Deep Earth: Leveraging neural networks for seismic exploration objectives

PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyDeep Earth: Leveraging neural networks for seismic exploration objectivesAuthors: Tariq AlkhalifahClaire BirnieRandy HarsukoHanchen WangOleg OvcharenkoTariq AlkhalifahKAUSTSearch for more papers by this author, Claire BirnieKAUSTSearch for more papers by this author, Randy HarsukoKAUSTSearch for more papers by this author, Hanchen WangKAUSTSearch for more papers by this author, and Oleg OvcharenkoKAUSTSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3750966.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractMachine learning has already made many inroads in developments related to acquisition, processing, imaging, inverting, and interpreting seismic data. In spite of the many success stories, its commercial use has been limited as the challenges mount. These challenges include cost of training, availability of training samples, the applicability of the trained model to real data (generalization), and more importantly, the availability of practitioners who actually know what the neural networks (NNs) are doing. Taking a step back, I will review what worked in deep learning and what we are still waiting on to work. We will look into the various ML algorithms, from supervised to unsupervised, transformers to contrastive learning, and identify the potential role of these various algorithms on seismic data, with examples. The examples include seismic data denoising, data extrapolation, first arrival picking, microseismic location, velocity inversion all on real data.Keywords: machine learning, seismic processing, velocity inversion, denoisingPermalink: https://doi.org/10.1190/image2022-3750966.1FiguresReferencesRelatedDetails 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 Tariq Alkhalifah, Claire Birnie, Randy Harsuko, Hanchen Wang, and Oleg Ovcharenko, (2022), "Deep Earth: Leveraging neural networks for seismic exploration objectives," SEG Technical Program Expanded Abstracts : 2372-2375. https://doi.org/10.1190/image2022-3750966.1 Plain-Language Summary Keywordsmachine learningseismic processingvelocity inversiondenoisingPDF DownloadLoading ...