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• W3197450056 abstract "PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsDual-band generative learning for low-frequency extrapolation in seismic land dataAuthors: Oleg OvcharenkoVladimir KazeiDaniel PeterIlya SilvestrovAndrey BakulinTariq AlkhalifahOleg OvcharenkoKAUSTSearch for more papers by this author, Vladimir KazeiAramco Services CompanySearch for more papers by this author, Daniel PeterKAUSTSearch for more papers by this author, Ilya SilvestrovSaudi AramcoSearch for more papers by this author, Andrey BakulinSaudi AramcoSearch for more papers by this author, and Tariq AlkhalifahKAUSTSearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3579442.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractThe presence of low-frequency energy in seismic data can help mitigate cycle-skipping problems in full-waveform inversion. Unfortunately, the generation and recording of low-frequency signals in seismic exploration remains a non-trivial task. Extrapolation of missing low-frequency content in field data might be addressed in a data-driven framework. In particular, deep learning models trained on synthetic data could be used for inference on the field data. Such an implementation of switching application domains remains challenging. We, therefore, propose the concept of generative dual-band learning to facilitate the knowledge transfer between synthetic and field seismic data applications of low-frequency data extrapolation. We first explain the two-step procedure for training a generative adversarial network (GAN) that extrapolates low frequencies. Then, we describe the workflow for synthetic dataset generation. Finally, we explore the feasibility of the dual-band learning concept on real nearsurface land data acquired in Saudi Arabia.Keywords: machine, learning, low frequency, land, elastic, artificial intelligencePermalink: https://doi.org/10.1190/segam2021-3579442.1FiguresReferencesRelatedDetails 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 Oleg Ovcharenko, Vladimir Kazei, Daniel Peter, Ilya Silvestrov, Andrey Bakulin, and Tariq Alkhalifah, (2021), Dual-band generative learning for low-frequency extrapolation in seismic land data, SEG Technical Program Expanded Abstracts : 1345-1349. https://doi.org/10.1190/segam2021-3579442.1 Plain-Language Summary Keywordsmachine learninglow frequencylandelasticartificial intelligencePDF DownloadLoading ..." @default.
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• W3197450056 title "Dual-band generative learning for low-frequency extrapolation in seismic land data" @default.
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