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Zhou, Yuanzhi, Zhang, Zhenjie, Yang, Jie, Ge, Yunzhao, Cheng, Qiuming (2022) Machine Learning and Singularity Analysis Reveal Zircon Fertility and Magmatic Intensity: Implications for Porphyry Copper Potential. Natural Resources Research, 31 (6) 3061-3078 doi:10.1007/s11053-022-10122-y

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Reference TypeJournal (article/letter/editorial)
TitleMachine Learning and Singularity Analysis Reveal Zircon Fertility and Magmatic Intensity: Implications for Porphyry Copper Potential
JournalNatural Resources Research
AuthorsZhou, YuanzhiAuthor
Zhang, ZhenjieAuthor
Yang, JieAuthor
Ge, YunzhaoAuthor
Cheng, QiumingAuthor
Year2022 (December)Volume31
Issue6
PublisherSpringer Science and Business Media LLC
DOIdoi:10.1007/s11053-022-10122-ySearch in ResearchGate
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Mindat Ref. ID15501784Long-form Identifiermindat:1:5:15501784:1
GUID0
Full ReferenceZhou, Yuanzhi, Zhang, Zhenjie, Yang, Jie, Ge, Yunzhao, Cheng, Qiuming (2022) Machine Learning and Singularity Analysis Reveal Zircon Fertility and Magmatic Intensity: Implications for Porphyry Copper Potential. Natural Resources Research, 31 (6) 3061-3078 doi:10.1007/s11053-022-10122-y
Plain TextZhou, Yuanzhi, Zhang, Zhenjie, Yang, Jie, Ge, Yunzhao, Cheng, Qiuming (2022) Machine Learning and Singularity Analysis Reveal Zircon Fertility and Magmatic Intensity: Implications for Porphyry Copper Potential. Natural Resources Research, 31 (6) 3061-3078 doi:10.1007/s11053-022-10122-y
In(2022, December) Natural Resources Research Vol. 31 (6) Springer Science and Business Media LLC

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