Multilayer perceptron and Bayesian neural network based implicit elastic full-waveform inversion

Abstract

PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyMultilayer perceptron and Bayesian neural network based implicit elastic full-waveform inversionAuthors: Tianze ZhangJian SunDaniel O. TradKristopher A. InnanenTianze Zhang University of CalgarySearch for more papers by this author, Jian Sun Ocean University of ChinaSearch for more papers by this author, Daniel O. Trad University of CalgarySearch for more papers by this author, and Kristopher A. Innanen University of CalgarySearch for more papers by this authorhttps://doi.org/10.1190/image2022-3746334.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract We introduce and analyze implicit full-waveform inversion (IFWI), which uses a neural network to generate velocity models and perform full-waveform inversion (FWI). IFWI carries out inversion by linking two main networks: a neural network that generates velocity models, and a recurrent neural network to perform the modeling. The approach is distinct from conventional waveform inversion in two key ways. First, it reduces reliance on accurate initial models, relative to conventional FWI. Instead, it invokes general information about the target area, for instance estimates of means and standard deviations of medium properties in the target area or, alternatively, well-log information in the target area. Second, iterative updating affects the weights in the neural network, rather than the velocity model directly. Velocity models can be generated in the first part of the IFWI process in either of two ways: through use of a multilayer perceptron (MLP) network, or a Bayesian neural network (BNN). Numerical testing is suggestive that the MLP based IFWI approach in principle build accurate models in the absence of an explicit initial model, and the BNN based IFWI could give the uncertainty analysis for the prediction results. Keywords: full-waveform inversion, initial model, uncertainty analysisPermalink: https://doi.org/10.1190/image2022-3746334.1FiguresReferencesRelatedDetails 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 Tianze Zhang, Jian Sun, Daniel O. Trad, and Kristopher A. Innanen, (2022), "Multilayer perceptron and Bayesian neural network based implicit elastic full-waveform inversion," SEG Technical Program Expanded Abstracts : 1639-1643. https://doi.org/10.1190/image2022-3746334.1 Plain-Language Summary Keywordsfull-waveform inversioninitial modeluncertainty analysisPDF DownloadLoading ...