Abstract: Geoscience shared databases (GEOROC, PetDB, etc.) provide important basic data for geoscience research. However, there is an obvious defect in these databases, i.e., in database samples, the nine major elements (SiO2, TiO2, Al2O3, CaO, MgO, MnO, K2O, Na2O and P2O5) are mostly present, but rare earth element (REE) data are often missing. In view of the important role of REE in geochemistry, here we attempt to provide a solution for supplementing the missing REE data by using random forest method of machine learning to predict REE values by major elements. Taking Ocean Island Basalt (OIB) as an example, 1283 OIB samples collected from the GEOROC database were divided into two groups: 80% of the data were used as training data for modeling and the remaining 20% were test data for model validation. Comparing the modeling and prediction results using random forest and multivariable linear regression methods on the same data, we found that the random forest method was superior in both aspects with clear advantage; however, the relationship between input and output parameters was not simple. The random forest method predicted the test data very well for light REEs, but prediction power decreased gradually with increasing atomic number, possibly due to a weaker or more complex relationship between heavy rare earth and major elements. The predicted REE distribution pattern by the random forest method matched the actual REE distribution pattern, with good distinguishing power to reflect the relative difference between the actual distribution patterns, which is particularly important to infer the geochemical process. With increasing training data, the model established by the random forest method will be more stable thus to provide more accurate prediction results. Ultimately, REE value prediction will be more reliable and feasible with continuous improvement of databases.

Key words: machine learning, random forest, oceanic island basalt, major elements, rare earth elements