Identification of morphostructural domains using Clustering Large Applications, a case study in Quadrilátero Ferrífero

Detalhes bibliográficos
Autor(a) principal: Ayache, Naim Khalil
Data de Publicação: 2022
Outros Autores: Santos, Allan Erlikhman Medeiros, Valente Neto, Francisco de Castro, Silva, Denise de Fátima Santos da
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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spelling 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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