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Computational Geo-Electromagnetics: Methods, Models, and Forecasts, Volume 5 [Pehme köide]

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(Head, Lab EM Data Interpretation Methodology, Geoelectromagnetic Research Centre IPE RAS, Moscow, Russia)
  • Formaat: Paperback / softback, 462 pages, kõrgus x laius: 235x191 mm, kaal: 1060 g, 140 illustrations (70 in full color); Illustrations, unspecified
  • Sari: Computational Geophysics
  • Ilmumisaeg: 04-Feb-2020
  • Kirjastus: Elsevier Science Publishing Co Inc
  • ISBN-10: 0128196319
  • ISBN-13: 9780128196311
Teised raamatud teemal:
Computational Geo-Electromagnetics: Methods, Models, and Forecasts, Volume 5Suurem pilt
  • Formaat: Paperback / softback, 462 pages, kõrgus x laius: 235x191 mm, kaal: 1060 g, 140 illustrations (70 in full color); Illustrations, unspecified
  • Sari: Computational Geophysics
  • Ilmumisaeg: 04-Feb-2020
  • Kirjastus: Elsevier Science Publishing Co Inc
  • ISBN-10: 0128196319
  • ISBN-13: 9780128196311
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780128196311.html
Märksõnad:

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.

  • Provides algorithms for inversion of incomplete, rare or irregularly distributed EM data
  • Features methodological issues of building geoelectrical models
  • Offers techniques for retrieving petrophysical properties from EM sounding data and well logs
Preface xi
Part 1 Methodology of EM data interpretation
Chapter 1 3-D EM forward modeling techniques
3(44)
1.1 Introduction
3(1)
1.2 Methods of integral equations
4(3)
1.3 Methods of differential equations
7(5)
1.4 Hybrid schemes
12(2)
1.5 Analog (physical) modeling approaches
14(1)
1.6 Balance technique for EM field computation
15(9)
1.7 Method of the EM field computation in axially symmetrical media
24(12)
1.8 Conclusions
36(11)
References
37(10)
Chapter 2 Three-dimensional Bayesian statistical inversion
47(26)
2.1 Introduction
47(1)
2.2 Technique for solving inverse problem using Bayesian statistics
48(7)
2.3 Assessment of prior information and data effects on the inversion results
55(8)
2.4 Case study: modeling of the aquifer salinity assessment with AMT data
63(4)
2.5 Conclusions
67(6)
References
68(5)
Chapter 3 Methodology of the neural network estimation of the model macro-parameters
73(42)
3.1 Introduction
73(1)
3.2 Backpropagation technique
74(4)
3.3 Statement of the modeling problem
78(2)
3.4 Artificial Neural Network architecture
80(6)
3.5 Effect of the type, volume, and structure of the teaching data pool
86(9)
3.6 ANN generalization ability
95(1)
3.7 Effect of noise
96(2)
3.8 Case study: ANN reconstruction of the Minou fault parameters
98(13)
3.9 Conclusions
111(4)
References
111(4)
Chapter 4 Building of 3-D geoelectrical models at the lack of magnetotelluric data
115(18)
4.1 Introduction
115(1)
4.2 Single profile case
115(4)
4.3 Effect of additional profile
119(4)
4.4 Effect of using scalar archive data around profile (case study of Eastern Siberia profile)
123(6)
4.5 Conclusions
129(4)
References
130(3)
Chapter 5 Methods for joint inversion and analysis of EM and other geophysical data
133(34)
5.1 Introduction
133(2)
5.2 Simultaneous inversion
135(10)
5.3 Cooperative inversion
145(3)
5.4 Classification methods
148(10)
5.5 Conclusions
158(9)
References
160(7)
Part 2 Models of geological medium
Chapter 6 Electromagnetic study of geothermal areas
167(40)
6.1 Introduction
167(1)
6.2 Conceptual models of geothermal areas
168(2)
6.3 Factors affecting electrical resistivity of rocks
170(7)
6.4 EM imaging of geothermal areas
177(9)
6.5 Electromagnetic mapping faults and fracturing
186(6)
6.6 EM monitoring of the geothermal reservoirs
192(1)
6.7 Constraining locations for drilling boreholes
193(4)
6.8 Conclusions
197(10)
References
198(9)
Chapter 7 3-D magnetotelluric sounding of volcanic interiors: methodological aspects
207(36)
7.1 Introduction
207(1)
7.2 Geological noise and relief topography treatment (Kilauea volcano, Hawaii, case study)
208(4)
7.3 Fast 3-D inversion of MT data (Komagatake volcano, Japan, case study)
212(4)
7.4 Assessment of the magma chamber parameters (Vesuvius volcano, Italy, case study)
216(9)
7.5 Modeling of remote MT monitoring of the melt condition in the magma chamber
225(4)
7.6 Remote imaging magma chamber from MT sounding data and satellite photo (Elbrus volcano, Caucasus, case study)
229(9)
7.7 Conclusions
238(5)
References
239(4)
Chapter 8 A conceptual model of the Earth's crust of Icelandic type
243(34)
8.1 Introduction
243(1)
8.2 Geological and geophysical information
244(6)
8.3 Building of 3-D resistivity model
250(6)
8.4 Temperature recovering from EM data
256(3)
8.5 3-D temperature model
259(7)
8.6 Heat sources
266(2)
8.7 Seismicity sources
268(2)
8.8 Conceptual model of the crust
270(2)
8.9 Conclusions
272(5)
References
272(5)
Chapter 9 Conceptual model of a lens in the upper crust (Northern Tien Shan case study)
277(34)
9.1 Introduction
277(11)
9.2 Density model
288(3)
9.3 Model of lithotypes
291(2)
9.4 Temperature model
293(5)
9.5 Porosity and fluid saturation
298(3)
9.6 Conceptual model
301(4)
9.7 Conclusions
305(6)
References
306(5)
Chapter 10 Conceptual model of the copper-porphyry ore formation (Sorskoe copper-molybdenum ore deposit case study)
311(20)
10.1 Introduction
311(1)
10.2 Geological and geophysical setting
312(3)
10.3 Characteristics of the Sorskoe copper-molybdenum deposit
315(1)
10.4 Electromagnetic studies
316(4)
10.5 Seismic tomography
320(3)
10.6 3-D density model
323(1)
10.7 3-D lithology model
324(1)
10.8 Conceptual model of the deposit
325(3)
10.9 Conclusions
328(3)
References
328(3)
Chapter 11 Electromagnetic sounding of hydrocarbon reservoirs
331(18)
11.1 Introduction
331(1)
11.2 Mapping zones of hydrocarbon fluids migration
332(1)
11.3 Decreasing the probability of drilling dry holes
333(2)
11.4 Ranking drilling targets
335(1)
11.5 Oil or water?
336(2)
11.6 Estimation of porosity beyond boreholes
338(1)
11.7 Constraining spatial boundaries of a deposit
339(2)
11.8 Optimization of a working cycle
341(1)
11.9 Forecasting reservoir rock properties while drilling
341(3)
11.10 Conclusions
344(5)
References
344(5)
Part 3 Forecasting petrophysical properties of rocks
Chapter 12 Temperature forecasting from electromagnetic data
349(44)
12.1 Introduction
349(1)
12.2 Electromagnetic geothermometer
350(2)
12.3 Interpolation in the interwell space
352(8)
12.4 EM temperature extrapolation in depth
360(14)
12.5 Building temperature model from MT sounding data (Soultz-sous-Forets, France, case study)
374(13)
12.6 Conclusions
387(6)
References
388(5)
Chapter 13 Recovering seismic velocities and electrical resistivity from the EM sounding data and seismic tomography
393(22)
13.1 Introduction
393(2)
13.2 Geological setting
395(2)
13.3 Geophysical surveys
397(3)
13.4 Methodology of modeling
400(1)
13.5 Recovering of seismic velocities from electrical resistivity
401(6)
13.6 Recovering of electrical resistivity from seismic velocities
407(4)
13.7 Conclusions
411(4)
References
412(3)
Chapter 14 Porosity forecast from EM sounding data and resistivity logs
415(16)
14.1 Introduction
415(2)
14.2 Lithology and porosity data
417(2)
14.3 Electrical resistivity data
419(3)
14.4 Modeling methodology
422(1)
14.5 Porosity forecast in depth
423(1)
14.6 Porosity forecast in the interwell space
424(3)
14.7 Conclusions
427(4)
References
427(4)
Appendix A Empirical formulas relating electrical conductivity, seismic velocities, and porosity 431(8)
Index 439
With over 30 years geophysics experience, Dr. Spichaks main research interests include joint interpretation of electromagnetic and other geophysical data, indirect estimation of the Earths physical properties from the ground electromagnetic data, and computational electromagnetics. Spichak has authored and edited 8 books with Elsevier, including Electromagnetic Sounding of the Earth's Interior (2015). He is the winner of the Gamburtsev award for the monograph Magnetotelluric fields in three-dimensional models of geoelectrics” (1999) and the Schmidt medal for outstanding achievements in Geophysics (2010).