Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data

TL;DR

This paper introduces a 2D probabilistic inversion method for plane-wave electromagnetic data using MCMC, emphasizing the importance of model constraints and joint inversion with resistivity data to reduce uncertainty and improve model interpretation.

Contribution

The paper presents a novel hierarchical Bayesian MCMC inversion approach for 2D EM data, incorporating model constraints and joint inversion with ERT to enhance stability and interpretability.

Findings

Model constraints reduce parameter uncertainty.

Joint inversion with ERT improves model resolution.

Hierarchical Bayesian approach effectively captures data error properties.

Abstract

Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space is high dimensional. Here, we present a two-dimensional (2D) pixel-based MCMC inversion of plane-wave electromagnetic (EM) data. Using synthetic data, we investigate how model parameter uncertainty depends on model structure constraints using different norms of the likelihood function and the model constraints, and study the added benefits of joint inversion of EM and electrical resistivity tomography (ERT) data. Our results demonstrate that model structure constraints are a necessity to stabilize the MCMC inversion results of a highly-discretized model. These constraints decrease model parameter uncertainty and facilitate model interpretation. A drawback is that these constraints may lead to posterior distributions that do not fully include the true underlying model, because some of its features exhibit a low sensitivity to the EM data, and hence are difficult to resolve. This problem can be partly mitigated if the plane-wave EM data is augmented with ERT observations. The hierarchical Bayesian inverse formulation introduced and used herein is able to successfully recover the probabilistic properties of the measurement data errors and a model regularization weight. Application of the proposed inversion methodology to field data from an aquifer demonstrates that the posterior mean model realization is very similar to that derived from a deterministic inversion with similar model constraints.