Deep learning-based seismic surface-related multiple adaptive subtraction with synthetic primary labels

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsDeep learning-based seismic surface-related multiple adaptive subtraction with synthetic primary labelsAuthors: Dong ZhangMike de LeeuwEric VerschuurDong ZhangDelft University of TechnologySearch for more papers by this author, Mike de LeeuwDelft University of TechnologySearch for more papers by this author, and Eric VerschuurDelft University of TechnologySearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3584041.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractSurface-related multiple elimination remains one of the most robust primary estimation approaches for decades, in which the adaptive subtraction step is a non-trivial task. Due to imperfections in the made assumptions during prediction, the perfect adaptive subtraction is a highly non-linear and non-stationary process, which is suitable for the popular deep learning (DL)-based image processing. Different from the most straightforward DL-based adaptive subtraction (i.e., the full wavefield and the advanced estimated primary training pair), we propose to include both the original full wavefield and the initial globally estimated surface multiples as the two-channel input, and train a DL neural network (UNet) on synthetic modeled primaries. In this way, the robust physics (i.e., the globally estimated multiples) is utilized, and the ground truth primary labels can be beneficial to the framework. Both synthetic and field examples are provided to demonstrate the current performance of our proposed framework.Keywords: SRME (surface-related muliple, elimination), adaptive subtraction, machine learningPermalink: https://doi.org/10.1190/segam2021-3584041.1FiguresReferencesRelatedDetails First International Meeting for Applied Geoscience & Energy Expanded AbstractsISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2021 Pages: 3561 publication data© 2021 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 01 Sep 2021 CITATION INFORMATION Dong Zhang, Mike de Leeuw, and Eric Verschuur, (2021), "Deep learning-based seismic surface-related multiple adaptive subtraction with synthetic primary labels," SEG Technical Program Expanded Abstracts : 2844-2848. https://doi.org/10.1190/segam2021-3584041.1 Plain-Language Summary KeywordsSRME (surface-related muliple elimination)adaptive subtractionmachine learningPDF DownloadLoading ...