Machine learning applications on lunar meteorite minerals : From classification to mechanical properties prediction
Peña-Asensio, Eloy (Politecnico di Milano. Dipartimento di Scienze e Tecnologie Aerospaziali)
Trigo-Rodríguez, Josep M. (Institut de Ciències de l'Espai)
Sort, Jordi (Universitat Autònoma de Barcelona. Departament de Física)
Ibañez-Insa, Jordi (Geosciences Barcelona - CSIC)
Rimola, Albert (Universitat Autònoma de Barcelona. Departament de Química)

Data:	2024
Resum:	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.
Ajuts:	Agencia Estatal de Investigación PID2021-128062NB-I00 Agencia Estatal de Investigación PGC2018-097374-B-I00 European Commission 865657 Agencia Estatal de Investigación PID2021-126427NB-I00 Agencia Estatal de Investigación PID2020-116844RB-C21 Agència de Gestió d'Ajuts Universitaris i de Recerca 2021/SGR-00651
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Meteorites ; Moon ; Mineralogy ; Machine learning ; Mechanical properties
Publicat a:	International Journal of Mining Science and Technology, Vol. 34, Issue 9 (September 2024) , p. 1283-1292, ISSN 2589-062X

DOI: 10.1016/j.ijmst.2024.08.001

10 p, 2.0 MB

El registre apareix a les col·leccions:
Articles > Articles de recerca
Articles > Articles publicats