Reversible-jump Markov chain Monte Carlo and Shamos–Hoey algorithms for two-dimensional gravity inversion

SUMMARY In this study, a modified 2-D gravimetric inversion algorithm is presented that is based on the reversible-jump Markov chain Monte Carlo method with the Talwani equation. To ensure the validity of the Talwani equation and accurate gravity anomaly calculations, the Shamos–Hoey algorithm is incorporated as an additional acceptance condition to prevent intersections in the model polygon. This improves upon the method proposed by Luo by iteratively refining a polygon model based on gravity anomalies while maintaining physical validity. Additionally, we suggest a revision of the prior density function to better test the proposal models. This method estimates the shape and location of subsurface intrusions, providing valuable insights into subsurface geological structures. This positions the algorithm as a valuable tool for geophysical research.