Year: 2020
Author: Xiaofeng Jia, Bin Chen, Qihua Li
Communications in Computational Physics, Vol. 28 (2020), Iss. 2 : pp. 723–742
Abstract
Conventional shot-record reverse time migration (RTM) suffers from a high computational burden when dealing with massive data. The computational cost of RTM can be reduced by shot-encoding techniques, and plane-wave encoding is a commonly used and effective shot-encoding scheme. However, plane-wave encoding requires long time padding to avoid information loss, which decreases the efficiency of the time-domain wavefield extrapolator, and the time padding becomes longer with the increasing distance between multiple sources. The piecewise plane-wave encoding scheme cuts multiple sources into several segments prior to implementing plane-wave encoding, hence reduces the time padding, but brings new crosstalk due to the mutual interference between shots in different source segments. We suppress the crosstalk artifacts by a new bipolar-bisection amplitude encoding method, in which half of the encoding array of each migration is different from that of any other migrations to reduce the number of crosstalk terms with as few migrations as possible. We embed the bipolar-bisection method into piecewise plane-wave encoding. Compared with plane-wave encoding, the proposed scheme requires considerably shorter time padding and thus works more efficiently and can generate a qualified imaging result. The feasibility of the proposed method is tested on the 2D SEG/EAGE salt model and the Marmousi model.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/cicp.OA-2018-0247
Communications in Computational Physics, Vol. 28 (2020), Iss. 2 : pp. 723–742
Published online: 2020-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 20
Keywords: Reverse time migration crosstalk artifacts shot encoding computational efficiency.
Author Details
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Deep Learning for Enhancing Multisource Reverse Time Migration
Li, Yaxing
Jia, Xiaofeng
Wu, Xinming
Geng, Zhicheng
IEEE Transactions on Geoscience and Remote Sensing, Vol. 60 (2022), Iss. P.1
https://doi.org/10.1109/TGRS.2022.3206283 [Citations: 5]