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• W2981223997 endingPage "R39" @default.
• W2981223997 startingPage "R29" @default.
• W2981223997 abstract "Seismic waveform inversion is a nontrivial optimization task, which is often complicated by the nonlinear relationship between the elastic attributes of interest and the large amount of data obtained in seismic experiments. Quantifying the solution uncertainty can be even more challenging, and it requires considering the problem in a probabilistic setting. Consequently, the seismic inverse problem is placed in a Bayesian framework, using a sequential filtering approach to invert for the elastic parameters. The method uses an iterative ensemble smoother to estimate the subsurface parameters, and from the ensemble, a notion of estimation uncertainty is readily available. The ensemble implicitly linearizes the relation between the parameters and the observed waveform data; hence, it requires no tangent linear model. The approach is based on sequential conditioning on partitions of the whole data record (1) to regularize the inversion path and effectively drive the estimation process in a top-down manner and (2) to circumvent a consequence of the ensemble reduced rank approximation. The method is exemplified on a synthetic case, inverting for elastic parameters in a 1D medium using a seismic shot record. Our results indicate that the iterative ensemble method is applicable to seismic waveform inversion and that the ensemble representation indeed indicates estimation uncertainty." @default.
• W2981223997 created "2019-10-25" @default.
• W2981223997 creator A5009816747 @default.
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• W2981223997 creator A5048150386 @default.
• W2981223997 date "2020-01-01" @default.
• W2981223997 modified "2023-10-16" @default.
• W2981223997 title "Ensemble-based seismic inversion for a stratified medium" @default.
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• W2981223997 doi "https://doi.org/10.1190/geo2019-0017.1" @default.
• W2981223997 hasPublicationYear "2020" @default.
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