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• W2029359498 abstract "PreviousNext No AccessSEG Technical Program Expanded Abstracts 2011Abrupt feature extraction via the combination of sparse representationsAuthors: Wei WangWenchao ChenWencheng LiuJin XuJinghuai GaoWei WangInstitute of Wave and Information, Xi'an Jiaotong UniversitySearch for more papers by this author, Wenchao ChenInstitute of Wave and Information, Xi'an Jiaotong UniversitySearch for more papers by this author, Wencheng LiuInstitute of Wave and Information, Xi'an Jiaotong UniversitySearch for more papers by this author, Jin XuInstitute of Wave and Information, Xi'an Jiaotong UniversitySearch for more papers by this author, and Jinghuai GaoInstitute of Wave and Information, Xi'an Jiaotong UniversitySearch for more papers by this authorhttps://doi.org/10.1190/1.3627378 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract The complexity of channels in 3D seismic data always makes detailed interpretation challenging. Similarly, definition of sand bars and beaches is also complicated as they are only partially visible when seismic amplitude is examined. To improve the imaging of these features, current interpretation workflows use advanced color and opacity based co‐rendering techniques to merge multiple attributes information. Other than to enhance these sedimentary features in 3D views with combinations of different attributes, this paper proposes to separate their reflection waveforms directly from 3D imaging data by exploiting the waveform diversity mechanism. Our separation model is set up upon the assumption that seismic data are composed of coherent events and abrupt features (correspond to sedimentary features). According to their appearance in vertical sections, we model these two kinds of seismic features as linear structures and punctate structures respectively. Two appropriate waveform dictionaries are chosen, one of which is used for the representation of the coherent events and the other for the sedimentary features. The separation process is promoted by the sparsity of both waveform components in their corresponding representing dictionaries. The capacity of the proposed method is illustrated using modeling data and real 3D seismic data with complex depositional systems.Permalink: https://doi.org/10.1190/1.3627378FiguresReferencesRelatedDetailsCited ByA dictionary learning method with atom splitting for seismic footprint suppressionDawei Liu, Lei Gao, Xiaokai Wang, and Wenchao Chen19 October 2021 | GEOPHYSICS, Vol. 86, No. 6Texture attribute analysis based on strong background interference suppressionShi’an Shen, Siqi Chi, Wenchao Chen, Xiaokai Wang, Cheng Wang, and Binke Huang3 April 2020 | Interpretation, Vol. 8, No. 23D seismic waveform of channels extraction by artificial intelligenceDawei Liu, Xiaokai Wang, Wenchao Chen, Yanhui Zhou, Wei Wang, Zhensheng Shi, Cheng Wang, and Chunlin Xie10 August 2019Three-dimensional seismic texture attributes analysis based on removed strong background noiseSiqi Chi, Wenchao Chen, Lu Zhang, Dawei Liu, and Jianyou Chan11 December 2018Monochromatic Noise Removal via Sparsity-Enabled Signal Decomposition MethodIEEE Geoscience and Remote Sensing Letters, Vol. 10, No. 3 SEG Technical Program Expanded Abstracts 2011ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2011 Pages: 4424 Publisher:Society of Exploration Geophysicists HistoryPublished: 25 May 2012 CITATION INFORMATION Wei Wang, Wenchao Chen, Wencheng Liu, Jin Xu, and Jinghuai Gao, (2011), Abrupt feature extraction via the combination of sparse representations, SEG Technical Program Expanded Abstracts : 1019-1024. https://doi.org/10.1190/1.3627378 Plain-Language Summary PDF DownloadLoading ..." @default.
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