Seismic data interpolation with conditional generative adversarial network in time and frequency domain

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Seismic data interpolation with conditional generative adversarial network in time and frequency domainAuthors: D. K. ChangW. Y. YangX. S. YongH. S LiD. K. ChangResearch institute of petroleum exploration & development-NWGI, PetroChinaSearch for more papers by this author, W. Y. YangResearch institute of petroleum exploration & development-NWGI, PetroChinaSearch for more papers by this author, X. S. YongResearch institute of petroleum exploration & development-NWGI, PetroChinaSearch for more papers by this author, and H. S LiResearch institute of petroleum exploration & development-NWGI, PetroChinaSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3210118.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractSeismic traces are missing due to limitations in acquisition conditions, bad sectors, etc., which seriously affects the quality of seismic dataset. Seismic data interpolation technology is an effective way to reconstruct missing seismic traces and improve the quality of seismic dataset. In this paper, we propose a method for seismic data interpolation by using the conditional generative adversarial network in time and frequency domain (TF-CGAN). This network consists of two parts, a generation network and a discrimination network. Seismic data and the FFT-transformed data are used for training of the network model to realize dual-domain feature learning. Experimental results show that the TF-CGAN can simultaneously discriminate the mathematical distribution of the interpolated seismic traces in the time and frequency domains, which makes the interpolated seismic traces have the same characteristics with the complete seismic dataset in time and frequency domain.Presentation Date: Wednesday, September 18, 2019Session Start Time: 1:50 PMPresentation Time: 1:50 PMLocation: Poster Station 2Presentation Type: PosterKeywords: artificial intelligence, data reconstruction, interpolation, neural networks, processingPermalink: https://doi.org/10.1190/segam2019-3210118.1FiguresReferencesRelatedDetailsCited byGenerative adversarial networks review in earthquake-related engineering fields28 February 2023 | Bulletin of Earthquake Engineering, Vol. 10Unsupervised deep learning for 3D interpolation of highly incomplete dataOmar M. Saad, Sergey Fomel, Raymond Abma, and Yangkang Chen13 December 2022 | GEOPHYSICS, Vol. 88, No. 1Regeneration-Constrained Self-Supervised Seismic Data InterpolationIEEE Transactions on Geoscience and Remote Sensing, Vol. 61Big gaps seismic data interpolation using conditional Wasserstein generative adversarial networks with gradient penalty26 October 2021 | Exploration Geophysics, Vol. 53, No. 5Improved Anomalous Amplitude Attenuation Method Based on Deep Neural NetworksIEEE Transactions on Geoscience and Remote Sensing, Vol. 60Attention and Hybrid Loss Guided Deep Learning for Consecutively Missing Seismic Data ReconstructionIEEE Transactions on Geoscience and Remote Sensing, Vol. 60Self-Supervised Learning for Efficient Antialiasing Seismic Data InterpolationIEEE Transactions on Geoscience and Remote Sensing, Vol. 60Seismic data interpolation using a POCS-guided deep image priorMin Jun Park, Joseph Jennings, Bob Clapp, and Biondo Biondi30 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 D. K. Chang, W. Y. Yang, X. S. Yong, and H. S Li, (2019), "Seismic data interpolation with conditional generative adversarial network in time and frequency domain," SEG Technical Program Expanded Abstracts : 2589-2593. https://doi.org/10.1190/segam2019-3210118.1 Plain-Language Summary Keywordsartificial intelligencedata reconstructioninterpolationneural networksprocessingPDF DownloadLoading ...