Collaborative Research: Calibration of Thermochemical Models using Bayesian Methods--Building MELTS 2.0.

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Molten rocks are essential to the structure and long-term evolution of the Earth and other planets. The properties of rocks when heated above their melting point (including composition, density, heat content, and thermal expansion) all play important roles in a vast array of geologic processes. These range widely from the creation of deep magma oceans during the earliest stages of planet formation all the way to the steady progress of plate tectonics, responsible for creating, shaping, and destroying the Earth's oceans and continents. Unfortunately, these geologic processes occur over planetary length-scales and time-scales, playing out over thousands of kilometers and billions of years. Due to this complexity, it is not possible to perform experiments that directly probe the evolution of planetary interiors. Instead, scientist rely upon thermodynamic models, which can predict the physical and energetic properties of rocks, melts, and fluids based on the results of laboratory experiments performed under controlled conditions. The usefulness of these models thus depends entirely upon how well they are calibrated, including the amount and variety of experimental data as well as the statistical methods used to extract the thermodynamic modeling parameters. In this proposal, the team will develop new modeling techniques needed to improve and extend the geologic thermodynamic models used to predict the properties of solid and molten rocks at planetary conditions, relevant to our understanding of rocky planets both within and outside our solar system.To accomplish these overarching goals, the collaborators will design the analytic tools needed to construct flexible and robust self-consistent thermodynamic models for the geologic community using Bayesian statistical methods. Currently, the calibration procedure for thermodynamic databases (like the popular MELTS model) is an unfortunately onerous task. Updating these models is time-consuming and restricted to the very few experts with the skills required to integrate new information without breaking the accuracy and self-consistency of the model. The purpose of this proposal is to dramatically reduce the challenges associated with recalibration, enabling simple and rapid incorporation of new experimental data into the database by a wide variety of users. The proposal objectives are: (1) Design and create statistical calibration tools (using novel Bayesian techniques) to simplify thermochemical model building; (2) Expand and augment the calibration database with the large quantity of previously ignored data, including solid phase-absent constraints, melt-free sub-solidus experiments, and observations of melt coexisting with solid phases of unmeasured-composition; (3) Use the new methods and data to produce MELTS 2.0, a new tunable silicate melts model that provides model prediction uncertainties, enabling users to rapidly pinpoint and address model weaknesses. The resulting calibration tool will thus be able to generate and visualize model prediction distributions - useful for geologic process modeling, teaching, and future experimental planning -and help to close the large gap that still exists between model-use and model-design.