基于岩浆锆石微量元素的机器学习模型对斑岩铜矿成矿潜力的评估

吴川川; 孟旭阳; 刘品; 辛茹月; 成嘉伟

doi:10.3799/dqkx.2026.084

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基于岩浆锆石微量元素的机器学习模型对斑岩铜矿成矿潜力的评估

更新时间：2026-10-15

- 基于岩浆锆石微量元素的机器学习模型对斑岩铜矿成矿潜力的评估
- Application of Machine Learning Models Based on Magmatic Zircon Trace Elements for Discriminating the Metallogenic Potential of Porphyry Copper Deposits
- 地球科学 2026年51卷第9期页码：3530-3546
- 作者机构：
  
  1.中国地质大学（北京）地球科学与资源学院，自然资源部战略性金属矿产找矿理论与技术重点实验室，北京 100083
  2.中国地质大学（北京）地球科学与资源学院，地质过程与成矿预测全国重点实验室，北京 100083
  3.中国地质大学（北京）人工智能学院，北京 100083
- 作者简介：
  
  吴川川（2001-），男，博士研究生，地质资源与地质工程专业.ORCID： 0009⁃0006⁃7692⁃0665.E⁃mail： 3001250147@email.cugb.edu.cn
  孟旭阳，E⁃mail: mengxuyang1991@163.com
  刘品，E⁃mail: liupin@cugb.edu.cn
- 基金信息：
  
  国家深地重大专项(2024ZD1001401);国家自然科学基金项目(42572091)
- DOI：10.3799/dqkx.2026.084
  中图分类号： P612
- 收稿：2025-12-15，
  
  纸质出版：2026-09-30
- 稿件说明：
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吴川川，孟旭阳，刘品，辛茹月，成嘉伟，2026.基于岩浆锆石微量元素的机器学习模型对斑岩铜矿成矿潜力的评估.地球科学，51（9）：3530-3546.

Wu Chuanchuan，Meng Xuyang，Liu Pin，Xin Ruyue，Cheng Jiawei，2026.Application of Machine Learning Models Based on Magmatic Zircon Trace Elements for Discriminating the Metallogenic Potential of Porphyry Copper Deposits.Earth Science，51（9）：3530-3546.
吴川川，孟旭阳，刘品，辛茹月，成嘉伟，2026.基于岩浆锆石微量元素的机器学习模型对斑岩铜矿成矿潜力的评估.地球科学，51（9）：3530-3546. DOI： 10.3799/dqkx.2026.084.

Wu Chuanchuan，Meng Xuyang，Liu Pin，Xin Ruyue，Cheng Jiawei，2026.Application of Machine Learning Models Based on Magmatic Zircon Trace Elements for Discriminating the Metallogenic Potential of Porphyry Copper Deposits.Earth Science，51（9）：3530-3546. DOI： 10.3799/dqkx.2026.084.

摘要

针对传统二元判别图在处理锆石微量元素数据时维度低、难以揭示非线性成矿机制的问题，本研究引入机器学习方法以提升斑岩型铜矿成矿潜力判别的准确性与客观性.基于全球斑岩型矿床锆石微量元素数据，系统对比了XGBoost、随机森林、支持向量机、逻辑回归和神经网络五种模型的分类性能，并结合SHAP可解释性框架解析模型决策机制.结果表明：XGBoost模型分类准确率最高（86.85%），其次为随机森林（83.84%）和逻辑回归（83.84%），支持向量机（78.90%）和多层感知器（69.86%）表现相对较低.SHAP分析显示，Eu

/Eu

*、10 000（Eu

/Eu

*）/Y、Ti、Y、△FMQ和Gd/Dy是判别成矿潜力的关键指标.本研究构建的XGBoost、随机森林等模型能有效识别斑岩型铜矿成矿潜力并筛选关键勘查标识，不仅为智能化矿产勘查提供了新思路，也为实际应用中的模型选择和优化提供了重要参考.

Abstract

To address the limitations of traditional binary discrimination diagrams

which arise from low dimensionality and difficulty in revealing nonlinear metallogenic mechanisms when processing zircon trace element data

this study introduces machine learning methods to enhance the accuracy and objectivity of porphyry copper mineralization potential assessment. Based on global zircon trace element data from porphyry deposits

we systematically compared the classification performance of five models⁃XGBoost

Random Forest

Support Vector Machine

Logistic Regression

and Neural Network⁃ and interpreted the model decision-making mechanisms using the SHAP explainable framework. The results show that XGBoost achieves the highest classification accuracy (86.85%)

followed by Random Forest (83.84%) and Logistic Regression (83.84%)

while Support Vector Machine (78.90%) and Multilayer Perceptron (69.86%) exhibit relatively lower performance. SHAP analysis identifies Eu

/Eu

10 000(Eu

/Eu

*)/Y

△FMQ

and Gd/Dy as key indicators for discriminating mineralization potential. The models constructed in this study

such as XGBoost

and Random Forest

can effectively identify porphyry copper mineralization potential and screen critical exploration indicators

offering not only new insights into intelligent mineral exploration but also important references for model selection and optimization in practical applications.

关键词

Keywords

references

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