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Iterative sparse deconvolution using seislet-domain constraint

MIN BAI,  JUAN WU
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Key Laboratory of Exploration Technology for Oil and Gas Resources of the Ministry of Education, Yangtze University, Wuhan 430100, P.R. China,
JSE 2019, 28(1), 73–88;
Submitted: 21 May 2018 | Accepted: 10 December 2018 | Published: 1 February 2019
© 2019 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Bai, M., and Wu, J., 2019. Iterative sparse deconvolution using seislet-domain constraint. Journal of Seismic Exploration, 28: 73-88. Deconvolution can help improve the resolution of seismic data. We introduce a deconvolution formulation that can arbitrarily select the resolution level of the seismic data by defining a simple squeezing factor. Considering the ill-posedness of the deconvolution problem, some proper regularizations should be added when iteratively solving the deconvolution-related inverse problem. Traditionally used Fourier-domain constraint can be effective only when the seismic data contains linear events. We propose a seislet-domain constraint to regularize the deconvolution problem to deal with the curved events in seismic data. The seislet transform compressed the seismic data along structural direction, and thus can obtain the optimal sparsity. We apply the proposed method to both synthetic and field data examples and obtain encouraging performance.

Keywords
deconvolution
noise attenuation
seislet transform
sparse inversion
regularization
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