期刊论文

Fu, Lihua

英文

IEEE Transactions on Geoscience and Remote Sensing

0196-2892

2020

58

9

6336-6343

10.1109/TGRS.2020.2976664

Manuscript received June 6, 2019; revised October 29, 2019 and January 15, 2020; accepted February 19, 2020. Date of publication March 13, 2020; date of current version August 28, 2020. This study was financially supported by the National Key R&D Program of China (No. 2018YFC1503705), Science and Technology Research Project of Hubei Provincial Department of Education (B2017597), Hubei Subsurface Multi-scale Imaging Key Laboratory (China University of Geosciences) under grants (SMIL-2018-06) and the Fundamental Research Funds for the Central Universities under Grants CCNU19TS020. (Corresponding author: Lihua Fu.) Wanjuan Zhang, Lihua Fu, and Wenting Cheng are with the School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China (e-mail: wanj.zhang@cug.edu.cn; lihuafu@cug.edu.cn; wtcheng@cug.edu.cn).

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Rank-reduction (RR) methods have been widely applied for reconstructing seismic data. The popular convex relaxation formulation of RR is nuclear norm minimization (NNM), which is capitalized on its convexity. Consequently, global optimization can be effectively achieved with NNM method. However, NNM minimizes the summation of all singular values and is therefore not equivalent to the minimum of the rank function; in fact, the rank of the matrix is equal to the number of nonzero singular values. Thus, NNM is not a good approximation to the origi...