A NEW APPROACH TO QUANTIFYING OPEN-SYSTEM TRANSCRUSTAL MAGMA STORAGE AND TRANSPORT SYSTEMS: MONTE CARLO MAGMA CHAMBER SIMULATOR

Igneous rocks record abundant evidence for magma evolution via crystallization, magma mixing, crustal contamination, and entrainment of earlier formed cumulates in transcrustal magma storage and transport systems. Quantifying processes and architectures of transcrustal systems is a key goal of igneous petrology, but modeling these complex open systems is a challenge due to the abundance of variables and associated geologic uncertainties. To address this challenge, we have developed a new tool, the Monte Carlo Magma Chamber Simulator (MC-MCS). MCS is a multicomponent, multiphase thermodynamic modeling tool that quantifies the pressure, mass and thermochemical history of open system processes in an evolving magma system. MC-MCS provides a Monte Carlo interface for choosing a set of variables (e.g., pressure; number, mass, composition of magma recharge events; initial temperature, composition of wallrock) and identifying geologically informed ranges for these parameters. MC-MCS simulations involve hundreds to thousands of runs, with stochastic combinations of relevant parameters, using Bayesian priors when appropriate. Archived results are assessed with a post-processing statistical-graphing package to determine a family of best-fit results based on user-defined criteria. We will illustrate how MC-MCS works by considering a well-defined theoretical MCS case where a continental tholeiite evolves at 0.1 GPa via fractional crystallization, crustal assimilation, and 2 recharge events (R2AFC). Using randomly chosen results from the R2AFC MCS calculation, we define a set of hypothetical volcanic samples and investigate how well MC-MCS reproduces the hypothetical data set when varying 7 parameters (i.e., pressure, temperature and mass of each of the two R events, mass and initial temperature of wallrock). Statistical evaluation of many thousands of runs is employed to define the best-fit MC-MCS results. Comparison between the hypothetical data set and the MC-MCS best-fit results illuminates the efficacy of MC-MCS. Our goal is to make this tool available to the petrology community and significantly enhance petrologists’ abilities to quantify complex processes in transcrustal open magmatic systems, thereby distinguishing the most likely petrologic scenarios from a sizable set of simulated possibilities.