FAS-XAI: An Interpretable Framework for the Comparative Morphological Analysis of Lunar and Martian Impact Craters

Abstract

Impact craters are among the most abundant geological structures on solid planetary surfaces and provide valuable information about impact processes and surface evolution. However, the systematic characterization of crater morphology remains challenging due to dataset heterogeneity, measurement uncertainty, and gradual transitions between morphological classes. This study proposes FAS-XAI, an interpretable framework for the comparative analysis of planetary crater datasets that combines fuzzy clustering and explainable artificial intelligence (XAI). The methodology combines exploratory data analysis, measurement-uncertainty assessment, unsupervised learning, supervised consistency analysis, and interpretable machine learning to identify and characterize crater morphologies through a structured workflow. The framework is applied to the Moon Crater Database v1 and the Robbins Mars Crater Database, two large-scale crater catalogs sharing a common geometric parameterization of crater properties. Using the variables available in both datasets, Fuzzy C-Means identifies morphological crater groups, while XGBoost assesses how consistently the resulting dominant cluster labels can be reconstructed from the same descriptor space. XAI techniques are then used to explain the contribution of each variable to the identified groups. The results reveal distinct structural patterns in the organization of lunar and Martian crater populations.