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• W3091445434 abstract "PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Structure enhanced least-squares migration by deep learning based structural preconditioningAuthors: Cheng ChengYang HeBin WangYi HuangCheng ChengTGSSearch for more papers by this author, Yang HeTGSSearch for more papers by this author, Bin WangTGSSearch for more papers by this author, and Yi HuangTGSSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3426676.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractIterative data-domain least-squares migration can overcome acquisition limitations and recover the reflectivity for desired amplitudes and resolutions. However, migration noise due to velocity errors and multiple scattering energy related to strong contrasts in the velocity model can be erroneously enhanced as well. In this complex case, it needs many extra iterations to achieve the final desired image. Regularization or preconditioning can be applied at each least-squares iteration to suppress migration artifacts, speed up convergency and improve inversion efficiency. However, in sedimentary layers, without proper fault constraints, it cannot preserve the real geological features in the image. In this work, we propose to use convolutional neural networks (CNNs) to automatically detect faults on the migration image first, and then to use the picked fault information as an additional constraint for preconditioning during least-squares migration. With proper training, our 3D predictive model can learn to detect true fault features and avoid erroneous picks of swing noise on the validation dataset. An offshore Brazil field data example in the Santos Basin demonstrates that our final least-squares migration images show enhanced fault structure, minimized migration artifacts, significantly increased image bandwidth and improved illumination after only a few iterations.Presentation Date: Wednesday, October 14, 2020Session Start Time: 8:30 AMPresentation Time: 9:45 AMLocation: 361APresentation Type: OralKeywords: least-squares migration, machine learning, faults, illumination, broadbandPermalink: https://doi.org/10.1190/segam2020-3426676.1FiguresReferencesRelatedDetailsCited byLeast-squares reverse time migration via deep learning-based updating operatorsKristian Torres and Mauricio Sacchi22 September 2022 | GEOPHYSICS, Vol. 87, No. 6 SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Cheng Cheng, Yang He, Bin Wang, and Yi Huang, (2020), Structure enhanced least-squares migration by deep learning based structural preconditioning, SEG Technical Program Expanded Abstracts : 2908-2912. https://doi.org/10.1190/segam2020-3426676.1 Plain-Language Summary Keywordsleast-squares migrationmachine learningfaultsilluminationbroadbandPDF DownloadLoading ..." @default.
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• W3091445434 title "Structure enhanced least-squares migration by deep learning based structural preconditioning" @default.
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