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<p>Regolith-hosted rare earth element (REE) deposits in South China provide a critical source of heavy rare earth elements (HREEs), yet the geochemical signals that discriminate protolith sources and track REE enrichment remain obscured by intense weathering. Here, the Renju REE deposit in Guangdong Province was investigated using an integrated approach combining zircon U–Pb dating, trace element geochemistry, and unsupervised machine learning. This paper reports three findings. (1) Zircon ages and ternary degree of saprolitization trends reveal that the Renju regolith is a composite of weathering products from Late Cretaceous rhyolite (~97&#xa0;Ma, 0–41&#xa0;m) and Jurassic quartz diorite (~189&#xa0;Ma, 44–60&#xa0;m). Pearson correlation analysis validates that weakly mobile trace elements such as Ti, Th, and V serve as effective indicators for tracing this protolith heterogeneity. (2) Cerium (Ce) is decoupled from all other REEs in the oxidized surface horizon (0–10&#xa0;m). The pronounced positive Ce anomaly (δCe &gt; 2) in the surface (A horizon) is geochemically complementary to negative Ce anomalies in deeper (B horizon) ion-adsorption-type REE ore bodies, suggesting that the surface Ce anomaly can serve as an indicator for underlying REE mineralization. (3) Gallium (Ga) shows co-migration behavior with light rare earth elements (LREEs), while strontium (Sr) and barium (Ba) correlate with HREEs. Consequently, Ga, Sr, and Ba can serve as practical geochemical pathfinders for LREE and HREE enrichment. This paper demonstrates that accurate identification of protolith heterogeneity is a critical prerequisite for understanding the formation mechanisms of regolith-hosted REE deposits in South China. Moreover, unsupervised machine learning can extract interpretable geochemical signals from high-dimensional weathering datasets, and provides transferable trace element fingerprints for cost-effective exploration of regolith-hosted REE deposits.</p>

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Unsupervised Machine Learning Identifies Trace Element Indicators for Protolith Discrimination and REE Targeting of Granitoid Regolith

  • 期刊论文

  • 发表日期：

    2026年7月31日

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  • Long Guo,
  • Wei Tan,
  • Mengqi Han,
  • Lianying Luo,
  • Qihang Yin,
  • Xiaoliang Liang,
  • Jianxi Zhu

摘要

Regolith-hosted rare earth element (REE) deposits in South China provide a critical source of heavy rare earth elements (HREEs), yet the geochemical signals that discriminate protolith sources and track REE enrichment remain obscured by intense weathering. Here, the Renju REE deposit in Guangdong Province was investigated using an integrated approach combining zircon U–Pb dating, trace element geochemistry, and unsupervised machine learning. This paper reports three findings. (1) Zircon ages and ternary degree of saprolitization trends reveal that the Renju regolith is a composite of weathering products from Late Cretaceous rhyolite (~97 Ma, 0–41 m) and Jurassic quartz diorite (~189 Ma, 44–60 m). Pearson correlation analysis validates that weakly mobile trace elements such as Ti, Th, and V serve as effective indicators for tracing this protolith heterogeneity. (2) Cerium (Ce) is decoupled from all other REEs in the oxidized surface horizon (0–10 m). The pronounced positive Ce anomaly (δCe > 2) in the surface (A horizon) is geochemically complementary to negative Ce anomalies in deeper (B horizon) ion-adsorption-type REE ore bodies, suggesting that the surface Ce anomaly can serve as an indicator for underlying REE mineralization. (3) Gallium (Ga) shows co-migration behavior with light rare earth elements (LREEs), while strontium (Sr) and barium (Ba) correlate with HREEs. Consequently, Ga, Sr, and Ba can serve as practical geochemical pathfinders for LREE and HREE enrichment. This paper demonstrates that accurate identification of protolith heterogeneity is a critical prerequisite for understanding the formation mechanisms of regolith-hosted REE deposits in South China. Moreover, unsupervised machine learning can extract interpretable geochemical signals from high-dimensional weathering datasets, and provides transferable trace element fingerprints for cost-effective exploration of regolith-hosted REE deposits.