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• W3197153445 abstract "PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsA recurrent neural network for ℓ1 anisotropic viscoelastic full-waveform inversion with high-order total variation regularizationAuthors: Tianze ZhangJian SunKristopher A. InnanenDaniel O. TradTianze ZhangUniversity of CalgarySearch for more papers by this author, Jian SunUniversity of CalgarySearch for more papers by this author, Kristopher A. InnanenThe Pennsylvania State UniversitySearch for more papers by this author, and Daniel O. TradUniversity of CalgarySearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3594168.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractA theory-guided deep learning approach to full waveform inversion has features which make it increasingly relevant as we treat problems of greater and greater complexity (e.g., when number of parameter classes increases). Here, we design a recurrent neural network (RNN) for anisotropic viscoelastic full waveform inversion. Eight parameters are simultaneously inverted for, the elastic parameters C11, C13, C33, C44 and their corresponding attenuation parameters QC11, QC13, QC33, QC44. The RNN is built around the velocity-stress anisotropic viscoelastic wave equation. It is a convenient environment in which to compute medium property FWI updates, given a non self-adjoint wave operator, and when the objective function is not analytically differentiable, but some experimentation is required to determine best choices of optimization and regularization in this multi-parameter setting. An objective function based on the l1 norm with high-order total variation (TV) regularization is selected, and the numerical response of the network, given simulated data for a range of acquisition geometries, is examined. Given accurate starting models, we observe that the high-order TV regularization is adept at recovering sharp edges in model parameters. We also observe a significant improvement in parameter recovery given a combination of cross-well and surface data in comparison with surface acquisition.Keywords: machine learning, full-waveform inversion, viscoelastic, VTI, attenuationPermalink: https://doi.org/10.1190/segam2021-3594168.1FiguresReferencesRelatedDetails 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 Tianze Zhang, Jian Sun, Kristopher A. Innanen, and Daniel O. Trad, (2021), A recurrent neural network for ℓ1 anisotropic viscoelastic full-waveform inversion with high-order total variation regularization, SEG Technical Program Expanded Abstracts : 1374-1378. https://doi.org/10.1190/segam2021-3594168.1 Plain-Language Summary Keywordsmachine learningfull-waveform inversionviscoelasticVTIattenuationPDF DownloadLoading ..." @default.
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• W3197153445 title "A recurrent neural network for ℓ1 anisotropic viscoelastic full-waveform inversion with high-order total variation regularization" @default.
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