Seismic waveform classification via a new similarity measure

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Seismic waveform classification via a new similarity measureAuthors: Chengyun SongZhining LiuBin SheKunhong LiGuangmin HuChengyun SongSchool of Computer Science and Engineering, Chongqing University of TechnologySearch for more papers by this author, Zhining LiuCenter for Information Geoscience, University of Electronic Science and Technology of ChinaSearch for more papers by this author, Bin SheCenter for Information Geoscience, University of Electronic Science and Technology of ChinaSearch for more papers by this author, Kunhong LiCenter for Information Geoscience, University of Electronic Science and Technology of ChinaSearch for more papers by this author, and Guangmin HuCenter for Information Geoscience, University of Electronic Science and Technology of ChinaSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3208031.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractSeismic waveform classification is an important technique for seismic facies analysis. The most existing methods use a fixed time window to extract seismic data along the interpretation horizon and then classify them. However, when the thickness of target layer has a large change at different points, an appropriate time window size is difficult to choose. Instead, by using both the top and bottom horizons to extract seismic waveforms, we propose a new similarity measure called dynamic sub-windows matching (DSWM) for calculating directly the distance between two waveforms with different lengths in this paper. DSWM is the average distance of multiple short signals and uses dynamic programming to find the best matching sub-windows. Furthermore, the KNN (K-Nearest Neighbors)-DSWM method is developed for supervised seismic facies analysis. The comparison and analysis of the results from the synthetic and real field data demonstrate that the proposed approach is suitable for reservoir with varying thickness, and provides an effective seismic interpretation tool.Presentation Date: Monday, September 16, 2019Session Start Time: 1:50 PMPresentation Time: 2:15 PMLocation: Poster Station 2Presentation Type: PosterKeywords: interpretation, machine learning, algorithmPermalink: https://doi.org/10.1190/segam2019-3208031.1FiguresReferencesRelatedDetailsCited byApplication of Dynamic Time Warping in Weighted Stacking of Seismic DataIEEE Geoscience and Remote Sensing Letters, Vol. 19A Hierarchical Clustering Method of SOM Based on DTW Distance for Variable-Length Seismic WaveformIEEE Geoscience and Remote Sensing Letters, Vol. 19Robust K-means algorithm with weighted window for seismic facies analysis20 August 2021 | Journal of Geophysics and Engineering, Vol. 18, No. 5 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 Chengyun Song, Zhining Liu, Bin She, Kunhong Li, and Guangmin Hu, (2019), "Seismic waveform classification via a new similarity measure," SEG Technical Program Expanded Abstracts : 2438-2442. https://doi.org/10.1190/segam2019-3208031.1 Plain-Language Summary Keywordsinterpretationmachine learningalgorithmPDF DownloadLoading ...