MONTE CARLO METHODS IN GEOPHYSICAL INVERSE PROBLEMS

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Resolution analysis in full waveform inversion (2011, unknown): “(4) The size of the model space and the costs of the forward problem solution prohibit the application of probabilistic approaches that account for non-linearity using Monte Carlo sampling (Sambridge & Mosegaard 2002; Tarantola 2005) or neural networks (Devilee et al. 1999; Meier et al. 2007a,b).”
Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks (2019, methodology): “Consequently, the MC method and it’s advanced variants have been extensively applied to a variety of UQ problems [6, 7, 8, 9, 10].”
An overview of full-waveform inversion in exploration geophysics (2009, methodology): “For sampling the initial model, sophisticated investigations with global and semiglobal techniques (cid:1) Koren et al., 1991; Jin and Madariaga, 1993, 1994; Mosegaard and Tarantola, 1995; Sambridge and Mosegaard, 2002 (cid:2) have been performed.”
Inversion of Surface Deformation Data for Rapid Estimates of Source Parameters and Uncertainties: A Bayesian Approach (2018, methodology): “These have shown to be successful in solving a variety of optimization problems to study different geophysical problems (Sambridge & Mosegaard, 2002), and a detailed analysis of these methodologies, applied to the inversion of InSAR data, is presented by Shirzaei and Walter (2009).”