Augment time-domain FWI with iterative deep learning

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Augment time-domain FWI with iterative deep learningAuthors: Tao ZhaoAria AbubakarXin ChengLei FuTao ZhaoSchlumbergerSearch for more papers by this author, Aria AbubakarSchlumbergerSearch for more papers by this author, Xin ChengSchlumbergerSearch for more papers by this author, and Lei FuSchlumbergerSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3424983.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractWe introduce an iterative workflow that uses data-driven methods to augment time-domain full waveform inversion (FWI) by predicting low frequency seismic data. The predicted data are used to invert a low wavenumber velocity model. When this low wavenumber model is used as an initial velocity model, it helps to reduce the risk of cycleskipping in FWI. Synthetic tests on both acoustic and elastic data demonstrate that when FWI starts with the updated low wavenumber velocity model, it produces more accurate results compared to an initial model without this update.Presentation Date: Wednesday, October 14, 2020Session Start Time: 9:20 AMPresentation Time: 9:20 AMLocation: Poster Station 3Presentation Type: PosterKeywords: full-waveform inversion, machine learning, time-domainPermalink: https://doi.org/10.1190/segam2020-3424983.1FiguresReferencesRelatedDetailsCited byExtrapolated surface-wave dispersion inversionHongyu Sun and Laurent Demanet15 August 2022Innovative application of full-waveform inversion applied to extended wide-azimuth marine streamer seismic data in a complex salt environmentPeter Lanzarone, Xukai Shen, Andrew Brenders, Ganyuan Xia, Joe Dellinger, Gabriel Ritter, and John Etgen25 February 2022 | GEOPHYSICS, Vol. 87, No. 3Seismic nonstationary deconvolution with physics-guided autoencoderSon Phan and Mrinal K. Sen1 September 2021 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 Tao Zhao, Aria Abubakar, Xin Cheng, and Lei Fu, (2020), "Augment time-domain FWI with iterative deep learning," SEG Technical Program Expanded Abstracts : 850-854. https://doi.org/10.1190/segam2020-3424983.1 Plain-Language Summary Keywordsfull-waveform inversionmachine learningtime-domainPDF DownloadLoading ...