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Basics of Computational Geophysics - 1st Edition - ISBN: 9780128205136, 9780128209011

Basics of Computational Geophysics

1st Edition

Editors: Pijush Samui Barnali Dixon Dieu Tien Bui
Paperback ISBN: 9780128205136
eBook ISBN: 9780128209011
Imprint: Elsevier
Published Date: 27th November 2020
Page Count: 432
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Description

Basics of Computational Geophysics provides a one-stop, collective resource for practitioners on the different techniques and models in geoscience, their practical applications, and case studies. The reference provides the modeling theory in an easy-to-read format that is verified with onsite models for specific regions and scenarios, including the use of big data and artificial intelligence. This book offers a platform whereby readers will learn theory, practical applications, and the comparison of real-world problems surrounding geomechanics, modeling and optimizations.

Key Features

  • Covers various advanced computational techniques for solving different problems in geophysics, including the use of Big Data and artificial intelligence
  • Includes case studies that provide examples surrounding practical applications
  • Provides an assessment of the capabilities of commercial software

Readership

Researchers, students, professionals, and engineers in the geosciences, civil engineering fields, and environmental sciences (i.e., atmospheric science)

Table of Contents

Part I: COMPUTATION & GEOPHYSICS APPLICATIONS

1. Synthetic ground motions of the 2005 Kashmir M7.6 earthquake at the bedrock and at surface using stochastic dynamic finite fault modelling with a dynamic corner

Hamid Sana

2. Global particle swarm optimization technique in the interpretation of residual magnetic anomalies due to simple geo-bodies with idealized structure

Arkoprovo Biswas and Anand Singh

3. Emerging Techniques to Simulate Strong Ground Motion

Sandeep Arora, Parveen Kumar and A. Joshi

4. Earthquakes: Basics of seismology and seismic computational techniques

Naresh Kumar Sr., Devajit Hazarika Sr. and Kalachand Sain

5. Significance and limit of electrical resistivity survey for detection sub surface cavity: a case study from, Southern Western Ghats, India

Mayank Joshi, Alka Gond, Prasobh. P. Rajan, B. S. S, Padma Rao B and Vivekanandan Nandakumar

6. A review on Geophysical parameters comparison in Garhwal and Kumaun Himalaya region, India

Sandeep Arora and Parveen Kumar

7. Liquefaction Susceptibility of High Seismic region of Bihar considering Fine Content

Sunita Kumari and Sufyan Ghani

8. Evaluating the reliability of various geospatial prediction models in landslide risk zoning

Chalantika L. Salui

9. Fractals and Complex networks Applied to Earthquakes

Denisse Pasten

10. Liquefaction as a seismic hazard: scales, examples and analysis

Hamid Sana

11. Landslide Prediction and Field Monitoring for Darjeeling Himalayas: A case study from Kalimpong

Neelima Satyam

12. Improvement of Shear Strength of Cohesive Soils by Additives: A Review

Amir H. Gandomi and Tamur Salik

13. Static stress change from 6 February, 2017 (M 5.8) earthquake Northwestern Himalaya, India

Mahesh prasad Parija, Arkoprovo Biswas and Shubhasmita Biswal

14. Remote Sensing for Geology-Geophysics

Surajit Panda and Krishnendu Banerjee

PART II: COMPUTATION & GEOSCIENCE APPLICATIONS

15. Prediction of Petrophysical Parameters using Probabilistic Neural NetworkTechnique

Nagendra Pratap Singh

16. Interpretation and Resolution of multiple structures from residual gravity anomaly data and application to mineral exploration

Arkoprovo Biswas

17. On fractal based estimations of soil subsidence.

Tatyana P. Mokritskaya and Anatolii Tushev

18. A Neural Network to predict spectral acceleration

Amir H. Gandomi, Ali R. Kashani, Mohsen Akhani and Charles V. Camp

19. Body tide prediction

Sung-Ho Na

20. Time series analysis of hydrometeorological data for the characterization of meltwater storage in glaciers of Garhwal Himalaya

Amit Kumar, Akshaya Verma, Rakesh Bhambri and Kalachand Sain

21. Trends in Frequency and Intensity of Tropical Cyclones in the Bay of Bengal: 1972-2015

OMVIR SINGH and Pankaj Bhardwaj

22. Application of machine learning models in hydrology: case study of stream temperature forecasting in the Drava River using coupled wavelet analysis and adaptive neuro-fuzzy inference systems model

Senlin Zhu, Marijana Hadzima-Nyarko and Ognjen Bonacci

Details

No. of pages:
432
Language:
English
Copyright:
© Elsevier 2020
Published:
27th November 2020
Imprint:
Elsevier
Paperback ISBN:
9780128205136
eBook ISBN:
9780128209011

About the Editors

Pijush Samui

Professor Pijush Samui is Associate Professor at National Institute of Technology, Patna, India. He is an established researcher in the application of Artificial Intelligence (AI) for solving different problems in engineering. Samui has published journal articles, peer reviewed conference papers, book chapters and 4 books. He is also holding the position of Visiting Professor at Far East Federal University (Russia).

Affiliations and Expertise

Associate Professor, National Institute of Technology, Patna, India

Barnali Dixon

Barnali Dixon is a professor and executive director of Initiative on Coastal Resilience and Adaptation (iCAR) and the director of Geospatial Analytics lab (G-SAL) at the University of South Florida. Her research interests include the development and application of spatially integrated decision support tools (SDST) using GIS, GPS and remote sensing tools for modeling and managing soil, land use and land-water interfaces (terrestrial sources and aquatics sinks, including coastal waters) using approximation tools. She is particularly interested in transdisciplinary modeling of land-water interface under climate change in the context of planning, adaptation, and resilience. I have secured over $1.5 million in funding, published 50+ refereed publications, nineteen monographs, and technical reports.

Affiliations and Expertise

Professor, School of Geosciences and Director of Geo-spatial Analytics Lab, University of South Florida, St. Petersburg, FL, United States

Dieu Tien Bui

Dieu Tien Bui is Professor in GIS, in the Department of Business and IT at the University of South-Eastern Norway, Norway. He obtained a Master of Engineering, at Hanoi University of Mining and Geology, Hanoi, Vietnam, a PhD at the Department of Mathematical Sciences and Technology (IMT), Norwegian University, and was postdoctoral researcher in the same department. His research interests include GIS, remote sensing, artificial intelligence and machine learning. He published journal and review articles, and book chapters. .

Affiliations and Expertise

Professor, Geographic Information System group, University of South-Eastern Norway, Norway

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