基于GEOROC数据库的全球辉长岩大数据的大地构造环境智能判别研究

辉长岩是化学成分与玄武岩类似的侵入岩,前人认为它的形成过程太复杂,对应的岩浆可能经过了分离结晶作用、混染作用等,不能用Pearce判别图来判断岩浆岩形成的构造环境。本文利用GEOROC数据库的资料对辉长岩进行大数据挖掘。首先根据前人成果,将GEOROC数据库的辉长岩形成的大地构造环境分为大陆玄武岩环境、汇聚边界环境、板内火山岩环境和大洋岛弧玄武岩环境等4类;然后在数据清洗基础上,利用Python语言,依托sklearn库,实现支持向量机、K近邻和随机森林等3种机器学习算法,获得3种对应的分类器结果输出。对辉长岩的构造环境进行智能判别结果显示,随机森林方法效果最好,判断准确率可达97%,利用辉长岩的地球化学大数据来判断岩浆岩的构造环境是完全可行的。

Study on intelligent discrimination of tectonic settings based on global gabbro data from GEOROC

The study of discrimination diagrams began in the 1970s. The basalt tectonic environmental discriminant diagrams are the most commonly used in academic circles and have achieved very good results. With the accumulation of data, many scholars have gradually discovered the limitations of the discriminating diagrams and tried to establish new discrimination diagrams with better effect. The gabbro is an intrusive rock with a chemical composition similar to that of basalt. The predecessors thought that the formation process of gabbro was too complicated. The magma may have undergone fractional crystallization, mixing, hybridization, and it cannot be used to determine the tectonic setting formed by magmatic rocks. In this paper, the data mining of gabbro was studied using the data from the database of Geochemistry of Rocks of the Oceans and Continents (GEOROC), and three different algorithms of machine learning (Support Vector Machine, K Nearest Neighbor, and Random Forest) were used for gabbro. The intelligent discriminant research for tectonic settings, compared with the previous discrimination diagrams, has obtained a better discriminant effect. The random forest method has the best effect, and the judgment accuracy rate can reach 97%. Therefore, it is considered that the geochemical data of gabbro can be used to determine the tectonic setting of magmatic rocks. Based on the existing results, the random forest algorithm has the best effect.