Principal component analysis of mineral and element composition of ores from the Bayan Obo Nb-Fe-REE deposit: Implication for mineralization process and ore classification

Abstract

The Bayan Obo Nb-Fe-REE polymetallic deposit in northern China is the world’s largest light REE deposit. The deposit was formed through multiperiodic alteration and mineralization, and thus mineral species and assemblages are extremely complex. To date, there is still no consensus on the classification of ore types, and research on the paragenetic association of minerals is still inadequate. Eighty ore samples are divided into seven types according to altered minerals, including dolomite, iron oxide, aegirine, riebeckite, biotite, fluorite, and barite types. These samples were analyzed by a TESCAN Integrated Mineral Analyzer (TIMA) for their mineral and element composition. The data set was investigated by principal component analysis (PCA) to reveal the mineralization and alteration information and classify the ores.PCM1 to PCM3 and PCE1 to PCE3 represent the first three principal components for PCA results of minerals and elements, respectively. The results indicate that PCM1 may distinguish rare earth mineralization from barite and silicification alteration that is associated with the latest fluids. PCM2 can distinguish mineralization and alteration associated with Nb from those associated with REE. The combination of PCM2 and PCE1 indicates that the precipitation of REE may be closely related to fluorination and phosphate, while the combination of PCM2 and PCE3 indicates that the Nb mineralization may be more closely related to alteration of riebeckite and biotite, as well as to Ti-rich fluids. In addition, PCM3 distinguishes the Th mineralization and associated fluorination from other alteration and mineralization. Based on PCA results, the above seven ore types can be classified into three types: dolomite REE ore, Fe-REE-F ore, and Nb-rich ore, which provides a simplified and comprehensive classification for Bayan Obo ores. PCA-based approach combining geochemical and mineralogical data can quickly identify ore types, which is helpful in mineral exploration.