Characterizing Extreme Compositions on the Moon Using Thermal Infrared Spectroscopy

Abstract The ultramafic and silicic rocks on the lunar surface have played an important role in expanding our knowledge regarding its thermal and magmatic evolution. The surface identification and quantification of these rocks on the global scale can significantly improve our understanding of their spatial extents, relationships and formation mechanisms. Christiansen feature positions using Diviner data have aided in global identification and mapping of relatively silica‐rich and silica‐poor lithologies on the lunar surface. We have used laboratory thermal infrared spectra of silicic and ultramafic rocks to analyze the variation in Christiansen feature in simulated lunar environment. We have characterized the absolute bulk silica content of the rocks and minerals and their Silica, Calcium, Ferrous iron, Magnesium index. We find that they are linearly correlated to the Christiansen feature despite particle size variations. Furthermore, we find that the Christiansen feature shifts toward longer wavelengths with increase in ilmenite content in the ilmenite‐basalt mixtures. We have explored the effect of instrument's spectral band position on the accuracy of the parabolic method that is currently used for the estimation of Christiansen feature position from Diviner data. We find that this method performs poorly for the estimation of the Christiansen feature for ultramafic and silicic rocks and minerals/mineral mixtures. We propose using a machine learning algorithm to estimate the Christiansen feature with higher accuracy for all kinds of silicate compositions on the Moon. This method will lead to increased accuracy in absolute quantification of bulk silicate composition of the lunar surface at varying spatial scales.