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Tytuł artykułu

Multi-trace post-stack seismic data sparse inversion with nuclear norm constraint

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Among many seismic inversion methods, the sparse spike inversion for post-stack seismic data uses the migrated and stacked seismic data which is regarded as zero ofset refection seismic data in the case of normal incidence to extract refectivity and impedance of underground rocks. The seismic refectivity and impedance can refect underground rocks’ lithology, petrophysical property, oil–gas possibility, and so forth. However, the common used post-stack seismic inversion adopts single trace in the process of inversion and completes the whole data cube’s inversion through trace by trace. It cannot use lateral regularization. Hence, the lateral continuity of single trace inversion result is poor. It is difcult to represent the lat eral variation features of underground rocks. Based on the conventional sparse spike inversion, the nuclear norm of matrix in the matrix completion theory is introduced in the process of post-stack seismic inversion. At the same time, the strategy of multi-trace seismic data simultaneous inversion is used to carry out lateral regularization constraint. Numerical tests on 2D model indicate that the inversion results obtained from the proposed method can clearly represent not only the vertical variation features but also the lateral variation features of underground rocks. At last, the inversion results of real seismic data further show the feasibility and superiority of the proposed method in practical application.
Czasopismo
Rocznik
Strony
53--64
Opis fizyczny
Bibliogr. 30 poz.
Twórcy
autor
  • School of Mathematics and Information, China West Normal University, Nanchong, Sichuan Province, People’s Republic of China
  • School of Geosciences, China University of Petroleum (East China), Qingdao, Shandong Province, People’s Republic of China
autor
  • School of Geoscience and Technology, Southwest Petroleum University, Chengdu, Sichuan Province, People’s Republic of China
autor
  • School of Mathematics and Information, China West Normal University, Nanchong, Sichuan Province, People’s Republic of China
Bibliografia
  • 1. Aki K, Richards PG (2002) Quantitative seismology, 2nd edn. University Science Books, Mill Valley
  • 2. Bai Y, Yang C, Jing X (2002) The geology-constrained wave impedance inversion. GeophysProspec Pet 41:61–64 (in Chinese with English abstract)
  • 3. Berteussen KA, Ursin B (1983) Approximate computation from seismic data. Geophysics 48:1351–1358
  • 4. Cooke DA, Schneider WA (1983) Generalized linear inversion of reflections seismic data. Geophysics 48:665–676
  • 5. Dai RH, Yin C, Liu Y, Zhang XD, Zhao H, Yan K, Zhang W (2019) Estimation of generalized Stein’s unbiased risk and selection of the regularization parameter in geophysical inversion problems. Chin J Geophys (Chin) 62:982–992 (in Chinese with English abstract)
  • 6. Dai R, Yin C, Yang S, Zhang F (2018) Seismic deconvolution and inversion with erratic data. Geophys Prospect 66:1684–1701
  • 7. Dai R, Zhang F, Liu H (2016) Seismic inversion based on proximal objective function optimization algorithm. Geophysics 81:R237–R246
  • 8. Davis G, Mallat S, Avellaneda M (1997) Adaptive greedy approximations. Constructive Approximation 13:57–98
  • 9. Donoho D, Maleki A, Montanari A (2009) Message passing algorithm for compressed sensing. ProcNatlAcadSci USA 106:18914–18919
  • 10. Donoho D, Maleki A, Montanari A (2010) Message passing algorithm for compressed sensing II: analysis and validation. In: Proceedings of the IEEE Information Theory Workship, pp 1–5.
  • 11. Elad M (2009) Sparse and redundant representations: from theory to applications in signal and image processing. Springer, Berlin
  • 12. Gholami A (2015) Nonlinear multichannel impedance inversion by total-variation regularization. Geophysics 80:R217–R224
  • 13. Gholami A, Sacchi MD (2012) A fast and automatic sparse deconvolution in the presence of outliers. IEEE Trans Geosci Remote Sens 50:4105–4116
  • 14. Hamid H, Pidlisecky A (2015) Multitrace impedance inversion with lateral constraints. Geophysics 80:M101–M111
  • 15. JamaliHondori E, Mikada H, Goto T, Takekawa J, (2013) A random layer-stripping method for seismic reflectivity inversion. ExplorGeophys 44:70–76
  • 16. Keshavan RH, Montanari A, Oh S (2010) Matrix completion from a few entries. IEEE Trans Inf Theory 56:2980–2998
  • 17. Ma J (2013) Three-dimensional irregular seismic data reconstruction via low-rank matrix completion. Geophysics 78:V181–V192
  • 18. Mallat SG, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process 41:3397–3415
  • 19. Menke W (1984) Geophysical data analysis: Discrete inverse theory. Academic Press, Inc, Cambridge
  • 20. Natarajan BK (1995) Sparse approximate solutions to linear systems. SIAM J Comput 24:227–234
  • 21. Recht B, Fazel M, Parrilo PA (2010) Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization. SIAM Rev 52:471–501
  • 22. Robinson EA, Treitel S (1980) Geophysical Signal Analysis. Prentice-Hall, Upper Saddle River
  • 23. Wang Y, Yang J, Yin W, Zhang Y (2008) A new alternating minimization algorithm for total variation image reconstruction. SIAM J Imaging Sci. 1:248–272
  • 24. Xu L, Lu C, Xu Y, Jia J (2011) Image smoothing via L0 gradient minimization. ACM Trans Graph 30:6 (Article 174)
  • 25. Yin X, Liu X, Wu G, Zong Z (2016) Basis pursuit inversion method under model constraint. Geophys Prospect Pet 55:115–122 (in Chinese with English abstract)
  • 26. Zhang F, Dai R, Liu H (2014) Seismic inversion based on L1-norm misfit function and total variation regularization. J ApplGeophys 109:111–118
  • 27. Zhang H, He Q (1995) Broad-band constrained inversion. Geophys Prospect Pet 34:1–10 (in Chinese with English abstract)
  • 28. Zhou Z, He J, Zhao H (1998) Solving a seismic trace inversion problem by using generalized conjugate gradient algorithm. Oil Geophys Prospect 33:439–447 (in Chinese with English abstract)
  • 29. Zhang F, Liu H, Niu X, Dai R (2014) High resolution seismic inversion by convolutional neural network. Oil Geophys Prospect 49:1165–1169 (in Chinese with English abstract)
  • 30. Zhang Y, Luo Y, Ling F (2001) Seismic trace multi-scale inversion using logging data and seismic data. Earth Sci J Chin UnivGeosci 26:533–537 (in Chinese with English abstract)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-62d79ad0-f200-4320-a1d9-8ad6400d39b7
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