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Coding and Decoding: Seismic Data: The Concept of Multishooting 2nd edition, Volume 1 [Pehme köide]

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(Faculty of Petroleum Geology, Texas A&M University, College Station, USA)
  • Formaat: Paperback / softback, 718 pages, kõrgus x laius: 235x191 mm, kaal: 1380 g
  • Sari: Computational Geophysics
  • Ilmumisaeg: 07-Dec-2017
  • Kirjastus: Elsevier Science Publishing Co Inc
  • ISBN-10: 0128110988
  • ISBN-13: 9780128110980
Teised raamatud teemal:
Coding and Decoding: Seismic Data: The Concept of Multishooting 2nd edition, Volume 1Suurem pilt
  • Formaat: Paperback / softback, 718 pages, kõrgus x laius: 235x191 mm, kaal: 1380 g
  • Sari: Computational Geophysics
  • Ilmumisaeg: 07-Dec-2017
  • Kirjastus: Elsevier Science Publishing Co Inc
  • ISBN-10: 0128110988
  • ISBN-13: 9780128110980
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780128110980.html
Märksõnad:

Coding and Decoding Seismic Data: The Concept of Multishooting, Second Editionoffers a thorough investigation of modern techniques for collecting, simulating, and processing multishooting data. Currently, the acquisition of seismic surveys is performed as a sequential operation in which shots are computed separately, one after the other. The cost of performing various shots simultaneously is almost identical to that of one shot; thus, the benefits of using the multishooting approach for computing seismic surveys are enormous.

By using this approach, the longstanding problem of simulating a three-dimensional seismic survey can be reduced to a matter of weeks. Providing both theoretical and practical explanations of the multishooting approach, including case histories, this book is an essential resource for exploration geophysicists and practicing seismologists.

  • Investigates how to collect, stimulate, and process multishooting data
  • Addresses the improvements in seismic characterization and resolution that can be expected from multishooting data
  • Provides information for the oil and gas exploration and production business on the benefits of multishooting data, helping to influence their day-to-day surveying techniques
  • Includes robust decoding methods of undetermined mixtures, nonlinear decoding, the use of constraints in decoding processes, and nonlinear imaging of undecoded data
  • Includes access to a companion site with answers to questions posed in the book

Muu info

Comprehensive resource on how to collect, simulate, and process multishooting data that also explores tactics to improve seismic characterization and resolution
Preface (First Edition) xi
Preface (Second Edition) xv
1 Introduction to Multishooting: Challenges and Rewards
1.1 Dimensions and Notation Conventions
6(3)
1.1.1 Coordinate systems
6(1)
1.1.2 Dimensions of heterogeneous media
7(1)
1.1.3 Notation conventions
7(1)
1.1.4 The f-x and f-k domains
8(1)
1.2 Scattering Experiments in Petroleum Seismology
9(18)
1.2.1 Principles of seismic acquisition
11(6)
1.2.2 Seismic data
17(2)
1.2.3 Shot, receiver, midpoint, and offset gathers
19(4)
1.2.4 Multiazimuthal data
23(4)
1.3 Acquisition of Multishot Data
27(8)
1.3.1 Multiazimuth surveys
27(1)
1.3.2 Flip-flop acquisition
28(1)
1.3.3 Source encoding: the marine style
29(2)
1.3.4 Source encoding: the land-vibroseis style
31(2)
1.3.5 The challenges of multishooting acquisition
33(2)
1.4 Processing of Multishot Data
35(10)
1.4.1 Reciprocity theorems
35(3)
1.4.2 Cross-talk and challenges of the decoding process
38(5)
1.4.3 The challenges of imaging multishot data without decoding
43(2)
1.5 The Nonlinear Elasticity and the Superposition Principle
45(9)
1.5.1 Linear and nonlinear media
45(1)
1.5.2 Second-order nonlinear media
46(3)
1.5.3 Volterra series
49(5)
1.6 The Cocktail-Party Problem: A Multidisciplinary Problem
54(8)
1.6.1 A brief review of the cocktail-party problem
54(2)
1.6.2 Coding and decoding in communication theory
56(2)
1.6.3 Processing of multishot data without decoding
58(1)
1.6.4 Nearly simultaneous earthquakes
58(3)
1.6.5 Nearly simultaneous sources of volcanic activities
61(1)
1.7 Scope and Content of This Book
62(7)
Exercises
63(6)
2 Decoding of Linear Instantaneous Mixtures
2.1 Seismic Data Representation as Random Variables
69(23)
2.1.1 Examples of random variables
69(7)
2.1.2 From seismic signals to seismic random variables
76(1)
2.1.3 Probability-density function (PDF) of seismic random variables
77(4)
2.1.4 Moments and cumulants of seismic random variables
81(6)
2.1.5 Negentropy: a measurement of non-Gaussianity
87(5)
2.2 Uncorrelatedness and Independence
92(35)
2.2.1 Joint probability-density functions and Kullback-Leibler divergence
93(8)
2.2.2 Joint moments and joint cumulants
101(5)
2.2.3 Uncorrelatedness and whiteness of random variables
106(1)
2.2.4 Independence of random variables
107(2)
2.2.5 Analysis of uncorrelatedness and independence with scatterplots
109(14)
2.2.6 Whitening
123(4)
2.3 ICA Decoding
127(43)
2.3.1 Decoding by maximizing contrast functions
128(17)
2.3.2 Decoding by cumulant--tensor diagonalization
145(6)
2.3.3 ICA decoding by negentropy maximizing
151(7)
2.3.4 ICA decoding methods for noisy mixtures
158(6)
2.3.5 Decoding by joint diagonalization of the autocovariance matrices
164(6)
2.4 Constrained Independent Component Analysis (cICA) Decoding
170(28)
2.4.1 Negentropy maximization
171(9)
2.4.2 Cumulant-tensor diagonalization
180(4)
2.4.3 Nonorthogonal cICA, based on maximum likelihood
184(2)
Exercises
186(12)
3 Decoding of Linear Convolutive Mixtures
3.1 Motivation and Foundation for Working in the T-F-X Domain
198(16)
3.1.1 Convolutive mixtures in the T-X domain
205(5)
3.1.2 Convolutive mixtures in the F-X domain
210(2)
3.1.3 Convolutive mixtures in the T-F-X domain
212(2)
3.2 Statistics of Complex Random Variables and Vectors
214(44)
3.2.1 The complex-valued gradient and the Hessian matrix
214(6)
3.2.2 Statistics of complex random variables
220(14)
3.2.3 Statistics of complex random vectors
234(15)
3.2.4 An analysis of the statistical independence of seismic data in the T-F-X domain
249(9)
3.3 Decoding in the T-F-X Domain
258(39)
3.3.1 Whiteness of complex random variables
258(1)
3.3.2 Decoding by negentropy maximization of complex random vectors
259(10)
3.3.3 Permutation inconsistency problem
269(2)
3.3.4 Cascaded and constrained ICA approaches
271(2)
3.3.5 Numerical examples
273(24)
3.4 Decoding In other Domains and Nonlinear Mixtures
297(21)
3.4.1 Decoding in the F-X domain
297(1)
3.4.2 Decoding in the T-X domain
298(7)
3.4.3 Decoding of convolutive post-nonlinear mixtures
305(1)
Exercises
306(12)
4 Decoding of Underdetermined Mixtures
4.1 Estimation of the Mixing Matrix
318(33)
4.1.1 Histograms of data-concentration directions
321(7)
4.1.2 Expectation maximization
328(11)
4.1.3 Histogram approach in the T-F-X domain
339(1)
4.1.4 Isolated single-shot estimation in the T-F-X domain
340(8)
4.1.5 Isolated single-shot estimation in the T-F domain
348(3)
4.2 ICA- and Sparsity-Based Decoding: 2M3S
351(12)
4.2.1 Combinatory search
351(3)
4.2.2 ICA-based decoding: formulation
354(1)
4.2.3 ICA-based decoding: examples
355(3)
4.2.4 The compressive sensing relationship with multishooting
358(5)
4.3 Decoding Using Denoising Tools: 1M2S and 1M4S
363(40)
4.3.1 Phase encoding and cross-talk
363(8)
4.3.2 Basic formulation of phase encoding
371(2)
4.3.3 ICA-based decoding
373(4)
4.3.4 Denoising-based decoding: C1-norm and total variations
377(4)
4.3.5 Compressive sensing and phase decoding data
381(5)
4.3.6 Denoising-based decoding: dictionary filtering
386(8)
4.3.7 Denoising-based decoding: median filtering
394(4)
4.3.8 Decoding with reference shots
398(5)
4.4 Multicomponent-Based Decoding: 1M2S, 1MS4, and 1M8S
403(4)
4.4.1 Simultaneous deghosting and decoding of multishot data
403(2)
4.4.2 Decoding of deghosted multishot data
405(2)
4.5 Array-Based Decoding
407(7)
4.6 ICA-Based Decoding: 1M16S
414(15)
4.6.1 Decoding in the T-F-X domain with a known mixing matrix
414(1)
4.6.2 Decoding based on isolated single-shot estimations
415(2)
4.6.3 Decoding in the T-F-X domain with an unknown mixing matrix
417(5)
Exercises
422(7)
5 Decoding of Nonlinear Mixtures
5.1 Models of Nonlinear Mixtures
429(5)
5.1.1 The general nonlinear mixing model
429(2)
5.1.2 Post-nonlinear (PNL) mixtures
431(1)
5.1.3 Multilayer perceptron model
432(2)
5.1.4 Convolutive nonlinear mixtures
434(1)
5.2 Scatterplots of Nonlinear Mixtures
434(12)
5.2.1 2D scatterplots
434(6)
5.2.2 3D scatterplots
440(6)
5.3 Decoding of Post-Nonlinear Mixtures
446(44)
5.3.1 Alternating conditional expectations (ACE)
446(30)
5.3.2 Inverse of a specified cumulative distribution function (ICDF)
476(7)
5.3.3 Geometrical-transformation approach
483(7)
5.4 Kernel-Based Decoding of Nonlinear Mixtures
490(28)
5.4.1 The making of nonlinear mixtures
491(1)
5.4.2 A brief background on linear principal component analysis
491(3)
5.4.3 A nonlinear form of principal component analysis
494(10)
5.4.4 Construction of linearized mixtures and decoding
504(14)
5.5 Kernel Canonical Correlation Analysis
518(18)
5.5.1 Canonical correlation analysis
518(7)
5.5.2 Kernel canonical correlation analysis
525(7)
Problems
532(4)
6 Imaging of Multishot Data Without Decoding
6.1 Linearized Inversion
536(54)
6.1.1 The NMF-based demultiple
537(20)
6.1.2 The sea-level-based demultiple
557(9)
6.1.3 Migration/inversion
566(8)
6.1.4 Velocity estimation
574(8)
6.1.5 Tomography
582(8)
6.2 Nonlinear Inversion
590(11)
6.2.1 Automated imaging
590(8)
6.2.2 Seismic imaging machine
598(3)
6.3 Modeling. Decoding, and Imaging in Snapshot Domain
601(8)
6.3.1 Multicomponent recordings
601(1)
6.3.2 Alternating conditional expectations (ACE)
602(2)
Problems
604(5)
A Some Background on Sparsity Optimization
A.1 C0-Norms
609(4)
A.1.1 C2-minimization
609(2)
A.1.2 C0-minimization: definition
611(1)
A.1.3 Various ways of measuring sparsity
611(1)
A.1.4 C0-minimization: uniqueness
612(1)
A.2 C1-Norm
613(21)
A.2.1 An example of a linear system
613(1)
A.2.2 Convex and nonconvex optimization problems
614(4)
A.2.3 A practical implementation of the C1-minimization
618(2)
A.2.4 C1-optimization of complex-valued data
620(14)
B ICA Decomposition
C Nonnegative Matrix Factorization
C.1 Lee-Seung Matrix Factorization Algorithm
634(14)
C.1.1 Mathematical formulation
634(4)
C.1.2 Numerical illustrations of the forward and inverse transform
638(4)
C.1.3 Selecting the number of elements of a dictionary
642(2)
C.1 4 Nonnegative matrix factorization with auxiliary constraints
644(3)
C.1.5 NMF optimization criteria
647(1)
C.2 Other Nonnegative Matrix Factorization Algorithms
648(11)
C.2.1 Project-gradient algorithm
649(4)
C.2.2 Alternating least-squares algorithm
653(6)
C.3 Decoding Challenges
659(2)
D Nonnegative Tensor Factorization
D.1 PARAFAC Decomposition Model
661(6)
D.2 Tucker Tensor Factorization
667(4)
E A Review of 3D Finite-Difference Modeling
E.1 Basic Equations for Elastodynamic Wave Motion
671(1)
E.2 Discretization In Both Time and Space
672(2)
E.3 Staggered-Grid Implementation
674(4)
E.4 Stability of the Staggered-Grid Finite-Difference Modeling
678(1)
E.5 Grid Dispersion in Finite-Difference Modeling
678(1)
E.6 Boundary Conditions
679(2)
Bibliography 681(12)
Index 693
Dr. Luc Ikelle is a Professor in Geology and Geophysics at Texas A&M University. He received his PhD in Geophysics from Paris 7 University in 1986 and has sense cultivated expertise in: seismic data acquisition, modeling, processing, and interpretation for conventional and unconventional energy production; inverse problem theory, signal processing, linear and nonlinear elastic wave propagation, linear and nonlinear optics, and continuum and fracture mechanics. His research interests include a combined analysis of petroleum systems, earthquakes, and volcanic eruptions based on geology, geophysics, statistical modeling, and control theory.He is a founding member of Geoscientists Without Borders, for which he received an award from SEG in 2010. He is a member of the editorial board of the Journal of Seismic Exploration and has published 107 refereed publications in international journals.