Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Czasopismo
2019 | Vol. 67, no. 6 | 1535--1550
Tytuł artykułu

High-resolution reflectivity inversion based on joint sparse representation

Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
High-resolution reflectivity inversion is termed as a fundamental yet essential step for the prediction of thin-bedded hydrocarbon reservoirs. However, algorithms suffer from two key issues: (1) seismic inversion is an ill-posed problem that has multiple solutions, and the results of trace-by-trace seismic inversion are quite poor in lateral continuity, and (2) algorithm stability is likely to be decreased owing to the noise and distortion associated with the acquisition and processing flows. In the current article, we formulate a new joint sparse representation through the combination with L2,1- norm misfit function, which possesses superior noise robustness, in particular in the presence of outliers. On the basis of the L2,1- norm regularization, this specific approach enforces a common sparsity profile, together with consistently lowering the multiplicity of solution. Subsequent to that, the resultant algorithm is applied to the multi-trace seismic inversion. Besides, the wedge model trial and practical applications suggest that the proposed inversion algorithm is stable, in addition to having good noise robustness and lateral continuity; moreover, the vertical resolution of λ/8 is realized under the noise and outliers interference. The logging data calibration illustrates that the proposed methodology is accurate and credible.
Wydawca

Czasopismo
Rocznik
Strony
1535--1550
Opis fizyczny
Bibliogr. 56 poz.
Twórcy
  • College of Geophysics, Chengdu University of Technology, Chengdu 610059, China, zhouhuailai06@cdut.cn
  • Engineering and Technical College, Chengdu University of Technology, Leshan 614000, China
  • College of Geophysics, Chengdu University of Technology, Chengdu 610059, China
  • College of Geophysics, Chengdu University of Technology, Chengdu 610059, China
  • School of Education, China West Normal University, Nanchong 637002, China
autor
  • CNOOC Research Institute, Beijing 100027, China
autor
  • CNOOC Research Institute, Beijing 100027, China
Bibliografia
  • 1. Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2:183–202. https://doi.org/10.1137/080716542
  • 2. Becker S, Bobin J, Candès E (2011) NESTA: a fast and accurate first-order method for sparse recovery. SIAM J Imaging Sci 4:1–39. https://doi.org/10.1137/090756855
  • 3. Chopra S, Castagna J, Portniaguine O (2006) Seismic resolution and thin-bed reflectivity inversion. CSEG Rec 2006:19–25
  • 4. Chen J, Huo X (2006) Theoretical results on sparse representations of multiple-measurement vectors. IEEE Trans Signal Process 54:4634–4643. https://doi.org/10.1109/TSP.2006.881263
  • 5. Chen S, Donoho D, Saunders M (2001) Atomic decomposition by basis pursuit. SIAM Rev 43:129–159. https://doi.org/10.1137/S003614450037906X
  • 6. Chung H, Lawton D (1995) Amplitude responses of thin beds: sinusoidal approximation versus Ricker approximation. Geophysics 60:223–230. https://doi.org/10.1190/1.1443750
  • 7. Claerbout J, Muir F (1973) Robust modeling with erratic data. Geophysics 38:826–844. https://doi.org/10.1190/1.1440378
  • 8. Cotter SF, Rao BD, Kjersti E, Kreutz-Delgado K (2005) Sparse solutions to linear inverse problems with multiple measurement vectors. IEEE Trans Signal Process 53:2477–2488. https://doi.org/10.1109/TSP.2005.849172
  • 9. Deng W, Yin W, Zhang Y (2013) Group sparse optimization by alternating direction method. In: Proceedings of SPIE 8858, wavelets and sparsity XV, pp 88580R. https://doi.org/10.1117/12.2024410
  • 10. de Voogd N, den Rooijen H (1983) Thin-layer response and spectral bandwidth. Geophysics 48:12–18. https://doi.org/10.1190/1.1441400
  • 11. Eldar YC, Mishali M (2009) Robust recovery of signals from a structured union of subspaces. IEEE Trans Inf Theory 55:5302–5316. https://doi.org/10.1109/TIT.2009.2030471
  • 12. Fuchs J (2009) Fast implementation of a ℓ1–ℓ1 regularized sparse representations algorithm. In: Proceedings of 2009 IEEE international conference on acoustics, speech and signal processing. Institute of Electrical and Electronics Engineers, New York. pp 19–24. https://doi.org/10.1109/ICASSP.2009.4960337
  • 13. Gabay D, Mercier BA (1976) Dual algorithm for the solution of nonlinear variational problems via finite element approximation. Comput Math Appl 2:17–40. https://doi.org/10.1016/0898-1221(76)90003-1
  • 14. He B, Yuan X (2012) On the O(1/n) convergence rate of the Douglas–Rachford alternating direction method. SIAM J Numer Anal 50:700–709. https://doi.org/10.1137/110836936
  • 15. Jiang J, Wang Z, Chen C, Lu T (2016) L1–L1 norms for face super-resolution with mixed Gaussian-impulse noise. In: 2016 IEEE international conference on acoustics, speech and signal processing. Institute of Electrical and Electronics Engineers, New York, pp 2089–2093. https://doi.org/10.1109/ICASSP.2016.7472045
  • 16. Kallweit R, Wood L (1982) The limits of resolution of zero-phase wavelets. Geophysics 47:1035–1046. https://doi.org/10.1190/1.1441367
  • 17. Kazemi N, Sacchi M (2014) Sparse multichannel blind deconvolution. Geophysics 79:V143–V152. https://doi.org/10.1190/geo2013-0465.1
  • 18. Kazemi N, Gholami A, Sacchi MD (2016) Modified sparse multichannel blind deconvolution. In: 78th EAGE conference and exhibition. European Association of Geoscientists and Engineers, Houten. https://doi.org/10.3997/2214-4609.201601244
  • 19. Kobayashi K, Kim S, Kojima M (2008) Sparse second order cone programming formulations for convex optimization problems. J Oper Res Soc Jpn 51:241–264. https://doi.org/10.15807/jorsj.51.241
  • 20. Koefoed O, de Voogd N (1980) The linear properties of thin layers, with an application to synthetic seismograms over coal seams. Geophysics 45:1254–1268. https://doi.org/10.1190/1.1441122
  • 21. Li F, Xie R, Song W, Zhao T, Marfurt K (2017) Optimal Lq norm regularization for sparse reflectivity inversion. In: SEG technical program expanded abstracts 2017. Society of Exploration Geophysicists, Tulsa. pp 677–681 https://doi.org/10.1190/segam2017-17666814.1
  • 22. Li F, Xie R, Song W, Chen H (2019) Optimal seismic reflectivity inversion: data-driven ℓpℓp-loss-ℓqℓq-regularization sparse regression. IEEE Geosci Remote Sens Lett 16:806–810. https://doi.org/10.1109/LGRS.2018.2881102
  • 23. Liu J, Ji S, Ye J (2010) SLEP: sparse learning with efficient projections. Dissertation, Arizona State University
  • 24. Liu Q, Davoine F, Yang J, Cui Y, Jin Z, Han F (2018) A fast and accurate matrix completion method based on QR decomposition and L 2,1-norm minimization. IEEE Trans Neural Netw Learn Syst 30:803–817. https://doi.org/10.1109/TNNLS.2018.2851957
  • 25. Mallat SG, Zhifeng Z (1993) Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process 41:3397–3415. https://doi.org/10.1109/78.258082
  • 26. Marfurt K, Kirlin R (2001) Narrow-band spectral analysis and thin-bed tuning. Geophysics 66:1274–1283. https://doi.org/10.1190/1.1487075
  • 27. Nguyen T, Castagna J (2010) High-resolution reflectivity inversion. J Seism Explor 19:303–320
  • 28. Nie F, Huang H, Cai X, Ding CHQ (2010) Efficient and robust feature selection via joint ℓ2,1-norms minimization. In: Proceedings of the 23rd international conference on neural information processing systems
  • 29. Partyka G, Gridley J, Lopez J (1999) Interpretational applications of spectral decomposition in reservoir characterization. Lead Edge 18:353–360. https://doi.org/10.1190/1.1438295
  • 30. Puryear C, Castagna J (2006) An algorithm for calculation of bed thickness and reflection coefficients from amplitude spectrum. In: SEG technical program expanded abstracts 2006. Society of Exploration Geophysicists, Tulsa. pp 1767–1770. https://doi.org/10.1190/1.2369866
  • 31. Puryear C, Castagna J (2008) Layer-thickness determination and stratigraphic interpretation using spectral inversion: theory and application. Geophysics 73:R37–R48. https://doi.org/10.1190/1.2838274
  • 32. Puryear C, Castagna J, Portniaguine O, Cobos C (2012) Constrained least-squares spectral analysis: application to seismic data. In: SEG technical program expanded abstracts 2012. Society of Exploration Geophysicists, Tulsa, pp 1–5. https://doi.org/10.1190/segam2012-0822.1
  • 33. Russell B (1988) Part 6—sparse–spike inversion. In: Domenico S (ed) Introduction to seismic inversion methods. Society of Exploration Geophysicists, Tulsa. pp 6-1–6-34. https://doi.org/10.1190/1.9781560802303.ch6
  • 34. Schuster G (2017) Chapter 1: introduction to seismic inversion. In: Sun Y (ed) Seismic inversion. Society of Exploration Geophysicists, Tulsa, pp 3–12. https://doi.org/10.1190/1.9781560803423.ch1
  • 35. Stojnic M, Parvaresh F, Hassibi B (2009) On the reconstruction of block-sparse signals with an optimal number of measurements. IEEE Trans Signal Process 57:3075–3085. https://doi.org/10.1109/TSP.2009.2020754
  • 36. Tropp JA, Gilbert AC, Strauss MJ (2006) Algorithms for simultaneous sparse approximation. Part I: greedy pursuit. Signal Process 86:572–588. https://doi.org/10.1016/j.sigpro.2005.05.030
  • 37. Tropp JA (2006) Algorithms for simultaneous sparse approximation. Part II: convex relaxation. Signal Process 86:589–602. https://doi.org/10.1016/j.sigpro.2005.05.031
  • 38. van den Berg E, Friedlander M (2008) Probing the Pareto frontier for basis pursuit solutions. SIAM J Sci Comput 31:890–912. https://doi.org/10.1137/080714488
  • 39. van Riel P, Berkhout A (1985) Resolution in seismic trace inversion by parameter estimation. Geophysics 50:1440–1455. https://doi.org/10.1190/1.1442012
  • 40. Wang B, Guo Z, Chen X, Lu W (2016) Nonstationary sparse-reflectivity inversion using nonconvex constraint in frequency domain. In: SEG technical program expanded abstracts 2016. Society of Exploration Geophysicists, Tulsa, pp 3741–3745 https://doi.org/10.1190/segam2016-13858493.1
  • 41. Wang S, Yuan S, Ma M, Zhang R, Luo C (2015) Wavelet phase estimation using ant colony optimization algorithm. J Appl Geophys 122:159–166. https://doi.org/10.1016/j.jappgeo.2015.09.013
  • 42. Widess M (1973) How thin is a thin bed? Geophysics 38:1176–1180. https://doi.org/10.1190/1.1440403
  • 43. Widess M (1982) Quantifying resolving power of seismic systems. Geophysics 47:1160–1173. https://doi.org/10.1190/1.1441379
  • 44. Xiao Y, Zhu H, Wu SY (2013) Primal and dual alternating direction algorithms for ℓ1–ℓ1-norm minimization problems in compressive sensing. Comput Optim Appl 54:441–459. https://doi.org/10.1007/s10589-012-9475-x
  • 45. Yang J, Peng Y, Xu W, Dai Q (2009) Ways to sparse representation: an overview. Sci China Ser F Inf Sci 52:695–703. https://doi.org/10.1007/s11432-009-0045-5
  • 46. Yang J, Zhang Y (2011) Alternating direction algorithms for ℓ1-problems in compressive sensing. SIAM J Sci Comput 33:250–278. https://doi.org/10.1137/090777761
  • 47. Yin XY, Liu XJ, Zong ZY (2015) Pre-stack basis pursuit seismic inversion for brittleness of shale. Pet Science 12:618–627. https://doi.org/10.1007/s12182-015-0056-3
  • 48. Yuan SY, Liu JZ, Zhang R, Tian N, Wang SX (2014) Seismic deconvolution via total variation regularization. In: 76th EAGE conference and exhibition. European Association of Geoscientists and Engineers, Houten. https://doi.org/10.3997/2214-4609.20141594
  • 49. Yuan S, Wang S, Tian N, Wang Z (2016) Stable inversion-based multitrace deabsorption method for spatial continuity preservation and weak signal compensation. Geophysics 81:V199–V212. https://doi.org/10.1190/geo2015-0247.1
  • 50. Yuan S, Wang S, Ma M, Ji Y, Deng L (2017) Sparse Bayesian learning-based time-variant deconvolution. IEEE Trans Geosci Remote Sens 55:6182–6194. https://doi.org/10.1109/TGRS.2017.2722223
  • 51. Yuan S, Wang S, Luo C, Wang T (2018) Inversion-Based 3-D seismic denoising for exploring spatial edges and spatio-temporal signal redundancy. IEEE Geosci Remote Sens Lett 15:1682–1686. https://doi.org/10.1109/LGRS.2018.2854929
  • 52. Zhang F, Dai R, Liu H (2014) Seismic inversion based on L1-norm misfit function and total variation regularization. J Appl Geophys 109:111–118. https://doi.org/10.1016/j.jappgeo.2014.07.024
  • 53. Zhang R, Castagna J (2011) Seismic sparse-layer reflectivity inversion using basis pursuit decomposition. Geophysics 76:R147–R158. https://doi.org/10.1190/geo2011-0103.1
  • 54. Zhang R, Mrinal KS, Sanjay S (2013) Multi-trace basis pursuit inversion with spatial regularization. J Geophys Eng 10:035012. https://doi.org/10.1088/1742-2132/10/3/035012
  • 55. Zhang Z, Xu Y, Yang J, Li X, Zhang D (2015) A survey of sparse representation: algorithms and applications. IEEE Access 3:490–530. https://doi.org/10.1109/ACCESS.2015.2430359
  • 56. Zhang Z, Li F, Zhao M, Zhang L, Yan S (2017) Robust neighborhood preserving projection by nuclear/L 2,1-norm regularization for image feature extraction. IEEE Trans Image Process 26:1607–1622. https://doi.org/10.1109/TIP.2017.2654163
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-1bb2efc0-7373-4476-9b4b-b59c6a4c6725
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.