Journal
JOURNAL OF GEOPHYSICS AND ENGINEERING
Volume 12, Issue 6, Pages 1024-1035Publisher
IOP PUBLISHING LTD
DOI: 10.1088/1742-2132/12/6/1024
Keywords
shearlet; total generalized variation; sparse representation; seismic random noise attenuation
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Funding
- National Natural Science Foundation of China [41274127, 40874066]
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Seismic denoising from a corrupted observation is an important part of seismic data processing which improves the signal-to-noise ratio (SNR) and resolution. In this paper, we present an effective denoising method to attenuate seismic random noise. The method takes advantage of shearlet and total generalized variation (TGV) regularization. Different regularity levels of TGV improve the quality of the final result by suppressing Gibbs artifacts caused by the shearlet. The problem is formulated as mixed constraints in a convex optimization. A Bregman algorithm is proposed to solve the proposed model. Extensive experiments based on one synthetic datum and two post-stack field data are done to compare performance. The results demonstrate that the proposed method provides superior effectiveness and preserve the structure better.
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