¹Denggao Qiu,¹Fei Li,¹Jianguo Yan,¹Wutong Gao,¹Zheng Chong
Icarus (in Press) Link to Article [https://doi.org/10.1016/j.icarus.2021.114778]
¹State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430070, China
Copyright Elsevier

The FeO and TiO2 contents are critical for distinguishing petrological properties of the Moon and for studying the distribution of the lunar maria and its multi-period volcanic activity. Traditional methods used the ratio between spectral reflectances to estimate FeO and TiO2 contents, which are empirical models. The development of machine learning algorithms offered new ideas for solving inversion problems, and these algorithms can automatically mine the data for potential correlations wherever possible. In this work, by using the Kaguya Multiband Imager data, we construct an optimized spectral inversion model using the Convolutional Neural Network (CNN) algorithm to produce a map of the FeO and TiO2 content on the lunar surface. The CNN models were compared with the traditional linear model and the Random Forest (RF) model. The results were indicated that the CNN models had higher accuracy and the CNN model eliminated the shortcoming of the RF model that the inversion results were limited by the training data, and certainly optimizes the impact of data striping. The CNN models can better describe the nonlinear relationship between spectral reflectance and oxide content. This also provides the basis for the inversion of the other oxides (e.g., MgO, Al2O3, CaO and SiO2). These new maps from the CNN model provide reference information for further studies of the geological evolution of the Moon.

¹Timo Hopp,¹Nicolas Dauphas,²Fridolin Spitzer,²Christoph Burkhardt,²Thorsten Kleine
Earth and Planetary Science Letters 577, 117245 Link to Article [https://doi.org/10.1016/j.epsl.2021.117245]
¹Origins Laboratory, Department of the Geophysical Sciences and Enrico Fermi Institute, The University of Chicago, 5743 South Ellis Avenue, Chicago, IL 60637, USA
²Institut für Planetologie, University of Münster, Wilhelm-Klemm-Str. 10, 48149 Münster, Germany
Copyright Elsevier

Nucleosynthetic Fe isotopic anomalies in meteorites may be used to learn about the early evolution of the solar system and to identify the origin and nature of the material that built the terrestrial planets. Using high-precision iron isotopic data of 23 iron meteorites from nine major chemical groups we show that all iron meteorites define the same dichotomy between non-carbonaceous (NC) and a carbonaceous (CC) meteorites previously observed for other elements. The Fe isotopic anomalies are predominantly produced by variations in 54Fe, where all CC iron meteorites are characterized by an excess in 54Fe relative to NC iron meteorites. This excess in 54Fe is accompanied by an excess in 58Ni observed in the same CC meteorite groups. Together, these overabundances of 54Fe and 58Ni are explained by nuclear statistical equilibrium either in type Ia supernovae or in the Si/S shell of core-collapse supernovae.

The Fe isotopic composition of Earth’s mantle plots on or close to correlations defined by Fe, Mo, and Ru isotopic anomalies in iron meteorites, indicating that throughout Earth’s accretion, the isotopic composition of its building blocks did not drastically change. While Earth’s mantle has a similar Fe isotopic composition to CI chondrites, the latter are clearly distinct from Earth’s mantle for other elements (e.g., Cr and Ni) whose delivery to Earth coincided with Fe. The fact that CI chondrites exhibit large Cr and Ni isotopic anomalies relative to Earth’s mantle, therefore, demonstrates that CI chondrites are unlikely to have contributed significant Fe to Earth and are not its main building blocks.