An Open‐Source Markov Chain Monte Carlo Inversion Software for the Analysis of Experimental Rock Deformation Data

Abstract

Despite its importance in modeling the interior dynamics of a planet, our understanding of interior rheology remains incomplete because of several reasons, including insufficient experimental data and inadequate analysis of the available data. To address the latter, Korenaga and Karato (2008, https://doi.org/10.1029/2007jb005100 ) developed a new and statistically robust approach to analyze experimental data on the plastic deformation of mineral assemblages, and its functionality has since been expanded. It is based on Bayesian statistics and is implemented by a Markov chain Monte Carlo (MCMC) method with Gibbs sampling. It is designed to explain the experimentally observed strain rates with a more realistic but highly nonlinear rheological model—the composite rheological model—which accounts for the simultaneous operation of multiple creep mechanisms. Our software, named labmc , can efficiently handle the inversion of experimental data for such complex rheologies. Moreover, it incorporates a thorough treatment of data uncertainties, which yields more robust estimates on the flow‐law parameters for the assumed rheological model. In this paper, we describe the labmc software and provide representative examples to explain how the software is used and the MCMC inversion results analyzed to obtain meaningful constraints on rock rheologies.