Joint Inversion of DC Resistivity and MT Data using Multi-Objective Grey Wolf Optimization

TL;DR

This paper demonstrates the application of Multi-Objective Grey Wolf Optimization for joint inversion of DC resistivity and MT data, effectively estimating subsurface resistivity profiles and quantifying uncertainty without requiring weighting parameters.

Contribution

It introduces the use of MOGWO for joint geophysical inversion, providing a set of Pareto optimal solutions and uncertainty assessment, which improves over traditional methods.

Findings

MOGWO successfully produced non-dominated solutions for joint inversion.

Results are comparable to established inversion techniques.

The method provides reliable resistivity estimates with quantified uncertainty.

Abstract

Joint inversion of geophysical datasets is instrumental in subsurface characterization and has garnered significant popularity, leveraging information from multiple geophysical methods. In this study, we implemented the joint inversion of DC resistivity with MT data using the Multi-Objective Grey Wolf Optimization (MOGWO) algorithm. As an extension of the widely-used Grey Wolf Optimization algorithm, MOGWO offers a suite of pareto optimal non-dominated solutions, eliminating the need for weighting parameters in the objective functions. This set of non-dominated predictions also facilitates the understanding of uncertainty in the predicted model parameters. Through a field case study in the region around Broken Hill in South Central Australia, the paper showcases MOGWO's capabilities in joint inversion, providing confident estimates of the model parameters (resistivity profiles), as indicated by a narrow spread in the suite of solutions. The obtained results are comparable to well established methodologies and highlight the efficacy of MOGWO as a reliable tool in geophysical exploration.