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A fast joint seismic data reconstruction by sparsity-promoting inversionNormal access

Authors: Lanshu Bai, Huiyi Lu, Yike Liu and Majid Khan
Journal name: Geophysical Prospecting
Issue: Vol 65, No 4, July 2017 pp. 926 - 940
DOI: 10.1111/1365-2478.12455
Organisations: Wiley
Language: English
Info: Article, PDF ( 15.58Mb )

Summary:
Seismic field data are often irregularly or coarsely sampled in space due to acquisition limits. However, complete and regular data need to be acquired in most conventional seismic processing and imaging algorithms. We have developed a fast joint curveletdomain seismic data reconstruction method by sparsity-promoting inversion based on compressive sensing. We have made an attempt to seek a sparse representation of incomplete seismic data by curvelet coefficients and solve sparsity-promoting problems through an iterative thresholding process to reconstruct the missing data. In conventional iterative thresholding algorithms, the updated reconstruction result of each iteration is obtained by adding the gradient to the previous result and thresholding it. The algorithm is stable and accurate but always requires sufficient iterations. The linearised Bregman method can accelerate the convergence by replacing the previous result with that before thresholding, thus promoting the effective coefficients added to the result. The method is faster than conventional one, but it can cause artefacts near the missing traces while reconstructing small-amplitude coefficients because some coefficients in the unthresholded results wrongly represent the residual of the data. The key process in the joint curvelet-domain reconstruction method is that we use both the previous results of the conventional method and the linearised Bregman method to stabilise the reconstruction quality and accelerate the recovery for a while. The acceleration rate is controlled through weighting to adjust the contribution of the acceleration term and the stable term. A fierce acceleration could be performed for the recovery of comparatively small gaps, whereas a mild acceleration is more appropriate when the incomplete data has a large gap of high-amplitude events. Finally, we carry out a fast and stable recovery using the trade-off algorithm. Synthetic and field data tests verified that the joint curvelet-domain reconstruction method can effectively and quickly reconstruct seismic data with missing traces.

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