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- W3090477321 abstract "PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Deep learning for characterizing paleokarst features in 3D seismic imagesAuthors: Xinming WuShangsheng YanJie QiHongliu ZengXinming WuUniversity of Science and Technology of ChinaSearch for more papers by this author, Shangsheng YanUniversity of Science and Technology of ChinaSearch for more papers by this author, Jie QiUniversity of OklahomaSearch for more papers by this author, and Hongliu ZengUniversity of Texas at AustinSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3427708.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractWe propose a supervised deep convolutional neural network (CNN) to automatically and accurately characterize paleokarst features in 3D seismic images. To avoid the time-consuming and subjective manual labelling for training the deep CNN, we propose an efficient workflow to automatically generate numerous 3D training data pairs including synthetic seismic images and the corresponding label images of the paleokarst features. With this workflow, we are able to simulate realistic and diverse geologic structure patterns and paleokarst features in the training datasets from which the CNN can effectively learn to recognize the paleokarst features in field seismic images which are not included in the training datasets. Two field examples in the Fort Worth Basin demonstrate that our CNNbased method is significantly superior to the conventional automatic methods in delineating paleokarst features from seismic images and yielding a clear 3D view of all the paleokarst systems from which the geometric parameters of each paleokarst can be automatically and quantitatively measured.Presentation Date: Monday, October 12, 2020Session Start Time: 1:50 PMPresentation Time: 3:30 PMLocation: 351FPresentation Type: OralKeywords: machine learning, interpretation, seismic attributes, algorithm, 3DPermalink: https://doi.org/10.1190/segam2020-3427708.1FiguresReferencesRelatedDetailsCited byKarst cave detection and prediction by using two fully convolutional neural networksXingyu Yan, Hanming Gu, and Youping Yan24 February 2022Seismic Coherence for Discontinuity Interpretation6 November 2021 | Surveys in Geophysics, Vol. 42, No. 6 SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Xinming Wu, Shangsheng Yan, Jie Qi, and Hongliu Zeng, (2020), Deep learning for characterizing paleokarst features in 3D seismic images, SEG Technical Program Expanded Abstracts : 1454-1459. https://doi.org/10.1190/segam2020-3427708.1 Plain-Language Summary Keywordsmachine learninginterpretationseismic attributesalgorithm3DPDF DownloadLoading ..." @default.
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- W3090477321 title "Deep learning for characterizing paleokarst features in 3D seismic images" @default.
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