Quartz Ti/Ge-P discrimination diagram: A machine learning based approach for deposit classification

Wang, Yu; Qiu, Kaibin; Hou, Zhaoliang; Yu, Hao‐Cheng

doi:10.18654/1000-0569/2022.01.18

Cited by 4 publications

(1 citation statement)

References 44 publications

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“…As a rapidly growing approach to analyzing high-throughput experimental data in novel ways, machine learning focuses on the underlying relationships between features (measurable properties) and research targets (Jordan & Mitchell, 2015). In recent years, it has been successfully applied to a diverse suite of classification challenges on high-dimensional datasets in the geosciences (Petrelli & Perugini, 2016;Schönig et al, 2021;Zhong et al, 2021;Wang et al, 2022). These include estimating pre-eruptive temperatures and pressures using clinopyroxene-melt (Petrelli et al, 2020), evaluating the occurrence of H diffusion in the clinopyroxene phenocrysts of basaltic magma (Chen et al, 2021), proposing and improving thermobarometry for different magma types (biotite-bearing magma: Li and Zhang, 2022, amphibole -bearing magma: Higgins et al, 2022, clinopyroxene-bearing magma: Jorgenson et al, 2022, and distinguishing S-, I-, and A-type granites (Gion et al, 2022).…”

Section: Diagnostic Geochemical Signatures From Apatite Trace Element...mentioning

confidence: 99%

Apatite trace element composition as an indicator of ore deposit types: A machine learning approach

Qiu,

Zhou,

Chew

et al. 2024

American Mineralogist

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The diverse suite of trace elements incorporated into apatite in ore-forming systems has important applications in petrogenesis studies of mineral deposits. Trace element variations in apatite can be used to distinguish between fertile and barren environments, and thus have potential as mineral exploration tools. Such classification approaches commonly employ two-variable scatterplots of apatite trace element compositional data. While such diagrams offer accessible visualization of compositional trends, they often struggle to effectively distinguish ore deposit types because they do not employ all the high-dimensional (i.e. multi-element) information accessible from high-quality apatite trace element analysis. To address this issue, we use a supervised machine learning-based approach (eXtreme Gradient Boosting, XGBoost) to correlate apatite compositions with ore deposit type, utilizing such highdimensional information. We evaluated 8629 apatite trace element data from five ore deposit types (porphyry, skarn, orogenic Au, iron oxide copper gold, and iron oxideapatite) along with unmineralized magmatic and metamorphic apatite to identify discriminating parameters for the individual deposit types as well as for mineralized systems. According to feature selection, eight elements (Th, U, Sr, Eu, Dy, Y, Nd and La) improve the model performance. We could show that the XGBoost classifier efficiently and accurately classifies high-dimensional apatite trace element data according to the ore deposit type (overall accuracy: 94% and F1 score: 89%).Interpretation of the model using the SHAPley Additive exPlanations (SHAP) tool shows that Th, U, Eu and Nd are the most indicative elements for classifying deposit types using apatite trace element chemistry. Our approach has broad implications for the better understanding of the sources, chemistry and evolution of melts and hydrothermal fluids resulting in ore deposit formation.

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Section: Diagnostic Geochemical Signatures From Apatite Trace Element...mentioning

confidence: 99%

Apatite trace element composition as an indicator of ore deposit types: A machine learning approach

Qiu,

Zhou,

Chew

et al. 2024

American Mineralogist

View full text Add to dashboard Cite

show abstract

Multistage sulfide and hydrothermal quartz as a record of precision ore-forming process in the epithermal Cu–Zn–Au system in the Kalatag arc, East Tianshan, NW China

Cheng,

Ling,

Liu

et al. 2024

Ore Geology Reviews

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Discrimination of deposit types using magnetite geochemistry based on machine learning

Wang,

Su,

Wang

et al. 2024

Ore Geology Reviews

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Quartz Ti/Ge-P discrimination diagram: A machine learning based approach for deposit classification

Cited by 4 publications

References 44 publications

Apatite trace element composition as an indicator of ore deposit types: A machine learning approach

Apatite trace element composition as an indicator of ore deposit types: A machine learning approach

Multistage sulfide and hydrothermal quartz as a record of precision ore-forming process in the epithermal Cu–Zn–Au system in the Kalatag arc, East Tianshan, NW China

Discrimination of deposit types using magnetite geochemistry based on machine learning

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