Tectonic discrimination of olivine in basalt using data mining techniques based on major elements: a comparative study from multiple perspectives

ABSTRACT The olivine in basalt records much information about formation and evolution of basaltic magma, which may help to discriminate basalt tectonic settings. However, the viewpoint that olivine is connected with the tectonic setting where it formed is controversial. To verify the hypothesis, we intend to discriminate the basalt tectonic settings by geochemical characteristics of olivine. The data mining technique is selected as an effective tool for this study, which is a new attempt in geochemical research. The geochemical data of olivine used is extracted from open-access and comprehensive petrological databases. The classification performance of Logistic regression classifier, Na？ve Bayes, Random Forest and Multi-layer perception (MLP) algorithms is firstly compared under some constraints. The results of the basic experiment indicate that MLP has the highest classification accuracy of about 88% based on raw data, followed by Random Forest. But this does not fully prove the hypothesis is credible. Then, the cross-validation method and other measurement criteria are integrated for scientific and in-depth comparative analysis. The advanced experiments mainly include the comparison of different data preprocessing methods, combinations of geochemical characteristics and sample data volumes. It turns out that chemical composition of olivine in basalt has the function of discriminating tectonic settings