Relative geologic time estimation using a deep convolutional neural network

Abstract

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Relative geologic time estimation using a deep convolutional neural networkAuthors: Zhicheng GengXinming WuYunzhi ShiSergey FomelZhicheng GengThe University of Texas at AustinSearch for more papers by this author, Xinming WuThe University of Texas at AustinSearch for more papers by this author, Yunzhi ShiThe University of Texas at AustinSearch for more papers by this author, and Sergey FomelThe University of Texas at AustinSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3214459.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractWe propose applying a deep learning technique to directly and automatically generate Relative Geologic Time (RGT) volumes from seismic images. In this method, a multi-layer convolutional neural network (CNN) is constructed and trained with synthetic input seismic images and target RGT images. Although the network is trained using only synthetic images, it generates accurate results on real seismic images. The method automatically captures complex geologic structures in the input, including multiple crossing faults and significantly folded horizons, without any need for manual picking.Presentation Date: Tuesday, September 17, 2019Session Start Time: 8:30 AMPresentation Time: 8:30 AMLocation: 221CPresentation Type: OralKeywords: machine learning, interpretation, neural networks, seismic attributesPermalink: https://doi.org/10.1190/segam2019-3214459.1FiguresReferencesRelatedDetailsCited byAutomated active learning for seismic facies classificationHaibin Di, Leigh Truelove, and Aria Abubakar15 August 2022Synthetic seismic data generation for automated AI-based procedures with an example application to high-resolution interpretationFernando Vizeu, Joao Zambrini, Anne-Laure Tertois, Bruno de Albuquerque da Graça e Costa, André Queiroz Fernandes, and Anat Canning1 June 2022 | The Leading Edge, Vol. 41, No. 6Using relative geologic time to constrain convolutional neural network-based seismic interpretation and property estimationHaibin Di, Zhun Li, and Aria Abubakar27 December 2021 | GEOPHYSICS, Vol. 87, No. 2Fault-Guided Seismic Stratigraphy Interpretation via Semi-Supervised Learning9 December 2021Using relative geologic time to constrain seismic facies classification using neural networksHaibin Di, Zhun Li, and Aria Abubakar1 September 2021Imposing interpretational constraints on a seismic interpretation convolutional neural networkHaibin Di, Cen Li, Stewart Smith, Zhun Li, and Aria Abubakar21 April 2021 | GEOPHYSICS, Vol. 86, No. 3Complete sequence stratigraphy from seismic optical flow without human labelingZhun Li and Aria Abubakar30 September 2020Kernel prediction network for common image gather stackingZiang Li, Xinming Wu, Luming Liang, and Xiaofeng Jia30 September 2020Deep learning for characterizing paleokarst features in 3D seismic imagesXinming Wu, Shangsheng Yan, Jie Qi, and Hongliu Zeng30 September 2020Real-time seismic attributes computation with conditional GANsJoão Paulo Navarro, Pedro Mário Cruz e Silva, Doris Pan, and Ken Hester30 September 20203D seismic data compression with multi-resolution autoencodersAna Paula Schiavon, Kevyn Swhants dos Santos Ribeiro, João Paulo Navarro, Marcelo Bernardes Vieira, and Pedro Mário Cruz e Silva30 September 2020Building realistic structure models to train convolutional neural networks for seismic structural interpretationXinming Wu, Zhicheng Geng, Yunzhi Shi, Nam Pham, Sergey Fomel, and Guillaume Caumon16 January 2020 | GEOPHYSICS, Vol. 85, No. 4Seismic stratigraphy interpretation by deep convolutional neural networks: A semisupervised workflowHaibin Di, Zhun Li, Hiren Maniar, and Aria Abubakar30 April 2020 | GEOPHYSICS, Vol. 85, No. 4Building realistic structure models to train convolutional neural networks for seismic structural interpretationXinming Wu, Zhicheng Geng, Yunzhi Shi, Nam Pham, and Sergey Fomel10 August 2019 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 Zhicheng Geng, Xinming Wu, Yunzhi Shi, and Sergey Fomel, (2019), "Relative geologic time estimation using a deep convolutional neural network," SEG Technical Program Expanded Abstracts : 2238-2242. https://doi.org/10.1190/segam2019-3214459.1 Plain-Language Summary Keywordsmachine learninginterpretationneural networksseismic attributesPDF DownloadLoading ...