Amid the scarcity of lunar meteorites and the imperative to preserve their scientific value, non-destructive testing methods are essential. This translates into the application of microscale rock mechanics experiments and scanning electron microscopy for surface composition analysis. This study explores the application of Machine Learning algorithms in predicting the mineralogical and mechanical properties of DHOFAR 1084, JAH 838, and NWA 11444 lunar meteorites based solely on their atomic percentage compositions. Leveraging a prior-data fitted network model, we achieved near-perfect classification scores for meteorites, mineral groups, and individual minerals. The regressor models, notably the K-Neighbor model, provided an outstanding estimate of the mechanical properties—previously measured by nanoindentation tests—such as hardness, reduced Young’s modulus, and elastic recovery. Further considerations on the nature and physical properties of the minerals forming these meteorites, including porosity, crystal orientation, or shock degree, are essential for refining predictions. Our findings underscore the potential of Machine Learning in enhancing mineral identification and mechanical property estimation in lunar exploration, which pave the way for new advancements and quick assessments in extraterrestrial mineral mining, processing, and research.

中文翻译：

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月球陨石矿物的机器学习应用：从分类到机械性能预测


在月球陨石稀缺且必须保持其科学价值的情况下，无损检测方法至关重要。这转化为微尺度岩石力学实验和扫描电子显微镜在表面成分分析中的应用。本研究探讨了机器学习算法在仅根据 DHOFAR 1084、JAH 838 和 NWA 11444 月球陨石的原子百分比组成预测其矿物学和机械特性中的应用。利用先前数据拟合的网络模型，我们对陨石、矿物组和单个矿物获得了近乎完美的分类分数。回归模型，特别是 K 邻域模型，提供了对机械性能的出色估计（以前通过纳米压痕测试测量），例如硬度、约化杨氏模量和弹性恢复。进一步考虑形成这些陨石的矿物的性质和物理性质，包括孔隙率、晶体取向或冲击程度，对于改进预测至关重要。我们的研究结果强调了机器学习在增强月球勘探中的矿物识别和机械性能估计方面的潜力，这为地外矿物开采、加工和研究的新进展和快速评估铺平了道路。