Identification of morphostructural domains using Clustering Large Applications, a case study in Quadrilátero Ferrífero
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/36235 |
Resumo: | Among the stages of a mining project, mineral research stands out, with the objective of identifying, studying and evaluating mineral deposits. In this specific stage, the inferred mineral resources are transformed into indicated and finally measured, and if their exploitation is feasible, into probable and/or proven mineral reserves. The discovery of these reserves is an impacting milestone for the industrial, technological and economic development of a society. The main objective of this article is to present the use of a machine learning technique to identify structures of particular geological interest, from satellite images. The technique applied was the Clustering Large Applications (CLARA) which is an unsupervised algorithm for clustering data, with high performance in massive databases. The area used as a case study was the Quadrilátero Ferrífero, one of the largest mineral provinces on the planet, located in the state of Minas Gerais, Brazil. The results of the CLARA model allowed the delineation of all the features that form the Quadrilátero Ferrífero. In this context, it is believed that this can be a good tool for selecting exploratory targets, reducing uncertainty and risk to investors. This not only attracts new companies for mineral research, but also expands the reserves of Brazilian mineral resources. |
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Research, Society and Development |
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Identification of morphostructural domains using Clustering Large Applications, a case study in Quadrilátero FerríferoIdentificación de dominios morfoestructurales mediante Clustering Large Applications, un caso de estudio en Quadrilátero FerríferoIdentificação de padrões morfoestruturais utilizando Clustering Large Applications, um estudo de caso no Quadrilátero FerríferoCLARAAnálisis de conglomeradosBúsqueda de mineralesQuadrilátero ferrífero.CLARAAnálise de agrupamentoPesquisa mineralQuadrilátero ferrífero.CLARACluster analysisMineral searchQuadrilátero ferrífero.Among the stages of a mining project, mineral research stands out, with the objective of identifying, studying and evaluating mineral deposits. In this specific stage, the inferred mineral resources are transformed into indicated and finally measured, and if their exploitation is feasible, into probable and/or proven mineral reserves. The discovery of these reserves is an impacting milestone for the industrial, technological and economic development of a society. The main objective of this article is to present the use of a machine learning technique to identify structures of particular geological interest, from satellite images. The technique applied was the Clustering Large Applications (CLARA) which is an unsupervised algorithm for clustering data, with high performance in massive databases. The area used as a case study was the Quadrilátero Ferrífero, one of the largest mineral provinces on the planet, located in the state of Minas Gerais, Brazil. The results of the CLARA model allowed the delineation of all the features that form the Quadrilátero Ferrífero. In this context, it is believed that this can be a good tool for selecting exploratory targets, reducing uncertainty and risk to investors. This not only attracts new companies for mineral research, but also expands the reserves of Brazilian mineral resources.Entre las etapas de un proyecto minero se destaca la investigación minera, con el objetivo de identificar, estudiar y evaluar yacimientos minerales. En esta etapa específica, los recursos minerales inferidos se transforman en indicados y finalmente medidos, y si su explotación es factible, en reservas minerales probables y/o probadas. El descubrimiento de estas reservas es un hito impactante para el desarrollo industrial, tecnológico y económico de una sociedad. El objetivo principal de este artículo es presentar el uso de una técnica de aprendizaje automático para identificar estructuras de particular interés geológico, a partir de imágenes satelitales. La técnica aplicada fue el Clustering Large Applications (CLARA) que es un algoritmo no supervisado para el agrupamiento de datos, con alto rendimiento en bases de datos masivas. El área utilizada como caso de estudio fue el Quadrilátero Ferrífero, una de las mayores provincias mineras del planeta, ubicada en el estado de Minas Gerais, Brasil. Los resultados del modelo CLARA permitieron delimitar todos los rasgos que forman el Quadrilátero Ferrífero. En este contexto, se cree que esta puede ser una buena herramienta para seleccionar objetivos exploratorios, reduciendo la incertidumbre y el riesgo para los inversores. Esto no solo atrae nuevas empresas para la investigación minera, sino que también amplía las reservas de recursos minerales brasileños.Dentre as etapas de um projeto de mineração destaca-se a pesquisa mineral, com objetivos de identificar, estudar e avaliar os depósitos minerais. Nesta etapa específica ocorre a transformação dos recursos minerais inferidos, em indicados e por fim medidos, e caso seja viável sua explotação, em reservas minerais prováveis e/ou provadas. A descoberta destas reservas é marco impactante para o desenvolvimento industrial, tecnológico e econômico de uma sociedade. Este artigo tem como objetivo principal apresentar a utilização de uma técnica de machine learning para identificação de estruturas de particular interesse geológico, a partir de imagens de satélite. A técnica aplicada foi o Clustering Large Applications (CLARA) que é um algoritmo não-supervisionado para agrupamento de dados, com alta performance em banco de dados massivos. A área utilizada como estudo de caso foi o Quadrilátero Ferrífero, uma das maiores províncias minerais do planeta, localizada no estado de Minas Gerais, Brasil. Os resultados do modelo CLARA permitiram delinear todas as feições que formam o Quadrilátero Ferrífero. Neste contexto acredita-se que esta possa ser uma boa ferramenta para seleção de alvos exploratórios reduzindo incerteza e risco aos investidores. O que propicia não somente a atração de novas empresas para pesquisa mineral, além da ampliação das reservas dos recursos minerais brasileiros.Research, Society and Development2022-10-22info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/3623510.33448/rsd-v11i14.36235Research, Society and Development; Vol. 11 No. 14; e140111436235Research, Society and Development; Vol. 11 Núm. 14; e140111436235Research, Society and Development; v. 11 n. 14; e1401114362352525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/36235/30451Copyright (c) 2022 Naim Khalil Ayache; Allan Erlikhman Medeiros Santos; Francisco de Castro Valente Neto; Denise de Fátima Santos da Silvahttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessAyache, Naim Khalil Santos, Allan Erlikhman Medeiros Valente Neto, Francisco de Castro Silva, Denise de Fátima Santos da 2022-11-08T13:36:27Zoai:ojs.pkp.sfu.ca:article/36235Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:50:51.875897Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Identification of morphostructural domains using Clustering Large Applications, a case study in Quadrilátero Ferrífero Identificación de dominios morfoestructurales mediante Clustering Large Applications, un caso de estudio en Quadrilátero Ferrífero Identificação de padrões morfoestruturais utilizando Clustering Large Applications, um estudo de caso no Quadrilátero Ferrífero |
title |
Identification of morphostructural domains using Clustering Large Applications, a case study in Quadrilátero Ferrífero |
spellingShingle |
Identification of morphostructural domains using Clustering Large Applications, a case study in Quadrilátero Ferrífero Ayache, Naim Khalil CLARA Análisis de conglomerados Búsqueda de minerales Quadrilátero ferrífero. CLARA Análise de agrupamento Pesquisa mineral Quadrilátero ferrífero. CLARA Cluster analysis Mineral search Quadrilátero ferrífero. |
title_short |
Identification of morphostructural domains using Clustering Large Applications, a case study in Quadrilátero Ferrífero |
title_full |
Identification of morphostructural domains using Clustering Large Applications, a case study in Quadrilátero Ferrífero |
title_fullStr |
Identification of morphostructural domains using Clustering Large Applications, a case study in Quadrilátero Ferrífero |
title_full_unstemmed |
Identification of morphostructural domains using Clustering Large Applications, a case study in Quadrilátero Ferrífero |
title_sort |
Identification of morphostructural domains using Clustering Large Applications, a case study in Quadrilátero Ferrífero |
author |
Ayache, Naim Khalil |
author_facet |
Ayache, Naim Khalil Santos, Allan Erlikhman Medeiros Valente Neto, Francisco de Castro Silva, Denise de Fátima Santos da |
author_role |
author |
author2 |
Santos, Allan Erlikhman Medeiros Valente Neto, Francisco de Castro Silva, Denise de Fátima Santos da |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Ayache, Naim Khalil Santos, Allan Erlikhman Medeiros Valente Neto, Francisco de Castro Silva, Denise de Fátima Santos da |
dc.subject.por.fl_str_mv |
CLARA Análisis de conglomerados Búsqueda de minerales Quadrilátero ferrífero. CLARA Análise de agrupamento Pesquisa mineral Quadrilátero ferrífero. CLARA Cluster analysis Mineral search Quadrilátero ferrífero. |
topic |
CLARA Análisis de conglomerados Búsqueda de minerales Quadrilátero ferrífero. CLARA Análise de agrupamento Pesquisa mineral Quadrilátero ferrífero. CLARA Cluster analysis Mineral search Quadrilátero ferrífero. |
description |
Among the stages of a mining project, mineral research stands out, with the objective of identifying, studying and evaluating mineral deposits. In this specific stage, the inferred mineral resources are transformed into indicated and finally measured, and if their exploitation is feasible, into probable and/or proven mineral reserves. The discovery of these reserves is an impacting milestone for the industrial, technological and economic development of a society. The main objective of this article is to present the use of a machine learning technique to identify structures of particular geological interest, from satellite images. The technique applied was the Clustering Large Applications (CLARA) which is an unsupervised algorithm for clustering data, with high performance in massive databases. The area used as a case study was the Quadrilátero Ferrífero, one of the largest mineral provinces on the planet, located in the state of Minas Gerais, Brazil. The results of the CLARA model allowed the delineation of all the features that form the Quadrilátero Ferrífero. In this context, it is believed that this can be a good tool for selecting exploratory targets, reducing uncertainty and risk to investors. This not only attracts new companies for mineral research, but also expands the reserves of Brazilian mineral resources. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-10-22 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/36235 10.33448/rsd-v11i14.36235 |
url |
https://rsdjournal.org/index.php/rsd/article/view/36235 |
identifier_str_mv |
10.33448/rsd-v11i14.36235 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/36235/30451 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 11 No. 14; e140111436235 Research, Society and Development; Vol. 11 Núm. 14; e140111436235 Research, Society and Development; v. 11 n. 14; e140111436235 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
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1797052799036424192 |