Ore/waste identification in underground mining through geochemical calibration of drilling data using machine learning techniques

[EN] Chemical X-ray fluorescence (XRF) analyses of drill cuttings and measurement-while-drilling (MWD) records were jointly collected in two production levels with different mineralogical characteristics of a narrow vein underground mine. More than 840 chemical samples and 23,000 MWD records with ge...

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Detalles Bibliográficos
Autores: Fernández Albertos, José, Segarra, Pablo, Sanchidrián, José A., Navarro Domínguez, Rafael
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/369467
Acceso en línea:http://hdl.handle.net/10261/369467
https://api.elsevier.com/content/abstract/scopus_id/85190990064
Access Level:acceso abierto
Palabra clave:X-ray Fluorescence
Assaying while drilling
Machine learning
Measurement while drilling
Ore/waste identification
Underground mining
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spelling Ore/waste identification in underground mining through geochemical calibration of drilling data using machine learning techniquesFernández Albertos, JoséSegarra, PabloSanchidrián, José A.Navarro Domínguez, RafaelX-ray FluorescenceAssaying while drillingMachine learningMeasurement while drillingOre/waste identificationUnderground mining[EN] Chemical X-ray fluorescence (XRF) analyses of drill cuttings and measurement-while-drilling (MWD) records were jointly collected in two production levels with different mineralogical characteristics of a narrow vein underground mine. More than 840 chemical samples and 23,000 MWD records with geological and geotechnical accompanying data constitute the database used in this study. A classification methodology and a site-specific model were developed following two approaches: a k-means clustering heuristic algorithm to define rock classes based on their physicochemical features, and a machine learning (ML) ensemble model trained with those classes to distinguish ore and waste as function of drilling parameters. Oversampling and under sampling techniques are required due to the unbalanced size of the rock classes. The prediction success rate (F1 score) is about 70% for the ore when associated with a high content of silica, and about 86% for the waste. When continuous drilling chips sampling is carried out, the operator bias is reduced and the F1 scores exceed 80% for all classes using different ML techniques. The study presents a complete analysis of the correlations between rock mineralogical characteristics and their mechanical response during drilling, highlighting the benefits that MWD-based information can bring to the analysis-while-drilling (AWD) for ore grading.This work has been conducted under the illuMINEation project, funded by the European Union’s Horizon 2020 research and innovation program under grant agreement no. 869379.Peer reviewedElsevierEuropean CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/369467https://api.elsevier.com/content/abstract/scopus_id/85190990064reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/H2020/869379https://doi.org/10.1016/j.oregeorev.2024.106045Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3694672026-05-22T06:33:51Z
dc.title.none.fl_str_mv Ore/waste identification in underground mining through geochemical calibration of drilling data using machine learning techniques
title Ore/waste identification in underground mining through geochemical calibration of drilling data using machine learning techniques
spellingShingle Ore/waste identification in underground mining through geochemical calibration of drilling data using machine learning techniques
Fernández Albertos, José
X-ray Fluorescence
Assaying while drilling
Machine learning
Measurement while drilling
Ore/waste identification
Underground mining
title_short Ore/waste identification in underground mining through geochemical calibration of drilling data using machine learning techniques
title_full Ore/waste identification in underground mining through geochemical calibration of drilling data using machine learning techniques
title_fullStr Ore/waste identification in underground mining through geochemical calibration of drilling data using machine learning techniques
title_full_unstemmed Ore/waste identification in underground mining through geochemical calibration of drilling data using machine learning techniques
title_sort Ore/waste identification in underground mining through geochemical calibration of drilling data using machine learning techniques
dc.creator.none.fl_str_mv Fernández Albertos, José
Segarra, Pablo
Sanchidrián, José A.
Navarro Domínguez, Rafael
author Fernández Albertos, José
author_facet Fernández Albertos, José
Segarra, Pablo
Sanchidrián, José A.
Navarro Domínguez, Rafael
author_role author
author2 Segarra, Pablo
Sanchidrián, José A.
Navarro Domínguez, Rafael
author2_role author
author
author
dc.contributor.none.fl_str_mv European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv X-ray Fluorescence
Assaying while drilling
Machine learning
Measurement while drilling
Ore/waste identification
Underground mining
topic X-ray Fluorescence
Assaying while drilling
Machine learning
Measurement while drilling
Ore/waste identification
Underground mining
description [EN] Chemical X-ray fluorescence (XRF) analyses of drill cuttings and measurement-while-drilling (MWD) records were jointly collected in two production levels with different mineralogical characteristics of a narrow vein underground mine. More than 840 chemical samples and 23,000 MWD records with geological and geotechnical accompanying data constitute the database used in this study. A classification methodology and a site-specific model were developed following two approaches: a k-means clustering heuristic algorithm to define rock classes based on their physicochemical features, and a machine learning (ML) ensemble model trained with those classes to distinguish ore and waste as function of drilling parameters. Oversampling and under sampling techniques are required due to the unbalanced size of the rock classes. The prediction success rate (F1 score) is about 70% for the ore when associated with a high content of silica, and about 86% for the waste. When continuous drilling chips sampling is carried out, the operator bias is reduced and the F1 scores exceed 80% for all classes using different ML techniques. The study presents a complete analysis of the correlations between rock mineralogical characteristics and their mechanical response during drilling, highlighting the benefits that MWD-based information can bring to the analysis-while-drilling (AWD) for ore grading.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/369467
https://api.elsevier.com/content/abstract/scopus_id/85190990064
url http://hdl.handle.net/10261/369467
https://api.elsevier.com/content/abstract/scopus_id/85190990064
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/H2020/869379
https://doi.org/10.1016/j.oregeorev.2024.106045
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
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