A Julia software package for transdimensional Bayesian inversion of electromagnetic data over horizontally stratified media

A quantitative assessment of model parameter uncertainty is vital for a reliable interpretation of electromagnetic (EM) data due to the nonuniqueness inherent to EM inverse problems. Conventional gradient-based inversion approaches typically produce a single preferred model with limited information about parameter uncertainty. The inverse problems can be alternatively postulated into a sampling-based Bayesian inference framework where the solution is represented by a posterior probability distribution of model parameters, which can provide an effective way to rigorously estimate parameter uncertainty related to the recovered solution. In this work, we have implemented a Bayesian inversion framework for probabilistic inversion of EM data, which is an open-source software package implemented in the Julia programming language. The key feature of the framework is that it allows the model complexity to be adaptively adjusted to an appropriate level compatible with the data by implementing a reversible jump Markov chain Monte Carlo (MCMC) algorithm, thus allowing the data to infer the appropriate level of model complexity and associated parameter uncertainty. We have elaborated the structure of the package with a focus on code modularity and extensibility. Finally, we demonstrate the capacity and versatility of the software package through three synthetic examples that simulate different EM scenarios. The inversion results show the performance and inherent resolving abilities of different EM surveys for conductive and/or resistive structures. In addition, the model ensemble produced by the transdimensional Bayesian inversion conveys a wealth of information. Many important quantities for data interpretation such as parameter uncertainty and correlation between model parameters of interest can be measured by exploring the model ensemble.