Closed-loop SRME based on 3D L1-norm sparse inversion

Wang, T.; Wang, D.; Sun, J.; Hu, B.; Liu, C.

doi:10.1007/s11600-017-0098-6

Artykuł - szczegóły

Tytuł artykułu

Closed-loop SRME based on 3D L1-norm sparse inversion

Autorzy

Wang T. , Wang D. , Sun J. , Hu B. , Liu C.

Wybrane pełne teksty z tego czasopisma

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

Identyfikatory

DOI

10.1007/s11600-017-0098-6

Warianty tytułu

Języki publikacji

Abstrakty

In many situations, the quality of seismic imaging is largely determined by a proper multiple attenuation as preprocessing step. Despite the widespread application of surface-related multiple elimination (SRME) and estimation of primaries by sparse inversion (EPSI) for the removal of multiples, there still exist some limitations in the process of prediction and subtraction (SRME) or inversion (EPSI), which make the efficiency of multiple attenuation less satisfactory. To solve these problems, a new fully data-driven method called closed-loop SRME was proposed, which combines the robustness of SRME and the multi-dimensional inversion strategy of EPSI. Due to the selection of inversion approach and constraint, primary estimation by closed-loop SRME may fall into a local optimum during the solving process, which lowers the accuracy of deep information and weakens the continuity of seismic events. To avoid these shortcomings, we first modified the solving method for closed-loop SRME to an L1 norm-based bi-convex optimization method, which stabilizes the solution. Meanwhile, in the L1 norm constraint-based optimization process, the 3D sparsifying transform, being a 2D Curvelet-1D wavelet transform, is brought in as a 3D sparse constraint. In the 3D sparsifying domain, the data become sparser, thus making the result of optimization more accurate, the information of seismic events more continuous and the resolution higher. Examples on both synthetic and field data demonstrate that the method proposed in this paper, compared with the traditional SRME and closed-loop SRME, have an excellent effect on primary estimation and suppress multiples effectively.

Słowa kluczowe

primary estimation closed-loop SRME L1-norm bi-convex optimization 2D curvelet-1D wavelet transform 3D sparse constraint

ocena podstawowa

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2017

Tom

Vol. 65, no. 6

Strony

1145--1152

Opis fizyczny

Bibliogr. 24 poz.

Twórcy

autor

Wang T.

College of Geo-Exploration Science and Technology, Jilin University, Changchun, People’s Republic of China

autor

Wang D.

wangdeli@jlu.edu.cn

College of Geo-Exploration Science and Technology, Jilin University, Changchun, People’s Republic of China

autor

Sun J.

College of Geo-Exploration Science and Technology, Jilin University, Changchun, People’s Republic of China

autor

Hu B.

College of Geo-Exploration Science and Technology, Jilin University, Changchun, People’s Republic of China

autor

Liu C.

College of Geo-Exploration Science and Technology, Jilin University, Changchun, People’s Republic of China

Bibliografia

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3. Boyd S, Vandenberghe L (2004) Convex Optimization. Cambridge University Press, USA, pp 127–152
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6. Daubechies I (1992) Ten lectures on Wavelets. SLAM Books, Philadelphia
7. Feng F, Wang DL, Zhu H, Cheng H (2013) Estimating primaries by sparse inversion of the 3D curvelet transform and the L1-norm constraint. Appl Geophys 10(2):201–209. https://doi.org/10.1007/s11770-013-0378-0
8. Hennenfent G, Van Den, Berg E, Friedlander MP et al (2008) New insights into one-norm solvers from the Pareto curve. Geophysics 73(4):A23–A26. https://doi.org/10.1190/1.2944169
9. Herrmann FJ (2010) Randomized sampling and sparsity: Getting more information from fewer samples. Geophysics 75(6):WB173–WB187. https://doi.org/10.1190/1.3506147
10. Herrmann FJ (2012) Pass on the message: recent insights in largescale sparse recovery, Presented at the EAGE gram, EAGE, 74th EAGE Conference and Exhibition incorporating SPE EUROPEC 2012, Copenhagen, Denmark, 4–7 June 2012
11. Kinneging NK, Budejicky V, Wapenaar CPA, Berkhout AJ (1989) Efficient 2D and 3D shot record redatuming. Geophys Prospect 37:493–530. https://doi.org/10.1111/j.1365-2478.1989.tb02220.x
12. Lin TTY, Herrmann FJ (2010) Stabilized estimation of primaries by sparse inversion. In: 72nd EAGE Conference and Exhibition incorporating SPE EUROPEC 2010, Barcelona, Spain, 14–17 June 2010
13. Lin TTY, Herrmann FJ (2011) Estimating primaries by sparse inversion in a Curvelet-like representation domain. In: 73rd EAGE Conference and Exhibition incorporating SPE EUROPEC 2011
14. Lin TTY, Herrmann FJ (2013) Robust signature deconvolution and the estimation of primaries by sparse inversion. Geophysics 78(3):R133–150. https://doi.org/10.1190/geo2012-0097.1
15. Lopez GA, Verschuur DJ (2014) Closed-loop SRME—a new direction in surface multiple removal algorithms. In: 76th Annual International Meeting, EAGE, Extended Abstracts, E102
16. Lopez GA, Verschuur DJ (2015) Closed-loop surface-related multiple elimination and its application to simultaneous data reconstruction. Geophysics 80(6):V189–V199. https://doi.org/10.1190/geo2015-0287.1
17. Song JW, Verschuur DJ, Chen XH (2014) Research status and progress in multiple elimination. Progress Geophys 29(1):240–247. https://doi.org/10.6038/pg20140134(in Chinese)
18. Van den Berg E, Friedlander MP (2009) Probing the Pareto frontier for basis pursuit solution. SIAM J Sci Comput 31(2):890–912. https://doi.org/10.1137/080714488
19. Van den Berg E, Friedlander MP (2011) Sparse optimization with least-squares constraints. SIAM J Optim 21(4):1201–1229. https://doi.org/10.1137/100785028
20. Van Groenestijn GJ, Verschuur DJ (2009a) Estimating primaries by sparse inversion and application to near-offset data reconstruction. Geophysics 74(3):A23–A28. https://doi.org/10.1190/1.3111115
21. Van Groenestijn GJ, Verschuur DJ (2009b) Estimation of primaries and near-offset reconstruction by sparse inversion: marine data application. Geophysics 74(6):R119–R128. https://doi.org/10.1190/1.3213532
22. Verschuur DJ, Berkhout AJ (1997) Estimation of multiple scattering by iterative inversion, part II: practical aspects and examples. Geophysics 62(5):1596–1611. https://doi.org/10.1190/1.1444262
23. Verschuur DJ, Berkhout AJ, Wapenaar CPA (1992) Adaptive surface-related multiple elimination. Geophysics 57(9):1166–1177. https://doi.org/10.1190/1.1443330
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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-081e84bf-ea79-4f59-99d6-348ac8917a91