Machine learning applications on lunar meteorite minerals:From classification to mechanical properties prediction  

作　　者：Eloy Peña-Asensio Josep M.Trigo-Rodríguez Jordi Sort Jordi Ibáñez-Insa Albert Rimola 

机构地区：[1]Department of Aerospace Science and Technology,Politecnico di Milano,Milano 20156,Italy [2]Institut de Ciències de l’Espai(ICE-CSIC),Campus UAB,08193 Cerdanyola del Vallès,Spain [3]Institut d’Estudis Espacials de Catalunya(IEEC),Barcelona 08034,Spain [4]Departament de Física,Universitat Autònoma de Barcelona,Cerdanyola del Vallès E-08193,Spain [5]InstitucióCatalana de Recerca i Estudis Avançats(ICREA),Barcelona E-08010,Spain [6]Geosciences Barcelona(GEO3BCN-CSIC),E-08028 Barcelona,Spain [7]Departament de Química,Universitat Autònoma de Barcelona,Bellaterra 08193,Spain

出　　处：《International Journal of Mining Science and Technology》2024年第9期1283-1292,共10页矿业科学技术学报（英文版）

基　　金：EP-A and JMT-R acknowledges financial support from the project PID2021-128062NB-I00 funded by MCIN/AEI/10.13039/501100011033;The lunar samples studied here were acquired in the framework of grant PGC2018-097374-B-I00(P.I.JMT-R);This project has received funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(No.865657)for the project“Quantum Chemistry on Interstellar Grains”(QUANTUMGRAIN),AR acknowledges financial support from the FEDER/Ministerio de Ciencia e Innovación-Agencia Estatal de Investigación(No.PID2021-126427NB-I00);Partial financial support from the Spanish Government(No.PID2020-116844RB-C21);the Generalitat de Catalunya(No.2021-SGR-00651)is acknowledged;This work was supported by the LUMIO project funded by the Agenzia Spaziale Italiana(No.2024-6-HH.0).

摘　　要：Amid the scarcity of lunar meteorites and the imperative to preserve their scientific value,nondestructive 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 KNeighbor 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.

关 键 词：METEORITES MOON MINERALOGY Machine learning Mechanical properties 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] P184[自动化与计算机技术—控制科学与工程] P185.83[天文地球—天文学]