Stochastic Joint Inversion of Seismic and Controlled-Source Electromagnetic Data

Abstract

Stochastic inversion approaches provide a valuable framework for geophysical applications due to their ability to explore multiple plausible models rather than offering a single deterministic solution. In this paper, we introduce a probabilistic joint inversion framework combining the very fast simulated annealing optimization technique with generalized fuzzy c-means clustering for coupling of model parameters. Since very fast simulated annealing requires extensive computational resources to converge when dealing with a large number of inversion parameters, we employ sparse parameterization, where models are sampled at sparse nodes and interpolated back to the modelling grid for forward computations. By executing multiple independent inversion chains with varying initial models, our method effectively samples the model space, thereby providing insights into model variability. We demonstrate our joint inversion methodology through numerical experiments using synthetic seismic traveltime and controlled-source electromagnetic datasets derived from the SEAM Phase I model. The results illustrate that the presented approach offers a practical compromise between computational efficiency and the ability to approximate model uncertainties, making it suitable as an alternative for realistic larger-scale joint inversion purposes.