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A compound method for random noise attenuationNormal access

Authors: D. Gemechu, J. Ma and X. Yong
Journal name: Geophysical Prospecting
Issue: Vol 66, No 8, October 2018 pp. 1548 - 1567
DOI: 10.1111/1365-2478.12672
Organisations: Wiley
Language: English
Info: Article, PDF ( 9.06Mb )

Random noise attenuation, preserving the events and weak features by improving signal-to-noise ratio and resolution of seismic data are the most important issues in geophysics. To achieve this objective, we proposed a novel seismic random noise attenuation method by building a compound algorithm. The proposed method combines sparsity prior regularization based on shearlet transform and anisotropic variational regularization. The anisotropic variational regularization which is based on the linear combination of weighted anisotropic total variation and anisotropic secondorder total variation attenuates noises while preserving the events of seismic data and it effectively avoids the fine-scale artefacts due to shearlets from the restored seismic data. The proposed method is formulated as a convex optimization problem and the split Bregman iteration is applied to solve the optimization problem. To verify the effectiveness of the proposed method, we test it on several synthetic seismic datasets and real datasets. Compared with three methods (the linear combination of weighted anisotropic total variation and anisotropic second-order total variation, shearlets and shearlet-based weighted anisotropic total variation), the numerical experiments indicate that the proposed method attenuates random noises while alleviating artefact and preserving events and features of seismic data. The obtained result also confirms that the proposed method improves the signal-to-noise ratio.

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