Physics informed neural networks for velocity inversion

Abstract

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Physics informed neural networks for velocity inversionAuthors: Yiran XuJingye LiXiaohong ChenYiran XuState Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, 102249, ChinaDivision of Applied Mathematics, Brown University, Providence, RI 02912, USASearch for more papers by this author, Jingye LiState Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, 102249, ChinaSearch for more papers by this author, and Xiaohong ChenState Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, 102249, ChinaSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3216823.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractIn this abstract, a new neural network, physics-informed neural networks (PINNs) (M. Raissi, 2019) are introduced and implemented to solve the inversion problems of wave equations. PINNs employ standard feedforward neural networks (NNs) with the partial differential equations (PDEs) explicitly encoded into the NN using automatic differentiation, while the sum of the mean-squared error in initial/boundary conditions is minimized with respect to the NN parameters. Specifically, here we use this network structure to produce an accurate velocity model from seismic data. Our approach relies on training deep neural networks that are extended to encode the acoustic wave equation. In the first case, given analytical solution to an initial boundary value problem (IBVP) to infer very accurately the velocity parameter. In the second case, given seismic wavefield data in space-time, we use several coupled deep neural networks to infer vary accuracy the velocity field. After compared the results with full waveform inversion (FWI), the promising results for synthetic 2D data demonstrate a new way of using seismic data to identify key structures in the subsurface from machine learning approaches.Presentation Date: Wednesday, September 18, 2019Session Start Time: 9:20 AMPresentation Time: 9:45 AMLocation: Poster Station 2Presentation Type: PosterKeywords: machine learning, inversion, acousticPermalink: https://doi.org/10.1190/segam2019-3216823.1FiguresReferencesRelatedDetailsCited bySeismic Inversion Based on Acoustic Wave Equations Using Physics-Informed Neural NetworkIEEE Transactions on Geoscience and Remote Sensing, Vol. 61Active training of physics-informed neural networks to aggregate and interpolate parametric solutions to the Navier-Stokes equationsJournal of Computational PhysicsDeep convolutional neural network and sparse least-squares migrationZhaolun Liu, Yuqing Chen, and Gerard Schuster13 June 2020 | GEOPHYSICS, Vol. 85, No. 4 SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION Yiran Xu, Jingye Li, and Xiaohong Chen, (2019), "Physics informed neural networks for velocity inversion," SEG Technical Program Expanded Abstracts : 2584-2588. https://doi.org/10.1190/segam2019-3216823.1 Plain-Language Summary Keywordsmachine learninginversionacousticPDF DownloadLoading ...