Precise estimation of pressure-temperature paths from zoned minerals using Markov random field modeling: theory and synthetic inversion

摘要

The chemical zoning profile in metamorphic minerals is often used to deduce the pressure-temperature (P-T) history of rock. However, it remains difficult to restore detailed paths from zoned minerals because ther-mobarometric evaluation of metamorphic conditions involves several uncertainties, including measurement errors and geological noise. We propose a new stochastic framework for estimating precise P-T paths from a chemical zoning structure using the Markov random field (MRF) model, which is a type of Bayesian stochastic method that is often applied to image analysis. The continuity of pressure and temperature during mineral growth is incorporated by Gaussian Markov chains as prior probabilities in order to apply the MRF model to the P-T path inversion. The most probable P-T path can be obtained by maximizing the posterior probability of the sequential set of P and T given the observed compositions of zoned minerals. Synthetic P-T inversion tests were conducted in order to investigate the effectiveness and validity of the proposed model from zoned Mg-Fe-Ca garnet in the divariant KNCFMASH system.