Unsupervised Learning for Thermophysical Analysis on the Lunar Surface

We investigate the use of unsupervised machine learning to understand and extract valuable information from thermal measurements of the lunar surface. We train a variational autoencoder (VAE) to reconstruct observed variations in lunar surface temperature from over 9 yr of Diviner Lunar Radiometer Experiment data and in doing so learn a fully data-driven thermophysical model of the lunar surface. The VAE defines a probabilistic latent model that assumes the observed surface temperature variations can be described by a small set of independent latent variables and uses a deep convolutional neural network to infer these latent variables and to reconstruct surface temperature variations from them. We find it is able to disentangle five different thermophysical processes from the data, including (1) the solar thermal onset delay caused by slope aspect, (2) effective albedo, (3) surface thermal conductivity, (4) topography and cumulative illumination, and (5) extreme thermal anomalies. Compared to traditional physics-based modeling and inversion, our method is extremely efficient, requiring orders of magnitude less computational power to invert for underlying model parameters. Furthermore our method is physics-agnostic and could therefore be applied to other space exploration data sets, immediately after the data is collected and without needing to wait for physical models to be developed. We compare our approach to traditional physics-based thermophysical inversion and generate new, VAE-derived global thermal anomaly maps. Our method demonstrates the potential of artificial intelligence-driven techniques to complement existing physical models as well as for accelerating lunar and space exploration in general.