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• W4291718616 abstract "PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyInternal multiple elimination with an inverse-scattering theory guided deep neural networkAuthors: Zhiwei GuLiurong TaoHaoran RenRu-Shan WuJianhua GengZhiwei GuTongji UniversitySearch for more papers by this author, Liurong TaoZhejiang UniversitySearch for more papers by this author, Haoran RenZhejiang UniversitySearch for more papers by this author, Ru-Shan WuUniversity of CaliforniaSearch for more papers by this author, and Jianhua GengTongji UniversitySearch for more papers by this authorhttps://doi.org/10.1190/image2022-3748726.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractDeep neural networks can automatically mine specific features from seismic data, which can be used in the process of multiple elimination. Surface-related multiple elimination (SRME) can provide good labels for the neural network based multiple elimination. Different from SRME, it is difficult to distinguish internal multiples from primary reflections when the subsurface is complex. An extended single-sided autofocusing guided by inverse-scattering theory is introduced to remove internal multiples in a data-driven manner. In this study, we explore the potential of neural networks in identifying the internal multiples with the guidance of inverse-scattering theory. We feed the neural network with training data, consisting of the shot records with internal multiples, and the primary-only datasets as labels, which are generated by an extended single-sided autofocusing method. The primary-only labels can be beneficial to the U-net framework. The test results show that internal multiple elimination via the neural network takes the advantage of the extended single-sided autofocusing method and is cheaper when the neural network is well-trained. The corresponding reverse time migration (RTM) results show the validity of our workNote: This paper was accepted into the Technical Program but was not presented at IMAGE 2022 in Houston, Texas.Keywords: Internal multiple elimination, inverse-scattering theory, deep neural networkPermalink: https://doi.org/10.1190/image2022-3748726.1FiguresReferencesRelatedDetails Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Zhiwei Gu, Liurong Tao, Haoran Ren, Ru-Shan Wu, and Jianhua Geng, (2022), Internal multiple elimination with an inverse-scattering theory guided deep neural network, SEG Technical Program Expanded Abstracts : 2832-2836. https://doi.org/10.1190/image2022-3748726.1 Plain-Language Summary KeywordsInternal multiple eliminationinverse-scattering theorydeep neural networkPDF DownloadLoading ..." @default.
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• W4291718616 date "2022-08-15" @default.
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• W4291718616 title "Internal multiple elimination with an inverse-scattering theory guided deep neural network" @default.
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