Material Characterization While Drilling on the Moon: Comparing the Atmospheric and Cryogenic Drilling Data

The sustainable presence of humans on the Moon relies heavily on using the water-ice available on the Moon. However, the large variability in our understanding of the form, quantity, vertical & spatial distribution, and composition of water-ice has been a roadblock in designing the technologies to identify, excavate, and process the water-ice. This uncertainty can be reduced significantly by acquiring ground-truth data. Upcoming missions such as VIPER and Luna-27 contain a drill that will help reduce this uncertainty. The goal of this NASA ESI funded project is to develop pattern-recognition algorithms to characterize lunar subsurface based on drilling data. This work focuses on developing a part of the algorithm for low-porosity water-bearing lunar regolith. Three low-porosity analogs were created to acquire high-frequency drilling data. This data was used to train a complex pattern-recognition algorithm which was then tested on water-bearing lunar regolith simulant with varying water-content to estimate the UCS of the sample, identify layer boundaries, detect auger choking, and identify the form of the water-bearing lunar regolith.