The Joint Bayesian Inversion of CSAMT and DC Data for the Jinba Gold Mine in Xinjiang Using Physical Property Priors

We perform Bayesian joint inversion on controlled-source audio-frequency magnetotelluric (CSAMT) and direct current (DC) resistivity data using geostatistical modeling to incorporate prior constraints on physical properties. This study focuses on the Jinba gold deposit in Xinjiang, China, demonstrating the effectiveness of integrating CSAMT and DC resistivity data in enhanced subsurface imaging. By leveraging prior knowledge and employing Markov chain Monte Carlo (MCMC) sampling, we quantify the uncertainty in inversion results and compare the improvements offered by joint inversion over single-method approaches.