Uncertainty quantification for regularized inversion of electromagnetic geophysical data—Part I: motivation and theory
Author:
Affiliation:
1. Scripps Institution of Oceanography, University of California , San Diego, La Jolla, CA 92037, USA
2. Lamont-Doherty Earth Observatory, Columbia University , Palisades, NY 10964, USA
Abstract
Funder
Office of Naval Research
Columbia University
Publisher
Oxford University Press (OUP)
Subject
Geochemistry and Petrology,Geophysics
Link
https://academic.oup.com/gji/advance-article-pdf/doi/10.1093/gji/ggac241/44264648/ggac241.pdf
Reference57 articles.
1. Flexible coupling in joint inversions: a bayesian structure decoupling algorithm;Agostinetti;J. geophys. Res.,2018
2. Optimization-based markov chain monte carlo methods for nonlinear hierarchical statistical inverse problems;Bardsley;SIAM/ASA J. Uncertain. Quant.,2021
3. Randomize-then-optimize: a method for sampling from posterior distributions in nonlinear inverse problems;Bardsley;SIAM J. Sci. Comput.,2014
4. Randomize-then-optimize for sampling and uncertainty quantification in electrical impedance tomography;Bardsley;SIAM/ASA J. Uncertain. Quant.,2015
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