Reviewerly/exHarmony huggingface dataset:
Using support vector regression to characterize uncertainty of geological surface

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Using support vector regression to characterize uncertainty of geological surfaceAuthors: Shicheng YuCai LuGuangmin HuShicheng YuSchool of Communication & Information Engineering, University of Electronic Science and Technology of China, ChinaThe Engineering & Technical College of Chengdu University of Technology, ChinaCenter for Information Geoscience, University of Electronic Science and Technology of China, ChinaSearch for more papers by this author, Cai LuSchool of Communication & Information Engineering, University of Electronic Science and Technology of China, ChinaSearch for more papers by this author, and Guangmin HuSchool of Communication & Information Engineering, University of Electronic Science and Technology of China, ChinaSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3215028.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractComplex geological surface reconstruction is widely used in petroleum exploration. However, the sparsity and inaccuracy of structural interpretation data, and the lack of structural morphological information will lead to uncertainty in the reconstruction of geological surface. Based on support vector regression (SVR), a method for uncertainty characterization of geological surface is proposed in this paper. The method integrates highconfidence samples such as well picks by confidence constraints; integrates well paths points by giving appropriate inequality constraints to the prediction points corresponding to well paths points. The results on a realworld fault data set show that the uncertainty envelopes generated by the proposed method are constrained by well observations and along well paths, which can effectively reduce the uncertainty.Presentation Date: Tuesday, September 17, 2019Session Start Time: 1:50 PMPresentation Start Time: 2:15 PMLocation: 304APresentation Type: OralKeywords: modeling, machine learning, 3DPermalink: https://doi.org/10.1190/segam2019-3215028.1FiguresReferencesRelatedDetails 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 Shicheng Yu, Cai Lu, and Guangmin Hu, (2019), "Using support vector regression to characterize uncertainty of geological surface," SEG Technical Program Expanded Abstracts : 3845-3849. https://doi.org/10.1190/segam2019-3215028.1 Plain-Language Summary Keywordsmodelingmachine learning3DPDF DownloadLoading ...
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