As part of NASA’s 2019 Frontier Development Lab, we used AI to search for resources on the surface of the Moon which could one day help humans settle there.
The Moon is a mysterious place that most of us have only seen through the lens of a telescope, but with companies like SpaceX offering commercial space flights, NASA’s new plans to put humans back on the Moon by 2024 and talk of a growing lunar economy it may start to feel much closer in the future.
Finding resources on the surface is a key part of building permanent lunar bases and sustaining our presence there.
The difficulty is that we are unsure where these resources are. Part of the problem is that the majority of the Moon is unexplored; there is a large amount of satellite data covering the surface, but without the presence of ground truth observations (i.e. humans or probes actually going there) we are not entirely confident which parts of this data correspond to resources.
Locating metal
An important resource is metal, and one place we might expect to find it is around the Moon’s craters. We know that some of the asteroids which have impacted the Moon would have been metallic and so it is likely they left deposits in these areas.
Their temperature may be a key way to detect them. The Moon is heated by the Sun during the day and cools during the evening. Just like when you recoil from your car seatbelt in the summer because it is too hot to touch, metals are excellent at retaining their heat and we expect these craters to be hotter in the evening compared to their surroundings.
The DIVINER instrument aboard the Lunar Reconnaissance Orbiter has recorded the surface temperature of the Moon over 10+ years, however understanding whether a temperature anomaly relates to metal is difficult because there are many factors of variation on the lunar surface which can affect its temperature. One example is the albedo of the surface, or how reflective it is, which can affect how much heat it absorbs.
Unsupervised machine learning
We used a fully data-driven, unsupervised machine learning approach to disentangle these factors of variation in the surface temperature and uncover those which may relate to metals. We trained a variational autoencoder (VAE) to reconstruct examples of the surface temperature variation over the lunar day and then looked inside its hidden representation to understand what it found were the major factors of variation.
We found that the VAE could disentangle different physical effects, such as the onset time of radiation and the albedo, allowing us to isolate and map those areas which potentially have high thermal conductivity relating to metals. Fig. 3 shows anomaly maps creating using our VAE which potentially locates metallic-like signatures over the Tycho crater.
Conclusion
Our AI-generated maps could help future mission planners and progress our vision of a fully data-driven approach to space exploration. See our final presentation below for more information!
Read the full NeurIPS 2019 workshop paper here:
https://ml4physicalsciences.github.io/2019/files/NeurIPS_ML4PS_2019_115.pdf
The team: Ben Moseley, Valentin Bickel, Jérôme Burelbach, Nicole Relatores, Daniel Angerhausen, Frank Soboczenski and Dennis Wingo
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