Automated Mineral Identification and Rock‐Type Classification of Lunar Mare Basalts Using SEM Images

Abstract We present an automated system for identifying minerals and classifying rock types in Apollo lunar mare basalts using scanning electron microscopy (SEM) imagery. Mineral segmentation is based on a U‐Net architecture, supplemented by two scale‐aware models designed to incorporate pixel size information. We find that a single U‐Net without explicit scale input achieves performance comparable to the scale‐aware models. From a total of 17,248 augmented images (i.e., 1,078 unique base images), 12,800 were used for training, and the final model achieved average pixel‐wise accuracies of 0.85, 0.81, and 0.82 on the training, validation, and test sets, respectively. For rock classification, we constructed a separate data set by compiling reported modal mineral abundances from the literature to train both a rule‐based classifier and a Gaussian Naive Bayes model. These classifiers were then applied to modal abundances derived from the segmented images, achieving accuracies of 0.78 in distinguishing between ilmenite, pigeonite, and olivine basalts. Our framework enables rapid, scalable, first‐order mineral identification and rock classification of lunar mare basalt petrography. However, it also reveals several limitations, including challenges in identifying minor phases, the need for phase subclassification, and inaccuracies of human bias in training annotations. These limitations underscore the continued importance of expert interpretation for detailed mineralogical and petrological studies. Nevertheless, our system provides a useful baseline and benchmark with applicability to both existing Apollo collections and lunar meteorites, as well as to future returned‐sample missions.