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Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization
[article]
2020
arXiv
pre-print
Uncertainty quantification for full-waveform inversion provides a probabilistic characterization of the ill-conditioning of the problem, comprising the sensitivity of the solution with respect to the starting model and data noise. This analysis allows to assess the confidence in the candidate solution and how it is reflected in the tasks that are typically performed after imaging (e.g., stratigraphic segmentation following reservoir characterization). Classically, uncertainty comes in the form
arXiv:2004.07871v1
fatcat:3vnq27hmobeljiikrt7e4yvcte