Deblending of Simultaneous-source Seismic Data using Fast Iterative Shrinkage-thresholding Algorithm with Firm-thresholding

In this paper, an improved algorithm is proposed to separate blended seismic data. We formulate the deblending problem as a regularization problem in both common receiver domain and frequency domain. It is suitable for different kinds of coding methods such as random time delay discussed in this pap...

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Veröffentlicht in:Acta geophysica Jg. 64; H. 4; S. 1064 - 1092
Hauptverfasser: Qu, Shan, Zhou, Hui, Liu, Renwu, Chen, Yangkang, Zu, Shaohuan, Yu, Sa, Yuan, Jiang, Yang, Yahui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.08.2016
De Gruyter
Springer Nature B.V
Schlagworte:
Algorithms
Data analysis
deblending
Earth and Environmental Science
Earth Sciences
firmthresholding
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
regularization
Seismology
simultaneous-source
Structural Geology
deblending
firm-thresholding
simultaneous-source
regularization
ISSN:1895-6572, 1895-7455
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Zusammenfassung:In this paper, an improved algorithm is proposed to separate blended seismic data. We formulate the deblending problem as a regularization problem in both common receiver domain and frequency domain. It is suitable for different kinds of coding methods such as random time delay discussed in this paper. Two basic approximation frames, which are iterative shrinkage-thresholding algorithm (ISTA) and fast iterative shrinkage-thresholding algorithm (FISTA), are compared. We also derive the Lipschitz constant used in approximation frames. In order to achieve a faster convergence and higher accuracy, we propose to use firm-thresholding function as the thresholding function in ISTA and FISTA. Two synthetic blended examples demonstrate that the performances of four kinds of algorithms (ISTA with soft- and firm-thresholding, FISTA with soft- and firm-thresholding) are all effective, and furthermore FISTA with a firm-thresholding operator exhibits the most robust behavior. Finally, we show one numerically blended field data example processed by FISTA with firm-thresholding function.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:1895-6572
1895-7455
DOI:10.1515/acgeo-2016-0043