Abstract
The lunar mare regolith preserves tripartite records of volcanism, impacting, and space weathering. However, previous studies based on limited soil particle numbers were hindered by issues of sample representativeness. Here we conduct micro-CT scans of bulk soil samples from Chang'e-5 (nearside) and Chang'e-6 (farside), and develop machine learning-based image segmentation and classification methods to identify a vast number of basalt, agglutinate, breccia, and monomineralic particles. The Chang'e-5 basalt exhibits higher plagioclase content than Chang'e-6, while agglutinates from Chang'e-6 have lower void ratios, respectively indicating different lava origins and more intense micrometeorite bombardment for farside Chang'e-6. Despite their contrasting volcanic and impacting histories, the soil particles for these youngest nearside/farside samples exhibit similar morphometric distributions, suggesting that space weathering reached saturation in shaping surficial soil particle morphology in ~ 2.2 million years or less. These findings may extend to other mare regions and help establish space weathering models for other airless bodies.