PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
  • Sesja wygasła!
Tytuł artykułu

A seismic interpolation and denoising method with curvelet transform matching filter

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A new seismic interpolation and denoising method with a curvelet transform matching filter, employing the fast iterative shrinkage thresholding algorithm (FISTA), is proposed. The approach treats the matching filter, seismic interpolation, and denoising all as the same inverse problem using an inversion iteration algorithm. The curvelet transform has a high sparseness and is useful for separating signal from noise, meaning that it can accurately solve the matching problem using FISTA. When applying the new method to a synthetic noisy data sets and a data sets with missing traces, the optimum matching result is obtained, noise is greatly suppressed, missing seismic data are filled by interpolation, and the waveform is highly consistent. We then verified the method by applying it to real data, yielding satisfactory results. The results show that the method can reconstruct missing traces in the case of low SNR (signal-to-noise ratio). The above three problems can be simultaneously solved via FISTA algorithm, and it will not only increase the processing efficiency but also improve SNR of the seismic data.
Czasopismo
Rocznik
Strony
1029--1042
Opis fizyczny
Bibliogr. 42 poz.
Twórcy
autor
  • College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
  • Key Laboratory of Geo-exploration Instruments, Ministry of Education of China (Jilin University), Changchun, China
autor
  • College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
  • Key Laboratory of Geo-exploration Instruments, Ministry of Education of China (Jilin University), Changchun, China
autor
  • College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
  • Key Laboratory of Geo-exploration Instruments, Ministry of Education of China (Jilin University), Changchun, China
autor
  • College of Geo-Exploration Science and Technology, Jilin University, Changchun, China
autor
  • College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
  • Key Laboratory of Geo-exploration Instruments, Ministry of Education of China (Jilin University), Changchun, China
Bibliografia
  • 1. Aster RC, Borchers B, Thurber CH (2012) Parameter estimation and inverse problems, 2nd edn. Academic Press, Cambridge
  • 2. Beck A, Teboulle M (2009a) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imag Sci 2:183–202
  • 3. Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm with application to wavelet-based image deblurring. In: IEEE international conference on acoustics, speech and signal processing, pp 693–696
  • 4. Berkhout AJ (1977) Least-squares inverse filtering and wavelet deconvolution. Geophysics 42:1369–1383
  • 5. Candès EJ, Demanet L (2003) Curvelets and Fourier integral operators. CR Math 336:395–398
  • 6. Candès EJ, Donoho DL (2004) New tight frames of curvelets and optimal representations of objects with C2 singularities. Commun Pure Appl Math 57:219–266
  • 7. Candès EJ, Demanet L, Donoho DL, Ying LX (2006) Fast discrete curvelet transforms. Multiscale Model Simul 5:861–899
  • 8. Chen QS (1993) Digital signal processing of principia mathematica. Petroleum Industry Press, Beijing
  • 9. Chen SS, Donoho DL, Saunders MA (1998) Atomic decomposition by basis pursuit. SIAM J Sci Comput 20:33–61
  • 10. Cheng JX, Zhu LH, Yang CC, Chen J (2004) Putting 3-D seismic data together based on wavelet transform. Oil Geophys Prospect 39:406–408
  • 11. Daubechies I, Defrise M, De Mol C (2004) An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun Pure Appl Math 57:1413–1457
  • 12. Douma H, de Hoop M (2007) Leading-order seismic imaging using curvelets. Geophysics 72:S231–S248
  • 13. Gill PR, Wang A, Molnar A (2011) The in-crowd algorithm for fast basis pursuit denoising. IEEE Trans Signal Process 59:4595–4605
  • 14. Górszczyk A, Adamczyk A, Malinowski M (2014) Application of curvelet denoising to 2D and 3D seismic data—practical considerations. J Appl Geophys 105:78–94
  • 15. Górszczyk A, Cyz M, Malinowski M (2015a) Improving depth imaging of legacy seismic data using curvelet-based gather conditioning: a case study from Central Poland. J Appl Geophys 117:73–80
  • 16. Górszczyk A, Malinowski M, Bellefleur G (2015b) Enhancing 3D post-stack seismic data acquired in hardrock environment using 2D curvelet transform. Geophys Prospect 63:903–918
  • 17. Hennenfent G, Cole J, Kustowski B (2011) Interpretative noise attenuation in the curvelet domain. In: 81th Annual international meeting, SEG, expanded abstracts, pp 3566–3570
  • 18. Herrmann FJ (2009) Curvelet-domain matched filtering. In: 79th Annual international meeting, SEG, expanded abstracts, pp 3643–3649
  • 19. Herrmann FJ, Wang DL, Hennenfent G, Moghaddam PP (2008) Curvelet-based seismic data processing: a multiscale and nonlinear approach. Geophysics 73:A1–A5
  • 20. Jin L, Chen XH, Li JY (2005) A new method for time-lapse seismic matching filter based on error criteria and cyclic iteration. Chin J Geophys 48:698–703
  • 21. Liu Y, Li CC (1997) Some methods for estimating the signal/noise ratio of seismic data. Oil Geophys Prospect 32:257–262
  • 22. Long Y, Han LG, Han L, Tan CQ (2012) L1 norm optimal solution match processing in the wavelet domain. Appl Geophys 9:451–458
  • 23. Malioutov DM, Cetin M, Willsky AS (2005a) Homotopy continuation for sparse signal representation. IEEE Int Conf Acoust Speech Signal Process 5:733–736
  • 24. Malioutov DM, Cetin M, Willsky AS (2005b) A sparse signal reconstruction perspective for source localization with sensor arrays. IEEE Trans Signal Process 53:3010–3022
  • 25. Norman K, Townsley J (2006) Using an interactive match filter to advance interpretetion. In: 76th 85th Annual international meeting, SEG, expanded abstracts, pp 1068–1072
  • 26. Robinson EA (1957) Predictive decomposition of seismic traces. Geophysics 22:767–778
  • 27. Scales JA, Gersztenkorn A (1988) Robust methods in inverse theory. Inverse Prob 4:1071–1091
  • 28. Shen HL, Tian G, Shi ZJ (2013) Partial frequency band match filtering based on high sensitivity data: method and applications. Appl Geophys 10:15–24
  • 29. Shi D, Milkereit B (2015) Migration-induced noise reduction using fast discrete curvelet transform. In: 85th Annual International Meeting, SEG, expanded abstracts, pp 2001–2006
  • 30. Shi ZJ, Tian G, Dong SX, He HY, Wang ZJ (2005) Matching filtering of geophone coupling for high-frequencies of seismic data in desert area. Oil Geophys Prospect 44:261–263
  • 31. van den Berg E, Friedlander MP (2008) Probing the Pareto Frontier for basis pursuit solutions. SIAM J Sci Comput 31:890–912
  • 32. Wallace R, Gray FD (1992) Network match filters: a least-squares technique for minimizing seismic mis-ties. In: 62th Annual international meeting, SEG, expanded abstracts, pp 1112–1115
  • 33. Wang YB, Zhu ZY, Jiang XD (2011) A pseudo-multichannel matching filter application to time-lapse seismic matching processing. In: Shenzhen 2011 international geophysical conference technical program expanded abstracts, pp 1803–1807
  • 34. Wang SX, Yuan SY, Ma M, Zhang R, Luo CM (2015) Wavelet phase estimation using ant colony optimization algorithm. J Appl Geophys 122:159–166
  • 35. Wiener N (1949) Extrapolation, interpolation, and smoothing of stationary time series, with engineering applications. Technology Press of the Massachusetts Institute of Technology, Cambridge
  • 36. Wright S, Nowak RD, Figueiredo MAT (2009) Sparse reconstruction by separable approximation. IEEE Trans Signal Process 57:2479–2493
  • 37. Wu DL, Jiang Y, Chen ZM (2006) Application of cascade matched filtering in mixed source data processing. Geophysl Prospect Pet 45:611–614
  • 38. Yuan SY, Wang SX (2013) Spectral sparse Bayesian learning reflectivity inversion. Geophys Prospect 61:735–746
  • 39. Yuan SY, Wang SX, Li GF (2012) Random noise reduction using Bayesian inversion. J Geophys Eng 9:60–68
  • 40. Yuan SY, Wang SX, Luo CM, He YX (2015) Simultaneous multitrace impedance inversion with transform-domain sparsity promotion. Geophysics 80:R71–R80
  • 41. Zhang LL, Wang HX, Xu YB, Wang D (2011) A fast iterative shrinkage-thresholding algorithm for electrical resistance tomography. WSEAS Trans Circuits Syst 10:393–402
  • 42. Zhang M, Wang YH, Li ZC, Yu HC, Ge DM (2014) Prestack cross-equalization based on pseudo-multichannel matching filter in time-lapse seismic. In: 84th Annual international meeting, SEG, expanded abstracts, pp 4940–4944
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e68e2e6b-5d58-4d61-8c0f-04d9a6bc894c
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.