We propose to investigate the relationships between major and trace elements by using the random forest algorithm of machine learning. The relationship between Zr and major elements in ocean island basalts（OIB）is selected as an example because：1）OIB are hardly affected by crust contamination and the relationship between the elements is relatively stable；2）the trace element Zr is a high field strength element and is chemically stable. Five major elements（TiO2, CaO, MgO, Na2O and P2O5）are selected as predictors based on the variable importance measurements of random forest method combining with the results of Pearson correlation analysis. A random forest model containing a thousand decision trees with three features in each tree is determined by these five elements, and the predictive result of this model to the Zr is superior to the general multivariable regression method. Furthermore, the empirical formula between Zr and these five major elements is analyzed and the fitted formula is also good for the prediction of Zr. By providing a demonstrating example, this study introduces a machine learning method to the research discipline of geochemistry which owns massive data and requires new techniques of data mining.
Hong Jin Gan Chengshi Liu Jie. Preliminary study on the relationship between trace and major elements in rocks based on machine learning：A case study of Zr in OIB[J]. Chinese Journal of Geology, 2018, 53(4): 1285-1299.