Numerical analysis of a deep learning formulation of multi-parameter elastic full waveform inversion

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Numerical analysis of a deep learning formulation of multi-parameter elastic full waveform inversionAuthors: Tianze ZhangKristopher A. InnanenJian SunDaniel O. TradTianze ZhangUniversity of CalgarySearch for more papers by this author, Kristopher A. InnanenUniversity of CalgarySearch for more papers by this author, Jian SunPennsylvania State UniversitySearch for more papers by this author, and Daniel O. TradUniversity of CalgarySearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3426826.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractIn this paper, we formulate seismic full waveform inversion within a deep learning environment. We are motivated both by the possibilities of incorporating the training of multiple datasets with the relatively low dimensionality of theoryguided network design and by the fact that by doing so we implement an FWI algorithm ready-made for new computational architectures. A recurrent neural network is set up with rules enforcing elastic wave propagation, with the wavefield projected onto a measurement surface acting as the labeled data to be compared with observed seismic data. Training this network amounts to carrying out elastic FWI. Based on the Automatic Differential method, the gradients can be accurately and efficiently constructed by inspection and use of the computational graph, a gradient which acts to update the elastic model. Under the theory-guided network design, the Automatic Differential method provide efficiency and flexibility for different misfits and parameterization alterations. We use different misfits, which are the l2, l1 and Huber norm, to improve the inversion results for parameters in eFWI. We also prepare our approach to mitigate cross-talk, which is a general property of multiparameter full waveform inversion algorithms, by allowing relative freedom to vary the eFWI parameterizations.Presentation Date: Tuesday, October 13, 2020Session Start Time: 1:50 PMPresentation Time: 3:05 PMLocation: 351FPresentation Type: OralKeywords: artificial intelligence, full-waveform inversion, elastic, finite difference, time-domainPermalink: https://doi.org/10.1190/segam2020-3426826.1FiguresReferencesRelatedDetailsCited byImplicit Seismic Full Waveform Inversion With Deep Neural Representation27 February 2023 | Journal of Geophysical Research: Solid Earth, Vol. 128, No. 3Elastic-AdjointNet: A physics-guided deep autoencoder to overcome crosstalk effects in multiparameter full-waveform inversionArnab Dhara and Mrinal Sen15 August 2022Multilayer perceptron and Bayesian neural network based implicit elastic full-waveform inversionTianze Zhang, Jian Sun, Daniel O. Trad, and Kristopher A. Innanen15 August 2022A recurrent neural network for 𝓁1 anisotropic viscoelastic full-waveform inversion with high-order total variation regularizationTianze Zhang, Jian Sun, Kristopher A. Innanen, and Daniel O. Trad1 September 2021Physics-guided deep learning for seismic inversion with hybrid training and uncertainty analysisJian Sun, Kristopher A. Innanen, and Chao Huang19 March 2021 | GEOPHYSICS, Vol. 86, No. 3 SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Tianze Zhang, Kristopher A. Innanen, Jian Sun, and Daniel O. Trad, (2020), "Numerical analysis of a deep learning formulation of multi-parameter elastic full waveform inversion," SEG Technical Program Expanded Abstracts : 1531-1535. https://doi.org/10.1190/segam2020-3426826.1 Plain-Language Summary Keywordsartificial intelligencefull-waveform inversionelasticfinite differencetime-domainPDF DownloadLoading ...