成都理工大学学报（自然科学版）2025，Vol.52Issue(5)：873-887,15.DOI:10.12474/cdlgzrkx.2025051904
基于机器学习的榍石微量元素特征判别岩浆岩类型
Machine-learning-based classification of igneous rock type using titanite trace element data
摘要
Abstract
Titanite,as an accessory mineral in igneous rocks,provides a record of the evolution of geochemical environments during magma differentiation,and its trace element characteristics hold significant potential in rock type identification.Although binary discrimination plots have been applied in traditional classifications of igneous rocks based on titanite trace element content,their limitations in classifying complex rock types have become increasingly evident.In recent years,with the accumulation of geological data and the development of machine-learning technologies,data-driven geological research methods have been widely applied.This study utilized publicly available titanite trace element data and employed genetic programming symbolic regression to construct a mathematical expression for discriminating igneous rock type.By applying four machine-learning algorithms(support vector machine,k-nearest neighbors,XGBoost,and random forest),a classification model for igneous rock type was constructed,which accurately distinguishes ultramafic,mafic,intermediate,and felsic rocks.SHAP analysis revealed significant associations between titanite trace element compositions and igneous rock types,indicating that titanite geochemistry can serve as an effective indicator for lithological classification.关键词
榍石/微量元素/岩浆岩/遗传编程符号回归/机器学习Key words
titanite/trace elements/igneous rocks/genetic programming symbolic regression/machine learning分类
天文与地球科学引用本文复制引用
滕文航,潘增锋,王瑞..基于机器学习的榍石微量元素特征判别岩浆岩类型[J].成都理工大学学报（自然科学版）,2025,52(5):873-887,15.基金项目
国家自然科学基金重大研究计划集成项目(92462304) （92462304）
中国地质大学(北京)2025年大学生创新创业训练计划项目资助. （北京）
