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Predicting ore content throughout a machine learning procedure: an Sn-W enrichment case study

2020Journal article Open access
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dc.contributor.authorIglesias, Carla
dc.contributor.authorAntunes, I.M.H.R.
dc.contributor.authorAlbuquerque, M.T.D.
dc.contributor.authorMartínez, Javier
dc.date.accessioned2020-02-10T11:51:54Z
dc.date.available2020-02-10T11:51:54Z
dc.date.issued2020
dc.description.abstractThe distribution patterns of trace elements are very useful for predicting mineral deposits occurrence. Machine learning techniques were used for the computation of adequate models in trace elements’ prediction. The main subject of this research is the definition of an adequate model to predict the amounts of Sn and W in the abandoned mine area of Lardosa (Central Portugal). Stream sediment samples (333) were collected within the study area and their geochemical composition - As, B, Be, Cd, Co, Cr, Cu, Fe, Ni, P, Sn, U, V, W, Y, and Zn - used as input attributes. Different machine learning techniques were tested: Decision Trees (CART), Multilayer Perceptron (MLP) and Support Vector Machines (SVM). For regression and clustering, CART, MLP approaches were tested and for the classification, problem SVM was used. These algorithms used six different inputs – N1 to N6 – aiming to pick out the best-performing model.The results show that CART is the optimized predictor for Sn and W. Concerning the regression approach, correlation coefficients of 0.67 for Sn (with Input N1) and 0.70 for W (with Input N3) were obtained. Regarding the classification problem, an error rate of 0.10 was reached for both Sn (Input N1) and W (Input N2). The classification process is the best methodology to predict Sn and W, using as input the trace element concentrations in the collected stream sediment samples, Lardosa area, Portugal.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationIGLESIAS, C. [et al.] (2020] - Predicting ore content throughout a machine learning procedure: an Sn-W enrichment case study. Journal of Geochemical Exploration. ISSN 0375-6742. Vol. 208, p. 1-12. Doi 10.1016/ j.gexplo.2019.106405pt_PT
dc.identifier.doi10.1016/j.gexplo.2019.106405pt_PT
dc.identifier.issn0375-6742
dc.identifier.urihttp://hdl.handle.net/10400.11/6918
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectOre potentialpt_PT
dc.subjectMachine learningpt_PT
dc.subjectClassification modelpt_PT
dc.subjectSn-W predictionpt_PT
dc.subjectStream sedimentspt_PT
dc.subjectPortugalpt_PT
dc.titlePredicting ore content throughout a machine learning procedure: an Sn-W enrichment case studypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleJournal of Geochemical Explorationpt_PT
person.familyNameantunes
person.familyNameAlbuquerque
person.givenNameIsabel Margarida
person.givenNameMaria Teresa
person.identifier.ciencia-idCB1E-FAD2-37D6
person.identifier.ciencia-id5A1C-8956-4C0A
person.identifier.orcid0000-0003-3456-5926
person.identifier.orcid0000-0002-8782-6133
person.identifier.ridM-1043-2013
person.identifier.ridB-1536-2013
person.identifier.scopus-author-id6701817085
person.identifier.scopus-author-id55507421600
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication1db83c95-f80c-41bb-b5c6-437ab32d9683
relation.isAuthorOfPublicatione2c2d171-e148-4c23-9cf8-0eb6d810c15e
relation.isAuthorOfPublication.latestForDiscovery1db83c95-f80c-41bb-b5c6-437ab32d9683

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