Uncertainty estimation in elastic {FWI} images for structural interpretations
in: 2023 {RING} meeting, pages 17, ASGA
Abstract
Uncertainty quantification in seismic imaging is important for a proper interpretation of the structural elements (e.g., faults and horizons) within the investigated subsurface. Especially in seismic full-waveform inversion (FWI), which is a highly non-linear problem and hence prone to non-uniqueness, evaluating uncertainties associated with the estimated subsurface parameters is essential for interpreting inverted models. In this work, we address uncertainty estimation in elastic FWI by calculating the posterior covariance matrix based on the data-misfit Hessian matrix. In particular, to make the computation tractable for large scale problems, we rely on a low-rank approximation of the Hessian, which avoids the prohibitive computation of the full matrix.
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BibTeX Reference
@inproceedings{ruggiero_RM2023,
abstract = {Uncertainty quantification in seismic imaging is important for a proper interpretation of the structural elements (e.g., faults and horizons) within the investigated subsurface. Especially in seismic full-waveform inversion (FWI), which is a highly non-linear problem and hence prone to non-uniqueness, evaluating uncertainties associated with the estimated subsurface parameters is essential for interpreting inverted models. In this work, we address uncertainty estimation in elastic FWI by calculating the posterior covariance matrix based on the data-misfit Hessian matrix. In particular, to make the computation tractable for large scale problems, we rely on a low-rank approximation of the Hessian, which avoids the prohibitive computation of the full matrix.},
author = {Ruggiero, Giusi and Cupillard, Paul and Caumon, Guillaume},
booktitle = {2023 {RING} meeting},
language = {en},
pages = {17},
publisher = {ASGA},
title = {Uncertainty estimation in elastic {FWI} images for structural interpretations},
year = {2023}
}