Development of a data-driven subsurface thermal probing technique for lunar exploration

Abstract

Conducting in-situ measurements of planetary environments is a key scientific goal in the geoscience field for the extraterrestrial exploration, such as the Moon or Mars. Among these measurements, determining the thermal properties of regolith is particularly challenging due to the material’s inhomogeneities. In this paper, an active thermal probing technique is proposed, utilizing a transient hot-wire technique with a needle-like probe structure integrated with temperature sensors and a heater. It is capable of measuring in-situ temperature variations and inner heat flux without disturbing the surrounding regolith. In addition, a machine-learning-based method is proposed for inverting in-situ thermophysical properties, including density, thermal conductivity, and heat capacity. An embedded artificial neural network model has been developed and trained with data sets from both finite element simulations and experiments. This machine learning model is capable of efficiently predicting temperature responses for different lunar regolith simulants. For total 12 parameters, the errors of ten cases are less than 10%, and other two cases are 12.23% and 13.17%. Finally, the proposed method is verified by heat measurement experiments under normal pressure and vacuum, and the variation of thermal properties with different compactness is obtained. The proposed probing technique provides novel ideas to measure the in-situ thermal properties of extraterrestrial regolith.