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

详细信息    查看全文

• 作者：Shan Qu ; Hui Zhou ; Renwu Liu ; Yangkang Chen ; Shaohuan Zu ; Sa Yu…
• 关键词：Key wordsdeblending ; regularization ; simultaneous ; source ; firm ; thresholding
• 刊名：Acta Geophysica
• 出版年：2016
• 出版时间：August 2016
• 年：2016
• 卷：64
• 期：4
• 页码：1064-1092
• 全文大小：3222KB
• 刊物主题：Geophysics/Geodesy; Structural Geology; Geotechnical Engineering & Applied Earth Sciences;
• 出版者：Springer International Publishing
• ISSN：1895-7455
• 卷排序：64

文摘

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.