Catchment-based gold prospectivity analysis combining geochemical, geophysical and geological data across northern Australia

Cracknell, Matthew J.; Caritat, Patrice de

doi:10.1144/geochem2016-012

Cited by 10 publications

(3 citation statements)

References 46 publications

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“…Although PCA can be a machine learning algorithm if used in an appropriate data-driven context (e.g., dimensionality reduction), in this study, PCA was used strictly as a multivariate geochemical analysis tool to understand multidi-mensional elemental associations. Other methods such as self-organizing maps (Kohonen, 1982(Kohonen, , 2001 have also been used with success (e.g., Iwashita et al, 2011;Cracknell & de Caritat, 2017). However, in this study, the purpose of multivariate geochemical analysis was mainly to provide an in-discipline basis for the understanding of machine learning results and, therefore, PCA is an appropriate choice that is highly explainable and intuitive.…”

Section: Multivariate Geochemical Analysismentioning

confidence: 99%

Data-Driven Predictive Modeling of Lithofacies and Fe In-Situ Grade in the Assen Fe Ore Deposit of the Transvaal Supergroup (South Africa) and Implications on the Genesis of Banded Iron Formations

et al. 2022

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The Assen Fe ore deposit is a banded iron formation (BIF)-hosted orebody, occurring in the Penge Formation of the Transvaal Supergroup, located 50 km northwest of Pretoria in South Africa. Most BIF-hosted Fe ore deposits have experienced post-depositional alteration including supergene enrichment of Fe and low-grade regional metamorphism. Unlike most of the known BIF-hosted Fe ore deposits, high-grade hematite (> 60% Fe) in the Assen Fe ore deposit is located along the lithological contacts with dolerite intrusions. Due to the variability in alteration levels, identifying the lithologies present within the various parts of the Assen Fe ore deposit, specifically within the weathering zone, is often challenging. To address this challenge, machine learning was applied to enable the automatic classification of rock types identified within the Assen Fe ore mine and to predict the in-situ Fe grade. This classification is based on geochemical analyses, as well as petrography and geological mapping. A total of 21 diamond core drill cores were sampled at 1 m intervals, covering all the lithofacies present at Assen mine. These were analyzed for major elements and oxides by means of X-ray fluorescence spectrometry. Numerous machine learning algorithms were trained, tested and cross-validated for automated lithofacies classification and prediction of in-situ Fe grade, namely (a) k-nearest neighbors, (b) elastic-net, (c) support vector machines (SVMs), (d) adaptive boosting, (e) random forest, (f) logistic regression, (g) Naïve Bayes, (h) artificial neural network (ANN) and (i) Gaussian process algorithms. Random forest, SVM and ANN classifiers yield high classification accuracy scores during model training, testing and cross-validation. For in-situ Fe grade prediction, the same algorithms also consistently yielded the best results. The predictability of in-situ Fe grade on a per-lithology basis, combined with the fact that CaO and SiO2 were the strongest predictors of Fe concentration, support the hypothesis that the process that led to Fe enrichment in the Assen Fe ore deposit is dominated by supergene processes. Moreover, we show that predictive modeling can be used to demonstrate that in this case, the main differentiator between the predictability of Fe concentration between different lithofacies lies in the strength of multivariate elemental associations between Fe and other oxides. Localized high-grade Fe ore along with lithological contacts with dolerite intrusion is indicative of intra-basinal fluid circulation from an already Fe-enriched hematite. These findings have a wider implication on lithofacies classification in weathered rocks and mobility of economic valuable elements such as Fe.

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Section: Multivariate Geochemical Analysismentioning

confidence: 99%

Data-Driven Predictive Modeling of Lithofacies and Fe In-Situ Grade in the Assen Fe Ore Deposit of the Transvaal Supergroup (South Africa) and Implications on the Genesis of Banded Iron Formations

et al. 2022

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“…The data and several reports are freely available from http://www.ga.gov.au/ngsa (accessed 18 April 2018), and the atlas was published by Caritat and Cooper (2011). A number of peer-reviewed journal articles have been published on the NGSA data, including a compositional data compliant principal component analysis of part of this dataset by Caritat and Grunsky (2013), a discussion of the background variation and threshold values for 59 chemical elements in Australian surface soil (Reimann and Caritat, 2017), a self-organizing map method integrating geological and geophysical data with the geochemical dataset (Cracknell and Caritat, 2017), and a prospectivity analysis spanning the regional to continental scales for porphyry copper-gold systems . Caritat and Cooper (2016) recently reviewed the considerable body of research todate on the NGSA dataset.…”

Section: -2011: National Geochemical Survey Of Australia (Ngsa)mentioning

confidence: 99%

The history, progress, and future of global-scale geochemical mapping

Smith¹,

Demetriades²,

Caritat³

et al. 2018

Geochim. Bras.

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“…Results from this study shown that the ELM algorithm is stable and reproducible, and that the learning speed of the ELM regression is much faster than that of logistic regression, and the ELM regression algorithm slightly outperforms logistic regression in mapping polymetallic prospectivity. Additionally,Cracknell and Caritat (2017) used unsupervised ML clustering method, specifically Self-Organizing Maps (SOM), for catchment-based gold prospectivity analysis in northern Australia. The SOM was trained with geochemical analysis of stream and sediment samples, airborne gravity and magnetic data, terrain slope and surface geology.…”

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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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Catchment-based gold prospectivity analysis combining geochemical, geophysical and geological data across northern Australia

Cited by 10 publications

References 46 publications

Data-Driven Predictive Modeling of Lithofacies and Fe In-Situ Grade in the Assen Fe Ore Deposit of the Transvaal Supergroup (South Africa) and Implications on the Genesis of Banded Iron Formations

Data-Driven Predictive Modeling of Lithofacies and Fe In-Situ Grade in the Assen Fe Ore Deposit of the Transvaal Supergroup (South Africa) and Implications on the Genesis of Banded Iron Formations

The history, progress, and future of global-scale geochemical mapping

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

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