1887

Abstract

Summary

Sparse reconstruction for seismic data aims to reconstruct the missing traces from noise-contaminated or incomplete seismic datasets with a sparsity regularization. The L0 and L1 regularization are the most widely used to methods to constrain the transform-domain coefficients.However,because of the NP-hard difficulty of L0 regularization and non-sparsest solution of L1 regularization, the traditional approach can not obtain the optimal solutions to the seismic interpolation problems. We propose a novel L1/2 regularization model to solve the seismic interpolation problem and borrow the efficient iterative half-thresholding (IHT) solver from the signal-processing field to solve the proposed L1/2 regularization model. An irregularly sampled 3D seismic data with 50% randomly missing traces shows accurate reconstruction using the proposed approach. Comparison with the traditional L1 regularization also confirms the effectiveness of the proposed approach. Because of the simple and efficient implementation of IHT algorithm, the proposed approach can be conveniently used in industry.

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/content/papers/10.3997/2214-4609.201413447
2015-06-01
2024-04-28
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References

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