The oxide abundance and Mg# (Mg/(Mg + Fe)) of the lunar surface are critical for understanding the Moon's petrology and evolution. Previous
The oxide abundance and Mg# (Mg/(Mg + Fe)) of the lunar surface are critical for understanding the Moon's petrology and evolution. Previous studies primarily relied on a single remote sensing data source and traditional regression algorithms for lunar oxide abundance inversion, potentially compromising the accuracy of chemical composition results. In this study, multisource data, specifically KAGUYA multiband imager (MI) data and Diviner Christiansen feature (CF) products, referred to as MI-CF, combined with an ensemble learning algorithm optimized by improved particle swarm optimization (IPSO), were utilized to produce new maps of six oxides (FeO, TiO2, Al2O3, CaO, MgO, and SiO2) and Mg# at a resolution of 59 m/pixel within the Moon's 65° N/S range. The models were constructed using oxide abundances measured from lunar samples collected by the Apollo, Luna, and Chang'E missions. Among the models tested, the SXL algorithm (stacking of support vector machine regression, extreme gradient boosting, and linear regression), which was selected from a stack of 2 or 3 out of six typical algorithms, achieved the highest inversion accuracy. The SXL algorithm's accuracy was further enhanced using IPSO with nonlinear weights and learning factors, resulting in a significant improvement over previous studies. The results confirm that the IPSO-SXL approach effectively improves the accuracy of lunar oxide inversion. Furthermore, the study demonstrates that using MI-CF data together yields better results than using MI or CF data alone. The oxide and Mg# distributions in three craters from different geological units—Highlands, SPA basin, and Maria—indicate different magmatic evolution processes, providing new insights into lunar geological evolution and volcanism.
