ARTICLE

Decomposition of a Laplacian filter operator for reverse time migration

XIN TIAN1 SAMAN AZADBAKHT2 CHIYUAN REN3
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1 Engineering College, Southwest Petroleum University, Chengdu 610500, P.R. China. tianxinjxs@163.com,
2 Petroleum Systems Engineering, University of Regina, SK, Canada S4S 0A2.,
3 Science College, Southwest Petroleum University, Chengdu 610500, P.R. China.,
JSE 2018, 27(4), 1–22;
Submitted: 9 June 2025 | Revised: 9 June 2025 | Accepted: 9 June 2025 | Published: 9 June 2025
© 2025 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

Tian, X., Azadbakht, S. and Ren, C.Y., 2018. Decomposition of the Laplacian filter operator for reverse time migration. Journal of Seismic Exploration, 27: 349-370. Reverse time migration is considered as an effective approach to obtain the images of layers, but it usually produces some artifacts in the images. Applying a Laplacian filter is a conventional and effective approach to improve these images. In this work, the Laplacian operator is decomposed mathematically. Based on this decomposition, the reason why the Laplacian filter can improve images is investigated thoroughly, and the work part is identified. Then we discard the useless part and employ the work part to form a new imaging condition. At first, shown in a one-shot numerical experiment, the proposed imaging condition can simultaneously remove the low-and high-frequency artifacts effectively. Then, the Sigsbee2A velocity model is employed as a synthetic example to verify the proposed imaging condition. The numerical results show that the proposed imaging condition can effectively govern the principal energy in wave fields and damp artifacts in angle domain.

Keywords
reverse time migration
imaging condition
Laplacian operator
artifact removal
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing