Joint inversion of gravity gradiometry data by model-weighted clustering in logarithmic space

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Joint inversion of gravity gradiometry data by model-weighted clustering in logarithmic spaceAuthors: Zhengwei XuLe WanMuran HanMichael S. ZhdanovYue MaoZhengwei XuRock Physics Lab, University of HoustonSearch for more papers by this author, Le WanUniversity of UtahSearch for more papers by this author, Muran HanUniversity of UtahSearch for more papers by this author, Michael S. ZhdanovUniversity of UtahSearch for more papers by this author, and Yue MaoColorado School of MinesSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3214793.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractThis paper develops a method of joint inversion of the different individual components of full-tensor gradient (FTG) data using the re-weighted regularized Newton-Gauss algorithm based on the clustering method. The clustering technique is used to enforce the density to be distributed around the specific a priori values determined from either petrophysical data or the rock sample measurement. To keep the density of the inverse model within the imposed boundaries, we implemented the inversion algorithm in the logarithmic model space. In addition, we applied the model weighting matrix to the clustering functional in order to guarantee the robustness of the inversion. Compared with a standard smooth density inversion, the new inversion approach predicts accurately the values of the density, and also improves the spatial resolution of the anomalous bodies, which is important in studying the complex geological structures. We present a model study and a case study for the new inversion approach using FTG gravity gradiometry data from Nordkapp Basin, Barents Sea.Presentation Date: Monday, September 16, 2019Session Start Time: 1:50 PMPresentation Time: 3:55 PMLocation: Poster Station 1Presentation Type: PosterKeywords: 3D, gravity tensor, inversion, machine learningPermalink: https://doi.org/10.1190/segam2019-3214793.1FiguresReferencesRelatedDetails 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 Zhengwei Xu, Le Wan, Muran Han, Michael S. Zhdanov, and Yue Mao, (2019), "Joint inversion of gravity gradiometry data by model-weighted clustering in logarithmic space," SEG Technical Program Expanded Abstracts : 1779-1783. https://doi.org/10.1190/segam2019-3214793.1 Plain-Language Summary Keywords3Dgravity tensorinversionmachine learningPDF DownloadLoading ...