Machine learning applications on lunar meteorite minerals

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 expl...

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Autores: Peña-Asensio, Eloy|||0000-0002-7257-2150, Trigo-Rodríguez, Josep M., Sort, Jordi|||0000-0003-1213-3639, Ibañez-Insa, Jordi|||0000-0002-8909-6541, Rimola, Albert|||0000-0002-9637-4554
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:309173
Acceso en línea:https://ddd.uab.cat/record/309173
https://dx.doi.org/urn:doi:10.1016/j.ijmst.2024.08.001
Access Level:acceso abierto
Palabra clave:Meteorites
Moon
Mineralogy
Machine learning
Mechanical properties
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spelling Machine learning applications on lunar meteorite mineralsFrom classification to mechanical properties predictionPeña-Asensio, Eloy|||0000-0002-7257-2150Trigo-Rodríguez, Josep M.Sort, Jordi|||0000-0003-1213-3639Ibañez-Insa, Jordi|||0000-0002-8909-6541Rimola, Albert|||0000-0002-9637-4554MeteoritesMoonMineralogyMachine learningMechanical propertiesAmid 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. 22024-01-0120242024-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/309173https://dx.doi.org/urn:doi:10.1016/j.ijmst.2024.08.001reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengAgencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2021-128062NB-I00Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 PGC2018-097374-B-I00European Commission https://doi.org/10.13039/501100000780 865657Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2021-126427NB-I00Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2020-116844RB-C21Agència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2021/SGR-00651open accesshttp://purl.org/coar/access_right/c_abf2Aquest 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.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:3091732026-06-06T12:50:31Z
dc.title.none.fl_str_mv Machine learning applications on lunar meteorite minerals
From classification to mechanical properties prediction
title Machine learning applications on lunar meteorite minerals
spellingShingle Machine learning applications on lunar meteorite minerals
Peña-Asensio, Eloy|||0000-0002-7257-2150
Meteorites
Moon
Mineralogy
Machine learning
Mechanical properties
title_short Machine learning applications on lunar meteorite minerals
title_full Machine learning applications on lunar meteorite minerals
title_fullStr Machine learning applications on lunar meteorite minerals
title_full_unstemmed Machine learning applications on lunar meteorite minerals
title_sort Machine learning applications on lunar meteorite minerals
dc.creator.none.fl_str_mv Peña-Asensio, Eloy|||0000-0002-7257-2150
Trigo-Rodríguez, Josep M.
Sort, Jordi|||0000-0003-1213-3639
Ibañez-Insa, Jordi|||0000-0002-8909-6541
Rimola, Albert|||0000-0002-9637-4554
author Peña-Asensio, Eloy|||0000-0002-7257-2150
author_facet Peña-Asensio, Eloy|||0000-0002-7257-2150
Trigo-Rodríguez, Josep M.
Sort, Jordi|||0000-0003-1213-3639
Ibañez-Insa, Jordi|||0000-0002-8909-6541
Rimola, Albert|||0000-0002-9637-4554
author_role author
author2 Trigo-Rodríguez, Josep M.
Sort, Jordi|||0000-0003-1213-3639
Ibañez-Insa, Jordi|||0000-0002-8909-6541
Rimola, Albert|||0000-0002-9637-4554
author2_role author
author
author
author
dc.subject.none.fl_str_mv Meteorites
Moon
Mineralogy
Machine learning
Mechanical properties
topic Meteorites
Moon
Mineralogy
Machine learning
Mechanical properties
description 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.
publishDate 2024
dc.date.none.fl_str_mv 2
2024-01-01
2024
2024-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/309173
https://dx.doi.org/urn:doi:10.1016/j.ijmst.2024.08.001
url https://ddd.uab.cat/record/309173
https://dx.doi.org/urn:doi:10.1016/j.ijmst.2024.08.001
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2021-128062NB-I00
Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 PGC2018-097374-B-I00
European Commission https://doi.org/10.13039/501100000780 865657
Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2021-126427NB-I00
Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2020-116844RB-C21
Agència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2021/SGR-00651
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
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collection Dipòsit Digital de Documents de la UAB
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