High resolution geochemical mapping in the Mociços mine (Ossa-Morena Zone, Portugal). Contributes from machine learning methods
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10174/27469 |
Resumo: | The Mociços mine was exploited for copper in the early twentieth century. A recent soil geochemistry campaign with portable X-ray fluorescence equipment permited to map the surroundings of this ancient mine with high resolution. The analysis of the results using machine learning methods, namely, principal component analysis, hierarchical and k-mean clustering, and the mapping of the observations, allows a better understanding of the geochemical behavior of the elements. The principal component analysis and the k-means method have comparable results and allow to define the zone of mineralization and the outcropping of a dyke of acid rocks. The hierarchical agglomeration method allows to group the mineralized zones with the mine waste sites. Using the spatial mapping of the clusters it was possible to identify the regions marked by the geochemical behaviour of copper and zinc as well as to find relationships between the mineralized vein and outcropping acid rocks in the region. |
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High resolution geochemical mapping in the Mociços mine (Ossa-Morena Zone, Portugal). Contributes from machine learning methodsZona de Ossa-MorenaGeoquímicaMociçosCobrePortable X-Ray FluorescenseThe Mociços mine was exploited for copper in the early twentieth century. A recent soil geochemistry campaign with portable X-ray fluorescence equipment permited to map the surroundings of this ancient mine with high resolution. The analysis of the results using machine learning methods, namely, principal component analysis, hierarchical and k-mean clustering, and the mapping of the observations, allows a better understanding of the geochemical behavior of the elements. The principal component analysis and the k-means method have comparable results and allow to define the zone of mineralization and the outcropping of a dyke of acid rocks. The hierarchical agglomeration method allows to group the mineralized zones with the mine waste sites. Using the spatial mapping of the clusters it was possible to identify the regions marked by the geochemical behaviour of copper and zinc as well as to find relationships between the mineralized vein and outcropping acid rocks in the region.Universidade de Évora2020-02-28T10:17:36Z2020-02-282019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/27469http://hdl.handle.net/10174/27469engNOGUEIRA, P., VICENTE, S., MAIA, M., ROSEIRO, J., MATOS, J.X. (2019). High resolution geochemical mapping in the Mociços mine (Ossa-Morena Zone, Portugal). Contributes from machine learning methods. Congresso Ibérico de Geoquímica & XX Semana da Geoquímica, Évora, 251-254.pmn@uevora.ptsandrorpvicente@gmail.commcmaiageo@gmail.comze.roseiro45@gmail.comjoao.matos@lneg.pt250NOGUEIRA, PedroVICENTE, SandroMAIA, MiguelROSEIRO, JoséMATOS, João X.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T19:22:56Zoai:dspace.uevora.pt:10174/27469Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:17:32.164172Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
High resolution geochemical mapping in the Mociços mine (Ossa-Morena Zone, Portugal). Contributes from machine learning methods |
title |
High resolution geochemical mapping in the Mociços mine (Ossa-Morena Zone, Portugal). Contributes from machine learning methods |
spellingShingle |
High resolution geochemical mapping in the Mociços mine (Ossa-Morena Zone, Portugal). Contributes from machine learning methods NOGUEIRA, Pedro Zona de Ossa-Morena Geoquímica Mociços Cobre Portable X-Ray Fluorescense |
title_short |
High resolution geochemical mapping in the Mociços mine (Ossa-Morena Zone, Portugal). Contributes from machine learning methods |
title_full |
High resolution geochemical mapping in the Mociços mine (Ossa-Morena Zone, Portugal). Contributes from machine learning methods |
title_fullStr |
High resolution geochemical mapping in the Mociços mine (Ossa-Morena Zone, Portugal). Contributes from machine learning methods |
title_full_unstemmed |
High resolution geochemical mapping in the Mociços mine (Ossa-Morena Zone, Portugal). Contributes from machine learning methods |
title_sort |
High resolution geochemical mapping in the Mociços mine (Ossa-Morena Zone, Portugal). Contributes from machine learning methods |
author |
NOGUEIRA, Pedro |
author_facet |
NOGUEIRA, Pedro VICENTE, Sandro MAIA, Miguel ROSEIRO, José MATOS, João X. |
author_role |
author |
author2 |
VICENTE, Sandro MAIA, Miguel ROSEIRO, José MATOS, João X. |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
NOGUEIRA, Pedro VICENTE, Sandro MAIA, Miguel ROSEIRO, José MATOS, João X. |
dc.subject.por.fl_str_mv |
Zona de Ossa-Morena Geoquímica Mociços Cobre Portable X-Ray Fluorescense |
topic |
Zona de Ossa-Morena Geoquímica Mociços Cobre Portable X-Ray Fluorescense |
description |
The Mociços mine was exploited for copper in the early twentieth century. A recent soil geochemistry campaign with portable X-ray fluorescence equipment permited to map the surroundings of this ancient mine with high resolution. The analysis of the results using machine learning methods, namely, principal component analysis, hierarchical and k-mean clustering, and the mapping of the observations, allows a better understanding of the geochemical behavior of the elements. The principal component analysis and the k-means method have comparable results and allow to define the zone of mineralization and the outcropping of a dyke of acid rocks. The hierarchical agglomeration method allows to group the mineralized zones with the mine waste sites. Using the spatial mapping of the clusters it was possible to identify the regions marked by the geochemical behaviour of copper and zinc as well as to find relationships between the mineralized vein and outcropping acid rocks in the region. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01T00:00:00Z 2020-02-28T10:17:36Z 2020-02-28 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10174/27469 http://hdl.handle.net/10174/27469 |
url |
http://hdl.handle.net/10174/27469 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
NOGUEIRA, P., VICENTE, S., MAIA, M., ROSEIRO, J., MATOS, J.X. (2019). High resolution geochemical mapping in the Mociços mine (Ossa-Morena Zone, Portugal). Contributes from machine learning methods. Congresso Ibérico de Geoquímica & XX Semana da Geoquímica, Évora, 251-254. pmn@uevora.pt sandrorpvicente@gmail.com mcmaiageo@gmail.com ze.roseiro45@gmail.com joao.matos@lneg.pt 250 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade de Évora |
publisher.none.fl_str_mv |
Universidade de Évora |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799136657697406976 |