A seismic interpolation and denoising method with curvelet transform matching filter

Yang, H.; Long, Y.; Lin, J.; Zhang, F.; Chen, Z.

doi:10.1007/s11600-017-0078-x

Artykuł - szczegóły

Tytuł artykułu

A seismic interpolation and denoising method with curvelet transform matching filter

Autorzy

Yang H. , Long Y. , Lin J. , Zhang F. , Chen Z.

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-017-0078-x

Warianty tytułu

Języki publikacji

Abstrakty

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.

Słowa kluczowe

interpolation denoising curvelet transform FISTA sparse constrained inversion matching filter

interpolacja odszumianie transformata curvelet FISTA

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2017

Tom

Vol. 65, no. 5

Strony

1029--1042

Opis fizyczny

Bibliogr. 42 poz.

Twórcy

autor

Yang H.

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

Long Y.

longy@jlu.edu.cn

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

Lin J.

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

Zhang F.

College of Geo-Exploration Science and Technology, Jilin University, Changchun, China

autor

Chen Z.

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