Real time assessment of water content in icy regolith by analysing drilling parameters

The first target of mining activities on the surface of the moon is to find and extract water from the icy regolith on the moon. A test drilling unit was designed and fabricated under a NASA Early Stage Innovation (ESI) grant. A high-frequency data acquisition system was used to record drilling responses at 1000 Hz. Parameters like weight on bit (WOB), torque, rev/min, rate of penetration (ROP), mechanical specific energy (MSE), field penetration index (FPI), and the uniaxial compressive strength (UCS) were recorded for 40 boreholes in the analog samples made of regolith simulant and specialised cement. Development of the AI system and machine learning (ML) algorithms for analysis of data to evaluate strength and hence water content of icy regolith required a large data set. In this case, the data set comprising of more than one billion data points recorded while drilling into various analogue samples and cryogenic lunar formations to optimise power consumption and make bit wear adjustment during drilling operations. The tests were also repeated in the samples of icy regolith at various water content at -190C to verify the ability of the algorithms to analyse the data and to estimate the strength of the frozen formation and hence water content. The paper will discuss the objectives and methodologies used in the project and reviews the testing program and the machine learning (ML) algorithms ability to identify various formations along the exploration boreholes.