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• W2746757980 abstract "PreviousNext No AccessSEG Technical Program Expanded Abstracts 2017Seismic-fault detection based on multiattribute support vector machine analysisAuthors: Haibin DiMuhammad Amir ShafiqGhassan AlRegibHaibin DiGeorgia Institute of TechnologySearch for more papers by this author, Muhammad Amir ShafiqGeorgia Institute of TechnologySearch for more papers by this author, and Ghassan AlRegibGeorgia Institute of TechnologySearch for more papers by this authorhttps://doi.org/10.1190/segam2017-17748277.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Reliable fault detection is one of the major tasks of subsurface interpretation and reservoir characterization from three-dimensional (3D) seismic surveying. This study presents an innovative workflow based on multi-attribute support vector machine (SVM) analysis of a seismic volume, which consists of four steps. First, three groups of seismic attributes are selected and computed from the volume of seismic amplitude, including edge-detection, geometric, and texture, all of which clearly highlight the seismic faults in the attribute images. Second, two sets of training samples are prepared by manually picking on the faults and the nonfaulting zones, respectively. Third, the SVM analysis is performed on the training datasets that builds an optimal classification model for volumetric processing. Finally, applying the SVM model to the whole seismic survey leads to a binary volume, in which the presence of a fault is labelled as ones. The added values of the proposed method are verified through applications to the seismic dataset over the Great South Basin in New Zealand, where the dominant features are polygonal faults of varying sizes and orientations. The results demonstrate not only good match between the detected faults and the original seismic images, but also great potential for quantitative fault interpretation, such as semi-automatic/automatic fault extraction, to aid structural framework modeling and reservoir simulation in the exploration areas of numerous faults and fractures Presentation Date: Wednesday, September 27, 2017 Start Time: 8:30 AM Location: 350D Presentation Type: ORAL Keywords: interpretation, seismic, attributes, faultsPermalink: https://doi.org/10.1190/segam2017-17748277.1FiguresReferencesRelatedDetailsCited bySeismic multi-attribute approach using visual saliency for subtle fault visualization23 November 2022 | Exploration Geophysics, Vol. 71High-resolution seismic faults interpretation based on adversarial neural networks with a regularization techniqueTianqi Wang and Yanfei Wang8 September 2022 | GEOPHYSICS, Vol. 87, 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. 2Assessing the accuracy of fault interpretation using machine-learning techniques when risking faults for CO2 storage site assessmentEmma A. H. Michie, Behzad Alaei, and Alvar Braathen19 November 2021 | Interpretation, Vol. 10, No. 1Fault Detection in Seismic Data Using Graph Attention Network29 July 2022Seismic Fault Interpretation Using 3-D Scattering Wavelet Transform CNNIEEE Geoscience and Remote Sensing Letters, Vol. 19Application of Multitask Learning for 2-D Modeling of Magnetotelluric Surveys: TE CaseIEEE Transactions on Geoscience and Remote Sensing, Vol. 60Empirical Evaluation on Utilizing CNN-Features for Seismic Patch Classification25 December 2021 | Applied Sciences, Vol. 12, No. 13D scattering wavelet transform CNN for seismic fault detectionShi’an Shen, Xiaokai Wang, Yanhui Zhou, Zhensheng Shi, Wenchao Chen, and Cheng Wang1 September 2021Exhaustive probabilistic neural network for attribute selection and supervised seismic facies classificationDavid Lubo-Robles, Thang Ha, Sivaramakrishnan Lakshmivarahan, Kurt J. Marfurt, and Matthew J. Pranter7 April 2021 | Interpretation, Vol. 9, No. 2Seismic, Artificial Intelligence to Neural Intelligence for Advanced Interpretation27 May 2021Seismic Fault Analysis Using Curvature Attribute and Visual SaliencySuppressing migration image artifacts using a support vector machine methodYuqing Chen, Yunsong Huang, and Lianjie Huang26 June 2020 | GEOPHYSICS, Vol. 85, No. 5A comparison of seismic saltbody interpretation via neural networks at sample and pattern levels23 September 2019 | Geophysical Prospecting, Vol. 68, No. 2A 3D channel body interpretation via multiple attributes and supervoxel graph cutXingmiao Yao, Mengxin Zhang, Mengyang Sun, Cheng Zhou, Yang Yi, and Guangmin Hu9 September 2019 | Interpretation, Vol. 7, No. 4Fault Detection and Optimization in Seismic Dataset using Multiscale Fusion of a Geometric AttributeMachine learning-assisted seismic interpretation with geologic constraintsHaibin Di, Cen Li, Stewart Smith, and Aria Abubakar10 August 2019Improving seismic fault detection by super-attribute-based classificationHaibin Di, Mohammod Amir Shafiq, Zhen Wang, and Ghassan AlRegib7 August 2019 | Interpretation, Vol. 7, No. 3Semi‐automatic fault/fracture interpretation based on seismic geometry analysis13 March 2019 | Geophysical Prospecting, Vol. 67, No. 5Seismic Fault Detection Using Convolutional Neural Networks Trained on Synthetic Poststacked Amplitude MapsIEEE Geoscience and Remote Sensing Letters, Vol. 16, No. 3Towards understanding common features between natural and seismic imagesMuhammad A. Shafiq, Mohit Prabhushankar, Haibin Di, and Ghassan AlRegib27 August 2018Patch-level MLP classification for improved fault detectionHaibin Di, Muhammad Shafiq, and Ghassan AlRegib27 August 2018Why using CNN for seismic interpretation? An investigationHaibin Di, Zhen Wang, and Ghassan AlRegib27 August 2018Real-time seismic-image interpretation via deconvolutional neural networkHaibin Di, Zhen Wang, and Ghassan AlRegib27 August 2018Successful leveraging of image processing and machine learning in seismic structural interpretation: A reviewZhen Wang, Haibin Di, Muhammad Amir Shafiq, Yazeed Alaudah, and Ghassan AlRegib6 June 2018 | The Leading Edge, Vol. 37, No. 6 SEG Technical Program Expanded Abstracts 2017ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2017 Pages: 6093 publication data© 2017 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 17 Aug 2017 CITATION INFORMATION Haibin Di, Muhammad Amir Shafiq, and Ghassan AlRegib, (2017), Seismic-fault detection based on multiattribute support vector machine analysis, SEG Technical Program Expanded Abstracts : 2039-2044. https://doi.org/10.1190/segam2017-17748277.1 Plain-Language Summary KeywordsinterpretationseismicattributesfaultsPDF DownloadLoading ..." @default.
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