Deep learning for joint geophysical inversion of seismic and MT data sets

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsDeep learning for joint geophysical inversion of seismic and MT data setsAuthors: Abhinav Pratap SinghDivakar VashisthShalivahan SrivastavaAbhinav Pratap SinghIndian Institute of Technology, Indian School of Mines, India.Search for more papers by this author, Divakar VashisthIndian Institute of Technology, Indian School of Mines, India.Search for more papers by this author, and Shalivahan SrivastavaIndian Institute of Technology, Indian School of Mines, India.Search for more papers by this authorhttps://doi.org/10.1190/segam2021-3583955.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractUsing more than one geophysical technique provides a more reliable way to delineate the subsurface structure than a single geophysical method. In this paper, we present a novel way for the joint inversion of seismic and MT datasets using artificial neural networks. Different rock models are taken to compute the joint relation between the porosity, shale content, velocity and resistivity of the strata. The velocity and resistivity models thus generated were used to compute seismic traces and apparent resistivity curves, the dataset used for training and testing the neural network. Such a method of performing joint inversion is advantageous when we want to check the reliability of our Machine Learning model in regions of low velocity or when a particular physical property does not vary across the layers while another does since, unlike other optimisation schemes used for the geophysical inversion, we do not need to rerun the model for every initial model. The efficacy of artificial neural networks (ANN) to perform the join inversion is tested, and in the process, a particular type of ANN architecture is developed.Keywords: neural networks, magnetotelluric, inversion, integration, seismic impedancePermalink: https://doi.org/10.1190/segam2021-3583955.1FiguresReferencesRelatedDetails 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 Abhinav Pratap Singh, Divakar Vashisth, and Shalivahan Srivastava, (2021), "Deep learning for joint geophysical inversion of seismic and MT data sets," SEG Technical Program Expanded Abstracts : 1741-1745. https://doi.org/10.1190/segam2021-3583955.1 Plain-Language Summary Keywordsneural networksmagnetotelluricinversionintegrationseismic impedancePDF DownloadLoading ...