Machine Learning and Singularity Analysis Reveal Zircon Fertility and Magmatic Intensity: Implications for Porphyry Copper Potential

Zhou, Yuanzhi; Zhang, Zhenjie; Yang, Jie; Ge, Yunzhao; Cheng, Qiuming

doi:10.1007/s11053-022-10122-y

Cited by 12 publications

(3 citation statements)

References 88 publications

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“…Given the complexity and diversity of geochemistry data, ML-based classification methods have emerged as a promising approach that outperforms conventional methods, especially in large-scale geological processes, such as in predicting mantle metasomatism worldwide (Qin et al, 2022), revealing source compositions of intraplate basaltic rocks (Guo et al, 2021), identifying primary water concentrations in mantle pyroxene (Chen et al, 2021), determining the quartz-forming environments , and classifying the source rocks of detrital zircons (Zhong et al, 2023a(Zhong et al, , 2023b. In the field of mineral exploration, two studies tried to apply ML to characterize magma fertility based on zircon compositional data, aiming to identify porphyry copper mineralization potential (Zhou et al, 2022;Zou et al, 2022). Tan et al (2023) employed partial least squares discriminant analysis (PLS-DA) to the apatite trace element dataset (4,298 data) to distinguish between apatites from different types of deposits and rocks.…”

Section: Introductionmentioning

confidence: 99%

Machine learning applied to apatite compositions for determining mineralization potential

Zheng,

Xu,

Lentz

et al. 2024

American Mineralogist

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Apatite major and trace element chemistry is a widely used tracer of mineralization, as it sensitively records the characteristics of the magmatic-hydrothermal system at the time of its crystallization. Previous studies have proposed useful indicators and binary discrimination diagrams to distinguish between apatites from mineralized and unmineralized rocks; however, their efficiency has been found to be somewhat limited in other systems and larger scale datasets.This work applied a machine learning (ML) method to classify the chemical compositions of apatites from both fertile and barren rocks, aiming to help determine the mineralization potential of unknown system. Approximately 13,328 apatite compositional analyses were compiled and labeled from 241 locations in 27 countries worldwide, and three apatite geochemical datasets were established for XGBoost ML model training. The classification results suggest that the developed models (accuracy: 0.851-0.992; F1 score: 0.839-0.993) are much more accurate and efficient than conventional methods (accuracy: 0.242-0.553). Feature importance analysis of the models demonstrates that Cl, F, S, V, Sr/Y, V/Y, Eu*, (La/Yb) N , and La/Sm are important variables in apatite that discriminate fertile and barren host rocks and indicates that V/Y and Cl/F ratios and the S content, in particular, are crucial parameters to discriminating metal enrichment and mineralization potential. This study suggests that ML is a robust tool for processing highdimensional geochemical data and presents a novel approach that can be applied to mineral exploration.

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Section: Introductionmentioning

confidence: 99%

Machine learning applied to apatite compositions for determining mineralization potential

Zheng,

Xu,

Lentz

et al. 2024

American Mineralogist

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“…Recently, machine learning was also applied to mineral exploration and achieved excellent results compared with traditional discrimination diagrams (Gregory et al., 2019; H. T. Zhao et al., 2023; Y. Z. Zhou et al., 2022; Zou et al., 2022). Zou et al.…”

Section: Introductionmentioning

confidence: 99%

“…Z. Zhou et al. (2022) employed Support Vector Machines and Random Forest methods to classify metallogenic fertility based on igneous zircon trace element data. Nevertheless, given that orogenic gold deposits are distinct from magmatic hydrothermal related deposits and represent a unique type of deposit strongly influenced by other factors such as faulting (Goldfarb & Pitcairn, 2023; Goldfarb et al., 2005; Groves et al., 1998; Q. F. Wang et al., 2022), the research on mineral exploration methods using machine learning approaches from previous studies may not be applicable for orogenic gold deposits.…”

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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Mineral Informatics: Origins

Prabhu

Morrison

Hazen

2023

Springer Mineralogy

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Machine Learning and Singularity Analysis Reveal Zircon Fertility and Magmatic Intensity: Implications for Porphyry Copper Potential

Cited by 12 publications

References 88 publications

Machine learning applied to apatite compositions for determining mineralization potential

Machine learning applied to apatite compositions for determining mineralization potential

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

Mineral Informatics: Origins

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