Abstract
AbstractConventional linearized deterministic inversions of transient electromagnetic (TEM) data inherently simplify the non-uniqueness and ill-posed nature of the problem. While Monte-Carlo-type approaches allow for a comprehensive search of the solution space, gaining the ensemble of inferred solutions as comprehensive as possible may be limited utility in high-dimensional problems. To overcome these limitations, we utilize a Markov Chain Monte Carlo (MCMC) inversion approach for surface-based TEM data, which incorporates Bayesian concepts into Monte-Carlo-type global search strategies and can infer the posterior distribution of the models satisfying the observed data. The proposed methodology is first tested on synthetic data for a range of canonical earth models and then applied to a pertinent field dataset. The results are consistent with those obtained by standard linearized inversion approaches, but, as opposed to the latter, allow us to estimate the associated non-linear, non-Gaussian uncertainty.
Funder
Natural Science Foundation of Shandong Province
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences,General Physics and Astronomy,General Engineering,General Environmental Science,General Materials Science,General Chemical Engineering