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• W3090729638 abstract "PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Uncertainty analysis of joint inversion using mixed Lp-norm regularizationAuthors: Xiaolong WeiJiajia SunXiaolong WeiUniversity of HoustonSearch for more papers by this author and Jiajia SunUniversity of HoustonSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3428359.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractJoint inversion has received considerable attention recently because of the presumed decrease in uncertainty of the recovered models. However, quantification of uncertainty and its reduction (or, lack thereof) in joint inversion remains underexplored. We approach the problem by generating a large sequence of jointly inverted models that are all equally valid from the point of view of reproducing the observed data but are characterized by vastly different types of features. Specifically, we formulate our joint inversion using a mixed Lp norm formulation where various combinations of Lp (0 ≤ p ≤ 2) norms can be implemented on different components of the regularization terms. We randomly sample the p-norm values multiple times, and generate a large number of models. Uncertainty can then be quantitatively analyzed by measuring the degree of variability in the recovered physical property values. Following the same procedures, we also test how correct and incorrect prior information, when imposed on joint inversions, affects the uncertainty. Our work shows that uncertainty of joint inversion results can be empirically and efficiently assessed using this approach. Our numerical results also show that a reduction in uncertainty does not necessarily lead to an increase in accuracy.Presentation Date: Tuesday, October 13, 2020Session Start Time: 8:30 AMPresentation Time: 9:45 AMLocation: 362CPresentation Type: OralKeywords: inversion, 3D, gradiometry, magneticsPermalink: https://doi.org/10.1190/segam2020-3428359.1FiguresReferencesRelatedDetailsCited byA deep learning-enhanced framework for multiphysics joint inversionYanyan Hu, Xiaolong Wei, Xuqing Wu, Jiajia Sun, Jiuping Chen, Yueqin Huang, and Jiefu Chen27 December 2022 | GEOPHYSICS, Vol. 88, No. 1Sparse Radon transform in the mixed frequency-time domain with ℓ1-2 minimizationWeiheng Geng, Xiaohong Chen, Jingye Li, Jitao Ma, Wei Tang, and Fan Wu5 September 2022 | GEOPHYSICS, Vol. 87, No. 53D probabilistic geology differentiation based on airborne geophysics, mixed Lp norm joint inversion, and physical property measurementsXiaolong Wei and Jiajia Sun28 June 2022 | GEOPHYSICS, Vol. 87, No. 4Towards a better understanding of the recoverability of physical property relationships from geophysical inversions of multiple potential-field data sets31 March 2022 | Geophysical Journal International, Vol. 230, No. 33D high‐order sparse radon transform with L1–2 minimization for multiple attenuation4 March 2022 | Geophysical Prospecting, Vol. 70, No. 4UB-Net: Improved Seismic Inversion Based on Uncertainty BackpropagationIEEE Transactions on Geoscience and Remote Sensing, Vol. 60Deep learning-enhanced multiphysics joint inversionYanyan Hu, Xiaolong Wei, Xuqing Wu, Jiajia Sun, Jiefu Chen, Jiuping Chen, and Yueqing Huang1 September 2021Geophysical characterization of a buried niobium and rare earth element deposit using 3D joint inversion and geology differentiation: A case study on the Elk Creek carbonatiteKenneth Li, Xiaolong Wei, and Jiajia Sun1 September 20213D probabilistic geology differentiation using mixed Lp norm joint inversion constrained by petrophysical informationXiaolong Wei and Jiajia Sun1 September 2021 SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Xiaolong Wei and Jiajia Sun, (2020), Uncertainty analysis of joint inversion using mixed Lp-norm regularization, SEG Technical Program Expanded Abstracts : 925-929. https://doi.org/10.1190/segam2020-3428359.1 Plain-Language Summary Keywordsinversion3DgradiometrymagneticsPDF DownloadLoading ..." @default.
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