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• W2892287369 abstract "PreviousNext No AccessSEG Technical Program Expanded Abstracts 2018A fault-detection workflow using deep learning and image processingAuthors: Tao ZhaoPradip MukhopadhyayTao ZhaoGeophysical InsightsSearch for more papers by this author and Pradip MukhopadhyayGeophysical InsightsSearch for more papers by this authorhttps://doi.org/10.1190/segam2018-2997005.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractWithin the last a couple of years, deep learning techniques, represented by convolutional neural networks (CNNs), have been applied to fault detection problems on seismic data with impressive outcome. As is true for all supervised learning techniques, the performance of a CNN fault detector highly depends on the training data, and post-classification regularization may greatly improve the result. Sometimes, a pure CNN-based fault detector that works perfectly on synthetic data may not perform well on field data. In this study, we investigate a fault detection workflow using both CNN and directional smoothing/sharpening. Applying both on a realistic synthetic fault model based on the SEAM (SEG Advanced Modeling) model and also field data from the Great South Basin, offshore New Zealand, we demonstrate that the proposed fault detection workflow can perform well on challenging synthetic and field data.Presentation Date: Tuesday, October 16, 2018Start Time: 8:30:00 AMLocation: 204B (Anaheim Convention Center)Presentation Type: OralKeywords: faults, machine learning, neural networksPermalink: https://doi.org/10.1190/segam2018-2997005.1FiguresReferencesRelatedDetailsCited byFault Detection via 2.5D Transformer U-Net with Seismic Data Pre-Processing14 February 2023 | Remote Sensing, Vol. 15, No. 4Ensemble Deep Learning-Based Porosity Inversion From Seismic AttributesIEEE Access, Vol. 11A fuzzy entropy, Contourlet based automatic fault detection24 January 2022 | Exploration Geophysics, Vol. 53, No. 6Multiscale fracture prediction technique via deep learning, seismic gradient disorder, and aberrance: Applied to tight sandstone reservoirs in the Hutubi block, southern Junggar BasinZhiguo Cheng, Long Bian, Haidong Chen, Xiaotao Wang, Di Ye, and Luming He11 August 2022 | Interpretation, Vol. 10, No. 4High-resolution seismic faults interpretation based on adversarial neural networks with a regularization techniqueTianqi Wang and Yanfei Wang8 September 2022 | GEOPHYSICS, Vol. 87, No. 6Applying machine learning technologies in the Niobrara Formation, DJ Basin, to quickly produce an integrated structural and stratigraphic seismic classification volume calibrated to wellsCarolan Laudon, Jie Qi, and Yin-Kai Wang10 October 2022Fault surface extraction from a global perspectiveCheng Zhou, Ruoshui Zhou, Xianglin Zhan, Hanpeng Cai, Xingmiao Yao, and Guangmin Hu13 July 2022 | GEOPHYSICS, Vol. 87, No. 5Seismic data augmentation for automatic faults picking using deep learningNam Pham and Sergey Fomel15 August 2022An integrated machine learning-based fault classification workflowJie Qi, Carolan Laudon, and Kurt Marfurt15 August 2022Fault enhancement comparison among coherence enhancement, probabilistic neural networks, and convolutional neural networks in the Taranaki Basin area, New ZealandJosé P. 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super-attribute-based classificationHaibin Di, Mohammod Amir Shafiq, Zhen Wang, and Ghassan AlRegib7 August 2019 | Interpretation, Vol. 7, No. 3Attenuation of random noise using denoising convolutional neural networksXu Si, Yijun Yuan, Tinghua Si, and Shiwen Gao7 August 2019 | Interpretation, Vol. 7, No. 3Deep learning applied to seismic attribute computationDonald P. 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David Potter, and Francesco Menapace12 June 2019 | Interpretation, Vol. 7, No. 3FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentationXinming Wu, Luming Liang, Yunzhi Shi, and Sergey Fomel16 April 2019 | GEOPHYSICS, Vol. 84, No. 3 SEG Technical Program Expanded Abstracts 2018ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2018 Pages: 5520 publication data© 2018 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 27 Aug 2018 CITATION INFORMATION Tao Zhao and Pradip Mukhopadhyay, (2018), A fault-detection workflow using deep learning and image processing, SEG Technical Program Expanded Abstracts : 1966-1970. https://doi.org/10.1190/segam2018-2997005.1 Plain-Language Summary Keywordsfaultsmachine learningneural networksPDF DownloadLoading ..." @default.
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