Three-dimensional magnetotelluric Bayesian inversion based on Stein variational gradient descent

Abstract

To address the challenge of assessing the reliability of three-dimensional (3D) magnetotelluric (MT) inversion results, we have developed a variational inference (VI) inversion framework (VI-MT) based on the Stein Variational Gradient Descent (SVGD) method. Through parallel particle optimization, we efficiently approximate the model posterior distribution, overcoming the computational limitation of traditional Markov Chain Monte Carlo (MCMC) methods. Synthetic tests show that the VI-MT inversion can effectively recover the synthetic model while reducing the tailing effect, and it can approximate the multimodal posterior distribution, providing quantitative uncertainty estimates. Furthermore, the VI-MT inversion is applied to the field MT data collected at the Weishan volcano in northeast China, with the posterior mean model consistent with the deterministic inversion model. A clear low resistivity body of ∼5 Ω·m with small uncertainties is imaged at depths of ∼2-6 km beneath the volcanic crater, suggesting the existence of a shallow magma chamber. Our study shows that the VI-MT inversion based on the SVGD method can efficiently solve the 3D MT Bayesian inversion, providing reliable model uncertainties.