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• W4291752846 abstract "PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyAutomated hyper-parameter optimization for deep learning framework to simulate boundary conditions for wave propagationAuthors: Harpreet KaurSergey FomelNam PhamHarpreet KaurThe University of Texas at AustinSearch for more papers by this author, Sergey FomelThe University of Texas at AustinSearch for more papers by this author, and Nam PhamThe University of Texas at AustinSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3743090.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractWe propose a hyper-parameter optimization workflow for training the deep learning framework to simulate the effect of boundary conditions for wave propagation. Hyper-parameter selection is a crucial step in model building and has a direct impact on the performance of machine learning models. We implement three different hyper-parameter optimization techniques, namely random search, Hyperband, and Bayesian optimization, for the proposed network to simulate boundary conditions and compare the strengths and drawbacks of these techniques. The automated deep learning framework optimizes network training and significantly improves the efficiency of the workflow. The proposed method reduces human effort in the network tuning process, improves the performance of deep learning models by achieving the optimal minima, makes the model more reproducible, and can be extended to different deep learning based applications. Tests on different models verify the effectiveness of the proposed approach.Keywords: machine learning, wave propagation, boundary conditionsPermalink: https://doi.org/10.1190/image2022-3743090.1FiguresReferencesRelatedDetailsCited byAutomated hyperparameter optimization for simulating boundary conditions for acoustic and elastic wave propagation using deep learningHarpreet Kaur, Sergey Fomel, and Nam Pham5 January 2023 | GEOPHYSICS, Vol. 88, No. 1 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 Harpreet Kaur, Sergey Fomel, and Nam Pham, (2022), Automated hyper-parameter optimization for deep learning framework to simulate boundary conditions for wave propagation, SEG Technical Program Expanded Abstracts : 1935-1939. https://doi.org/10.1190/image2022-3743090.1 Plain-Language Summary Keywordsmachine learningwave propagationboundary conditionsPDF DownloadLoading ..." @default.
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