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 uncertainty. 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 biotite formula. The results show that PCA,t-SNE,and UMAP dimensionality reduction methods are not ineffective 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.
