Mineral Content Estimation of Lunar Soil of Lunar Highland and Lunar Mare Based on Diagnostic Spectral Characteristic and Partial Least Squares Method

Extraction of mineral and rock information of lunar regolith is of far-reaching significance to the study of material composition, geological structure and historical evolution of lunar regolith. Visible and near-infrared spectra can reflect mineral composition information, and can be used to extract mineral composition and distribution characteristics of lunar regolith. In this paper, the LSCC (Lunar Soil Characterization Consortium) data of lunar regolith is taken as the research object. The partial least squares (PLS) regression model is used to estimate the spectra of lunar regolith measured in RELAB laboratory of Brown University. The mineral contents of plagioclase, pyroxene, olivine, ilmenite, agglutinate and volcanic glass in lunar regolith have been optimized and retrieved. The LSCC spectra of lunar regolith have been pre-processed by multivariate scattering correction (MSC), which highlight the spectral features of lunar regolith. The optimal number of principal components has been selected by cross-validation test. The PLS regression have been established for samples from lunar highland and lunar mare respectively. Two-thirds of samples have been randomly selected as experimental group to establish the prediction relationship between the spectra of lunar regolith and mineral content. The remaining one-third of samples have been used as verification group to further validate the prediction relationship. The results show that the partial least squares regression model has high accuracy and good stability. It is of theoretical and practical significance to optimize the inversion of mineral content in lunar regolith using spectral data of lunar regolith.