1J ZhangZhou,2Yuan Li,3Proteek Chowdhury,4Sayan Sen,5,6Urmi Ghosh,2Zheng Xu,7Jingao Liu,8Zaicong Wang,9James M.D. Day
Geochimica et Cosmochimica Acta (in Press) Link to Article [https://doi.org/10.1016/j.gca.2023.11.029]
1Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou, China
2State Key Laboratory of Isotope Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
3Earth, Environment and Planetary Sciences, Rice University, TX 77005, USA
4Zuckerberg Institute for Water Research, Ben-Gurion University of the Negev, Midreshet Ben-Gurion 8499000, Israel
5Environmental and Biochemical Sciences, The James Hutton Institute, Craigiebuckler, Aberdeen AB15 8QH, UK
6Department of Geology and Geophysics, Indian Institute of Technology (IIT) Kharagpur, 721302 Kharagpur, India
7State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences (Beijing), Beijing, China
8State Key Laboratory of Geological Processes and Mineral Resources, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China
9Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093-0244, USA
Copyright Elsevier
The sulfur content at sulfide saturation (SCSS) of a silicate melt can regulate the stability of sulfides and, therefore, chalcophile elements’ behaviors in planetary magma oceans. Many studies have reported high-pressure experiments to determine SCSS using either linear or exponential regressions to parameterize the thermodynamics of the system. Although these empirical equations describe the effects of different parameters on SCSS, they perform poorly when predicting laboratory measurements. Here, we compiled 542 published analyses of experiments performed on a range of sulfide and silicate compositions at varying P–T conditions (<24 GPa, <2673 K). Using empirical equations, linear regression, Random Forest algorithms, and a hybrid algorithm employing empirical fits to P–T conditions and the Random Forest algorithm for compositions, we developed several SCSS models and compared them to laboratory measurements. The Random Forest and hybrid models (R2 = 0.82–0.91, mean average error [MAE] < 746 ppmw S, residual mean standard error [RMSE] < 972 ppmw S), significantly outperform previous empirical models (R2 = 0.28–0.69, MAE = 622–1,170 ppmw S, RMSE = 1,070–1,744 ppmw S), whereas linear regression performs moderately well, i.e., between the classic and machine learning models. We applied our hybrid model to predict SCSS during magma ocean solidification on Earth, Mars, and the Moon, and we compared our model results to expected S contents in the residual magma oceans calculated by mass balance. Our results confirm that during early accretion, sulfides precipitated from magma oceans and into the outer cores of Earth and Mars, but not the Moon. Subsequently, once the respective magma oceans began precipitating minerals with increasingly FeO-rich and SiO2-, Al2O3-, and MgO-depleted compositions, the increasing S concentration in the residual magma was offset by temperature and compositional effects on SCSS, preventing sulfide precipitation during intermediate stages of crystallization. Sulfides precipitated late during magma ocean crystallization, but failed to percolate through the underlying crystalline mantle, significantly contributing to the modern bulk-silicate sulfur abundances of Earth, Mars, and the Moon. Our calculations suggest that late-stage sulfide precipitation occurred at shallow depths of 120–220 km, 40–320 km, and <10 km in the magma oceans of Earth, Mars, and the Moon, respectively.