Automatic Bulk Composition Analysis of Lunar Basalts: Novel Big-Data Algorithm for Energy-Dispersive X-ray Spectroscopy

Abstract

The bulk composition of lunar basaltic meteorites and clasts provides crucial information for understanding their petrogenesis and thus lunar thermal evolution. Meanwhile, the basalt type of Chang’E-5 based on the bulk TiO2 contents remains debatable. Modal recombination based on mineral volume fraction, densities, and average compositions is currently the most popular method to determine the bulk composition of lunar samples. Yet, the latter two parameters can be biased markedly by ubiquitous compositional variations in pyroxene, olivine, and plagioclase. To rectify these issues and provide more accurate classifications, this study devises a novel big-data algorithm that analyzes maps of energy-dispersive X-ray spectroscopy (EDS) data of lunar basalts. The algorithm starts by labeling each point through a newly devised mineral classifier, then uses the mean of all points per mineral to represent average composition, and finally recalculates the true density per mineral to replace standard density. The accuracy of this mineral classifier is demonstrated by tests on a database of lunar minerals. The accuracy and precision of EDS mapping were verified by test analysis on certified reference minerals. Measurements on a lunar meteorite sample with a known composition, NWA 4734, are comparable to those measured using inductively coupled plasma optical emission spectrometry and confirm the reliability of the bulk composition algorithm. To demonstrate its utility for comprehensive understanding of petrographic features, the high-efficiency algorithm was applied to Chang’E-5 basalts. The results reveal that these basalts are characterized by low-Ti and low-Mg features, thus distinct from previous Apollo and Luna samples.