic hazard characterization and not the higher return period. Given the varying level of epistemic uncertainty contingent on the fundamental period and regional seismicity, a constant importance factor such as that specified by most

Exploring an Alternate Perspective of the Importance Factor for Seismic …

0.6

IS1893 IS1893 ꓫ 1.2 IS1893 ꓫ I84

0.5

Sa (g)

Sa (g)

0.4 0.3 0.2 0.1 0.0 0

1 2 Time Period (sec)

3

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

651

IS1893 IS1893 ꓫ 1.5 IS1893 ꓫ I99

0

1 2 Time Period (sec)

3

Fig. 6 Design spectra at East Khasi Hills (Meghalaya) with different importance factors

seismic standards may defeat the underlying objectivity. Further, a constant importance factor also defeats the objective of uniform mean annual rate of exceedance if higher mode participation is non-negligible. Based on the discussion presented above, the fractile hazard definition proposed in this paper scientifically accounts for the objectives of importance factor though choice of appropriate fractile contingent on the importance of structure requires a rigorous risk assessment, which is beyond the scope of this study.

4 Summary and Conclusion The importance factor is aimed to augment the design seismic hazard to reduce the probability of failure of a structure contingent on the associated risk to human life. A constant importance factor in seismic design recommended by most seismic standards may defeat the underlying objectivity of uniform risk-based seismic design. An alternate perspective while maintaining the same underlying principle is recommended in this paper that defines the ratio of fractile-to-weighted meanrepresentation of hazard as the importance factor, which gives an important information about location and time period dependent importance factor, rather than a constant. Choice of appropriate fractile however needs rigorous risk assessment which is beyond the scope of this paper. PSHA of the North-east Indian region is carried out using logic tree approach with due consideration of epistemic uncertainty contributed from several sources, and the results of few cities are presented here for the purpose of comparative illustrations. Results are reported in terms of different fractile and weighted mean hazard (and their ratio) for some of the major cities of the region. The location- and time perioddependent importance factor for 475 years return period is defined as a linear function of time period and contour maps of location-dependent constants.

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STEP-1 Carry out the site specific PSHA using logic tree approach

STEP-2 Compute the model hazard curves (at different time periods) resulting from each logic tree branch

STEP-4 Compute the cumulative distribution function of the IM vector at each time period considering cumulative branch factor (from logic tree) as the cumulative probability

STEP-5 Compute the weighted mean and fractile representation of site specific UHS Note: The site specific UHS accounting for importance factor can also be used for design

STEP-3 Compute the site specific UHS from horizontal dissection of each model hazard curve at a particular return period (say DBE)

STEP-6 Take the ratio of different fractiles (say 84th or 99th) to weighted mean and fit a linear relation, to get and in terms of a function of the time period

STEP-7 Repeat the STEP-1 to -6 at all the possible locations of the region and compute the location dependent constants of the linear relation ( STEP-8 Compute the site-specific time period dependent importance factor ( or ) and multiply

or

) and plot the contour map

with the code specific design spectra

Fig. 7 Flowchart for alternate perspective of the importance factor

Acknowledgements This research is funded by the Ministry of Earth Sciences, Seismology Division, Government of India, under the Grant No. MoES/P.O. (Seismo)/1(370)/2019, and the financial support is acknowledged.

Exploring an Alternate Perspective of the Importance Factor for Seismic …

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References 1. American Society of Civil Engineers: Minimum design loads and associated criteria for buildings and other structures. ASCE/SEI 7, Reston, VA (2016) 2. EN 1998–1: Eurocode 8: Design of structures for earthquake resistance—Part 1: General rules, seismic actions and rules for buildings (2004) 3. IS-1893: Indian standard criteria for earthquake resistant design of structures, Part-1: General provisions and buildings. In: Bureau of Indian Standards, New Delhi (2016) 4. Gurjar, N., Basu, D.: Epistemic uncertainty in PSHA and seismic hazard characterization using logic tree approach: part I-developing the framework. Pure Appl. Geophys. (2022). https://doi. org/10.1007/s00024-022-03143-4 5. Gurjar, N., Basu, D.: Epistemic uncertainty in PSHA and seismic hazard characterization using logic tree approach: part II-implementation over North-East India. Pure Appl. Geophys. (2022). https://doi.org/10.1007/s00024-022-03148-z 6. Gutenberg, B., Richter, C.F.: Frequency of earthquakes in California. Bull. Seism. Soc. Am. 34, 185–188 (1944) 7. Main, I.G., Burton, P.W.: Information theory and the earthquake frequency-magnitude distribution. Bull. Seism. Soc. Am. 74, 1409–1426 (1984) 8. Kijko, A., Singh, M.: Statistical tools for maximum possible earthquake magnitude estimation. Acta Geophys. 59, 674–700 (2011) 9. Gupta, I.D.: The state-of-the-art in seismic hazard analysis. ISET J. Earthquake Technol. 39(4), 311–346 (2002)

Simplified Damping Modification Factor for Vertical Response Spectra Ravi Kanth Sriwastav

and Dhiman Basu

Abstract Damping modification factor of vertical response spectra (DMFV ) for 1%, 2%, 8% and 10% damping levels with that at 5% of the critical is explored for a period range of 0–4.0 s. The dependence of DMFV on seismological parameters (magnitude, epicentral distance and soil type) is studied and is shown to be weak and insignificant for practical purposes. Simplified relation for computing DMFV as a function of the period is proposed irrespective of any seismological parameters. Keywords Vertical earthquake shaking · Damping modification factor

1 Introduction Significant effect of the vertical ground motion on the performance of certain reinforced concrete (RC) structures has been observed in the past [6, 9, 12–15]. The effects of vertical components in seismic design can be included by defining a design vertical response spectrum. Notable progress has been made over the past decade in the characterization of vertical ground motion. The procedures for construction of the vertical spectrum available in the literature can be grouped into two broad categories. The ground motion predictive model (GMPM) for direct construction for the vertical response spectrum is referred to as the first type. The second type refers to median prediction of the vertical-to-horizontal spectral ratio (V/H) through a GMPM. The product of V/H and the corresponding horizon tal spectrum is used to calculate the vertical spectrum. Another type of vertical spectrum constructed by simplifying the first two types was also proposed in an earlier work [5]. Most building codes (For example, IS1893:2016 Part 1, ASCE 7:16 [2] and EC8-part 1: 2008 [7]) have recognized the effect of vertical components R. K. Sriwastav (B) · D. Basu Department of Civil Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, India e-mail: [email protected] D. Basu e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_52

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and provide a simple recommendation for constructing the design vertical spectrum with the design horizontal spectrum as a basis. Recently, Sriwastav and Basu [18] proposed a simplified V /H spectrum using a large database of recorded events for the computation of vertical spectra for both scenario- and intensity-based assessments. Most vertical response spectra proposed in the literature and that recommended by the design codes are for a damping ratio (ξ ) of 5%. However, the vertical modes in the structures are relatively stiffer. It is anticipated that the damping ratios related to these modes would differ from the horizontal modes. Therefore, a factor is generally needed to convert the 5% damped vertical spectral ordinate to that associated with an arbitrary damping ratio. Such a factor may be referred to as the vertical damping modification factor (DMF). Design codes specify a constant DMF for different damping ratios irrespective of period and seismological parameters. As an example, Eurocode 8-Part 1 (2004) [Clause 3.2.2.2(3)] / / specifies the DMF for both horizontal and vertical components as DMF = 10 (5 + ξ )(≥ 0.55). ASCE 7:16 (Table 17.5-1) recommended a range of DMF between 1.25 and 0.5 for damping ratios up to 50% of the critical. The possible dependence on the seismological parameters and period of the structure is thus not accounted for in such grossly simplified recommendations. Few recent studies including Akkar et al. [1], Rezaeian et al. [17] and Xiang and Huang [19] extensively addressed the issues pertaining to the DMF of vertical component (DMFV ). Akkar et al. [1] developed DMFV model to modify the 5%-damped vertical pseudo-spectral acceleration (PSA) of Akkar et al. [1] for generating PSA at other damping levels between 1 and 50%. The dataset in this study consisted of 1041 accelerograms from 221 shallow crustal earthquakes recorded in the Mediterranean region and the Middle East. Mean DMFV was proposed as a function of M W , RJB and Vs30, damping ratio and period. Rezaeian et al. [17] also developed a model for DMFV for a range of damping ratios between 0.5% and 30%. Ground motion records from the PEER NGA West2 database [3] were used, and median DMFV was shown to depend upon the magnitude, distance, damping ratio and spectral period (but not Vs30). Xiang and Huang [19] proposed a regional (Japanese) DMFV utilizing 3198 ground motion records from the Japanese seismic database. The range of damping ratio studied were 0.5%, 1%, 2%, 3%, 4%, 6%, 7%, 8%, 9%, 10%, 12.5%, 15%, 17.5%, 20%, 25%, 30%, 35% and 40%. The peaks (for ξ less than 5%) or valleys (for ξ more than 5%) of DMFV spectra were observed at a spectral period of 0.12 s. It further deviates from unity with the increase in earthquake magnitude and epicentral distance. The dependence of hypocenter depth and site category on DMFV was reported to follow no distinct trend. [Therefore, there is no clear consensus on the dependence of DMFV on seismological parameters.] Additionally, the DMFV models in the literature are too complex to use in design practice, and hence, a simplified DMFV model is needed with a sufficient level of accuracy. This paper tries to develop such a simplified model based on a large database of recorded events. We took five damping ratios namely 1%, 2%, 5%, 8% and 10% of the critical to study the dependence of seismological parameters on DMFV using a comparatively larger dataset. A simplified DMFV model for these damping levels

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657

is also proposed. Following section discusses the ground motion dataset used in the paper.

2 Seismic Events and Ground Motions Considered The ground motion database in this paper is similar to that used in Sriwastav and Basu [18]. A set of 5962 records from PEER NGA West2 database with (i) MW 5.0– 8.0; (ii) epicentral distance (R)-0–200 km and (iii) Vs30-180–1500 m/s have been selected. This dataset is classified into a number of triplets defined by M-R-Vs30. An interval of 0.5 is used to construct magnitude bins. Six distance bins are formed namely (i) 0 < R ≤ 20 km; (ii) 20 < R ≤ 50 km; ; (iii) 50 < R ≤ 75 km; (iv) 75 < R ≤ 100 km; ; (v) 100 < R ≤ 150 km and (vi) 150 < R ≤ 200 km. Three bins for site category are constructed per NEHRP soil classification [8]; (i) medium soil (180–360 m/s), (ii) hard soil (360–760 m/s) and (iii) rock (760–1500 m/s). A total of 76 triplets are formed. The absolute acceleration spectrum is considered as IM of the vertical component.

3 Median Vertical Spectrum for Scenario-Based Assessment Let the vertical response spectra corresponding to damping ratio ‘ξ ’ and that at 5% of the critical are denoted by Vξ and V0.05 , respectively. Also, assume that the spectral ordinates (at any period T ) conditioned to an M-R (magnitude-distance) bin follows a lognormal distribution and logarithmic mean of Vξ and V0.05 are represented by μln Vξ and μln V0.05 , respectively. Thus, vertical spectral ordinate corresponding to damping ratio ‘ξ ’ at any period can be computed as Vξ (T ) = V0.05 (T )

  / Vξ Vξ (T ) = V0.05 (T ) ⇒ ln Vξ = ln V0.05 + ln Vξ V0.05 . V0.05 (T ) V0.05 (1)

 /  It follows from Eq. (1) that ln Vξ V0.05/ is also normally distributed with  μln(Vξ / V0.05 ) as the logarithmic mean of ln Vξ V0.05 . Further, μln Vξ (T ) is given by μln Vξ (T ) = μln V0.05 (T ) + μln(Vξ / V0.05 )

(2)

 /  Denoting θ(Vξ / V0.05 ) as the median of Vξ V0.05 with ln θ(Vξ / V0.05 ) = μln(Vξ / V0.05 ) , it may be calculated for a given M-R bin as

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   /  θ(Vξ / V0.05 )(T ) = ex p E ln Vξ V0.05  / = ex p(E[ln Vξ ] ex p(E[ln(V0.05 )] / ⇒ θ(Vξ / V0.05 )(T ) = θVξ (T ) θV0.05 (T )

(3)

where E[·] denotes the first moment. The ratio of median vertical response spectrum at any damping / level (θVξ (T ) ) to that at 5% of critical (θV0.05 (T ) ) is, therefore, the same as median Vξ V0.05 spectrum.

3.1 Damping Modification Factor Using a Set of Recorded Events Damping modification factor for vertical shaking (DMFV ) is defined here as the ratio of median vertical spectrum at any damping level (θVξ (T ) ) to that at 5% of critical / D M FV (T ) = θVξ (T ) θV0.05 (T )

(4)

The process of constructing DMFV is briefly discussed here. Given an M-R-Vs30 triplet, the median vertical response spectra at five damping levels (1%, 2%, 5%, 8% and 10%) are constructed. Next, the DMFV is computed for each damping ratio. This is followed for all the M-R-Vs30 triplets. Examples are presented for a few triplets covering a different range of magnitude and distance for medium, hard and rock soil sites in Figs. 1, 2 and 3, respectively. The median vertical spectrum for all damping ratios is presented in the left panel of the figures. The right panel presents the DMFV for the associated damping ratios. The shape of DMFV for a particular damping ratio is observed to be nearly the same regardless of the triplet characteristics.

Sa/g

0.40 0.30

1.80

1% 2% 5% 8% 10%

1.60 1.40 DMF

0.50

0.20 0.10 0.00 0.001

1.20

1% 2% 5% 8% 10%

1.00 0.80

0.01

0.1 Period (s)

1

0.60 0.001

0.01

0.1 Period (s)

1

Fig. 1 Vertical response spectra (left) and damping modification factor (DMFV ) (right) for a triplet defined by M: 6.5–7.0 R: 20–50 km Vs30: 180–360 m/s [medium soil sites]

Simplified Damping Modification Factor for Vertical Response Spectra

Sa/g

0.20

1.80

1% 2% 5% 8% 10%

1.60 1.40 DMF

0.30

0.10

1.20

659 1% 2% 5% 8% 10%

1.00 0.80

0.00 0.001

0.01

0.1

0.60 0.001

1

0.01

0.1

Period (s)

1

Period (s)

Fig. 2 Vertical response spectra (left) and damping modification factor (DMFV ) (right) for a triplet defined by M: 7.0–7.5 R: 50–75 km Vs30: 360–760 m/s [hard soil sites]

Sa/g

0.04

1.80

1% 2% 5% 8% 10%

1.60 1.40 DMF

0.06

0.02

1.20

1% 2% 5% 8% 10%

1.00 0.80

0.00 0.001

0.01

0.1 Period (s)

1

0.60 0.001

0.01

0.1 Period (s)

1

Fig. 3 Vertical response spectra (left) and damping modification factor (DMFV ) (right) for a triplet defined by M: 6.0–7.0 R: 50–100 km Vs30: 760–1500 m/s [rock sites]

4 Dependence of Damping Modification Factor on Seismological Parameters Guided by the similarity of DMFV shape irrespective of triplet characteristics, separate comparisons are performed in this section to verify the dependence of DMFV on M, R and Vs30. Figures 4 and 5 present a few representative plots of such comparison. The plots are shown separately for the four damping levels (1%, 2%, 8% and 10%) in each figure. A comparison of the DMFV for different magnitudes given a particular distance and site class is presented in Fig. 4. Similarly, a comparison of DMFV for distances given a magnitude and site class is conducted. However, it is not presented for brevity. Weak dependence of DMFV on the magnitude and distance is observed. Figure 5 presents the comparison against soil sites regardless of magnitude and epicentral distance. DMFV is observed to (i) decrease with Vs30 for ξ < 0.05 and (ii) increase with Vs30 for ξ > 0.05. However, this effect of soil category on DMFV is small. Therefore, for all practical purposes, it may be regarded as insignificant.

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1.6 1.3 DMF

DMF

1.6 1.3 M5.25 M6.25 M7.25

1.0 0.7 0.001

0.01

0.1

1.0

M5.75 M6.75 M7.75

0.7 0.001

1

M5.25 M6.25 M7.25 0.01

Period (s)

a)

M5.25 M6.25 M7.25

b)

DMF 0.8 0.6 0.001

2% damping R:100-150;Vs30:180-360

1.2

M5.75 M6.75 M7.75

DMF

1.0

1

Period (s)

1% damping R:20-50;Vs30:180-360

1.2

0.1

M5.75 M6.75 M7.75

M5.25 M6.25 M7.25

1.0

M5.75 M6.75 M7.75

0.8

0.01

0.1

1

Period (s)

c) 8% damping R:50-75; Vs30:180-360

0.6 0.001

0.01

0.1

1

Period (s)

d) 10% damping R:75-100km; Vs30: 180-360

Fig. 4 Dependence of DMFV on earthquake magnitude for a 1%, b 2% and c 8% d 10%

5 Simplified Damping Modification Factor for V/H Since, DMFV is observed to be weakly dependent on all the seismological parameters considered here (M, R and Vs30), all the triplets are clubbed and the DMFV is computed to study its relationship with the period for each damping level. DMFV spectra for all damping levels have a distinct shape and may be defined through their shape in three period ranges. Following simplified relation is observed to best represent the DMFV computed using the recorded ground motions | | | | 1.0 T ≤ 0.02s | | c3 T | DMFV (T ) = | c1 + c2 e 0.02s 0.2s

(5)

Here, c1 , c2 , c3 and c4 are the regression coefficients. These are provided in Table 1 for all damping levels. Figure 6 presents the comparison of DMFV computed using recorded events and that generated using simplified expression per Eq. (5). The difference is negligible for all practical purposes. Therefore, using the framework followed in this paper, a simplified DMFV model for a wide range of damping ratios may be developed for possible recommendation in the seismic code.

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6 Assessment of Proposed Damping Modification Factor The proposed model is compared with three DMFV GMPMs available in the literature namely Akkar et al. [1] (AK13), Rezaeian et al. [17] (RZ14) and Xiang and Huang [19] (XH18). Taking an example triplet defined by M: 5.5–6.0 R: 100–150 km Vs30: 180–360 m/s, 5% damped median vertical spectrum is computed using ground motions from PEER Database [3]. Also computed is the 1% damped median vertical spectrum. Utilizing the 5% damped median vertical spectrum, the corresponding 1% damped spectrum is computed using the proposed model and that per AK13, RZ14 and XH18. Figure 7a presents the comparison of these predicted median vertical spectra. Also included in the comparison are the vertical spectra computed per EC8part 1 and ASCE 7–16 recommendations. A similar comparison is provided for 2%, 8% and 10% damping ratios in Fig. 7b–d, respectively. Different inferences are observed from the above comparisons for damping ratios less than and greater than 5%. For damping ratios less than 5%, both the design codes underestimate the median vertical spectra for period greater than 0.03 s, whereas the prediction is overestimated for shorter periods. However, for damping ratios greater than 5%, the codal prediction is significantly lower than the recorded spectrum for

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period less than 0.03 s, whereas the prediction is comparatively better for longer periods. The median spectrum computed using the GMPMs and the proposed models are nearly equal and closely match the recorded median vertical spectrum for all damping levels. A similar comparison with (for the same triplet) is provided in Fig. 8 using the ground motions from PESMOS [15] and COSMOS [4] databases which are not utilized in the development of the DMFV model. Only 1% and 10% damped spectra are demonstrated for brevity. The above inferences also hold for this dataset.

7 Demonstration of Utility of Damping Modification Factor The proposed damping modification factor may be used for computing the median vertical spectrum for both scenario- and intensity-based assessments given the associated 5% damped spectrum. The utility of damping modification factor is demonstrated here through two examples: (i) site-specific spectrum and (ii) code-based spectrum.

7.1 Site-Specific Spectrum The PSHA of the horizontal component performed by Gurjar and Basu [11, 10] for Northeast India is revisited for computing the uniform hazard spectrum (UHS) and conditional mean spectrum (CMS) using the generalized causal rupture (GCR) approach [11, 10]. Two cities namely Golaghat in Assam and Noney in Manipur are selected and the ‘weighted mean representation of UHS and GCR-CMS (hereafter referred to as UHS and H-CMS, respectively)’ at a conditional period of 1.25 s are computed in hard soil site (Vs30: 360–760 m/s) for the 2475 year return period using the logic tree approach with GMPE rule.

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V/H spectra are computed for all the generalized causal rupture scenarios (mean M-R) contributing to the H-CMS using [18]. Weighted mean representation of V/H spectrum is computed using GCR and logic tree weights applied to each V/H spectra. The 5% damped vertical spectrum consistent with H-CMS (denoted by V_H-CMS) is computed as the product of weighted mean representation of V/H spectrum and H-CMS. This vertical spectrum is denoted as ‘vertical spectrum consistent with horizontal seismic hazard’. V_H-CMS is validated through a suite of ground motions selected and scaled to match the H-CMS. Two period ranges namely i) 0.1 s–1.5T H and ii) 0.1 s–2.0T H are studied, where T H (=1.25 s) is the fundamental horizontal period. The same set of scale factors is utilized to scale the associated vertical components of the suite as well. Next, the average of scaled vertical spectra (Avg_V ) and the average of scaled horizontal spectra (Avg_H) are computed. The ratio of average vertical-to-average-horizontal spectra, (denoted by V /H), are computed for both the period ranges and are compared with the weighted mean representation of V /H spectrum in Fig. 9a, c for Golaghat and Noney cities, respectively. The comparison of the average vertical spectra with V_H-CMS is also presented in Fig. 9b, d for these cities. The use of the wider period range (0.1–2.0T H ) is seen to better represent the vertical spectrum consistent with the horizontal seismic hazard (V_H-CMS). This is expected as the spectral shape of H-CMS is representative of the hazard and the ground motions selected using a wider period range better represent the target. With V_H-CMS as the 5% damped vertical spectrum, the vertical spectra associated with other damping ratios are computed utilizing the proposed DMFV model. A comparison of these vertical spectra is provided in Fig. 10a, b for Golaghat and Noney cities. In another example, the intensity-based vertical spectrum is computed using the product of horizontal UHS and the intensity-based (envelope) V/H spectrum proposed by Sriwastav and Basu [18]. The proposed DMFV model is again utilized to construct the vertical spectrum corresponding to other damping ratios. A comparison of these spectra is provided in Fig. 11a, b for these two cities.

7.2 Code Specific Spectrum The selected cities (Golaghat and Noney) lie in the same seismic zone per the zonation map of IS1893:2016 (Part 1). Thus, the design horizontal spectrum is the same for both cities. Design vertical spectrum is computed using the product of this 5% damped horizontal spectrum (for hard soil site) and the intensity-based (envelope) V/H spectrum per Sriwastav and Basu [18]. Vertical spectra corresponding to other damping ratios are also computed using the proposed DMFV model and compared in Fig. 12.

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8 Summary and Conclusion Damping modification factor of vertical response spectra (DMFV ) for 1%, 2%, 8% and 10% damping levels with that at 5% of the critical is explored for a period range of 0–4.0 s. DMFV is studied for 76 triplets, each defined by a range of MR and Vs30. The dependence of DMFV on seismological parameters (magnitude, epicentral distance and soil type) is observed to be weak and insignificant for all practical purposes. Simplified relations for computing DMFV as a function of the period is proposed for damping levels 1%, 2%, 8% and 10% irrespective of any seismological parameter. The proposed DMFV is demonstrated to be used for both scenario- and intensity-based assessments. A simplified model using the framework adopted in this paper may be developed for a larger range of damping factors for possible recommendation in the seismic codes. Acknowledgements This research is funded by the Ministry of Earth Sciences, Seismology Division, Go I, under the Grant No. MoES/P.O. (Seismo)/1(370)/2019 and the financial support is acknowledged.

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References 1. Akkar, S., Sandıkkaya, M.A., Ay, B.Ö.: Compatible ground-motion prediction equations for damping scaling factors and vertical-to-horizontal spectral amplitude ratios for the broader Europe region. Bull. Earthq. Eng. 12(1), 517–547 (2014) 2. American Society of Civil Engineers: Minimum design loads and associated criteria for buildings and other structures. ASCE/SEI 7, Reston, VA (2016) 3. Ancheta, T.D., Darragh, R.B., Stewart, J.P., Seyhan, E., Silva, W.J., Chiou, B.S.J., Wooddell, K.E., Graves, R.W., Kottke, A.R., Boore, D.M., Kishida, T.: NGA-West2 database. Earthq. Spectra 30(3), 989–1005 (2014) 4. Archuleta, R.J., Steidl, J., Squibb, M.: The COSMOS Virtual Data Center: A web portal for strong motion data dissemination. Seismol. Res. Lett. 77(6) (2006) 5. Bozorgnia, Y., Campbell, K.W.: The vertical-to-horizontal response spectral ratio and tentative procedures for developing simplified V/H and vertical design spectra. J. Earthq. Eng. 8(02), 175–207 (2004) 6. DiSarno, L., Elnashai, A.S. Manfredi, G.: Seismic Response of RC Members Subjected to the 2009 L’Aquila (Italy) Near-Field Earthquake Ground Motions. Report No. 10-01, Mid-America Earthquake Center (2010) 7. EN 1998-1: Eurocode 8: design of structures for earthquake resistance–Part 1: General rules, seismic actions and rules for buildings (2004) 8. FEMA 450.: NEHRP recommended provisions for seismic regulations for new buildings and other structures-Part 1: provisions. Building seismic safety council. Washington, DC 9. Ghobarah, A., Elnashai, A.S.: Contribution of vertical ground motion to the damage of RC buildings. In: Proceedings of the Eleventh European Conference on Earthquake Engineering (pp. 9–13). AA Balkema, Rotterdam (1998) 10. Gurjar, N., Basu, D.: Epistemic uncertainty in PSHA and seismic hazard characterization using the logic tree approach: part II, Implementation over North-East India. Pure Appl. Geophys. (2022) 11. Gurjar, N., Basu, D.: Epistemic uncertainty in PSHA and seismic hazard characterization using the logic tree approach: part I, Developing the framework. Pure Appl. Geophys. (2022) 12. Hosseinpour, F., Abdelnaby, A.E.: Effect of different aspects of multiple earthquakes on the nonlinear behaviour of RC structures. Soil Dyn. Earthq. Eng. 92, 706–725 (2017) 13. Kim, S., Kim, S.J., Chang, C.: Analytical assessment of the effect of vertical ground motion on RC frames designed for gravity loads with various geometric configurations. Adv. Civil Eng. (2018) 14. Mwafy, A.M., Elnashai, A.S.: October. Vulnerability of code-compliant RC buildings under multi-axial earthquake loading. In: 4th International Conference on Earthquake Engineering, Taiwan, Paper No. 115 (2006) 15. PESMOS. Department of Earthquake Engineering, IIT Roorkee, https://pesmos.com/ [last visited 10/02/2020] 16. Papazoglou, A.J., Elnashai, A.S.: Analytical and field evidence of the damaging effect of vertical earthquake ground motion. Earthq. Eng. Struct. Dynam. 25(10), 1109–1137 (1996) 17. Rezaeian, S., Bozorgnia, Y., Idriss, I.M., Abrahamson, N.A., Campbell, K.W., Silva, W.J.: Damping scaling factors for vertical elastic response spectra for shallow crustal earthquakes in active tectonic regions. Earthq. Spectra 30(3), 1335–1358 (2014) 18. Sriwastav, R.K., Basu, D.: Vertical spectra consistent with horizontal seismic hazard. Soil Dyn. Earthq. Eng. 157, 107242 (2022) 19. Xiang, Y., Huang, Q.L.: Damping modification factor for the vertical seismic response spectrum: a study based on Japanese earthquake records. Eng. Struct. 179, 493–511 (2019)

December 01, 2020, Haridwar, Earthquake: Fault Plane Solution and Tectonic Implications Pooja Mahto and S. C. Gupta

Abstract On December 01, 2020, a small-sized earthquake (M L = 4.0) occurred 25 km northwest of Haridwar after a gap of about 45 years. The location of the earthquake falls on Main Frontal Thrust (MFT) about 105 km southwest of the 1991 Uttarkashi earthquake (M w = 6.8). The earthquake was sourced in the crust-mantle boundary (40.6 km). Data from the local broadband seismic network around the Tehri region have been used for the study of the source mechanism. The fault plane solution of this event suggests that deformation occurred by strike-slip faulting with a significant reverse component. The trend/plunge of the major and minor principal axes P/T are 201°/10° and 296°/24°, respectively. This paper is based on the determination of the focal mechanism through the moment tensor inversion solution of waveforms derived from the records of the local event, employing ISOLA software. The moment tensor solution is considered a stable and more robust method to quantify the fault orientation of faults that slipped due to an earthquake and the method yields more accurate results than any other standard approach. A numerical technique, developed based on forward modeling, is used for the inversion of the observed waveforms for the components of the moment tensors and the earthquake source-time function (STF(t)). We used f min of 0.15 Hz and f max of 0.55 Hz to perform the waveform inversion of the earthquake. Keywords Moment tensor analysis · Source parameter · Focal plane solution · Seismicity

P. Mahto (B) · S. C. Gupta Department of Earthquake Engineering, IIT Roorkee, Roorkee, Uttarakhand, India e-mail: [email protected] S. C. Gupta e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_53

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1 Introduction The current knowledge of the crustal structure allows waveforms to be modeled at low frequencies only. The local earthquake (around 300 km) is constrained by natural noise. As a result, M = 4.0 waveforms can be integrated over a limited frequency and distance range. Also, the number of usable stations is small, so few waveforms seem to provide a relevant focal mechanism [2]. When the epicentral distances are relatively small, and the frequency is low, then the waveforms are less dependent on the crustal model. The synthetic seismograms which have been calculated for the region in the present study provided almost identical waveforms. The focal mechanism solution through MT inversion requires different filters to find out the usable frequency range and avoid the disturbances of instruments. It requires visualization of space and time-variation of the correlation between real and synthetic seismograms, plotting waveform fit, and beach-ball representation. A small-sized earthquake of magnitude M L = 4.0 occurred on December 01, 2020, about 25 km northwest of Haridwar at a focal depth of 40.6 km. The earthquake has been recorded by a VSAT-based local seismic network operative in the Tehri region around Tehri dam which was deployed by the Department of Earthquake Engineering, Indian Institute of Technology Roorkee to monitor the local seismicity of the region and sponsored by THDC India Ltd., Rishikesh. In the present study, the computer-code ISOLA is used, with the combination of the computational speed of FORTRAN code and the user comfort of MATLAB developed by Sokos and Zahradnik [18], and the local earthquake records are inverted to estimate the MT solutions. The determination of focal mechanisms solved by moment tensor waveform inversions shows stable and much more accurate results. Here, the deviatoric tensor is used because the inverse problem remains linear and does not create problems during the inversion process. The digital data has acquired through Broadband sensor 3ESPS (120–50 Hz) and short-period CMG-6 T (30–100 Hz) coupled with 24-bit DAS (DM24-53) at a sampling interval of 100 samples/sec/component. The present study lies in the Sub-Himalayan part of the Himalayas. The subHimalayas constitute the foothills part of the Himalayas and lie between the HFT and MBT. Geographically, Sub-Himalayas constitute the Siwalik range. Rivers have carried and deposited sandstone and mudstone from the Himalayan mountains for over 5000 years in this region. Approx. over a million years ago, the Himalayan Front Fault (or Thrust), which is still active, uplifted the foothills of the Siwalik Range (of the Himalayas). HFT is the boundary between the Siwalik range and the North Indian plains. During the uplifting of the Siwalik range by the Himalayan Frontal Fault, river valleys were created in the Sub-Himalayan zone; these basins are called duns (doons), and well-known examples include Dehra Dun in India and Chitwan in Nepal.

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2 Geology and Tectonic of the Area Since the collision of the Indian Plate and Eurasian Plate during the Tertiary Period, the Himalayan arc has formed along a plate boundary that is 2500 km long and continues to do so at a rate of 37–44 mm each year [5, 12, 8]. Over the past century, there have been several significant Himalayan earthquakes, including four great earthquakes (M ≥ 8.7). The chronological order of occurrence of them; the 1897 Shillong earthquake of M 8.7 (Milne et al. 1911), the 1905 Kangra earthquake of M 7.8 (Gutenberg and Richter et al. 1954), the 1934 Bihar-Nepal border earthquake of M 8.3 (Tandon and Srivastava et al. 1974, 75), and the 1950 Assam earthquake of M (Tandon and Srivastava et al. 1974, 75). These great earthquakes have ruptured approximately 300 km long sections of the Himalayan Plate Boundary in the past 100 years. The Garhwal Himalaya which forms the western part of the Himalayan mountain ranges is characterized by moderate to large-sized earthquake activity. In the last three decades, two moderate-sized earthquakes namely the Uttarkashi earthquake of 1991 and the Chamoli earthquake of 1999 were experienced by this region [8] that falls in the Seismic Zone IV of the seismic zoning map of India (IS: 1893-2002). Based on the statistical study of the local earthquakes of this region, Khattri et al., 1999, gave the opinion that there is a high probability to generate a great earthquake in the future. Focal mechanism solutions are being used extensively to study the type of displacement and the relative motion between the boundaries of different plates. In the early days, focal mechanisms have been revealed as a powerful tool in attaining seismotectonics and the state of stress, but their reliability of results for Garhwal Himalaya, where there is small-to-moderate earthquakes are occurring continuously, is inappropriate due to the lack of coverage of seismic stations. In response to thrust tectonics in the detachment zone along the northward dipping Indian plate, almost all of the higher magnitude earthquakes (M ≥ 5.0) in the Himalayas have originated at a depth of 10–20 km near the Main Central Thrust (MCT) [12]. It is referred to as the area that lies between the locked, shallow fault segments that ruptured during strong earthquakes and the smoothly sliding deeper aseismic zone. The source, path, and instrument response are three effects that are combined to create the ground motion record of an earthquake at a station. Therefore, to determine the source mechanism of each fault, an earthquake must be deconvoluted. There are multiple north-dipping overthrusts in the Himalayan tectonic zone, which is a plate collision boundary. These overthrusts originate at a decollement surface that dips 15 degrees to the north, at a depth of between 12 and 20 km [17]. Solutions of fault planes in moderately sized earthquakes indicate upthrust along shallowly dipping planes originating from the north (e.g., [13]). The Tethyan Himalayan Sequence (THS), the Higher Himalayan Crystalline Sequence (HHCS), the Lesser-Himalayan Sequence (LHS), and the Sub-Himalayan Sequence (SHS) are the major tectonic divisions of the Garhwal Himalaya, and they are stacked from north to south by the thrust and detachment faults viz. the

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Southern Tibetan Detachment (STD), the Main Central Thrust (MCT), the Main Boundary Thrust (MBT), and the Himalayan Frontal Thrust (HFT) [6, 16, 20, 21]. According to Molnar and Tapponnier et al. [12], the region is subjected to seismic activity due to the northward motion of the Indian plate against the Tibet block of the Eurasian plate at a rate of around 0.05–0.06 m/year, which deforms the rocks to create the higher Himalayas. The majority of the earthquakes that occur in the Kumaun-Garhwal Himalaya occur nearby or to the north and south of the MCT. Several seismological studies and efforts have been done in India and elsewhere to study the complex seismicity patterns in this zone of continental-continental collisions. Fault plane solutions to earthquakes in this region have been carried out by various authors using data from various sources. Molnar and his coworkers from 1973 to 1975 worked out the solutions by using P-wave first motion using long-period seismometers. Since then, this region has carried out a continuous approach to study fault orientations. Recently, a study has been done by Prasath and his team [15] to estimate the upper crustal stress and seismotectonic of the Garhwal Himalayas.

3 Methodology and Qualitative Analysis 3.1 Moment Tensor Inversion The waveform inversion of the event is a powerful tool for estimating the focal mechanism of the event as it provides information on the source properties and the deformation in the source region that generates the seismic waves to examine the solution, we perform the waveform inversion of December 01, 2020, Haridwar earthquake using a tool called ISOLA. Using least-squares inversion and iterative deconvolution of the entire waveform, ISOLA, a MATLAB-based program, determines the focal MT of an earthquake [9, 18]. The moment tensor and Green’s tensor are used to relate the event waveforms to the source and medium parameters [1]. Because the current study is focused on small-scale earthquakes, it analyzes the near-field effects at local stations. If the time and position of the source are to be determined, the leastsquare method is used along with the spatio-temporal grid-search method in MT inversion. ISOLA code can use both local as well as regional events. The computercode ISOLA by Sokos and Zahradnik [18] is used to estimate waveform inversion in several modes: full MT inversion, DC-constrained MT inversion, and deviatoric MT inversion. There are deviatoric and volumetric parts to the full moment tensor, and it is challenging to obtain the volumetric part. Considering [22, Benetatos et al. 2013] as references, the decomposition in ISOLA for the inversion process namely volumetric (ISO), compensated linear vector dipole (CLVD), and double-couple (DC), such that ISO% + DC% + CLVD% = 100%.

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In the present study, the deviatoric tensor is performed because the inverse problem remains linear and does not create problems during the inversion process. The deviatoric tensor can be decomposed into the double-couple (DC) and non-double-couple (CLVD) components. The relative sizes of double-couple and non-double-couple (CLVD) parts are (1–2t) and (2t), respectively, where 1, t–1, and –t are the normalized MT eigenvalues. The term 100 (1–2t) is referred to as the double-couple percentage DC%. During waveform inversion, DC% is calculated for all inversion. Green’s function is calculated by the discrete wavenumber method [4, Bouchon et al. 2003]. The overall variance reduction provides the match between the observed and synthetic data by the relation Σ Varred = 1 − A/R; where A = (Ri − Si )2 , Σ R = (Ri )2 , with R and S denoting the observed and synthetic data, along with a summation of overall samples, components, and stations. The ISOLA code enables histories of complicated ruptures by multiple pointsource subevents, with each event represented by a delta function [24].

4 Data Set and Analysis In the present study, data from the December 01, 2020, earthquake that has been recorded in the Sub-Himalaya close to HFT has been analyzed. A local seismological network of 18 stations around the Tehri dam in the Garhwal Himalaya has recorded this earthquake (Fig. 1). The Department of Earthquake Engineering at IIT Roorkee manages and maintains the network, which is sponsored by THDC India Ltd., Rishikesh. The location of the event marked on the tectonic map of the region is shown in Fig. 1. The short-period sensor CMG-6 T frequency range 30 s–100 Hz and the broadband sensor CMG-3ESPCS frequency range (120 s–50 Hz) have been used to collect the digital data for this earthquake at 20 stations each at a rate of 100 samples/sec/component. The location of the earthquake has been determined through the HYPO71 computer program used in SEISAN software. This step estimates the Green function using the trial source position. The best fit between the observed and the synthetic waveforms is determined using a grid search by ISOLA software. The grid of trial points can be set up on a plane, in a line, with different strikes and dips, or it can be fixed at the epicenter position to find the event’s optimum depth. Figure 4 illustrates the focal depth estimate for an event for which the highest correlation and DC% values were determined using a vertical grid-search method. Here, the frequency range (0.15 < f < 0.55 Hz) is used for investigation, and the frequency above 0.55 Hz cannot be used as above this range, the model misses the necessary detail of the crustal structure Therefore, the permitted ranges for both frequency and distance are unfortunately quite normal. A narrow range also reduces

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Fig. 1 18-station seismological network operative in the Tehri region along with the location of the Dec. 01, 2020, earthquake. Tectonics after GSI (2000)

data and makes users more vulnerable to noise, which is a drawback. Instrument corrections and baseline corrections for the specified event were applied during the raw data preparation in ISOLA. The spectral amplitude of the earthquake has been utilized in the present study for the MT inversions. The final step of analysis is the waveform inversion, which is performed at the best centroid position of each event. The final output shows the correlation between the observed and synthetic waveforms as shown in Fig. 2. For each event, the optimal depth is found, and by fixing this depth, the optimal epicenter position is calculated. The best location of the epicenter is one where the correlation coefficient has the maximum value in the horizontal plane. Based on the above-mentioned analysis procedure, out of 18 stations, only 4 stations are used for inversion. The waveform of the other stations is not inverted due to the instrumental disturbances and poor azimuthal coverage.

5 Results In the present study, data from the December 01, 2020, earthquake has been analyzed using a moment tensor inversion solution. Based on the analysis, we have obtained earthquake source parameters viz., hypocentral location consisting of centroid location, and centroid depth. It has been identified that earthquakes with M L > 3.0 hold

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Fig. 2 Waveform fit for the moment tensor solution for Dec. 01, 2020, earthquake. The plot of comparisons between the original (black waveform) and synthetic (red waveform) for four stations used for inversion. Inversion band 0.15–0.55 Hz is used for analysis

a good signal-to-noise ratio (SNR) to perform waveform inversion. The earthquake here is of magnitudes M L = 4.0. The value of the signal-to-noise ratio is found to be 3, which gives a good estimate over the frequency range of 0–15 to 0.55 Hz. The variation between the calculated Mw and the local magnitude (M L ) is 0.01. The centroid depth estimated has a difference of 4.4 km to the hypocenter of the earthquake. Figure 3 depicts the inverted earthquake event’s seismic moment (Mo) as 1.147e + 15 N-m. The moment magnitude (M w ) obtained through the MT inversion of the earthquake is 4.01.

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Fig. 3 Moment tensor solution of the event 01.12.2020 estimated using single source inversion in ISOLA software

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The fault plane solution of this earthquake has been obtained from the MT inversions using only 4 seismological stations of the network as shown in Fig. 2. The fault parameters of this earthquake along with beach-ball presentation are given below. From the obtained fault plane solutions, it has been found that the solution is strikeslip fault with thrust components. The epicentral location of the earthquake is located around the HFT zone as shown in Figs. 3, 4, and 5. For an event, when the correlation and the DC% are showing the highest values in the vertical grid search, the focal depth estimate is considered to be found. Here, the values of the correlation coefficient of moment tensors with trial focal depths are shown in Fig. 4. Based on the above analysis, finally, following results have been obtained: Origin time: 04:11:50.83 Centroid location: Lat. 30.10 N, Long. 78.020 E Centroid depth (km): 45 km Seismic moment: 1.15e+15 N-m Moment magnitude (Mw ): 4.01 DC %: 49.1 CLVD%:50.9 Plane 1: Strike: 71, Dip: 80, Rake: 25 Plane 2: Strike: 336, Dip: 66, Rake: 169 P-axis: Azimuth: 201, Plunge: 10

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Fig. 4 Correlation of original and synthetic waveforms performed at a trial source depth. DC% represent by colors coding for 01/12/2020 earthquake and the focal mechanism during the inversion process for the optimum time step for this event (+0.36 s) and various depths are shown

Fig. 5 Map showing beach-ball representation of FPS solution of the event marked on the tectonics map of the region

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T-axis: Azimuth: 296, Plunge: 24 Type of solution: Strike-slip with thrust faulting

The MT solution being with the previously available solutions studied in the Chamoli region with M > 3.0 earthquake showed the strike-slip MT solutions are well supported by the occurrence of a couple of strike-slip fault plane solutions [11]. But being very less availability of noise-free data along HFT and the Ganga foredeep area, the solution provided in the present study seems important and useful.

6 Discussion Garhwal Himalaya is a region with active earthquakes and is well-known for its interplate seismic zone of thrust regime. A detailed and accurate analysis of seismic sources is always performed using moment tensor inversion. The earthquake has been generated due to both double-couple and non-double-couple mechanisms. The high value of CLVD shows that there may be a presence of complex tectonic features which could be responsible for this earthquake, and it could be a multiple fractures zone showing numerous cracks. The inversion is showing a good correlation between centroid location and hypocentral location. And, the value of variance reduction is near 1 confirming that the trial source modeling at this depth is giving the most stable result causing this earthquake. The value of CLVD is very much equal to double-couple, it may show the presence of a geothermal province (may be hot springs), or may be the result of a crack opening under stress and strain and causing this earthquake. Further, the MT solution provided in this study being consistent will be important as very less earthquake activity with magnitude three and above occur in the region particularly along HFT which is considered the boundary between the Siwalik foothills and the Ganga foredeep plains. The centroid depth will also give clue the depth of Moho in this zone. Acknowledgements Authors acknowledge with thanks to the THDC India Ltd., Rishikesh for sponsoring the study. Authors are also thankful to the Head, Department of Earthquake Engineering, Indian Institute of Technology Roorkee and Prof. M. L. Sharma, P.I. of study for providing the data for carrying out the study and allowing to use departmental facilities to complete the study. Mrs. Pooja Mahto also expresses gratitude toward Dr. Arup Sen, Dr. Sanjay Kr. Jain and other team members of the Seismological Observatory, Department of Earthquake Engineering, IIT Roorkee for providing the help and suggestions at various stages of this study.

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References 1. Aki, K., Richards, P.G.: University Science Books. Sausalito, California (2002) 2. Antolik, M., Dreger, D., & Romanowicz, B. (1999). Rupture processes of large deep-focus earthquakes from inversion of moment rate functions. Journal of Geophysical Research: Solid Earth, 104(B1), 863-894. 3. Benioff, H., Gutenberg, B., & Richter, C. F. (1954). Progress Report, Seismological Laboratory, California Institute of Technology, 1953. Eos, Transactions American Geophysical Union, 35(6), 979-987. 4. Coutant, O.: Program of numerical simulation AXITRA. LGIT (in French), Universite Joseph Fourier, Grenoble, Res. Rep (1989) 5. Gansser, A.: Geology of the Himalayas (1964) 6. Jain, A.K.: Continental subduction in the NW-Himalaya and Trans-Himalaya. Italian J. Geosci. 136(1), 89–102 (2017) 7. Kanaujia, J., Kumar, A., Gupta, S.C.: Three-dimensional velocity structure around Tehri region of the Garhwal Lesser Himalaya: constraints on the geometry of the underthrusting Indian plate. Geophys. J. Int. 205(2), 900–914 (2016) 8. Kayal, J.R., Ram, S., Singh, O.P., Chakraborty, P.K., Karunakar, G.: Aftershocks of the March 1999 Chamoli earthquake and seismotectonic structure of the Garhwal Himalaya. Bull. Seismol. Soc. Am. 93(1), 109–117 (2003) 9. Kikuchi, M., Kanamori, H.: Inversion of complex body waves—III. Bull. Seismol. Soc. Am. 81(6), 2335–2350 (1991) 10. Kumar, R., Gupta, S.C., Kumar, A.: Effect of azimuthal coverage of an earthquake on moment tensor solutions estimated by waveform inversion. Arab. J. Geosci. 8(8), 5713–5726 (2015) 11. Kumar, N., Paul, A., Mahajan, A.K., Yadav, D.K., Bora, C.: The Mw 5.0 Kharsali, Garhwal Himalayan earthquake of 23 July 2007: source characterization and tectonic implications. Curr. Sci. 102(12), 1674–1682 (2012) 12. Molnar, P.: A review of seismicity and the rates of active underthrusting and deformation at the Himalaya. J. Himalayan Geol. 1, 131–154 (1990) 13. Ni, J., Barazangi, M.: Seismotectonics of the Himalayan collision zone: geometry of the underthrusting Indian plate beneath the Himalaya. J. Geophys. Res.: Solid Earth 89(B2), 1147–1163 (1984) 14. Paul, A., Prasath, A.R., Singh, R.: Slip heterogeneities evaluated for earthquakes M> 4.0 using waveform modelling in the Garhwal region of Central Seismic Gap in Northwest Himalaya, India. Him. Geol. 36, 153–160 (2015) 15. Prasath, R.A., Paul, A., Singh, S.: Upper crustal stress and seismotectonic of the Garhwal Himalaya using small-to-moderate earthquakes: implications to the local structures and free fluids. J. Asian Earth Sci. 135, 198–211 (2017) 16. Richards, A., Argles, T., Harris, N., Parrish, R., Ahmad, T., Darbyshire, F., Draganits, E.: Himalayan architecture constrained by isotopic tracers from clastic sediments. Earth Planet. Sci. Lett. 236(3–4), 773–796 (2005) 17. Seeber, L., Armbruster, J.G.: Great detachment earthquakes along the Himalayan arc and long-term forecasting. Earthquake Pred.: An Inter. Rev. 4, 259–277 (1981) 18. Sokos, E.N., Zahradnik, J.: ISOLA a Fortran code and a Matlab GUI to perform multiple-point source inversion of seismic data. Comput. Geosci. 34(8), 967–977 (2008) 19. Sokos, E., Zahradník, J.: Evaluating centroid-moment-tensor uncertainty in the new version of ISOLA software. Seismol. Res. Lett. 84(4), 656–665 (2013) 20. Srivastava, P., Mitra, G.: Thrust geometries and deep structure of the outer and lesser Himalaya, Kumaon and Garhwal (India): implications for evolution of the Himalayan fold-and-thrust belt. Tectonics 13(1), 89–109 (1994) 21. Valdiya, K.S.: The two intracrustal boundary thrusts of the Himalaya. Tectonophysics 66(4), 323–348 (1980) 22. Vavryˇcuk, V.: Inversion for parameters of tensile earthquakes. J. Geophys. Res.: Solid Earth. 106(B8), 16339–16355 (2001)

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23. Vavryˇcuk, V.: Tensile earthquakes: theory, modeling, and inversion. J. Geophys. Res.: Solid Earth 116(B12) (2011) 24. Zahradnik, J., Jansky, J., Plicka, V.: Detailed waveform inversion for moment tensors of M∼4 events: examples from the Corinth Gulf, Greece. Bull. Seismol. Soc. Am. 98(6), 2756–2771 (2008)

Local Seismicity Around Tehri Dam, Garhwal Himalaya M. L. Sharma, S. C. Gupta, J. P. Narayan, J. Das, A. Sen, S. K. Jain, A. K. Jindal, Subhash Patel, Prajawal Tandekar, Avichal Rastogi, Rajeev Vishnoi, Atul Jain, Virendra Singh, and S. K. Saxena

Abstract Tehri dam with a height of 260.5 m is the highest earth and rock fill dam in India. It is situated on the confluence of the Bhagirathi and the Bhilangana rivers in the Garhwal Himalaya and located in the highly strained region of the northwestern Himalaya. The region around Tehri dam lies in zone IV and V as per the seismic zoning map of India where the 1991 Uttarkashi earthquake Mb ~ 6.6 and the 1999 Chamoli earthquake Mb ~ 6.4 have occurred. The local seismicity in the environs of Tehri dam is being monitored for last more than two and half decades. For this purpose, a local seismological network was deployed by the Department of Earthquake Engineering in September 1993 under the scheme of Department of Science and Technology (DST). The network has been upgraded time to time. Presently, 18 stations of state-of-the-art seismological network is being operated around Tehri dam reservoir area. The spatial variation of local seismicity follows the trend of surface trace of MCT in the Garhwal Lesser Himalaya. The focal depth distribution of seismic events along and across the strike direction of the regional tectonic features reveal the confinement of activity within 10 to 15 km. In the present paper, attributes of local seismicity for more than two and half decades have been presented for Tehri dam. In addition, the changes in seismicity due to dam reservoir as a part of reservoir-induced seismicity (RIS) has been studied and no correlation of seismicity activity with the dam reservoir filling has been observed even after about seventeen years filling/drawdown of dam reservoir. Keywords Microearthquake · Seismicity · MBT · MCT

M. L. Sharma · S. C. Gupta (B) · J. P. Narayan · J. Das · A. Sen · S. K. Jain · A. K. Jindal · S. Patel · P. Tandekar · A. Rastogi Department of Earthquake Engineering, IIT Roorkee, Roorkee, Uttarakhand, India e-mail: [email protected] M. L. Sharma e-mail: [email protected] R. Vishnoi · A. Jain · V. Singh · S. K. Saxena THDC India Ltd, Rishikesh, Uttarakhand, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Shrikhande et al. (eds.), Proceedings of 17th Symposium on Earthquake Engineering (Vol. 4), Lecture Notes in Civil Engineering 332, https://doi.org/10.1007/978-981-99-1459-3_54

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1 Introduction Microearthquake networks are powerful tool in studying the nature and state of tectonic processes for mapping active faults for hazard evaluation. The modernization of seismological instrumentation has led to the development of seismological networks for monitoring of seismicity around important engineering projects such as HE projects. Such networks may contain different types of instrumentation depending on the objectives of the study. While broadband instruments are used to record the teleseismic and/or regional events, the short period seismometers are preferred to record the local events due to their high frequency contents. Such networks may also contain some of the strong motion recorders which are generally deployed in the dam bodies to study the response of the structure during the earthquake [1]. These instruments may become the part of the network when the data is collected at a central recording station or may be deployed as an individual unit. The basic processing of the data is carried out to locate the events and to estimate the size and the physical process in terms of the focal mechanism. The advance processing may contain the estimation of the temporal and spatial variation of seismicity, their source parameters and the physical modeling of the medium and the source in the region [2]. Further, specific studies may also be carried out which relates to the medium characterization in terms of its attenuation properties [3], 3D velocity structures [4], seismic hazard parameter estimations and the risk assessment for the region. The results of these studies are used to interpret them in terms of hazard for the region may it be for earthquake resistant designs or for studies like reservoir-induced seismicity and other by products being used by disaster managers. The Garhwal Himalaya that forms the part of north-western Himalayan mountain ranges is characterized by moderate to large sized earthquake activity. In the last two decades, two moderate sized earthquakes namely the Uttarkashi earthquake of 1991 and the Chamoli earthquake of 1999 were experienced by this region that falls in the Seismic Zone IV of seismic zoning map of India (IS: 1893-2002). To monitor the contemporary local seismicity covering parts of the Garhwal Himalaya, the earthquake data has been collected through the continuous operation of a local seismological network that commenced in the region during 1993. A general pattern of ambient tectonic strains prevailing in the region is attributed to the collision of India plate and Eurasia tectonic plate resulting in high level of inter-plate seismicity. This paper contains the various attributes of contemporary local seismicity around Tehri dam in the Garhwal Himalaya. Further, the paper also contains its seismic hazard parameter analysis along with its RIS behavior.

2 Seismological Network The region around Tehri dam falls in the Lesser Himalaya of the Garhwal Himalaya. It is composed of a mountain belt having an average elevation of about 2000 m and is

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made up of fossiliferous Riphean sediments overridden by several thrust sheets which have traveled from north to south in response to the geodynamic processes that gave birth to the Himalaya. To the south, the Lesser Himalaya is separated from the SubHimalaya by the Main Boundary Thrust (MBT), while in the north it is separated from the Higher Himalaya by the Main Central Thrust (MCT). The Main Central Thrust (MCT) is demarcated as a structural feature of great geotectonic significance and is marked as a boundary separating the Lesser Himalaya from the Higher Himalaya [5]. Besides the two prominent thrust boundaries trending northwest to southeast and running parallel to the Himalayan arc, there are structural features trending transverse to the Sub-Himalaya in the form of subsurface ridges and depressions, buried under the alluvium cover of the Indo-Gangetic Plains [6, 7]. Therefore, the location of Tehri dam that falls between two major tectonic features, the MBT and the MCT. Besides that, the region encompasses several local tectonic features, e.g., Sriguri thrust, Dunda thrust, Aglar thrust, Uttarkashi thrust, Srinagar thrust in the Garhwal Lesser Himalaya and transverse structural features in the Sub-Himalaya SW of dam site [8]. In view of presence of several tectonic features, structural features as well as the area around Tehri dam falls in seismic zone IV and V, as per the seismic zoning map of India [9], several local seismological networks were operated in the region time to time by several institutions to monitor the local seismicity of the region (e.g., Gaur et al. [10], Kumar et al. [11], Kumar [12], Khattri et al. [13], Kumar et al. [14]). The Department of Earthquake Engineering, IIT Roorkee has deployed its first seismological network in 1970 with three observatories. Later, a six-station local seismological network has been operated from 1987 onwards as part of DST project that later funded by the THDCIL since 1995. This network has been expanded and upgraded time to time and being operated continuously since last more than 27 years. Presently, a 18-station VSAT-based local seismological network is operative in the region around Tehri dam reservoir. Out of 18 stations, 9 stations are operated in radio telemetry mode while 9 stations are operated in VSAT mode and real time data is being received continuously to two central recording stations (CRSs) [15]. Figure 1 shows the locations of 18 stations of the seismological network along with the location of Tehri dam and reservoir area. The network has been deployed with the objective to collect small magnitude earthquake activity on long-term basis to study the contemporary local seismicity and to delineate active seismic source zones in the environs of Tehri dam reservoir and also to provide data of local seismicity in the region around Tehri dam before, during and after impounding of dam reservoir to allow to study the changes in the local seismicity if any, during and after impounding of Tehri dam reservoir.

3 Data Set The network, deployed by Department of Earthquake Engineering, IIT Roorkee and funded by the THDC India Limited, Rishikesh has acquired one of the most precious

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Fig. 1 Map showing 18-station seismological network around Tehri dam along with major tectonic features in the region. Tectonics after GSI [8]

data sets on local seismicity from the Garhwal Himalaya. Such data set is one of the assets for seismotectonic studies which is indispensable for seismic hazard and risk assessments. The present study is based on the local earthquake data collected continuously for last more than two and half decades from 1995 to 2021. Local earthquakes with magnitude two and above (ML ≥ 2.0) recorded within 200 km from each station, i.e., (S-P) time less than 25.0 s., have been considered for analysis. Only those events having standard errors less than 0.6 s in origin time (erms), less than 6.0 km in epicentral distance (erh) and less than 6.0 km focal depth (erz) have been considered estimation of hypocenter parameters, preparation of seismicity map and drawing fault plane solution, etc. The hypocenter parameters of events have been computed employing the HYPO71PC and HYPO71 program given in SEISAN computer program [16, 17] and using the velocity model given by Chander et al. [18], Kumar et al. [19], and Kanaujia et al. [20] for the region (Table 1). Table 1 Velocity model adopted in the study

P-wave velocity (km/sec) 3.0

Depth to the top of the layer (km) 0.0

5.2

1.0

6.0

16.0

7.91

46.0

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4 Data Analysis 4.1 Spatial Variation of Local Seismicity Based on the above analyses procedures, hypocenters of 2224 events having magnitude two and above and recorded within 200 km of Tehri dam (i.e., ML ≥ 2.0 and S-P ≤ 25 s) have been considered suitable for location and other interpretation. Based on the epicentral locations of these events, a seismicity map has been prepared and shown in Fig. 2. The distribution of seismic activity has brought out that the most of the contemporary local seismicity is non-uniformly distributed and occurs in the Lesser Himalaya and the Higher Himalaya (EQ: 2021-04). An endeavor has been made to associate the present seismicity with the existing tectonic features. Such correlation is important from seismic hazard point of view where the tectonic features have to be redefined as seismogenic features based on the contemporary seismicity [20]. The seismic hazard studies have been carried out in Himalayas using such data [21]. The seismicity associated with the existing features may include some of the seismicity part which is due to the features in their vicinity or may be considered as their interactions and/or transfer of stress regime during the dynamism of stress release process, i.e., earthquake occurrence in this area [22–25].

Fig. 2 Map showing epicenters of events around Tehri dam located during 1995 to 2021. Two depth cross-sections marked AA’ and BB’ are also shown. Tectonics after Seismotectonics Atlas [8]

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Based on the distribution (pl. see Fig. 2) about 85% of seismic activity is located along the segment of MCT and follow the trend of surface trace of the MCT. The distribution of activity defines about 400 km long and about 60 km wide NW– SE elongated local seismicity zone extending from the Himachal Himalaya in NW direction to Dharchula in the Kumaon Himalaya in SE direction, while 15% of remaining activity scattered between the Srinagar thrust and MBT in the Lesser Himalaya and between the MBT and HFT in the Sub-Himalaya as well as in the Indo-Gangetic plains.

4.2 Depth Distribution and Depth Sections of Seismicity The focal depth distribution of seismic activity shows that about 80% of activity occurred at shallow depths ranging between 5 and 20 km. There is a significant reduction in the occurrence of events having focal depths above 20 km (Fig. 3). Therefore, based on this sample of data set, it is attributed that there are maximum chances of occurrence of earthquake at shallow depth up to 20 km [26]. To study the depth-wise variation of seismic activity along traverses, two depth sections AA’ and BB’ are drawn. These traverses have been drawn along and across the major tectonic features in the region (Fig. 2). Hypocenters of events, confined to a narrow zone approximately ± 40 km wide on either side of these traverses, are projected onto a vertical plane passing through these traverses. Depth section AA’ passes in the close proximity of Tehri dam and runs across the regional strike directions of the MFT, MBT, MCT and Srinagar thrust (Figs. 2 and 5). The seismic activity along this section primarily confined to the three tectonic domains namely the Sub-Himalaya, the Lesser Himalaya and the Higher Himalaya. Fig. 3 Histograms showing depth distribution of seismic activity in the Garhwal Himalaya

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Fig. 4 Depth cross section of seismic activity along traverse AA’

Baring few events, most of the activity along this section confined to depth up to 50 km. The focal depth of events in the Sub-Himalaya between the HFT and MBT occur at around 12–50 km, while activity in the vicinity of dam site occurs at depth between 10 and 20 km. This activity seems to be related mainly to Srinagar thrust that dips in south-west direction. Further, NE of the section, the most of activity in the vicinity of MCT occurs at depths between near surface to 20 km, while further to the northeast of the MCT in the Higher Himalaya, events occur either at depth around 10–20 km or at depths above 45–55 km (Fig. 4). The activity at shallow depths around 10–15 km in the Lesser Himalaya and the Higher Himalaya seems to be associated with the upper part of the detachment surface of the under-thrusting Indian plate, whereas the activity at a level between 30 and 45 km seems to signify the depth of the brittle-ductile transition zone. The activity at this depth seems to define the geometry of the under-thrusting India plate. The depth section BB’ depicts the focal distribution of seismic activity along the regional strike direction of the major tectonic features in the region (Fig. 5). In this section, events are widely distributed all along section and focal depths of events vary from surface to 50 km forming the synclinal structure of activity close to the surface at both the end. Focal depths of most of events fall between 10 and 20 km. This activity occurs in the Lesser Himalaya either in the vicinity of the MCT or to the south of the MCT. Most of the activity that falls in the north of the MCT in the Higher Himalaya occurs at shallow depths between 10 and 15 km. Therefore, it is observed that the activity in the Garhwal Lesser Himalaya occurs at an average depth of about 15 km, whereas in the Higher Himalaya, the activity occurs at an average depth of about 10 to 12 km. The events at very shallow depths (