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Ge, Yun-Zhao; Zhang, Zhen-Jie; Zhou, Yuan-Zhi; Li, Qiang; Zhang, Feng (2025) Explainable machine learning reveals apatite fertility and porphyry copper mineralization processes in the syn- and post-subduction settings. Ore Geology Reviews, 183. doi:10.1016/j.oregeorev.2025.106679

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Reference TypeJournal (article/letter/editorial)
TitleExplainable machine learning reveals apatite fertility and porphyry copper mineralization processes in the syn- and post-subduction settings
JournalOre Geology Reviews
AuthorsGe, Yun-ZhaoAuthor
Zhang, Zhen-JieAuthor
Zhou, Yuan-ZhiAuthor
Li, QiangAuthor
Zhang, FengAuthor
Year2025 (August)Volume183
PublisherElsevier BV
DOIdoi:10.1016/j.oregeorev.2025.106679Search in ResearchGate
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Mindat Ref. ID18432989Long-form Identifiermindat:1:5:18432989:2
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Full ReferenceGe, Yun-Zhao; Zhang, Zhen-Jie; Zhou, Yuan-Zhi; Li, Qiang; Zhang, Feng (2025) Explainable machine learning reveals apatite fertility and porphyry copper mineralization processes in the syn- and post-subduction settings. Ore Geology Reviews, 183. doi:10.1016/j.oregeorev.2025.106679
Plain TextGe, Yun-Zhao; Zhang, Zhen-Jie; Zhou, Yuan-Zhi; Li, Qiang; Zhang, Feng (2025) Explainable machine learning reveals apatite fertility and porphyry copper mineralization processes in the syn- and post-subduction settings. Ore Geology Reviews, 183. doi:10.1016/j.oregeorev.2025.106679
In(2025) Ore Geology Reviews Vol. 183. Elsevier BV

References Listed

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Zheng, Yu-yu, Xu, Bo, Lentz, David R., Yu, Xiao-yan, Hou, Zeng-qian, Wang, Tao (2024) Machine learning applied to apatite compositions for determining mineralization potential. American Mineralogist, 109 (8) 1394-1405 doi:10.2138/am-2023-9115
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