A framework for coupled physics-deep learning inversion and multiparameter joint inversion

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsA framework for coupled physics-deep learning inversion and multiparameter joint inversionAuthors: Daniele ColomboErsan TurkogluWeichang LiDiego RovettaDaniele ColomboSaudi AramcoSearch for more papers by this author, Ersan TurkogluSaudi AramcoSearch for more papers by this author, Weichang LiAramco AmericasSearch for more papers by this author, and Diego RovettaAramco Overseas CompanySearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3583272.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractA new approach to the inversion and joint inversion of geophysical data is described. We take advantage of the domains of local optimization and of the machine learning (ML) or deep learning (DL) technique to generate efficient optimization schemes to reduce uncertainties in the model parameter estimations, exploit the image segmentation capability of DL techniques, and guarantee compliance with the requirement of physics for the wave propagation. The domains of physics driven (Phy) optimization, based on data misfit functionals, and of DL optimization, based on model misfit (loss), are coupled by multiple penalty functions imposed on the common model term of the physical domain such as performed in a joint inversion approach. The procedure is complemented by network retraining with partial inversion results to augment the network knowledge base and enable more physics-oriented DL predictions. After several iterations, the procedure tends to converge to models satisfying both physics and DL optimization schemes by providing at the same time better resolution and accuracy in parameter estimation. The developed method is demonstrated on synthetic and field transient EM data.Keywords: inversion, machine learning, electromagnetics, multiphysicsPermalink: https://doi.org/10.1190/segam2021-3583272.1FiguresReferencesRelatedDetails 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 Daniele Colombo, Ersan Turkoglu, Weichang Li, and Diego Rovetta, (2021), "A framework for coupled physics-deep learning inversion and multiparameter joint inversion," SEG Technical Program Expanded Abstracts : 1706-1710. https://doi.org/10.1190/segam2021-3583272.1 Plain-Language Summary Keywordsinversionmachine learningelectromagneticsmultiphysicsPDF DownloadLoading ...