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3D predictive modelling using Artificial Neural Network Analysis in the Neves-Corvo deposits [Resumo]

2026-06Documento de conferência Acesso aberto
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datacite.subject.fosCiências Naturais::Ciências da Terra e do Ambiente
datacite.subject.fosCiências Naturais::Outras Ciências Naturais
dc.contributor.authorBatista, Maria Joao
dc.contributor.authorRepresas, Patricia
dc.contributor.authorCarvalho, João
dc.contributor.authorAraújo, Vítor
dc.contributor.authorMarques, Fábio
dc.contributor.authorXavier Matos, João Manuel
dc.contributor.authorMorais, Igor
dc.contributor.authorAlbardeiro, Luís
dc.contributor.authorInverno, Carlos
dc.contributor.authorde Oliveira, Daniel Pipa Soares
dc.contributor.authorDias, P.
dc.date.accessioned2026-09-12T12:12:45Z
dc.date.available2026-09-12T12:12:45Z
dc.date.issued2026-06
dc.description.abstractABSTRACT: This study focuses on mineral and lithological vectoring to target new VMS deposits in the Neves-Corvo district, Portugal. Neves-Corvo is a world-class deposit in the Iberian Pyrite Belt, discovered in 1977 by ground gravity survey and one of Europe’s important copper-zinc producers. The deposit is hosted in volcano-sedimentary sequences within a moderately folded and thrust-faulted setting. Previous studies show differences between massive sulphide ores and stockwork ores, especially in trace-element associations. These geochemical particularities, combined with decades of exploration data, make the area well suited for advanced statistical and multivariate analysis. This study will apply an Artificial Neural Network Analysis (ANNA) methodology to the geochemical data, interpolated from boreholes, and a 3D rock density model, obtained by a constrained inversion of gravity data, to identify patterns that other methods might overlook, thereby improving targeting for new VMS mineralization. ANNA can extract trends from complex datasets and perform non-parametric analysis that do not assume a-priori relationships between variables. Therefore, instead of relying on linear or quadratic regressions approaches, this study employs the so-called black box to determine a mathematical function that can adequately approximate the representation of dependent and independent variables. In this study, the number of variables is considerably smaller than the number of training cases available. The training progresses iteratively, using the previous outputs to carefully and progressively adjust the predictors in an iterative manner, allowing for a continuous validation (based in a set of data taken from the total data before training to monitor the performance of the training in each cycle or iteration) of the different scenarios. The geological model was produced, and the drill-hole density database was used to constrain the 3D gravity inversion. All information was laid in a 5mx5mx5m 3D mesh, eliminating the blocks with missing data. The formatted data was then inputted into in STATISTICA’s ANNA software. The variables considered for the first step of ANNA were Cu, Zn, rock density, gravity inversion and Cu/Zn ratio for the Lombador, Neves Graça, Corvo and Zambujal deposits. From the training and validation procedures, it was possible to conclude that using gravity inversion as target and Cu/Zn and rock density as input data produced the more accurate prediction map.eng
dc.identifier.citationBatista, M.J., Represas, P., Carvalho, J., Araújo, V., Marques, F., Matos, J.X., Morais, I., Albardeiro, L., Inverno, C., Oliveira, D., & Dias, D. (2026). 3D predictive modelling using Artificial Neural Network Analysis in the Neves-Corvo deposits. In: Livro de resumos, XII Congresso Nacional de Geologia, Évora, Portugal, 21-26 junho, 2026, pp. 213
dc.identifier.isbn978-972-778-551-3
dc.identifier.urihttp://hdl.handle.net/10400.9/6454
dc.language.isoeng
dc.peerreviewedno
dc.publisherUniversidade de Évora
dc.relationSustainable mineral resources by utilizing new Exploration technologies
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.title3D predictive modelling using Artificial Neural Network Analysis in the Neves-Corvo deposits [Resumo]eng
dc.typeconference object
dspace.entity.typePublication
oaire.awardNumber775971
oaire.awardTitleSustainable mineral resources by utilizing new Exploration technologies
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/775971/EU
oaire.citation.conferenceDate2026-06-21
oaire.citation.conferencePlaceÉvora, Portugal
oaire.citation.startPage213
oaire.citation.titleXII Congresso Nacional de Geologia
oaire.fundingStreamH2020
oaire.versionhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43
person.familyNameBatista
person.familyNameRepresas
person.familyNameCarvalho
person.familyNameMarques
person.familyNameXavier Matos
person.familyNameMorais
person.familyNameAlbardeiro
person.familyNameInverno
person.familyNamede Oliveira
person.givenNameMaria Joao
person.givenNamePatricia
person.givenNameJoão
person.givenNameFábio
person.givenNameJoão Manuel
person.givenNameIgor
person.givenNameLuís
person.givenNameCarlos
person.givenNameDaniel Pipa Soares
person.identifier92033
person.identifierA-8080-2008
person.identifier2216267
person.identifier.ciencia-idC01A-B2B1-C2D0
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person.identifier.ciencia-idB514-EE2B-870D
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person.identifier.ciencia-idA11D-DDD7-29F2
person.identifier.orcid0000-0003-0197-1004
person.identifier.orcid0000-0002-2987-2836
person.identifier.orcid0000-0003-2227-3918
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person.identifier.orcid0000-0002-6108-1030
person.identifier.orcid0000-0001-9806-7139
person.identifier.orcid0000-0002-3826-260X
person.identifier.orcid0000-0002-0806-5330
person.identifier.orcid0000-0002-6338-8845
person.identifier.ridAGY-5000-2022
person.identifier.scopus-author-id7005481948
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameEuropean Commission
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