现代地质2025，Vol.39Issue(3)：552-559,8.DOI:10.19657/j.geoscience.1000-8527.2025.040
深时铁矿物分布特征及演化趋势预测初探
A Preliminary Study on the Temporal Distribution Characteristics of Iron Minerals and Prediction of the Deep-Time Evolution
摘要
Abstract
Minerals are important recorders of Earth's evolution over billions of years.Data-driven research in mineralogy makes it easier to understand the underlying laws and driving mechanisms of the evolutionary proces-ses of Earth.Iron plays a critical role in the matter and energy cycles,evolution of life,and environmental re-mediation,and is also recognized as a vitally important'oxygen fugacity buffer'during the evolution of Earth surficial system.A total amount of 949 iron minerals are collected in this study.And by exploring their evolu-tionary patterns and distributional characteristics,a preliminary framework is constructed to tap into the intrinsic links between the evolution of deep-time iron minerals,evolution of Earth's environment and evolution of life.The results reveal that the diversity of iron minerals has expanded episodically throughout geological history,and the peak of the growth stage coincides with the period of supercontinent collisions.The processes of plate mo-tion,atmospheric oxygenation,and life's metabolic activities have combined to promote the evolution of iron minerals in a more complex and diverse direction.Furthermore,back propagation neural network(BPNN),random forest(RF)and support vector regression(SVR),are used to establish and predict the evolution model of iron minerals since 4.0 Ga.The results show that RF is more appropriate to predict the evolution trend more accurately with stronger generalization ability than BPNN and SVR.关键词
铁矿物/矿物演化/机器学习/演化特征Key words
iron mineral/mineral evolution/machine learning/evolutionary feature分类
天文与地球科学引用本文复制引用
黄思艺,徐靖博,魏林宏,蔡元峰..深时铁矿物分布特征及演化趋势预测初探[J].现代地质,2025,39(3):552-559,8.基金项目
国家自然科学基金项目(42192500). （42192500）
