Computational Geo-Electromagnetics: Methods, Models, and Forecasts, Volume Five in the Computational Geophysics series, is devoted to techniques for building of geoelectrical models from electromagnetic data, featuring Bayesian statistical analysis and neural network algorithms. These models are applied to studying the geoelectrical structure of famous volcanoes (i.e., Vesuvio, Kilauea, Elbrus, Komagatake, Hengill) and geothermal zones (i.e., Travale, Italy; Soultz-sous-Forets, Elsace). Methodological recommendations are given on electromagnetic sounding of faults as well as geothermal and hydrocarbon reservoirs. Techniques for forecasting of petrophysical properties from the electrical resistivity as proxy parameter are also considered.
Computational Geo-Electromagnetics: Methods, Models, and Forecasts offers techniques and algorithms for building geoelectrical models under conditions of rare or irregularly distributed EM data and/or lack of prior geological and geophysical information. This volume also includes methodological guidelines on interpretation of electromagnetic sounding data depending on goals of the study. Finally, it details computational algorithms for using electrical resistivity for properties beyond boreholes.
1. 1.1. Introduction - p. 3 2. 1.2. Methods of integral equations - p. 4 3. 1.3. Methods of differential equations - p. 7 4. 1.4. Hybrid schemes - p. 12 5. 1.5. Analog (physical) modeling approaches - p. 14 6. 1.6. Balance technique for EM field computation - p. 15 7. 1.7. Method of the EM field computation in axially symmetrical media - p. 24 8. 1.8. Conclusions - p. 36 9. 2. Three-dimensional Bayesian statistical inversion - p. 47 10. 3. Methodology of the neural network estimation of the model macro-parameters - p. 73 11. 4. Building of 3-D geoelectrical models at the lack of magnetotelluric data - p. 115 12. 5. Methods for joint inversion and analysis of EM and other geophysical data - p. 133 13. Part 2. Models of geological medium - p. 164 14. Introduction - p. 165 15. 6. Electromagnetic study of geothermal areas - p. 167 16. 7. 3-D magnetotelluric sounding of volcanic interiors: methodological aspects - p. 207 17. 8. A conceptual model of the Earth’s crust of Icelandic type - p. 243 18. 9. Conceptual model of a lens in the upper crust (Northern Tien Shan case study) - p. 277 19. 10. Conceptual model of the copper–porphyry ore formation (Sorskoe copper–molybdenum ore deposit case study) - p. 311 20. 11. Electromagnetic sounding of hydrocarbon reservoirs - p. 331 21. Part 3. Forecasting petrophysical properties of rocks - p. 345 22. 12. Temperature forecasting from electromagnetic data - p. 349 23. 13. Recovering seismic velocities and electrical resistivity from the EM sounding data and seismic tomography - p. 393 24. 14. Porosity forecast from EM sounding data and resistivity logs - p. 415 25. Appendix A. Empirical formulas relating electrical conductivity, seismic velocities, and porosity - p. 431 26. Index - p. 439
Computational Geo-Electromagnetics: Methods, Models, and Forecasts, Volume Five in the Computational Geophysics series, is devoted to techniques for building of geoelectrical models from electromagnetic data, featuring Bayesian statistical analysis and neural network algorithms. These models are applied to studying the geoelectrical structure of famous volcanoes (i.e., Vesuvio, Kilauea, Elbrus, Komagatake, Hengill) and geothermal zones (i.e., Travale, Italy; Soultz-sous-Forets, Elsace). Methodological recommendations are given on electromagnetic sounding of faults as well as geothermal and hydrocarbon reservoirs. Techniques for forecasting of petrophysical properties from the electrical resistivity as proxy parameter are also considered.
Computational Geo-Electromagnetics: Methods, Models, and Forecasts offers techniques and algorithms for building geoelectrical models under conditions of rare or irregularly distributed EM data and/or lack of prior geological and geophysical information. This volume also includes methodological guidelines on interpretation of electromagnetic sounding data depending on goals of the study. Finally, it details computational algorithms for using electrical resistivity for properties beyond boreholes.
1. 1.1. Introduction - p. 3 2. 1.2. Methods of integral equations - p. 4 3. 1.3. Methods of differential equations - p. 7 4. 1.4. Hybrid schemes - p. 12 5. 1.5. Analog (physical) modeling approaches - p. 14 6. 1.6. Balance technique for EM field computation - p. 15 7. 1.7. Method of the EM field computation in axially symmetrical media - p. 24 8. 1.8. Conclusions - p. 36 9. 2. Three-dimensional Bayesian statistical inversion - p. 47 10. 3. Methodology of the neural network estimation of the model macro-parameters - p. 73 11. 4. Building of 3-D geoelectrical models at the lack of magnetotelluric data - p. 115 12. 5. Methods for joint inversion and analysis of EM and other geophysical data - p. 133 13. Part 2. Models of geological medium - p. 164 14. Introduction - p. 165 15. 6. Electromagnetic study of geothermal areas - p. 167 16. 7. 3-D magnetotelluric sounding of volcanic interiors: methodological aspects - p. 207 17. 8. A conceptual model of the Earth’s crust of Icelandic type - p. 243 18. 9. Conceptual model of a lens in the upper crust (Northern Tien Shan case study) - p. 277 19. 10. Conceptual model of the copper–porphyry ore formation (Sorskoe copper–molybdenum ore deposit case study) - p. 311 20. 11. Electromagnetic sounding of hydrocarbon reservoirs - p. 331 21. Part 3. Forecasting petrophysical properties of rocks - p. 345 22. 12. Temperature forecasting from electromagnetic data - p. 349 23. 13. Recovering seismic velocities and electrical resistivity from the EM sounding data and seismic tomography - p. 393 24. 14. Porosity forecast from EM sounding data and resistivity logs - p. 415 25. Appendix A. Empirical formulas relating electrical conductivity, seismic velocities, and porosity - p. 431 26. Index - p. 439
