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.
1. Cover image - p. Cover 2. Title page - p. iii 3. Table of Contents - p. iii 4. Copyright - p. iv 5. List of Contributors - p. xi 6. Preface - p. xiii 7. Part I: Computation & geophysics applications - p. xiv 8. Chapter 1. Synthetic ground motions of the October 8, 2005 Kashmir earthquake (Mw 7.6): An inference to the site response and seismic hazard of Kashmir basin, NW Himalaya - p. 3 9. Chapter 2. Global particle swarm optimization technique in the interpretation of residual magnetic anomalies due to simple geo-bodies with idealized structure - p. 13 10. Chapter 3. Emerging techniques to simulate strong ground motion - p. 33 11. Chapter 4. Earthquakes: Basics of seismology and computational techniques - p. 47 12. Chapter 5. Significance and limit of electrical resistivity survey for detection sub surface cavity: A case study from, Southern Western Ghats, India - p. 81 13. Chapter 6. A review on geophysical parameters comparison in Garhwal and Kumaun Himalaya region, India - p. 95 14. Chapter 7. Liquefaction susceptibility of high seismic region of Bihar considering fine content - p. 105 15. Chapter 8. Evaluating the reliability of various geospatial prediction models in landslide risk zoning - p. 121 16. Chapter 9. Fractals and complex networks applied to earthquakes - p. 139 17. Chapter 10. Liquefaction as a seismic hazard: Scales, examples and analysis - p. 153 18. Chapter 11. Landslide prediction and field monitoring for Darjeeling Himalayas: A case study from Kalimpong - p. 165 19. Chapter 12. Improvement of shear strength of cohesive soils by additives: A review - p. 189 20. Chapter 13. Static stress change from February 6, 2017 (M 5.8) earthquake Northwestern Himalaya, India - p. 213 21. Chapter 14. Remote sensing for geology-geophysics - p. 223 22. Part II: Computation & geoscience applications - p. 264 23. Chapter 15. Prediction of petrophysical parameters using probabilistic neural network technique - p. 273 24. Chapter 16. Interpretation and resolution of multiple structures from residual gravity anomaly data and its application to subsurface mineralization - p. 293 25. Chapter 17. On fractal based estimations of soil subsidence - p. 319 26. Chapter 18. A neural network to predict spectral acceleration - p. 335 27. Chapter 19. Prediction of Earth tide - p. 351 28. Chapter 20. Time series analysis of hydrometeorological data for the characterization of meltwater storage in glaciers of Garhwal Himalaya - p. 373 29. Chapter 21. Trends in frequency and intensity of tropical cyclones in the Bay of Bengal: 1972–2015 - p. 389 30. Chapter 22. Application of machine learning models in hydrology: Case study of river temperature forecasting in the Drava River using coupled wavelet analysis and adaptive neuro-fuzzy inference systems model - p. 399 31. Index - p. 413
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.
1. Cover image - p. Cover 2. Title page - p. iii 3. Table of Contents - p. iii 4. Copyright - p. iv 5. List of Contributors - p. xi 6. Preface - p. xiii 7. Part I: Computation & geophysics applications - p. xiv 8. Chapter 1. Synthetic ground motions of the October 8, 2005 Kashmir earthquake (Mw 7.6): An inference to the site response and seismic hazard of Kashmir basin, NW Himalaya - p. 3 9. Chapter 2. Global particle swarm optimization technique in the interpretation of residual magnetic anomalies due to simple geo-bodies with idealized structure - p. 13 10. Chapter 3. Emerging techniques to simulate strong ground motion - p. 33 11. Chapter 4. Earthquakes: Basics of seismology and computational techniques - p. 47 12. Chapter 5. Significance and limit of electrical resistivity survey for detection sub surface cavity: A case study from, Southern Western Ghats, India - p. 81 13. Chapter 6. A review on geophysical parameters comparison in Garhwal and Kumaun Himalaya region, India - p. 95 14. Chapter 7. Liquefaction susceptibility of high seismic region of Bihar considering fine content - p. 105 15. Chapter 8. Evaluating the reliability of various geospatial prediction models in landslide risk zoning - p. 121 16. Chapter 9. Fractals and complex networks applied to earthquakes - p. 139 17. Chapter 10. Liquefaction as a seismic hazard: Scales, examples and analysis - p. 153 18. Chapter 11. Landslide prediction and field monitoring for Darjeeling Himalayas: A case study from Kalimpong - p. 165 19. Chapter 12. Improvement of shear strength of cohesive soils by additives: A review - p. 189 20. Chapter 13. Static stress change from February 6, 2017 (M 5.8) earthquake Northwestern Himalaya, India - p. 213 21. Chapter 14. Remote sensing for geology-geophysics - p. 223 22. Part II: Computation & geoscience applications - p. 264 23. Chapter 15. Prediction of petrophysical parameters using probabilistic neural network technique - p. 273 24. Chapter 16. Interpretation and resolution of multiple structures from residual gravity anomaly data and its application to subsurface mineralization - p. 293 25. Chapter 17. On fractal based estimations of soil subsidence - p. 319 26. Chapter 18. A neural network to predict spectral acceleration - p. 335 27. Chapter 19. Prediction of Earth tide - p. 351 28. Chapter 20. Time series analysis of hydrometeorological data for the characterization of meltwater storage in glaciers of Garhwal Himalaya - p. 373 29. Chapter 21. Trends in frequency and intensity of tropical cyclones in the Bay of Bengal: 1972–2015 - p. 389 30. Chapter 22. Application of machine learning models in hydrology: Case study of river temperature forecasting in the Drava River using coupled wavelet analysis and adaptive neuro-fuzzy inference systems model - p. 399 31. Index - p. 413
