Reflectance spectroradiometry applied to a semi-quantitative analysis of the mineralogy of the N4ws deposit, Carajás Mineral Province, Pará, Brazil

Prado, Elias Martins Guerra; Silva, Adalene Moreira; Ducart, Diego Fernando; Toledo, Catarina Labouré Bemfica; Assis, Luciano Mozer de

doi:10.1016/j.oregeorev.2016.03.007

Cited by 18 publications

(8 citation statements)

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“…13B), and only small areas of mixtures between hematite and goethite are observed mainly in mafic laterites. This observation is consistent with results reported by Prado et al (2016). Reflectance spectra extracted from EO-1/Hyperion imagery (VNIR region) can be validated with hematite and goethite of USGS spectral library (Fig.…”

Section: Feature Extraction Applied To Eo-1/ Hyperion Imagerysupporting

confidence: 91%

“…2) by Tolbert et al (1971) and Resende and Barbosa (1972), and updated by Macambira (2003). The geology of the area was recently summarized by Assis (2013) and Prado et al (2016). The region is dominated by metamorphosed volcano-sedimentary sequences and granitoids formed between 2.76 and 2.68 Ga, as well as by the Pium and Xingu Mesoarchean complexes (Cordani et al 1979, Santos 2003, Santos et al 2000, 2006, Tassinari et al 2000, Tassinari & Macambira 1999, 2004.…”

Section: Geologymentioning

confidence: 99%

“…6A). These data were validated with field spectral data collected by Prado et al (2016) in drilling cores of the area. The USGS spectra were converted to the spectral resolution of Landsat-8/OLI (Fig.…”

Section: Spectral Characterization Of Materialsmentioning

confidence: 99%

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Mapping iron oxides with Landsat-8/OLI and EO-1/Hyperion imagery from the Serra Norte iron deposits in the Carajás Mineral Province, Brazil

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ABSTRACT:Mapping methods for iron oxides and clay minerals, using Landsat-8/Operational Land Imager (OLI) and Earth Observing 1 (EO-1)/Hyperion imagery integrated with airborne geophysical data, were applied in the N4, N5, and N4WS iron deposits, Serra Norte, Carajás, Brazil. Band ratios were achieved on Landsat-8/ OLI imagery, allowing the recognition of the main minerals from iron deposits. The Landsat-8/OLI imagery showed a robust performance for iron oxide exploration, even in vegetated shrub areas. Feature extraction and Spectral Angle Mapper hyperspectral classification methods were carried out on EO-1/Hyperion imagery with good results for mapping high-grade iron ore, the hematite-goethite ratio, and clay minerals from regolith. The EO-1/Hyperion imagery proved an excellent tool for fast remote mineral mapping in openpit areas, as well as mapping waste and tailing disposal facilities. An unsupervised classification was carried out on a data set consisting of EO-1/Hyperion visible near-infrared 74 bands, Landsat-8/OLIderived Normalized Difference Vegetation Index, Laser Imaging Detection and Ranging-derived Digital Terrain Model, and high-resolution airborne geophysical data (gamma ray spectrometry, Tzz component of gradiometric gravimetry data). This multisource classification proved to be an adequate alternative for mapping iron oxides in vegetated shrub areas and to enhance the geology of the regolith and mineralized areas. KEYWORDS:Remote sensing; Multispectral and hyperspectral imagery; Iron ore. RESUMO: Métodos de mapeamento para óxidos de ferro e argilas, aplicados em imagens Landsat-8/Operational Land Imager (OLI) e Earth Observing 1 (EO-1)/Hyperion e integrados com dados aerogeofísicos, foram testados nos depósitos de ferro de N4, N5 e N4WS, Serra

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Section: Feature Extraction Applied To Eo-1/ Hyperion Imagerysupporting

confidence: 91%

Section: Geologymentioning

confidence: 99%

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Mapping iron oxides with Landsat-8/OLI and EO-1/Hyperion imagery from the Serra Norte iron deposits in the Carajás Mineral Province, Brazil

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“…Spectral indices are established by recognizing spectral gradients, i.e., the contrast of high and low reflectance in specific wavelength positions that demark a certain mineral to target. This procedure is observed in case studies of different deposits or prospects in the literature, such as Tappert et al (2011), Yang et al (2011), Prado et al (2016, Mesquita et al (2019), andMedina et al (2021), mainly for estimating the mineral abundance of specific mineral groups based on the depth of diagnostic absorption features or using spectral arithmetic. In this research, we analyzed the spectral responses of each lithotype and alteration zone (Fig.…”

Section: Spectral Indices To Target Exploratory Sitesmentioning

confidence: 99%

Spectral characterization of the Umbuzeiro Doce skarn target (Brazil): insights for the exploration of a W-Mo-gemological vesuvianite system

et al. 2022

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The Umbuzeiro Doce skarn target in the Seridó Belt (NE Brazil) was studied using reflectance spectroscopy and petrography. Interpretation of the 61 reflectance spectra obtained in the study established the following zones: marble zone, with diagnostic absorption features at 2,340 and 2,475 nm (C-O); tremolite marble zone, with main absorption at 1,393 (OH − ) and 2,313 nm (Mg-OH); garnet-vesuvianite zone, with the main absorption at 2,215 nm (OH − ); diopside-hornblende zone, marked by a broad absorption at 1,153 nm (Fe 2+ ); late wollastonite zone, which lacks a diagnostic spectral signature; and a local violet vesuvianite-bearing marble, characterized by potential gemological vesuvianite marked by Cr 3+ absorption features (~548 and 680 nm). Centimeter-sized molybdenite aggregates occur at the interface of the marble and calc-silicate alteration zones. Spectral indices were produced to automate identification of potential zones to explore Mo, W, and gemological vesuvianite. Indices for the zones of marble, tremolite marble, garnet-vesuvianite, diopside-hornblende and violet vesuvianite were produced and tested. This methodology appears as a powerful exploratory guide for Mo, W, and violet vesuvianite occurrences or deposits that show a mineral zoning like the Umbuzeiro Doce target.

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“…For mineral quantification, the depth of the main absorption feature of certain minerals are correlated with geochemical analysis to produce a linear or polynomial regression to estimate the concentration of elements by means of the spectral features. Some of the products developed in these studies include: estimation of wt % Fe using the depth of iron (oxyhydr-)oxides (hematite, goethite) feature (~900 nm) (Ducart et al, 2016;Haest and Cudahy, 2012;Prado et al, 2016); estimation of wt % Al2O3 using the depth of Al clays (kaolinite group, white micas, and Al smectites) feature (~2200 nm) (Haest and Cudahy, 2012;Prado et al, 2016;Silversides and Murphy, 2017); estimation of wt % MgO using the depth of Talc feature (~2310 nm) (Prado et al, 2016); estimation of wt % K2O using the depth carnallite feature (~1200 nm) (J.-T. Qiu et al, 2017).…”

Section: Previous Workmentioning

confidence: 99%

Evaluation of machine learning methods for prospectivity modeling and vectoring of IOCG mineralization

Martins Guerra Prado

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é bacharel em Geologia (2012) pela Universidade de Brasília (UnB). Obteve mestrado na UnB em 2015, desenvolvendo um estudo sobre análise quantitativa da mineralogia de formações ferríferas por meio de dados espectrais. Em 2014, se tornou pesquisador em geociências do Serviço Geológico do Brasil (SGB), onde atuou no mapeamento geológico do estado de Rondônia por três anos. Atua desde 2019 como coordenador executivo do Centro de Geociências Aplicadas (CGA) do SGB, onde desenvolve pesquisas utilizando técnicas de inteligência artificial para criar soluções na área das geociências. Iniciou seu Ph.D. na Universidade de Campinas (Unicamp), em 2019, com o objetivo de investigar a aplicação de métodos de aprendizado de máquina na exploração de depósitos minerais do tipo IOCG. BIOGRAPHY Elias M. G. Prado has a bachelor's degree in Geology (2012) from the University of Brasília (UnB). He obtained a master's degree from UnB in 2015, developing a study on quantitative analysis of the mineralogy of iron formations through spectral data. In 2014, he became a researcher in geosciences at the Geological Survey of Brazil (SGB), where he worked on the geological mapping of the state of Rondônia for three years. He has served since 2019 as executive coordinator of the SGB's Center for Applied Geosciences (CGA), where he develops research using artificial intelligence techniques to create solutions in the geosciences. He started a Ph.D. at the University of Campinas (Unicamp) in 2019, aiming to investigate the application of machine learning methods in the exploration of IOCG-type mineral deposits. AGRADECIMENTO To my father, Edson, in loving memory. To my mother, Germana, and sister, Cássia, my sincere thanks for the caring, example, and full support. To my wife Flávia and son Caio, thanks for the companionship, encouragement and all the support. I thank Prof. Dr. Carlos Roberto de Souza Filho of the University of Campinas (UNICAMP) for his dedication and guidance during this learning period. To the Prof. E. John Carranza of the University of the Free State (Bloemfontein, South Africa) for the great collaboration in this work. I thank the Geological Survey of Brazil (SGB) for the release for training through internal selection process. I direct this acknowledgment to the head of the Applied Geosciences Center (CGA) of the SGB, Noevaldo Araujo Teixeira, who supports my work so many times To the various geologists and colleagues in Brazil who participate directly or indirectly in the research. To João Motta, Leandro, Rodrigo, Cimara, and Christian for all the support and knowledge exchange.

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Reflectance spectroradiometry applied to a semi-quantitative analysis of the mineralogy of the N4ws deposit, Carajás Mineral Province, Pará, Brazil

Cited by 18 publications

References 43 publications

Mapping iron oxides with Landsat-8/OLI and EO-1/Hyperion imagery from the Serra Norte iron deposits in the Carajás Mineral Province, Brazil

Mapping iron oxides with Landsat-8/OLI and EO-1/Hyperion imagery from the Serra Norte iron deposits in the Carajás Mineral Province, Brazil

Spectral characterization of the Umbuzeiro Doce skarn target (Brazil): insights for the exploration of a W-Mo-gemological vesuvianite system

Evaluation of machine learning methods for prospectivity modeling and vectoring of IOCG mineralization

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