Mineral prospectivity mapping using machine learning techniques for gold exploration in the Larder Lake area, Ontario, Canada

Liu, Haiming; Harris, Jeff; Sherlock, Ross; Behnia, Pouran; Grunsky, Eric; Naghizadeh, Mostafa; Rubingh, Kate; Tuba, Györgyi; Roots, Eric; Hill, G. J.

doi:10.1016/j.gexplo.2023.107279

Cited by 6 publications

(2 citation statements)

References 75 publications

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“…Machine learning has emerged as a powerful tool in the field of mineral potential mapping, and its successful applications have been reported across the world [8][9][10][11][12][13]. One of its primary strengths in this context lies in its capacity to efficiently handle extensive and intricate multivariate datasets, a substantial improvement over traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Domain Expertise in Machine Learning for Critical Metal Prospecting in the Oslo Rift: A Case Study for Fe-Ti-P-Rare Earth Element Mineralization

Wang,

Coint,

Mansur

et al. 2024

Minerals

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Global demand for critical raw materials, including phosphorus (P) and rare earth elements (REEs), is on the rise. The south part of Norway, with a particular focus on the Southern Oslo Rift region, is a promising reservoir of Fe-Ti-P-REE resources associated with magmatic systems. Confronting challenges in mineral exploration within these systems, notably the absence of alteration haloes and distal footprints, we have explored alternative methodologies. In this study, we combine machine learning with geological expertise, aiming to identify prospective areas for critical metal prospecting. Our workflow involves processing over 400 rock samples to create training datasets for mineralization and non-mineralization, employing an intuitive sampling strategy to overcome an imbalanced sample ratio. Additionally, we convert airborne magnetic, radiometric, and topographic maps into machine learning-friendly features, with a keen focus on incorporating domain knowledge into these data preparations. Within a binary classification framework, we evaluate two commonly used classifiers: a random forest (RF) and support vector machine (SVM). Our analysis shows that the RF model outperforms the SVM model. The RF model generates a predictive map, identifying approximately 0.3% of the study area as promising for mineralization. These findings align with legacy data and field visits, supporting the map’s potential to guide future surveys.

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Section: Introductionmentioning

confidence: 99%

Leveraging Domain Expertise in Machine Learning for Critical Metal Prospecting in the Oslo Rift: A Case Study for Fe-Ti-P-Rare Earth Element Mineralization

Wang,

Coint,

Mansur

et al. 2024

Minerals

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“…However, the challenging and costly nature of their exploration and prospecting arises from the fact that orogenic gold deposits can be found at varying depths within different rock formations (Q. F. Wang et al., 2022). This variability poses a substantial challenge to traditional exploration methods such as geochemical and geophysical survey data, as these struggle to furnish dependable exploration indicators (H. M. Liu et al., 2023; Zuo & Carranza, 2023). Consequently, there exists an immediate requirement for the establishment of novel exploration methods (H. Y. Chen, 2020; H. Y. Chen & Zhang, 2022).…”

Section: Introductionmentioning

confidence: 99%

Primary Controlling Factors of Apatite Trace Element Composition and Implications for Exploration in Orogenic Gold Deposits

Cao,

Chen,

Zhang

et al. 2024

Geochem Geophys Geosyst

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Significant and readily accessible orogenic gold deposits have been previously recognized, exploited, and progressively depleted. Innovative approaches are required to discover new and deeply buried deposits. Recently, trace element variations in apatite have been used to distinguish fertile and barren environments as reliable mineral exploration tools. In this study, machine learning models using Random Forest and Deep Neutral Network are utilized to assess the fertility of quartz veins and altered zones in the orogenic gold systems. The two models have been trained using trace element data of apatite, and the performance of both models yield good classification accuracy (∼90% on average) with low false positive rates. Feature importance analysis (Gini decrease and hidden layer weights) suggest that Pb, As, U, Sr, Eu, Mn, and Fe are the important parameters. Arsenic, U, Eu, Mn, and Fe are redox‐sensitive elements, with their concentrations responding to changes in fluid redox conditions. Strontium primarily originates from the breakdown of plagioclase, which is more likely to occur under oxidizing fluid conditions. Therefore, we infer that the main controlling factor of the models is the redox conditions. The two distinct models consistently highlight the most significant contribution of Pb to this differentiation, even though Pb is not a redox‐sensitive element and can only substitute for Ca2+ in apatite as Pb2+. We infer that the high contribution of Pb may be attributed to the potential transportation of Au in the form of a Pb‐(Bi)‐Au melt, and the Pb content in apatite is influenced by the Pb content in the melt, fluid oxygen, and sulfur fugacity. We also propose a novel discriminant plot using Linear Discriminant Analysis (LDA) to assess the mineralization potential in quartz veins and alteration zones based on apatite trace element data. The machine learning and LDA results suggest that apatite trace elements could be used effectively in the future orogenic gold deposit exploration.

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A New Sphalerite Thermometer Based on Machine Learning with Trace Element Geochemistry

Zhao,

Zhang,

Shao

et al. 2024

Nat Resour Res

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Mineral prospectivity mapping using machine learning techniques for gold exploration in the Larder Lake area, Ontario, Canada

Cited by 6 publications

References 75 publications

Leveraging Domain Expertise in Machine Learning for Critical Metal Prospecting in the Oslo Rift: A Case Study for Fe-Ti-P-Rare Earth Element Mineralization

Leveraging Domain Expertise in Machine Learning for Critical Metal Prospecting in the Oslo Rift: A Case Study for Fe-Ti-P-Rare Earth Element Mineralization

Primary Controlling Factors of Apatite Trace Element Composition and Implications for Exploration in Orogenic Gold Deposits

A New Sphalerite Thermometer Based on Machine Learning with Trace Element Geochemistry

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