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Passive Imaging with Ambient Noise [Kõva köide]

4.00/5 (2 hinnangut Goodreads-ist)
(Université de Paris VII (Denis Diderot)), (Stanford University, California)
  • Formaat: Hardback, 306 pages, kõrgus x laius x paksus: 252x179x17 mm, kaal: 760 g, 16 Halftones, color; 15 Line drawings, unspecified; 32 Line drawings, color
  • Ilmumisaeg: 21-Apr-2016
  • Kirjastus: Cambridge University Press
  • ISBN-10: 110713563X
  • ISBN-13: 9781107135635
Teised raamatud teemal:
Passive Imaging with Ambient NoiseSuurem pilt
  • Formaat: Hardback, 306 pages, kõrgus x laius x paksus: 252x179x17 mm, kaal: 760 g, 16 Halftones, color; 15 Line drawings, unspecified; 32 Line drawings, color
  • Ilmumisaeg: 21-Apr-2016
  • Kirjastus: Cambridge University Press
  • ISBN-10: 110713563X
  • ISBN-13: 9781107135635
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781107135635.html
Märksõnad:
"Waves generated by opportunistic or ambient noise sources and recorded by passive sensor arrays can be used to image the medium through which they travel. Spectacular results have been obtained in seismic interferometry, which open up new perspectives in acoustics, electromagnetics, and optics. The authors present, for the first time in book form, a self-contained and unified account of correlation-based and ambient noise imaging. In order to facilitate understanding of the core material, they also address a number of related topics in conventional sensor array imaging, wave propagation in random media, and high-frequency asymptotics for wave propagation. Taking a multidisciplinary approach, the book uses mathematical tools from probability, partial differential equations and asymptotic analysis, combined with the physics of wave propagation and modelling of imaging modalities. Suitable for applied mathematicians and geophysicists, it is also accessible to graduate students in applied mathematics, physics, and engineering"--

Muu info

This multidisciplinary book provides a systematic introduction to the analysis of passive sensor array imaging using ambient noise sources.
Preface xi
1 Introduction and overview of the book 1(16)
1.1 Why passive, correlation-based imaging?
1(9)
1.1.1 Travel time estimation
2(1)
1.1.2 Applications of travel time estimation
3(1)
1.1.3 Reflector imaging
4(2)
1.1.4 Auxiliary array or virtual source imaging
6(2)
1.1.5 Passive synthetic aperture imaging
8(1)
1.1.6 Imaging with intensity cross correlations
9(1)
1.2
Chapter-by-chapter description of the book
10(7)
2 Green's function estimation from noise cross correlations 17(34)
2.1 The scalar wave equation and its Green's function
17(8)
2.1.1 The Sommerfeld radiation condition
19(1)
2.1.2 Reciprocity
20(1)
2.1.3 The Helmholtz-Kirchhoff identity
21(2)
2.1.4 Application to time reversal
23(2)
2.2 The scalar wave equation with noise sources
25(4)
2.3 Green's function estimation with a uniform distribution of sources in a homogeneous open medium
29(2)
2.4 Green's function estimation with an extended distribution of sources in an inhomogeneous open medium
31(3)
2.5 Green's function estimation with an extended distribution of sources in an inhomogeneous cavity
34(4)
2.6 Green's function estimation with a limited distribution of sources in a one-dimensional inhomogeneous medium
38(10)
2.6.1 The one-dimensional wave equation
39(4)
2.6.2 Reflection seismology
43(2)
2.6.3 Daylight imaging
45(3)
2.7 Conclusion
48(1)
2.A Appendix: the covariance of the empirical cross correlation
48(3)
3 Travel time estimation from noise cross correlations using stationary phase 51(17)
3.1 High-frequency wave propagation
52(1)
3.2 High-frequency asymptotic analysis of the Green's function in a homogeneous medium
53(1)
3.3 High-frequency asymptotic analysis of the Green's function in a smoothly varying medium
53(7)
3.3.1 An introduction to geometrical optics
53(2)
3.3.2 Ray solution of the eikonal equation
55(2)
3.3.3 Fermat's principle for the travel time
57(1)
3.3.4 Properties of the travel time
58(2)
3.4 High-frequency asymptotic analysis of the cross correlation
60(7)
3.5 Conclusion
67(1)
4 Overview of conventional sensor array imaging 68(26)
4.1 Passive array imaging of sources
68(6)
4.1.1 Data acquisition
68(1)
4.1.2 Imaging function
69(1)
4.1.3 The linear forward operator
69(1)
4.1.4 The adjoint operator
70(1)
4.1.5 Least squares inversion
71(2)
4.1.6 The reverse-time imaging function
73(1)
4.1.7 Kirchhoff migration (or travel-time migration)
74(1)
4.2 Passive array imaging of sources: resolution analysis
74(10)
4.2.1 Full-aperture array
75(1)
4.2.2 Partial-aperture array
75(8)
4.2.3 Summary of resolution analysis for passive source imaging
83(1)
4.3 Active array imaging of reflectors
84(8)
4.3.1 Data acquisition
84(1)
4.3.2 Source and reflector array imaging: comparison
85(1)
4.3.3 Modeling
85(1)
4.3.4 Nonlinear inversion
86(1)
4.3.5 Linearization of the forward problem
86(2)
4.3.6 Linearized inversion
88(1)
4.3.7 The reverse-time imaging function
89(2)
4.3.8 Kirchhoff migration (or travel-time migration)
91(1)
4.3.9 Summary of resolution analysis for active reflector imaging
91(1)
4.4 A remark about time-reversal experiments
92(1)
4.5 Conclusion
92(2)
5 Passive array imaging of reflectors using ambient noise illumination 94(12)
5.1 Imaging configurations of noise sources, sensors, and reflectors
94(2)
5.2 Stationary phase analysis of the cross correlation with reflectors
96(3)
5.3 Migration imaging of cross correlations
99(6)
5.3.1 Migration imaging with daylight illumination
100(1)
5.3.2 Migration imaging with backlight illumination
101(2)
5.3.3 Migration imaging with surround light illumination
103(2)
5.4 Conclusion
105(1)
6 Resolution analysis for passive array imaging using ambient noise illumination 106(30)
6.1 A comparison of reflector imaging with active and passive arrays
107(1)
6.2 Imaging by cross correlation of signals generated by ambient noise sources
108(3)
6.2.1 The wave equation with noise sources
108(1)
6.2.2 Statistical stability of the cross correlation function
108(1)
6.2.3 Passive sensor imaging
109(1)
6.2.4 Hypothesis of small decoherence time and correlation radius for the noise sources
110(1)
6.3 Structure of the cross correlations in a homogeneous medium
111(4)
6.3.1 The background Green's function
111(1)
6.3.2 The peaks of the cross correlation in the presence of a reflector
111(4)
6.4 Resolution analysis of correlation-based imaging
115(11)
6.4.1 The daylight imaging function
115(7)
6.4.2 The backlight imaging function
122(2)
6.4.3 Numerical simulations
124(1)
6.4.4 Role of illumination diversity
125(1)
6.5 Conclusion
126(1)
6.A Appendix: Proof of Proposition 6.2
126(2)
6.B Appendix: Proof of Propositions 6.4-6.5
128(4)
6.C Appendix: Proof of Proposition 6.6
132(4)
7 Travel time estimation using ambient noise in weakly scattering media 136(16)
7.1 Role of scattering in travel time estimation with cross correlations
136(2)
7.2 A model for the scattering medium
138(2)
7.3 Signal-to-noise ratio reduction and enhanced resolution due to scattering
140(2)
7.4 Use of fourth-order cross correlations
142(3)
7.5 Conclusion
145(1)
7.A Appendix: Complete expression of the average cross correlation
146(2)
7.B Appendix: Proof of Proposition 7.1
148(1)
7.C Appendix: Proof of Proposition 7.2
149(3)
8 Correlation-based reflector imaging using ambient noise in weakly scattering media 152(35)
8.1 Role of scattering in correlation-based imaging
152(2)
8.2 Passive sensor imaging in a randomly scattering medium
154(11)
8.2.1 A model for the scattering medium
155(1)
8.2.2 The differential cross correlation
156(1)
8.2.3 Expansion of the clutter Green's function
157(2)
8.2.4 Expansion of the differential cross correlation
159(1)
8.2.5 Statistical analysis of the differential cross correlation
160(4)
8.2.6 On the trade-off between resolution enhancement and signal-to-noise ratio reduction
164(1)
8.2.7 Numerical simulation of migration imaging with cross correlations in the presence of scatterers
164(1)
8.3 Passive sensor imaging with a reflecting interface
165(5)
8.3.1 Stationary phase analysis of the cross correlation with a reflecting interface
166(2)
8.3.2 Numerical simulations of migration imaging with cross correlations in the presence of an interface
168(2)
8.4 Iterated cross correlations for passive imaging in a randomly scattering medium
170(2)
8.4.1 The coda cross correlation
170(2)
8.4.2 Numerical simulations of migration imaging with coda cross correlations
172(1)
8.5 Conclusion
172(2)
8.A Appendix: Proof of Proposition 8.1
174(4)
8.B Appendix: Proof of Proposition 8.2
178(4)
8.B.1 First group
178(2)
8.B.2 Second group
180(2)
8.C Appendix: Statistical analysis of the cross correlations
182(3)
8.C.1 The cross correlation at the difference of travel times
182(2)
8.C.2 The cross correlation at the sum of travel times
184(1)
8.D Appendix: Proof of Proposition 8.3
185(2)
9 Virtual source imaging in homogeneous media 187(19)
9.1 Introduction to virtual source imaging
187(3)
9.2 Ideal virtual source imaging with an infinite source array
190(1)
9.3 High-frequency analysis in a homogeneous background with a limited source array
191(6)
9.3.1 Direct scattering problem
191(1)
9.3.2 High-frequency analysis of the cross correlations
192(3)
9.3.3 High-frequency analysis of the imaging function
195(2)
9.4 Passive synthetic aperture imaging in a homogeneous background
197(4)
9.4.1 High-frequency analysis of the imaging function
198(1)
9.4.2 Comparison with classical synthetic aperture imaging
199(2)
9.5 Conclusion
201(1)
9.A Appendix: Proof of Proposition 9.2
202(1)
9.B Appendix: Proof of Proposition 9.3
203(3)
10 Vitual source imaging in scattering media 206(22)
10.1 The auxiliary array imaging setup
206(2)
10.2 Time-reversal interpretation of virtual source imaging
208(1)
10.3 The paraxial approximation in random media
209(3)
10.3.1 The main results in the paraxial approximation
210(1)
10.3.2 Validity of the paraxial approximation in random media
211(1)
10.4 Analysis of virtual source imaging in the random paraxial regime
212(6)
10.4.1 The cross correlation of the recorded field
212(4)
10.4.2 Migration of cross correlations
216(2)
10.5 Numerical simulations
218(1)
10.6 Passive synthetic aperture imaging in random media
219(3)
10.7 Conclusion
222(1)
10.A Appendix: Proofs of Propositions 10.1-10.2
223(4)
10.B Appendix: Proofs of Propositions 10.3-10.4
227(1)
11 Imaging with intensity cross correlations 228(17)
11.1 The ghost imaging setup
228(3)
11.2 The intensity correlation function
231(6)
11.2.1 The empirical and statistical correlations
231(2)
11.2.2 Paraxial regime
233(2)
11.2.3 Time-reversal interpretation
235(1)
11.2.4 Averaging with respect to the random medium
236(1)
11.3 Resolution analysis
237(5)
11.3.1 Resolution analysis for the fully incoherent case
237(3)
11.3.2 Resolution analysis for the partially coherent case
240(2)
11.4 Conclusion
242(1)
11.A Appendix: The fields in the white-noise paraxial regime
243(2)
12 A review of wave propagation in random media 245(24)
12.1 The random travel time model
245(8)
12.1.1 Domain of validity
245(2)
12.1.2 Statistics of the amplitude and phase perturbations
247(3)
12.1.3 The moments of the Green's function
250(3)
12.2 The random paraxial model
253(5)
12.2.1 The random paraxial regime
253(1)
12.2.2 The random paraxial wave equation
254(1)
12.2.3 The moments of the fundamental solution
255(3)
12.3 The randomly layered model
258(4)
12.3.1 The scaling regime
258(2)
12.3.2 Review of wave propagation in randomly layered media
260(1)
12.3.3 Statistics of the Green's function
261(1)
12.4 Conclusion
262(1)
12.A Appendix: Proof of Lemma 12.1
262(2)
12.B Appendix: Proof of Proposition 12.6
264(3)
12.C Appendix: Proof of Proposition 12.8
267(2)
13 Appendix: Basic facts from analysis and probability 269(16)
13.1 Fourier identities
269(1)
13.2 Divergence theorem
270(1)
13.3 Stationary phase method
270(2)
13.4 Sampling theorem
272(2)
13.5 Random processes
274(11)
13.5.1 Random variables
274(1)
13.5.2 Random vectors
275(1)
13.5.3 Gaussian random vectors
276(1)
13.5.4 Random processes
277(1)
13.5.5 Ergodic processes
278(1)
13.5.6 Mean square theory
279(2)
13.5.7 Gaussian processes
281(1)
13.5.8 Stationary Gaussian processes
282(1)
13.5.9 Vector- and complex-valued Gaussian processes
283(2)
References 285(8)
Index 293
Josselin Garnier is a professor in the Mathematics Department at the Université Paris Diderot, France. His background is in applied probability and he has many years of research experience in the field of wave propagation and imaging in random media. He received the Blaise Pascal Prize from the French Academy of Sciences in 2007 and the Felix Klein Prize from the European Mathematical Society in 2008. George Papanicolaou is the Robert Grimmett Professor in Mathematics at Stanford University, California. He specializes in applied and computational mathematics, partial differential equations, and stochastic processes. He received the John von Neumann Prize from the Society for Industrial and Applied Mathematics in 2006 and the William Benter Prize in Applied Mathematics in 2010. He was elected to the National Academy of Sciences in 2000 and he became a fellow of the American Mathematical Society in 2012.