Predicting rock type and detecting hydrothermal alteration using machine learning and petrophysical properties of the Canadian Malartic ore and host rocks, Pontiac Subprovince, Québec, Canada

Bérubé, Charles L.; Olivo, Gema R.; Chouteau, Michel; Perrouty, Stéphane; Shamsipour, Pejman; Enkin, Randolph J.; Morris, William A.; Feltrin, Leonardo; Thiémonge, Raphaël

doi:10.1016/j.oregeorev.2018.04.011

Cited by 54 publications

(21 citation statements)

References 41 publications

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“…Similarly, fine-resolution multi-parameter datasets obtained from drill cores cannot be used effectively in isolation without considering the large-scale structural architecture of the deposit. Current and future machine learning (ML) systems that maximize the knowledge gained from drill core data (Bérubé et al 2018;Liu et al 2019;Schnitzler et al 2019) hold great promise for the minerals industry. Even though the benefits of ML are great, it is paramount that geologists know how to train these artificial computer algorithms to recognize large-scale hierarchical structural controls, otherwise ML systems may never yield sensible exploration targets.…”

Section: Implications For Orogenic Gold Mineralization and Explorationmentioning

confidence: 99%

Deposit-scale structural architecture of the Sigma-Lamaque gold deposit, Canada—insights from a newly proposed 3D method for assessing structural controls from drill hole data

Cowan

2020

Miner Deposita

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Deposit-scale structural analysis is a practical method of structurally analyzing 3D drilling data. This interpretation method uses readily available distributed 3D data at the most relevant scale for mining and exploration. This method does not depend on collecting traditional structural data from core, such as orientation of planar and linear data, but relies on the premise that grade distributions sampled from drilling provide insight into the structural control of the deposit, with the interpretation of structural geometry done using a computer 3D viewer. This method is applied to the well-studied Archean Sigma-Lamaque gold deposit, which features late-orogenic vein mineralization, and which is controlled by three orders of structural features. The first-order control is the easterly plunging pipe-like conduit parallel to F 2 fold plunge defined by the intersection of bedding (S 0 ) and foliation (S 2 ) (~1.5 km radius × 3 km down-plunge). The second-order controls, nested within the first-order, are the younger subvertical Late Diorite plugs that intruded vertically into the host rocks along the established S 2 foliation anisotropy (1250:400:150 m dimension for Main Plug). The third-order control, laterally limited by first-and second-order structural controls, is the well-documented goldbearing vein sets (1-10 m thick and up to 1.5 km in extent) that developed in the Late Diorites and in the older folded volcanogenic stratigraphy and diorites. It is proposed that the first-order control defined by the moderately plunging F 2 fold is the main fluid pathway resulting from structural permeability formed during earlier folding. The second-order Late Diorite plugs and other competent lithological units, which intersected and fractured during late-stage D 2 compression, served as chemically reactive sites causing gold to precipitate along this first-order pathway. It is predicted that intrusions that do not intersect the first-order structural conduits are less likely to be mineralized. Greenfield orogenicgold exploration requires that the first-order controls are identified, and the geometry of hydrothermal alteration is best understood in the context of the structural architecture of the host rocks.

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Section: Implications For Orogenic Gold Mineralization and Explorationmentioning

confidence: 99%

Deposit-scale structural architecture of the Sigma-Lamaque gold deposit, Canada—insights from a newly proposed 3D method for assessing structural controls from drill hole data

Cowan

2020

Miner Deposita

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“…Here we have estimated Na to assess hydrothermal alteration, but RF and similar ensemble methods could be used to predict ore grades and the distribution of mineralisation. Other potential uses of such multiparameter databases and artificial intelligence in exploration include: the prediction of lithology (pseudologs) along the boreholes, the generation of predictive maps of metals, resource estimation, and sample classification (e.g., Rodriguez-Galiano et al, 2015;Bérubé et al, 2018;Caté et al, 2018;Chen et al, 2018).…”

Section: Other Possible Uses Of Machine Learning In Mining Explorationmentioning

confidence: 99%

“…Even at the diamond drill core characterization stage, which used to consist primarily of a visual log by the geologist, more and more data is becoming available (e.g., physical rock properties, geochemistry, mineralogy, …) (e.g., Ross et al, 2013Ross et al, , 2016aJácomo et al, 2015;Ross and Bourke, 2017;Wang et al, 2017;Bérubé et al, 2018;Chen et al, 2018). Utilizing such large multiparameter datasets to their full potential requires specific algorithms such as multivariate statistical analysis (e.g., Fresia et al, 2017) or ensemble trees (e.g., Bérubé et al, 2018;Caté et al, 2018;Chen et al, 2018). Artificial intelligence methods are already used by some mining companies, but generally remain little known in the mining sector.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning to estimate a key missing geochemical variable in mining exploration: Application of the Random Forest algorithm to multi-sensor core logging data

Schnitzler

Ross

Gloaguen

2019

Journal of Geochemical Exploration

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“…Os métodos de magnetometria e elétricos são comumente usados para entendimento e interpretações em estudos na prospecção mineral como: Williams (2009); Leão-Santos et al (2015) e Lesher et al (2017). Enquanto análises mais precisas que utilizam uma gama maior de dados, entre eles os geofísicos e geoquímicos como: Sandrin et al, 2009;Clark, 2014;Aguilef et al, 2017;Bérubé et al, 2018 ainda são raros. A integração destes dados será realizada para mapear e identificar se estes são métodos prospectivos efetivos dentro do contexto geológico em apreço.…”

Section: Introductionunclassified

Assinatura Geofísica e Geoquímica do Depósito Pb-Zn-(Cu-Ag) Santa Maria – RS, Brasil

et al. 2021

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O entendimento da assinatura geofísica em depósitos torna-se essencial na prospecção mineral, uma vez que a compreensão do footprint em profundidade é cada mais necessário devido a alvos e depósitos superficiais estarem cada vez mais escassos. A integração de outros tipos de dados aos dados geofísicos pode auxiliar na criação de processos de interpretações a partir do conhecimento avançado da resposta geofísica de diferentes rochas, alterações hidrotermais e mineralizações que futuramente podem aumentar a descoberta de novos alvos ou novas abordagens na prospecção. Esta pesquisa tem como alvo o depósito epitermal de Pb-Zn-(Cu-Ag) de Santa Maria, situado no distrito de Minas do Camaquã, município de Caçapava do Sul, Rio Grande do Sul, Brasil. Trata-se de um sistema magmático-hidrotermal distal, com mineralizações controladas por sistemas de falhas em arenitos e conglomerados, que hospedam zonas de alterações hidrotermais que contêm ilita, clorita e pirita, além de galena, esfalerita, calcopirita e bornita. O objetivo principal é mapear a assinatura geofísica do depósito a partir da integração de dados geoquímicos e geofísicos das rochas encaixantes, alterações hidrotermais e mineralizações. A metodologia inicial aplicada consiste na seleção de amostras de furos de sondagens representativas da litologia predominante, das rochas alteradas e das mineralizações hidrotermais do depósito; submissão das amostras a análises geoquímicas de elementos maiores, traço e terras raras; e aquisição de dados geofísicos para serem integrados e interpretados. A magnetometria terrestre, a resistividade e a polarização induzida mostraram-se eficientes para mapear a assinatura geofísica do Depósito Santa Maria, principalmente em termos estruturais, uma vez que se identificou o possível controlador de mineralizações da área. A geoquímica facilitou o entendimento das variações geofísicas, pois a presença de sulfetos em maiores quantidades e presença de óxidos como K2O, Al2O3 e MgO utilizados para mapear zonas de ilita e clorita, possibilitaram interpretações e associações à geofísica da área. Toda a integração de dados proporcionou uma maior confiabilidade na caracterização da assinatura geofísica do depósito.

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Predicting rock type and detecting hydrothermal alteration using machine learning and petrophysical properties of the Canadian Malartic ore and host rocks, Pontiac Subprovince, Québec, Canada

Cited by 54 publications

References 41 publications

Deposit-scale structural architecture of the Sigma-Lamaque gold deposit, Canada—insights from a newly proposed 3D method for assessing structural controls from drill hole data

Deposit-scale structural architecture of the Sigma-Lamaque gold deposit, Canada—insights from a newly proposed 3D method for assessing structural controls from drill hole data

Using machine learning to estimate a key missing geochemical variable in mining exploration: Application of the Random Forest algorithm to multi-sensor core logging data

Assinatura Geofísica e Geoquímica do Depósito Pb-Zn-(Cu-Ag) Santa Maria – RS, Brasil

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