A mineral potential mapping approach for supergene nickel deposits in southwestern São Francisco Craton, Brazil

Motta, João Gabriel; Júnior, Ilio Rodarte Faria

doi:10.1590/2317-4889201620160021

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…It is a futuristic framework for potential zone identification that involves geoscientific data from multiple sources, such as geochemical, mineralogical, geophysical and geological surveys (Figure 2). The details of these factors are provided [59][60][61].…”

Section: Detailed Description Of Factorsmentioning

confidence: 99%

A New Method to Evaluate Gold Mineralisation-Potential Mapping Using Deep Learning and an Explainable Artificial Intelligence (XAI) Model

et al. 2022

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Geoscientists have extensively used machine learning for geological mapping and exploring the mineral prospect of a province. However, the interpretation of results becomes challenging due to the complexity of machine learning models. This study uses a convolutional neural network (CNN) and Shapley additive explanation (SHAP) to estimate potential locations for gold mineralisation in Rengali Province, a tectonised mosaic of volcano-sedimentary sequences juxtaposed at the interface of the Archaean cratonic segment in the north and the Proterozoic granulite provinces of the Eastern Ghats Belt in Eastern India. The objective is to integrate multi-thematic data involving geological, geophysical, mineralogical and geochemical surveys on a 1:50 K scale with the aim of prognosticating gold mineralisation. The available data utilised during the integration include aero-geophysical (aeromagnetic and aerospectrometric), geochemical (national geochemical mapping), ground geophysical (gravity), satellite gravity, remote sensing (multispectral) and National Geomorphology and Lineament Project structural lineament maps obtained from the Geological Survey of India Database. The CNN model has an overall accuracy of 90%. The SHAP values demonstrate that the major contributing factors are, in sequential order, antimony, clay, lead, arsenic content and a magnetic anomaly in CNN modelling. Geochemical pathfinders, including geophysical factors, have high importance, followed by the shear zones in mineralisation mapping. According to the results, the central parts of the study area, including the river valley, have higher gold prospects than the surrounding areas. Gold mineralisation is possibly associated with intermediate metavolcanics along the shear zone, which is later intruded by quartz veins in the northern part of the Rengali Province. This work intends to model known occurrences with respect to multiple themes so that the results can be replicated in surrounding areas.

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Section: Detailed Description Of Factorsmentioning

confidence: 99%

A New Method to Evaluate Gold Mineralisation-Potential Mapping Using Deep Learning and an Explainable Artificial Intelligence (XAI) Model

et al. 2022

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“…Esse tipo de cobertura foi bastante estudado em todo o mundo (BUTT; ZEEGERS, 1992;ELIAS, 2002;HORTON, 2008;OGURA, 1986). No Brasil, há estudos como os de (GOLIGHTLY, 2010;MOTTA;JÚNIOR, 2016;SEOANE et al, 2009), trabalhos que utilizaram a geofísica como metodologia de investigação.…”

Section: Introductionunclassified

Caracterização geofísica da Cobertura Detrito-Laterítica e seu aquífero no Chapadão de São Gabriel do Oeste, MS

Neumann,

Gabas,

Lastoria

2024

FAAP

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A caracterização geofísica da cobertura detrito-laterítica existente no Chapadão de São Gabriel do Oeste-MS, de fundamental importância econômica para a agricultura da região, foi objeto de estudo desta pesquisa, destacando-se as cangas lateríticas associadas, amplamente utilizadas no revestimento primário dos mais de 3000 km de rodovias municipais. Buscou-se avaliar a eficácia do método geofísico GPR na identificação das cangas lateríticas em subsuperfície e na detecção dos contatos litológicos do TQdl com as camadas subjacentes. Foi dado um enfoque na verificação da continuidade da Formação Serra Geral na área de estudos, com obtenção do mapa de isoespessuras. Complementarmente, foi efetuada uma caracterização preliminar do aquífero relacionado à cobertura ainda não individualizada. Para alcançar tais objetivos, usou-se da obtenção de dados geofísicos com o GPR, Eletrorresistividade e dados geológicos e hidrogeológicos. Como resultados finais, são apresentadas: a viabilidade de emprego do GPR na identificação das cangas; a espessura da Cobertura detrito-laterítica e seus contatos; a definição da distribuição e da espessura dos basaltos da Fm. Serra Geral e a definição do mapa potenciométrico do aquífero representado pela cobertura. Adicionalmente, obteve-se um perfil geológico buscando o melhor entendimento do arcabouço geológico-estrutural, o qual sintetiza a interpretação das relações estratigráficas na área.

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Lateritic Ni–Co Prospectivity Modeling in Eastern Australia Using an Enhanced Generative Adversarial Network and Positive-Unlabeled Bagging

Wake,

Farahbakhsh,

Müller

2024

Nat Resour Res

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The surging demand for Ni and Co, driven by the acceleration of clean energy transitions, has sparked interest in the Lachlan Orogen of New South Wales for its potential lateritic Ni–Co resources. Despite recent discoveries, a substantial knowledge gap exists in understanding the full scope of these critical metals in this geological province. This study employed a machine learning-based framework, integrating multidimensional datasets to create prospectivity maps for lateritic Ni–Co deposits within a specific Lachlan Orogen segment. The framework generated a variety of data-driven models incorporating geological (rock units, metamorphic facies), structural, and geophysical (magnetics, gravity, radiometrics, and remote sensing spectroscopy) data layers. These models ranged from comprehensive models that use all available data layers to fine-tuned models restricted to high-ranking features. Additionally, two hybrid (knowledge-data-driven) models distinguished between hypogene and supergene components of the lateritic Ni–Co mineral systems. The study implemented data augmentation methods and tackled imbalances in training samples using the SMOTE–GAN method, addressing common machine learning challenges with sparse training data. The study overcame difficulties in defining negative training samples by translating geological and geophysical data into training proxy layers and employing a positive and unlabeled bagging technique. The prospectivity maps revealed a robust spatial correlation between high probabilities and known mineral occurrences, projecting extensions from these sites and identifying potential greenfield areas for future exploration in the Lachlan Orogen. The high-accuracy models developed in this study utilizing the Random Forest classifier enhanced the understanding of mineralization processes and exploration potential in this promising region.

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A mineral potential mapping approach for supergene nickel deposits in southwestern São Francisco Craton, Brazil

Cited by 6 publications

References 27 publications

A New Method to Evaluate Gold Mineralisation-Potential Mapping Using Deep Learning and an Explainable Artificial Intelligence (XAI) Model

A New Method to Evaluate Gold Mineralisation-Potential Mapping Using Deep Learning and an Explainable Artificial Intelligence (XAI) Model

Caracterização geofísica da Cobertura Detrito-Laterítica e seu aquífero no Chapadão de São Gabriel do Oeste, MS

Lateritic Ni–Co Prospectivity Modeling in Eastern Australia Using an Enhanced Generative Adversarial Network and Positive-Unlabeled Bagging

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