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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.
- 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
Researchers, students, professionals, and engineers in the geosciences, civil engineering fields, and environmental sciences (i.e., atmospheric science)
GEOCOMPUTATION AND GEOPHYSICS
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
2. Global particle swarm optimization technique in the interpretation of residual magnetic anomalies due to simple geo-bodies with idealized structure
3. Emerging Techniques to Simulate Strong Ground Motion
4. Earthquakes: Basics of seismology and seismic computational techniques
Naresh Kumar Sr., Devajit Hazarika and Kalachand Sain
5. Significance and limit of electrical resistivity survey for detection sub surface cavity: a case study from, Southern Western Ghats, India
6. A review on Geophysical parameters comparison in Garhwal and Kumaun Himalaya region, India
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
10. Liquefaction as a seismic hazard: scales, examples and analysis
11. Landslide Prediction and Field Monitoring for Darjeeling Himalayas: A case study from Kalimpong
12. Improvement of Shear Strength of Cohesive Soils by Additives: A Review
Amir H. Gandomi
13. Static stress change from 6 February, 2017 (M 5.8) earthquake Northwestern Himalaya, India
Mahesh prasad Parija
14. Remote Sensing for Geology-Geophysics
Surajit Panda and Krishnendu Banerjee
GEOCOMPUTATION AND GEOSCIENCE
15. Prediction of Petrophysical Parameters using Probabilistic Neural Network Technique
Nagendra Pratap Singh
16. Interpretation and Resolution of multiple structures from residual gravity anomaly data and application to mineral exploration
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
19. Body tide prediction
20. Seasonal Characterization of glacier meltwater storage and drainage from Garhwal Himalaya: Time Series Analysis of Hydrometeorological data
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
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
23. Issues of Resolution of Data for GIS-based Integrated Watershed Modelling: A Case Study
- No. of pages:
- © Elsevier 2021
- 27th February 2021
- Paperback ISBN:
Professor Pijush Samui is currently an 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. He developed a new method for prediction of response of soil during an earthquake. He has given charts for prediction of response of soil during an earthquake and developed equations for prediction of lateral spreading of soil due to earthquake. He developed equations for determination of seismic liquefaction potential of soil based on strain energy and prediction of seismic attenuation. He developed efficient models for prediction of magnitude of reservoir induced earthquake. He has developed models for determination of medical waste generation in hospitals with equations used for practical purpose. The developed models can be used for clean India project. He determined frequency effects on liquefaction by using Shake Table. He has applied AI techniques for determination of bearing capacity and settlement of foundation and equations for determination of bearing capacity and settlement of shallow foundation. He also developed equations for determination of compression index and angle of shearing resistance of soil. he developed equations for prediction of uplift capacity of suction caisson. He also developed equations for determination of fracture parameters of concrete. His active research activity is evident from his extensive citation of publications in google scholar (total frequency of 1280) with H-Index of 22. Dr Samui has published journal papers, books/book chapters and peer reviewed conference papers that involved co-authors from Australia, India, Korea and several other nations. He is also holding the position of Visiting Professor at Far East Federal University (Russia).
Associate Professor, National Institute of Technology, Patna, India
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.
Professor, School of Geosciences and Director of Geo-spatial Analytics Lab, University of South Florida, St. Petersburg, FL, United States
Dieu Tien Bui is currently an Associate Professor at the GIS group, University of South-Eastern Norway, Norway. An editor for several scientific journals, he received his M.Sc. degree in Cartographic Engineering from Hanoi University of Mining and Geology, Hanoi, Vietnam, in 2004, and his Ph.D. in Geomatics from the Norwegian University of Life Sciences, Aas, Norway, in 2013. He was a postdoctoral researcher at the Norwegian University of Life Sciences from 2013 to 2014. In 2007-2008, he was a geospatial analyst at Ugland IT Group, a geographic information services company in Lysaker, Oslo, Norway. He has more than 120 publications to his credit, including 85 articles published in Science Citation Index (SCI/SCIE) scientific journals. His research interests include GIS, remote sensing, and artificial intelligence and machine learning for natural hazards and environmental problems such as landslides, floods, forest fires, ground biomass, and structural displacement.
Professor, Geographic Information System group, University of South-Eastern Norway, Bø i Telemark, Norway
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