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

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外文摘要：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 mechan-ics experiments and scanning electron microscopy for surface composition analysis.This study explores the application of Machine Learning algorithms in predicting the mineralogical and mechanical proper-ties 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 con-siderations 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 under-score 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.

外文关键词：

MeteoritesMoonMineralogyMachine learningMechanical properties

作者：

Eloy Peña-Asensio、Josep M.Trigo-Rodríguez、Jordi Sort、Jordi Ibáñez-Insa、Albert Rimola

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作者单位：

Department of Aerospace Science and Technology,Politecnico di Milano,Milano 20156,Italy

Institut de Ciències de l'Espai(ICE-CSIC),Campus UAB,08193 Cerdanyola del Vallès,Spain

Institut d'Estudis Espacials de Catalunya(IEEC),Barcelona 08034,Spain

Departament de Física,Universitat Autònoma de Barcelona,Cerdanyola del Vallès E-08193,Spain

Institució Catalana de Recerca i Estudis Avançats(ICREA),Barcelona E-08010,Spain

Geosciences Barcelona(GEO3BCN-CSIC),E-08028 Barcelona,Spain

Departament de Química,Universitat Autònoma de Barcelona,Bellaterra 08193,Spain

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基金：

project PID2021-128062NB-I00 funded by MCIN/AEI/10.13039/5011 00011033European Research Council(ERC)under the European Union's Horizon 2020 research and innovation programme for the project''QuaFEDER/Ministerio de Ciencia e Innovación-Agencia Estatal de InvestigaciónSpanish GovernmentGeneralitat de CatalunyaLUMIO project funded by the Agenzia Spaziale Italiana

项目编号：

865657PID2021-126427NB-I00PID2020-116844RB-C212021-SGR-006512024-6-HH.0

出版年：

2024

DOI：

10.1016/j.ijmst.2024.08.001

矿业科学技术学报(英文版)

中国矿业大学

矿业科学技术学报(英文版)

CSTPCDEI

影响因子：1.222

ISSN：2095-2686

年,卷(期)：2024.34(9)