A Machine Learning-Based Approach to Discriminating Basaltic Tectonic Settings

Abstract

The geochemical characteristics of magmatic rocks can distinguish the tectonic setting of magma formation and their geochemical signatures are discriminated by using the whole-rock geochemical data. As a new attempt of artificial intelligence technology in geochemistry, the machine learning discrimination method is gradually complementary to the classical discriminative graphical method. However, the feature selection of high-dimensional data and the determination of many unknown parameters are the two main factors affecting the classification accuracy of the algorithm. In this paper, a particle swarm optimized support vector machine (PSO-SVM) model is established to classify the tectonic environments of basaltic rocks in the GEOROC database. The model mainly relies on the powerful search capability of the particle swarm algorithm to find the best parameter combination selected by the SVM based on experience to improve the accuracy. In this study, based on the basalt samples in the database and the confusion matrix, the performance of PSO-SVM model is evaluated by simulation experiments. The results show that the model proposed in this paper is more effective in distinguishing the basaltic tectonic environments, with an accuracy of more than 90%. Therefore, compared with the traditional discriminant map method, the machine learning method based on the fusion of two algorithms performs better in the tectonic environment classification problems.