Global Mapping of Fragmented Rocks on the Moon with a Neural Network: Implications for the Failure Mode of Rocks on Airless Surfaces

Abstract

Failure modes of lunar boulders depend both on rheology and the erosion agents acting in the lunar surface environment. Here, we address the failure modes of lunar boulders and their variations at a quasi-global scale (60°N to S). We deploy a neural network and map a total of ∼130,000 fragmented boulders (width > ∼10 m) scattered across the lunar surface and visually identify a dozen different disintegration morphologies corresponding to different failure modes. Our findings suggest that before a boulder is catastrophically shattered by an impact, there is an internal weakening period with minor morphological evidence of damage at the rock scale at the resolution of the used imagery. We find that some of the rare pre-shattering morphologies (e.g., fractures) are equivalent to morphologies observed on asteroid Bennu, suggesting that these morphologies on the Moon and on asteroids are likely not diagnostic of their formation mechanism (e.g., meteoroid impact, thermal stresses). In addition, we identify new morphologies such as breccia boulders with an advection-like erosion style. We publicly release the produced fractured boulder catalog along with this paper.