Spectral Unmixing Based Construction of Lunar Mineral Abundance Maps

Abstract

ABSTRACT:In this study we apply a nonlinear spectral unmixing algorithm to a nearly global lunar spectral reflectance mosaic derived from hyperspectral image data acquired by the Moon Mineralogy Mapper (M 3 ) instrument. Corrections for topographic effects and for thermal emission were performed. A set of 19 laboratory-based reflectance spectra of lunar samples published by the Lunar Soil Characterization Consortium (LSCC) were used as a catalog of potential endmember spectra. For a given spectrum, the multi-population population-based incremental learning (MPBIL) algorithm was used to determine the subset of endmembers actually contained in it. However, as the MPBIL algorithm is computationally expensive, it cannot be applied to all pixels of the reflectance mosaic. Hence, the reflectance mosaic was clustered into a set of 64 prototype spectra, and the MPBIL algorithm was applied to each prototype spectrum. Each pixel of the mosaic was assigned to the most similar prototype, and the set of endmembers previously determined for that prototype was used for pixel-wise nonlinear spectral unmixing using the Hapke model, implemented as linear unmixing of the singlescattering albedo spectrum. This procedure yields maps of the fractional abundances of the 19 endmembers. Based on the known modal abundances of a variety of mineral species in the LSCC samples, a conversion from endmember abundances to mineral abundances was performed. We present maps of the fractional abundances of plagioclase, pyroxene and olivine and compare our results with previously published lunar mineral abundance maps.

Lunar ore geology and feasibility of ore mineral detection using a far-IR spectrometer

“…Later studies of the M 3 spectrometer onboard Chandrayaan-1 confirmed mineralogical differences between these two groups of rocks, but only in rock-forming minerals. First, lunar mare basalts show 35-50 vol.% plagioclase (Bernhardt et al, 2017;Corley et al, 2018), and lunar highland anorthosites >90 vol.% plagioclase (Cheek et al, 2013;Bernhardt et al, 2017). However, no maps show the differences between the domains of basalt mare and anorthositic highlands regarding their content of ore minerals, such as ilmenite, troilite, and others.…”

“…These areas show slightly elevated TiO 2 (1-5 wt%) and FeO T (5-15 wt%) relative to the anorthositic highlands (Lucey et al, 1998), which would imply an increased ore mineral content. The orbit of the Chandrayaan-1 mission did not allow the collection of mineralogical information from these areas by the M 3 spectrometer (Bernhardt et al, 2017). Therefore, the role of MIRORES in recognizing polar regions may be crucial.…”

“…The third secondary goal will be insights into the geology of KREEP sites, which are located at the margins of lunar mare (Jolliff et al, 2000) and can be characterized by MIRORES for ore minerals, but also apatite (robust spectral features between 25 and 30 µm) rich in REE and containing water (Liu et al, 2022). So far, we have chemical data (Lucey et al, 1998) and rock-forming mineral data (Bernhardt et al, 2017) that indicate values intermediate between those for the lunar mare and the anorthositic highlands (Lucey et al, 1998;Bernhardt et al, 2017). However, orbital data on ore mineral and apatite contents are missing.…”