Seismic-facies analysis based on deep-encoder clustering

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2018Seismic-facies analysis based on deep-encoder clusteringAuthors: Yanting DuanXiaodong ZhengLianlian HuYanting DuanInstitute of Oil & Gas, Peking University, Beijing, ChinaSearch for more papers by this author, Xiaodong ZhengResearch Institute of Petroleum Exploration & Development, PetroChina, Beijing, ChinaSearch for more papers by this author, and Lianlian HuResearch Institute of Petroleum Exploration & Development, PetroChina, Beijing, ChinaSearch for more papers by this authorhttps://doi.org/10.1190/segam2018-2997763.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractUnsupervised seismic facies are a convenient and efficient method for interpretation. Current seismic facies analysis mostly focuses on the improvement of the precision of seismic facies belt prediction. In this paper, we propose a clustering method that simultaneously learns feature representations and cluster assignments by using deep auto-encoder network, which improves the clustering result. First, this method learns a mapping from the high-dimensional data space to a low-dimensional feature space. Then, it iteratively optimizes the clustering objective by minimizing the error between cluster centers and embedded points as the constraint condition. The application with actual data demonstrates that the proposed method is helpful for improving the precision of seismic facies prediction, which may be conducive to the seismic interpretation.Presentation Date: Tuesday, October 16, 2018Start Time: 9:20:00 AMLocation: Poster Station 1Presentation Type: PosterKeywords: interpretation, neural networks, faciesPermalink: https://doi.org/10.1190/segam2018-2997763.1FiguresReferencesRelatedDetailsCited byRecurrent autoencoder model for unsupervised seismic facies analysisYanhui Zhou and Wenchao Chen15 June 2022 | Interpretation, Vol. 10, 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 Yanting Duan, Xiaodong Zheng, and Lianlian Hu, (2018), "Seismic-facies analysis based on deep-encoder clustering," SEG Technical Program Expanded Abstracts : 2152-2156. https://doi.org/10.1190/segam2018-2997763.1 Plain-Language Summary Keywordsinterpretationneural networksfaciesPDF DownloadLoading ...