A new P-wave reconstruction method for VSP data using conditional generative adversarial network

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019A new P-wave reconstruction method for VSP data using conditional generative adversarial networkAuthors: Yanwen WeiHaohuan FuYunyue Elita LiJizhong YangYanwen WeiTsinghua University and China National Supercomputer Center in WuxiSearch for more papers by this author, Haohuan FuTsinghua University and China National Supercomputer Center in WuxiSearch for more papers by this author, Yunyue Elita LiNational University of SingaporeSearch for more papers by this author, and Jizhong YangNational University of SingaporeSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3206719.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractWave-mode separation is an essential step of multicomponent seismic data processing. Due to limited acquisition at sparse receiver locations, conventional physics-based wavemode decomposition methods are only feasible based on many strong assumptions such as homogeneous subsurface velocity and flat structures. We propose a new P-wave reconstruction method based on VSP data, aiming to learn the domain transformation directly from full waveform elastic VSP data to their P-wave components. This method is based on optimal transportation theory and implemented by Generative Adversarial Network. We train network on 22180 pairs of synthetic data produced on 2D subsurface model and test on 40596 pairs. The network outputs achieve an average accuracy rate of 97.87%. The results tested on the 2D synthetic data show the network is capable of learning the waveform of target separated data beyond phase recognition and wave classification.Presentation Date: Wednesday, September 18, 2019Session Start Time: 9:20 AMPresentation Time: 10:10 AMLocation: Poster Station 1Presentation Type: PosterKeywords: artificial intelligence, vertical seismic profile (VSP), P-wave, 2DPermalink: https://doi.org/10.1190/segam2019-3206719.1FiguresReferencesRelatedDetailsCited byDeep Learning-Based P- and S-Wave Separation for Multicomponent Vertical Seismic ProfilingIEEE Transactions on Geoscience and Remote Sensing, Vol. 60Building training data set for deep learning-based P- and S-wave separation: Field data caseYanwen Wei, Yunyue Elita Li, and Haohuan Fu1 September 2021Multi-task learning based P/S wave separation and reverse time migration for VSPYanwen Wei, Yunyue Elita Li, Jizhong Yang, Jingjing Zong, Jinwei Fang, and Haohuan Fu30 September 2020 SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION Yanwen Wei, Haohuan Fu, Yunyue Elita Li, and Jizhong Yang, (2019), "A new P-wave reconstruction method for VSP data using conditional generative adversarial network," SEG Technical Program Expanded Abstracts : 2528-2532. https://doi.org/10.1190/segam2019-3206719.1 Plain-Language Summary Keywordsartificial intelligencevertical seismic profile (VSP)P-wave2DPDF DownloadLoading ...