Apatite Eu/Y-Ce discrimination diagram: A big data based approach for provenance classification

Abstract

Apatite is a common and important accessory mineral in rocks. Apatite comprises rich various trace elements, which are formed in different apatite-generation environments. Constructing apatite-associated trace element discrimination diagrams is a classic; and efficient technique for the investigation of apatite provenance. Hie typical examples include the diagrams of Sr-Y, Sr-Mn, Y-( Eu/ Eu ∗ ) and ( Ce/Yb) N-HEE. However, the current discrimination diagrams cannot precisely distinguish apatite types due to the massive increase of apatite trace elements that are detected l>v the developing microscale analyses, which therefore cannot precisely predict the apatite-formation environment. Fortunately, the significant increase of apatite trace element data advances our understanding in apatite provenance by using the big data analysis technique. In this study, we applied the big data study for the collected 1925 published apatite trace element data, analyzing six apatite-associated rock types, which are alkali-rich igneous rocks, ultramafic rocks, mafic igneous rocks, felsic granitoids, low- and medium-grade metamorphic rocks and high-grade metamorphic rocks. Hie yield 7140 apatite- associated discrimination diagrams were further detailed evaluated by introducing the Silhouette coefficient. By the process, we proposed an Eu/\-Ce discrimination diagram to distinguish apatite.