Genesis of the Kuh-Panj porphyry copper deposit, Kerman, Iran: Constraints from mineralization, geochemistry, fluid inclusion, and zircon U Pb isotope systematics

Allahbakhshipoor, Atefeh; Alipour-Asll, Masood; Lentz, David R.

doi:10.1016/j.gexplo.2023.107276

Journal of Geochemical Exploration

2023

DOI: 10.1016/j.gexplo.2023.107276

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Genesis of the Kuh-Panj porphyry copper deposit, Kerman, Iran: Constraints from mineralization, geochemistry, fluid inclusion, and zircon U Pb isotope systematics

Atefeh Allahbakhshipoor,

Masood Alipour-Asll,

David R. Lentz

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2024

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Cited by 3 publications

References 69 publications

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Hidden Markov model for spatial analysis of three-dimensional mineralization distribution: Insights into magma flow and mineral exploration targets in the Jinchuan Ni-Cu-(PGE) sulfide deposit, China

Deng,

Huang,

Liu

et al. 2024

Applied Geochemistry

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Hidden Markov model for spatial analysis of three-dimensional mineralization distribution: Insights into magma flow and mineral exploration targets in the Jinchuan Ni-Cu-(PGE) sulfide deposit, China

Deng,

Huang,

Liu

et al. 2024

Applied Geochemistry

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Application of anomaly components of multi-element combination extracted from stream sediments data by BEMD in mapping ore prospectivity within the JGCR, Eastern China

Zheng,

Chen,

Chen

et al. 2024

Applied Geochemistry

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Application of Machine Learning to Characterize Metallogenic Potential Based on Trace Elements of Zircon: A Case Study of the Tethyan Domain

Guo,

2024

Minerals

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Amidst the rapid advancement of artificial intelligence and information technology, the emergence of big data and machine learning provides a new research paradigm for mineral exploration. Focusing on the Tethyan metallogenic domain, this paper conducted a series of research works based on machine learning methods to explore the critical geochemical element signals that affect the metallogenic potential of porphyry deposits and reveal the metallogenic regularity. Binary classifiers based on random forest, XGBoost, and deep neural network are established to distinguish zircon fertility, and these machine learning methods achieve higher accuracy, exceeding 90%, compared with the traditional geochemical methods. Based on the random forest and SHapley Additive exPlanations (SHAP) algorithms, key chemical element characteristics conducive to magmatic mineralization are revealed. In addition, a deposit classification model was constructed, and the t-SNE method was used to visualize the differences in zircon trace element characteristics between porphyry deposits of different mineralization types. The study highlights the promise of machine learning algorithms in metallogenic potential assessment and mineral exploration by comparing them with traditional chemical methods, providing insights into future mineral classification models utilizing sub-mineral geochemical data.

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Genesis of the Kuh-Panj porphyry copper deposit, Kerman, Iran: Constraints from mineralization, geochemistry, fluid inclusion, and zircon U Pb isotope systematics

Cited by 3 publications

References 69 publications

Hidden Markov model for spatial analysis of three-dimensional mineralization distribution: Insights into magma flow and mineral exploration targets in the Jinchuan Ni-Cu-(PGE) sulfide deposit, China

Hidden Markov model for spatial analysis of three-dimensional mineralization distribution: Insights into magma flow and mineral exploration targets in the Jinchuan Ni-Cu-(PGE) sulfide deposit, China

Application of anomaly components of multi-element combination extracted from stream sediments data by BEMD in mapping ore prospectivity within the JGCR, Eastern China

Application of Machine Learning to Characterize Metallogenic Potential Based on Trace Elements of Zircon: A Case Study of the Tethyan Domain

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