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• W2930688509 abstract "PreviousNext No AccessSEG 2018 Workshop: SEG Maximizing Asset Value Through Artificial Intelligence and Machine Learning, Beijing, China, 17–19 September 2018Generative Adversarial Networks for Seismic Data InterpolationAuthors: Dekuan ChangWuyang YangXueshan YongHaishan LiDekuan ChangResearch Institute of Petroleum Exploration & Development-Northwest, PetroChinaSearch for more papers by this author, Wuyang YangResearch Institute of Petroleum Exploration & Development-Northwest, PetroChinaSearch for more papers by this author, Xueshan YongResearch Institute of Petroleum Exploration & Development-Northwest, PetroChinaSearch for more papers by this author, and Haishan LiResearch Institute of Petroleum Exploration & Development-Northwest, PetroChinaSearch for more papers by this authorhttps://doi.org/10.1190/AIML2018-11.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract The interpolation of missing seismic traces has always been a research hotspot. The high-precision interpolation technology is an effective way to compensate for the missing seismic traces. In this paper, we present a method for seismic data interpolation by using the generative adversarial network (SIGAN). The objective optimization function of this method consists of the loss function of generator G and discriminator D. Generator G uses a deep residual network (ResNet), which is an end-to-end network, and discriminator D uses a conventional CNN network. After the SIGAN has been trained, we can use generator G to reconstruct missing seismic traces. Through numerical tests, the difference between the seismic data generated by SPGAN and the original data is very small, which illustrates the potential of SIGAN for seismic data interpolation. Keywords: interpolation, optimization, algorithmPermalink: https://doi.org/10.1190/AIML2018-11.1FiguresReferencesRelatedDetailsCited 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. 1Recent Development of Smart Field Deployment for Mature Waterflood Reservoirs1 January 2023 | Sustainability, Vol. 15, No. 1Seismic image-to-image translation using a conditional GAN with Bayesian inferenceXiaolei Song, Muhong Zhou, Petr Jilek, Rodney Johnston, Sean Cardinez, and Kareem Vincent15 August 2022Reconstruction of Missing Ground-Penetrating Radar Traces Using Simplified U-NetIEEE Geoscience and Remote Sensing Letters, Vol. 19Seismic Data Reconstruction via Wavelet-Based Residual Deep LearningIEEE 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 Dual-Domain Conditional Generative Adversarial NetworksIEEE Geoscience and Remote Sensing Letters, Vol. 18, No. 10Seismic data interpolation with conditional generative adversarial network in time and frequency domainD. K. Chang, W. Y. Yang, X. S. Yong, and H. S Li10 August 2019 SEG 2018 Workshop: SEG Maximizing Asset Value Through Artificial Intelligence and Machine Learning, Beijing, China, 17–19 September 2018ISSN (online):2159-6832Copyright: 2018 Pages: 244 publication data© 2018 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 14 Dec 2018 CITATION INFORMATION Dekuan Chang, Wuyang Yang, Xueshan Yong, and Haishan Li, (2018), Generative Adversarial Networks for Seismic Data Interpolation, SEG Global Meeting Abstracts : 40-43. https://doi.org/10.1190/AIML2018-11.1 Plain-Language Summary KeywordsinterpolationoptimizationalgorithmPDF DownloadLoading ..." @default.
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• W2930688509 title "Generative Adversarial Networks for Seismic Data Interpolation" @default.
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