期刊
COMPUTERS & GEOSCIENCES
卷 51, 期 -, 页码 449-461出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cageo.2012.07.023
关键词
Sparsity; Compressive sensing; Seismic trace interpolation; Iterative shrinkage-thresholding (IST); Projection onto convex sets (POCS)
资金
- National Natural Science Foundation of China [40730424]
- National Science & Technology Major Project [2011ZX05023-005]
Interpolating the missing traces of regularly or irregularly sampled seismic record is an exceedingly important issue in the geophysical community. Many modern acquisition and reconstruction methods are designed to exploit the transform domain sparsity of the few randomly recorded but informative seismic data using thresholding techniques. In this paper, to regularize randomly sampled seismic data, we introduce two accelerated, analysis-based two-step interpolation algorithms, the analysis-based FISTA (fast iterative shrinkage-thresholding algorithm) and the FPOCS (fast projection onto convex sets) algorithm from the IST (iterative shrinkage-thresholding) algorithm and the POCS (projection onto convex sets) algorithm. A MATLAB package is developed for the implementation of these thresholding-related interpolation methods. Based on this package, we compare the reconstruction performance of these algorithms, using synthetic and real seismic data. Combined with several thresholding strategies, the accelerated convergence of the proposed methods is also highlighted. (c) 2012 Elsevier Ltd. All rights reserved.
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