现代地质2025，Vol.39Issue(3)：523-540,18.DOI:10.19657/j.geoscience.1000-8527.2025.001
基于机器学习的黑云母成分判别花岗岩成因类型方法研究
Machine-learning Based Discrimination of Granite Type Using Biotite Composition
摘要
Abstract
The chemical composition of biotite can reflect the physical and chemical conditions and evolution process of magma.This study collected the EPMA data of 1455 biotite samples derived from I-type,S-type,and A-type granites to explore their differences and intrinsic association with granite type.The results show that the biotites from I-type granite are relatively rich in SiO2,TiO2,MgO,and MnO,while the biotites from S-type granite are relatively rich in Al2O3 and Na2O,and the biotites from A-type granite are relatively rich in FeOT.Based on the chemistry of biotites from I-type,S-type,and A-type granites,The I-type granite forms in a relatively high temperature,low pressure,and high oxygen fugacity environment.In contrast,the S-type granite usually forms in a relatively high-pressure environment,and the oxygen fugacity and temperature are lower than those of the I-type granite.The F and Cl contents and enrichment extent of biotite from different granite types are also significantly different,with relatively highest F and Cl contents in the biotites from A-type granites.Previous studies have shown that biotite composition has excellent potential to distinguish the genetic types of granites,but the existing classification models based on biotite composition still have significant uncer-tainty.PCA,t-SNE,UMAP dimensionality reduction methods and decision tree-based random forest(RF),extreme random tree(ERT),gradient boosting decision tree(GBDT),extreme gradient boosting(XGBoost),lightweight gradient boosting(LightGBM)and CatBoost machine learning model algorithms were applied to identify I-type,S-type and A-type granites based on calculated molar proportions of cation assignment in the bi-otite formula.The results show that PCA,t-SNE,and UMAP dimensionality reduction methods are not ineffec-tive in distinguishing different granite types.In contrast,machine learning models based on decision trees can effectively identify granite types with more than 94.5％accuracy.In the biotite formula,T.Al,T.Fe3+,M.Al,M.Mg,and M.Mn are the five key cation assignments that affect the classification of machine-learning models.关键词
黑云母/机器学习/I型花岗岩/S型花岗岩/A型花岗岩Key words
biotite/machine learning/I-type granite/S-type granite/A-type granite分类
天文与地球科学引用本文复制引用
孙玉洁,李晓彦,张超..基于机器学习的黑云母成分判别花岗岩成因类型方法研究[J].现代地质,2025,39(3):523-540,18.基金项目
科技部重点研发计划项目(2023YFF0804200). （2023YFF0804200）
