1Wen Xiang Xia et al. (>10)
Icarus (in Press) Link to Article [https://doi.org/10.1016/j.icarus.2018.10.031]
1Hubei Subsurface Multi-scale Imaging Key Laboratory, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China
The major oxides (SiO2, Al2O3, CaO, FeO, MgO, and TiO2) and Mg# are critical for revealing the petrological characteristics of the Moon and for testing models of lunar formation and geologic evolution. There are few high-spatial-resolution (<250 m/pixel) abundance maps for all the six major oxides and Mg# across the Moon. Furthermore, previous studies primarily employed the traditional regression methods to derive oxide contents from optical images, which may influence the inversion accuracies of the lunar chemical compositions. This paper reports the abundance maps of all the six major oxides and Mg# with a high spatial resolution of ∼200 m/pixel and compared them with the ones in the previous works. Neural networks algorithms along with the data from the Interference Imaging Spectrometer (IIM) onboard Chang’E-1 were employed in this paper to derive the abundances of the six oxides. Compared with the traditional linear regression models, the neural networks method suggested in this work is hopeful to better depict the complex nonlinear relations between the spectra and the chemical components, so it may improve the inversion performance of the lunar chemistry.