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Least-squares reverse time migration of blended data with low-rank constraint along structural direction

MIN BAI1 ,  JUAN WU1 ,  JIANYONG XIE2 ,  DONG ZHANG2
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1 School of Resources and Environment, North China University of Water Resources and Electric Power, Zhengzhou 450045, P.R. China,
2 State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Fuxue Road 18, Beijing 102200, P.R. China,
JSE 2018, 27(1), 29–48;
Submitted: 1 May 2017 | Accepted: 8 November 2017 | Published: 1 February 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/ )
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Abstract

Bai, M., Wu, J., Xie, J., and Zhang, D., 2018. Least-squares least reverse time migration of blended data with low-rank constraint along structural direction. Journal of Seismic Exploration, 27: 29-48. The simultaneous-source acquisition has been developed to tremendously improve the marine acquisition efficiency. The economic benefit is compromised by the complexity of recorded seismic data. We propose an effective direct imaging algorithm for migrating the blended simultaneous-source data without preprocessing, i.e., source separation. While the least-squares reverse time migration can help attenuate the crosstalk noise significantly, there are still a huge amount of artifacts existing in the final image. We propose to apply a low-rank constraint to regularize the model during the least-squares inversion. The low-rank constraint is applied along the geological structure of the reflectivity image in order to fully take advantage of the structural patterns of seismic images. Two numerical examples with different complexity validate the effectiveness of the proposed method.

Keywords
simultaneous-source acquisition
least-squares reverse time migration
low-rank constraint
geological structure
crosstalk noise
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
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