Compressive sensing for seismic data reconstruction via fast projection onto convex sets based on seislet transform
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- 作者：Shuwei Gana ; gsw19900128@126.com" class="auth_mail" title="E-mail the corresponding author ; Shoudong Wanga ; swang104@ucsc.edu" class="auth_mail" title="E-mail the corresponding author ; Yangkang Chenb ; ykchen@utexas.edu" class="auth_mail" title="E-mail the corresponding author ; Xiaohong Chena ; chenxh@cup.edu.cn" class="auth_mail" title="E-mail the corresponding author ; Weiling Huanga ; cup_hwl@126.com" class="auth_mail" title="E-mail the corresponding author ; Hanming Chena ; huichanming@126.com" class="auth_mail" title="E-mail the corresponding author
- 关键词：Compressive sensing ; Irregularly sampled seismic data ; Sparsity comparison ; Seislet transform ; Fast projection onto convex sets
- 刊名：Journal of Applied Geophysics
- 出版年：2016
- 出版时间：July 2016
- 年：2016
- 卷：130
- 期：Complete
- 页码：194-208
- 全文大小：4772 K
文摘
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We proposed a novel fast projection onto convex sets (POCS) algorithm using the seislet transform as the sparsity-promoting transform.
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The seislet is shown to be the sparsest among well-known transforms.
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The selection of FPOCS or FISTA depends on the noise level of the data.
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The seislet based FPOCS can obtain better and faster seismic data recovery.
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The local similarity is also used to measure the data recovery performance.