Computational Geo electromagnetics Methods Models and Forecasts Volume 5 1st Edition by Viacheslav Spichak 0128208205 9780128208205

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ISBN-10 :  0128208205 

ISBN-13 : 9780128208205

Author:  Viacheslav V. Spichak 

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: Volume 5 1st table of contents:

Part 1. Methodology of EM data interpretation
1. 3-D EM forward modeling techniques
1.1. Introduction
1.2. Methods of integral equations
1.3. Methods of differential equations
1.4. Hybrid schemes
1.5. Analog (physical) modeling approaches
1.6. Balance technique for EM field computation
1.7. Method of the EM field computation in axially symmetrical media
1.8. Conclusions
2. Three-dimensional Bayesian statistical inversion
2.1. Introduction
2.2. Technique for solving inverse problem using Bayesian statistics
2.3. Assessment of prior information and data effects on the inversion results
2.4. Case study: modeling of the aquifer salinity assessment with AMT data
2.5. Conclusions
3. Methodology of the neural network estimation of the model macro-parameters
3.1. Introduction
3.2. Backpropagation technique
3.3. Statement of the modeling problem
3.4. Artificial Neural Network architecture
3.5. Effect of the type, volume, and structure of the teaching data pool
3.6. ANN generalization ability
3.7. Effect of noise
3.8. Case study: ANN reconstruction of the Minou fault parameters
3.9. Conclusions
4. Building of 3-D geoelectrical models at the lack of magnetotelluric data
4.1. Introduction
4.2. Single profile case
4.3. Effect of additional profile
4.4. Effect of using scalar archive data around profile (case study of Eastern Siberia profile)
4.5. Conclusions
5. Methods for joint inversion and analysis of EM and other geophysical data
5.1. Introduction
5.2. Simultaneous inversion
5.3. Cooperative inversion
5.4. Classification methods
5.5. Conclusions
Part 2. Models of geological medium
Introduction
6. Electromagnetic study of geothermal areas
6.1. Introduction
6.2. Conceptual models of geothermal areas
6.3. Factors affecting electrical resistivity of rocks
6.4. EM imaging of geothermal areas
6.5. Electromagnetic mapping faults and fracturing
6.6. EM monitoring of the geothermal reservoirs
6.7. Constraining locations for drilling boreholes
6.8. Conclusions
7. 3-D magnetotelluric sounding of volcanic interiors: methodological aspects
7.1. Introduction
7.2. Geological noise and relief topography treatment (Kilauea volcano, Hawaii, case study)
7.3. Fast 3-D inversion of MT data (Komagatake volcano, Japan, case study)
7.4. Assessment of the magma chamber parameters (Vesuvius volcano, Italy, case study)
7.5. Modeling of remote MT monitoring of the melt condition in the magma chamber
7.6. Remote imaging magma chamber from MT sounding data and satellite photo (Elbrus volcano, Caucasus, case study)
7.7. Conclusions
8. A conceptual model of the Earth’s crust of Icelandic type
8.1. Introduction
8.2. Geological and geophysical information
8.3. Building of 3-D resistivity model
8.4. Temperature recovering from EM data
8.5. 3-D temperature model
8.6. Heat sources
8.7. Seismicity sources
8.8. Conceptual model of the crust
8.9. Conclusions
9. Conceptual model of a lens in the upper crust (Northern Tien Shan case study)
9.1. Introduction
9.2. Density model
9.3. Model of lithotypes
9.4. Temperature model
9.5. Porosity and fluid saturation
9.6. Conceptual model
9.7. Conclusions
10. Conceptual model of the copper–porphyry ore formation (Sorskoe copper–molybdenum ore deposit case study)
10.1. Introduction
10.2. Geological and geophysical setting
10.3. Characteristics of the Sorskoe copper–molybdenum deposit
10.4. Electromagnetic studies
10.5. Seismic tomography
10.6. 3-D density model
10.7. 3-D lithology model
10.8. Conceptual model of the deposit
10.9. Conclusions
11. Electromagnetic sounding of hydrocarbon reservoirs
11.1. Introduction
11.2. Mapping zones of hydrocarbon fluids migration
11.3. Decreasing the probability of drilling dry holes
11.4. Ranking drilling targets
11.5. Oil or water?
11.6. Estimation of porosity beyond boreholes
11.7. Constraining spatial boundaries of a deposit
11.8. Optimization of a working cycle
11.9. Forecasting reservoir rock properties while drilling
11.10. Conclusions
Part 3. Forecasting petrophysical properties of rocks
12. Temperature forecasting from electromagnetic data
12.1. Introduction
12.2. Electromagnetic geothermometer
12.3. Interpolation in the interwell space
12.4. EM temperature extrapolation in depth
12.5. Building temperature model from MT sounding data (Soultz-sous-Forêts, France, case study)
12.6. Conclusions
13. Recovering seismic velocities and electrical resistivity from the EM sounding data and seismic tomography
13.1. Introduction
13.2. Geological setting
13.3. Geophysical surveys
13.4. Methodology of modeling
13.5. Recovering of seismic velocities from electrical resistivity
13.6. Recovering of electrical resistivity from seismic velocities
13.7. Conclusions
14. Porosity forecast from EM sounding data and resistivity logs
14.1. Introduction
14.2. Lithology and porosity data
14.3. Electrical resistivity data
14.4. Modeling methodology
14.5. Porosity forecast in depth
14.6. Porosity forecast in the interwell space
14.7. Conclusions

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