Abstract:

Nominally anhydrous minerals (NAMs) are the primary carriers of water in rocky planet mantles. Therefore, studying water solubilities in the major mantle NAMs can help us estimate the water storage capacities of rocky planet mantles and indirectly constrain the actual water contents of their interiors. Using data science methods such as statistics and statistical learning algorithms, this paper presents and summarizes current modeling studies on the mantle water storage capacities of Earth, Mars, and exoplanets. The paper first reviews the thermodynamic model for mantle water storage capacity. Then, two case studies on Earth and Mars explore how to translate atomic-scale experimental data of water solubility and their measurement errors into planetary-scale models of mantle water storage capacity, based on robust regression, Monte Carlo methods, and bootstrap aggregation algorithms. Then, it is presented how a large sample from the exoplanet observational campaigns can help us understand the statistical nature of their mantle water storage capacities. Finally, the paper discusses the limitations of data science methods and how to combine statistics and statistical algorithms with mineral physics data.