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

• Published in: SN applied sciences Vol. 4; no. 10; pp. 1 - 12
• Main Authors: Deng, Shengqiang, Zhang, Nuoya, Kuang, Bo, Li, Yaohua, Sun, Huaifeng
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.10.2022; Springer
• Subjects: Applied and Technical Physics; Chemistry/Food Science; Earth Sciences; Engineering; Environment; Materials Science; Research Article
• ISSN: 2523-3963; 2523-3971
• DOI: 10.1007/s42452-022-05134-5

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. Article Highlights We propose a Bayesian MCMC procedure for surface-based TEM data inversion and apply it successfully both in synthetic data and field data. We use the modified Gaussian proposal distribution to improve the sampling efficiency and deep resistivity resolution. The results of Bayesian can provide uncertainty information on parameters and judge the reliability of inversion results.