Equivariant imaging for self-supervised regularly undersampled seismic data interpolation

PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyEquivariant imaging for self-supervised regularly undersampled seismic data interpolationAuthors: Weiwei XuVincenzo LipariPaolo BestaginiPolitecnico di MilanoWenchao ChenStefano TubaroWeiwei XuXi’an Jiaotong UniversitySearch for more papers by this author, Vincenzo LipariXi’an Jiaotong UniversitySearch for more papers by this author, Paolo BestaginiXi’an Jiaotong UniversitySearch for more papers by this author, Politecnico di MilanoXi’an Jiaotong UniversitySearch for more papers by this author, Wenchao ChenXi’an Jiaotong UniversitySearch for more papers by this author, and Stefano TubaroPolitecnico di MilanoSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3751148.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractBecause of the restriction of complex field conditions and economic circumstance, seismic data is usually undersampled in the spatial domain, which needs to be interpolated to meet the requirements of following seismic data processing such as seismic imaging. In this abstract, we present a seismic data interpolation method via an end-to-end self-supervised deep learning framework. Specifically, a CNN is trained only using the observed undersampled seismic data itself. Furthermore, based on the equivariance of seismic data with respect to shift and undersampling, a training strategy that enforces both the measurement consistency and the equivalence is utilized. Experiments on regularly undersampled synthetic and field data interpolation show the effectiveness of our presented method in comparison with deep image prior (DIP) based interpolation method.Keywords: regularly , undersampled seismic data interpolation, self-supervised deep learning, training strategy, convolutional neural network, equivariance of seismic dataPermalink: https://doi.org/10.1190/image2022-3751148.1FiguresReferencesRelatedDetails 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 Weiwei Xu, Vincenzo Lipari, Paolo Bestagini, Politecnico di Milano, Wenchao Chen, and Stefano Tubaro, (2022), "Equivariant imaging for self-supervised regularly undersampled seismic data interpolation," SEG Technical Program Expanded Abstracts : 1920-1924. https://doi.org/10.1190/image2022-3751148.1 Plain-Language Summary Keywordsregularly undersampled seismic data interpolationself-supervised deep learningtraining strategyconvolutional neural networkequivariance of seismic dataPDF DownloadLoading ...