Global mapping of fragmented rocks on the Moon with a neural network: Implications for the failure mode of rocks on airless surfaces

TL;DR

This study uses a neural network to map and analyze fragmented rocks on the Moon, revealing diverse failure modes and morphological features that inform understanding of rock failure processes on airless planetary surfaces.

Contribution

It introduces a neural network-based method to map and classify lunar boulder morphologies, providing new insights into their failure modes and morphological diversity.

Findings

Multiple boulder morphologies linked to different failure modes

Fragmentation often occurs suddenly after internal weakening

Discovery of new erosion-like morphologies such as breccia boulders

Abstract

It has been recently recognized that the surface of sub-km asteroids in contact with the space environment is not fine-grained regolith but consists of centimeter to meter-scale rocks. Here we aim to understand how the rocky morphology of minor bodies react to the well known space erosion agents on the Moon. We deploy a neural network and map a total of ~130,000 fragmented boulders scattered across the lunar surface and visually identify a dozen different desintegration morphologies corresponding to different failure modes. We find that several fragmented boulder morphologies 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. Our findings suggest that the boulder fragmentation process is characterized by an internal weakening period with limited morphological signs of damage at rock scale until a sudden highly efficient impact shattering event occurs. 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.