New maps of major oxides and Mg # of the lunar surface from additional
geochemical data of Chang’E-5 samples and KAGUYA multiband imager data
Abstract
In the past, global maps of major oxides and Mg # of the lunar surface
had been derived from spectral data with “ground truth” geochemical
information from Apollo and Luna samples. These compositional maps
provide insights into the chemical variations of different geologic
units, thus the regional and global geologic evolution. In this study,
we produced new global maps of major oxides
(Al2O3, CaO, FeO, MgO, and
TiO2) and Mg # with imaging spectral data of KAGUYA
multiband imager (MI) with the one dimensional-convolutional neural
network(1D-CNN)algorithm, taking advantage of recently acquired
geochemical information of China’s Chang’E-5 (CE-5) samples. The
coefficients of determination (R2) and Root Mean
Squared Error (RMSE) were selected as the model evaluation indicators,
and compared with the models used by Wang et al. (2021) and Xia et al.
(2019), the results showed that the 1D-CNN algorithm model used in this
study had a higher degree of fit and smaller dispersion between the
ground true value and the predicted value. The 1D-CNN algorithm
generally performs better in describing the complex nonlinear
relationship between spectra and chemical components. In addition, we
present regions of mare domes in Mairan Dome (43.76°N, 49.90°W), and
irregular mare patches (IMPs) in Sosigenes (8.34°N, 19.07°E) to
demonstrate the geologic implications of these new maps. With the
highest spatial resolution (~ 59 m / pixel), these new
maps of major oxides and Mg # will serve as an important guide in the
future study of lunar geology.