Strong random noise suppression using low-rank based simultaneously sparse coding, with selective extraction of nonlocal means estimates

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Strong random noise suppression using low-rank based simultaneously sparse coding, with selective extraction of nonlocal means estimatesAuthors: Wenhan SunQizhen DuQiang ZhaoLiyun FuWenhan SunThe Key laboratory of deep oil and gas, China University of Petroleum (East China), Changjiang West Road 66th, Qingdao, 266580, ChinaThe Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266071, ChinaSearch for more papers by this author, Qizhen DuThe Key laboratory of deep oil and gas, China University of Petroleum (East China), Changjiang West Road 66th, Qingdao, 266580, ChinaThe Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266071, ChinaSearch for more papers by this author, Qiang ZhaoThe Key laboratory of deep oil and gas, China University of Petroleum (East China), Changjiang West Road 66th, Qingdao, 266580, ChinaThe Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266071, ChinaSearch for more papers by this author, and Liyun FuThe Key laboratory of deep oil and gas, China University of Petroleum (East China), Changjiang West Road 66th, Qingdao, 266580, ChinaThe Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266071, ChinaSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3216454.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractRandom noise attenuation is one of the critical task for seismic data pre-conditioning. Especially, suppression of strong noise has been attracting increasing attention. In this abstract, the theory of simultaneously sparse coding is introduced to strong random noise suppression in seismic data. Combined with the low-rank based algorithm named SAIST, nonlocal means method is applied to extract an initial estimate from each cluster before solving the optimization problem. Furthermore, a designed filter which calculates the lateral coherence of vector sets in each cluster is used to determine the extracting process. Synthetic test on model corrupted by strong noise shows that better de-noising performance can be obtained by the proposed algorithm. Application of field seismic records also verifies the practical perspective of this method.Presentation Date: Monday, September 16, 2019Session Start Time: 1:50 PMPresentation Start Time: 1:50 PMLocation: Poster Station 13Presentation Type: PosterKeywords: random, noise, attenuationPermalink: https://doi.org/10.1190/segam2019-3216454.1FiguresReferencesRelatedDetails 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: 01 Aug 2019 CITATION INFORMATION Wenhan Sun, Qizhen Du, Qiang Zhao, and Liyun Fu, (2019), "Strong random noise suppression using low-rank based simultaneously sparse coding, with selective extraction of nonlocal means estimates," SEG Technical Program Expanded Abstracts : 4665-4669. https://doi.org/10.1190/segam2019-3216454.1 Plain-Language Summary KeywordsrandomnoiseattenuationPDF DownloadLoading ...