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High-order sparse Radon transform for deblending of simultaneous source seismic data
MIN WANG,
YARU XUE
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China University of Petroleum, State Key Laboratory of Petroleum Resources and Prospecting, Beijing 102249, P.R. China.,
JSE 2018, 27(2), 167–181;
Submitted: 27 July 2017 | Accepted: 15 January 2018 | Published: 1 April 2018
© 2018 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/ )
Abstract
Wang, M. and Xue, Y., 2018. High-order sparse Radon transform for deblending of simultaneous source seismic data. Journal of Seismic Exploration, 27: 167-181. This paper proposes an iterative high-order Radon transform based on matching pursuit (MP) algorithm to separate the blended seismic data. During each iteration, the matched subspace is picked by energy distribution in the high-order Radon domain. In thus small subspace, the high-order Radon transform is realized quickly to estimate the effective signals. The blending noise is then estimated by the estimate-deblended data with prior acquisition code and subtracted from the pseudo-deblended data. Thus an iteration is finished. The MP method shows more sparse than the iterative reweight least square method (IRLS). We compared the denoising effectiveness between these two methods. Synthetic and field data experiments prove that the matching pursuit algorithm has higher SNR and better denoising effectiveness than IRLS method.
Keywords
high-order Radon transform
matching pursuit algorithm
sparse constraint
simultaneous source acquisition
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