From Trace Elements to Petrogenesis: A Machine Learning Approach to Determine Ore Deposit Type from Trace Elements Analysis of Apatite

The diverse suite of trace elements incorporated into apatite in ore-forming systems has important applications in petrogenesis studies of mineral deposits. Trace element variations in apatite can be used to distinguish between different types of rocks as well as discriminating between deposit types, and thus have potential as mineral exploration tools. Such classification approaches commonly employ two-variable scatterplots of apatite trace element compositional data. While such diagrams offer easy and convenient visualization of compositional trends, they often struggle to effectively distinguish deposit types because they do not employ all the high-dimensional (i.e. multi-element) information accessible from high-quality apatite trace element analysis. To address this issue, we employ, for the first time, a supervised machine learning-based approach (eXtreme Gradient Boosting, XGBoost) to correlate apatite compositions with ore deposit type, utilizing high-dimensional information. We evaluated 8629 apatite trace element data from five deposit types (porphyry, skarn, orogenic Au, iron oxide copper gold, and iron oxide-apatite) along with unmineralized apatite to discriminate between apatite in mineralized vs unmineralized systems. We could show that the XGBoost classifier efficiently and accurately classifies high-dimensional apatite trace element data according to the ore deposit type (overall accuracy: 94% and F1 score: 89%). Interpretation of the model using the SHAPley Additive exPlanations tool (SHAP) shows that Th, U, Eu and Nd are the most indicative elements for classifying deposit types using apatite trace element chemistry. Our approach has broad implications for the understanding of the sources, chemistry and evolution of melts and hydrothermal fluids resulting in ore deposit formation.

Keywords: Machine learning; apatite; Trace elements; Ore deposit type; XGBoost