Bayesian Markov Chain Monte Carlo inversion of surface-based transient electromagnetic data

Conventional 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.