Real-time characterization of water-bearing lunar regolith while drilling on the Moon

Abstract

Various studies and missions proved the existence of significant quantities of water-ice in the permanently shadowed regions of the Moon. Successful excavation and processing of this water-ice on the Moon can revolutionize the space industry and help expedite the exploration of the Moon and other planetary bodies. The water-ice can be used for human consumption, radiation shielding, constructions, and most importantly to produce propellants. However, the uncertainty around the form, quantity, distribution, and composition of the water-ice remains the biggest obstacle in making this a reality, pointing to the need for further explorations and quantification of the available resources. This uncertainty makes it extremely difficult to make a business case for exploring the lunar poles. It also creates massive hurdles in designing the excavation systems for lunar polar conditions. An extensive exploratory drilling program in the lunar permanently shadowed region is needed to help deliver ground-truth information and help reduce this uncertainty. This study, conducted under NASA’ Early Stage Innovation (ESI) Grant, proposes methods that can assist the drilling rover or landers in extracting as much information as possible from the drilling operations on real time basis and to eliminate or reduce the need for obtaining physical samples for further testing. The goal is to develop comprehensive pattern-recognition algorithms to analyze high-frequency drilling data to characterize material properties on the Moon while drilling, which allows faster and more efficient exploration of the areas of interest. Based on the drilling systems developed for different extraterrestrial conditions, an experimental drilling setup with a high-frequency data acquisition system was developed to acquire drilling response from various samples. A cryogenic apparatus was also designed and fabricated to cool down water-bearing lunar regolith samples to lunar conditions. Four types of water-bearing lunar regolith were considered: low-porosity aqueous-icy, high-porosity aqueous-icy, unfused granular icy, and fused granular icy regolith. Low-porosity and high-porosity analog samples were designed. The drilling data collected from full scale testing in the analog samples was used to train, validate, and test the ‘Lunar Material Characterization while Drilling Algorithm’ which consists of three classification models and two regression models. The classification models include a drilling state classifier, batch classifier to identify layer boundaries, auger choking, and porosity type, and form classification to identify the form of water-bearing lunar regolith samples. The regression models developed include a torque calculation model and a Uniaxial Compressive Strength (UCS) prediction model, based on available data relating water content to strength properties of icy regolith in separate laboratory tests. The applicability and veracity of the ‘Lunar Material Characterization while Drilling Algorithm’ confirmed using blind data from the analog and cryogenic tests and using digital twin’s data estimated for a simulated complex 3D lunar subsurface sample. These tests showed that the algorithm built here are flexible and can adjust to various surface and subsurface conditions to deliver accurate results. The tests conducted on the simulated 3D lunar subsurface sample also showed an additional application of this algorithm in mapping subsurface strata across complex layers. The algorithm was tested on various forms of water-bearing regolith simulant at lunar conditions to detect layer boundaries, identify the form of water-ice, differentiate between the types of porosity, and calculate UCS. This algorithm can also identify drilling-state and predict auger choking. Such algorithms can be crucial in understanding the form, quantity, and spatial and vertical distribution of water-ice in the lunar permanently shadowed regions during the drilling tests. With multiple drilling missions slated for lunar polar exploration in the next decade, such algorithms can expedite the lunar exploration and be applied on the existing drilling units by simply feeding the data stream to the algorithm for identification of the formations being drilled on real time basis.