3D convolutional neural networks for efficient fault detection and orientation estimation

PreviousNext No AccessSEG Technical Program Expanded Abstracts 20193D convolutional neural networks for efficient fault detection and orientation estimationAuthors: Tao ZhaoTao ZhaoGeophysical InsightsSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3216307.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractRecently, convolutional neural networks (CNNs) have received great success in detecting faults in seismic data. However, to achieve good fault detection results, a neural network model needs training data of both high quality and quantity, usually from legacy interpretation. In this study, I introduce a 3D CNN-based workflow to detect faults while also estimating fault properties from seismic data in which no human interpretation is involved in training data preparation. I use examples from two different seismic surveys to demonstrate the effectiveness and efficiency of the proposed workflow.Presentation Date: Wednesday, September 18, 2019Session Start Time: 1:50 PMPresentation Time: 2:15 PMLocation: 221DPresentation Type: OralKeywords: machine learning, faults, neural networksPermalink: https://doi.org/10.1190/segam2019-3216307.1FiguresReferencesRelatedDetailsCited byAn integrated workflow for deep learning-accelerated seismic modelling of the Groningen gas field, the NetherlandsHaibin Di, Vanessa Simoes, Zhun Li, Cen Li, Anisha Kaul, and Aria Abubakar15 August 2022An integrated machine learning-based fault classification workflowJie Qi, Carolan Laudon, and Kurt Marfurt15 August 2022Deep learning for end-to-end subsurface modeling and interpretation: An example from the Groningen gas fieldAria Abubakar, Haibin Di, Anisha Kaul, Cen Li, Zhun Li, Vanessa Simoes, Leigh Truelove, and Tao Zhao1 April 2022 | The Leading Edge, Vol. 41, No. 4Distilling Knowledge From an Ensemble of Convolutional Neural Networks for Seismic Fault DetectionIEEE Geoscience and Remote Sensing Letters, Vol. 19Seismic Fault Interpretation Using 3-D Scattering Wavelet Transform CNNIEEE Geoscience and Remote Sensing Letters, Vol. 19Which seismic attributes are best for subtle fault detection?Marwa Hussein, Robert R. Stewart, and Jonny Wu4 May 2021 | Interpretation, Vol. 9, No. 2Seismic fault detection based on 3D Unet++ modelDun Yang, Yufei Cai, Guangmin Hu, Xingmiao Yao, and Wen Zou30 September 2020Comparing convolutional neural networking and image processing seismic fault detection methodsJie Qi, Bin Lyu, Xinming Wu, and Kurt Marfurt30 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 Tao Zhao, (2019), "3D convolutional neural networks for efficient fault detection and orientation estimation," SEG Technical Program Expanded Abstracts : 2418-2422. https://doi.org/10.1190/segam2019-3216307.1 Plain-Language Summary Keywordsmachine learningfaultsneural networksPDF DownloadLoading ...