Senior Project Advisor
Machine learning, geophysics, geochemistry, planetary geology, data science, interdisciplinary science
Within Earth’s core, light elements (Si, O, C, S, N, H) are known to make up a small fraction of the total mass of the core with respect to heavy elements. The degree to which these elements exist in the cores of terrestrial planets have geophysical and geochemical implications, most notably the presence of core convection and a geodynamo, thermal conductivity within the core, and core temperature. Comparison of the composition of chondrites to Earth’s mantle composition and the Preliminary Reference Earth Model have given an estimation of about 10 % light elements in Earth’s core. The concentrations of each light element have been estimated in previous literature by determining experimentally the partitioning of elements between metal and silicate phases at high pressure and temperature. Previous studies have constructed thermodynamic models using linear regressions, to predict the change of partition coefficients with pressure, temperature, and oxygen fugacity. However, there is a large variance among previous literature in resulting thermodynamic models, which is likely indicative in substantial regression errors. Here, we plan to use machine learning algorithms, including Random Forest Regressions and Neural Networks, to predict the partition coefficients of Si and O using MetSilDB, a database for metal-silicate equilibria (Boujibar et al., GSA Fall 2022 Conference). We will assess the accuracy of our models using cross-validation techniques. The project itself will additionally assess the impact and development of machine learning models in the field of geosciences and more specifically planetary geology. Using these methods, we will build a model predicting elemental partitioning coefficients, which are predicted to have a highly improved performance than previous models. In addition, machine learning algorithms will enable addressing non-linear effects of experimental variables. Our findings will help us infer the composition of the core more accurately.
Keane, Ruben, "Silicon and Oxygen in Earth’s Core: Applications of Machine Learning to Metal-Silicate Equilibria and Core Formation" (2023). WWU Honors College Senior Projects. 648.
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