A Machine Learning Zircon Trace Element Tool to Predict Porphyry Deposit Type and Resource Size

Abstract

Abstract Porphyry deposits are primarily known for their association with base metals like copper and to some extent molybdenum and gold. Here we present machine learning models, based on zircon composition, that provide quantitative distinction between different deposit types and resource sizes. Using a global zircon compositional database for different porphyry deposits (9,649 samples), we trained several machine learning models. A porphyry deposit type model (PDT model) was developed using XGBoost, which distinguishes between barren, Cu, and Mo bearing deposits. Furthermore, porphyry Cu and Mo reserve models (Porphyry Cu Reserve [PCR] and Porphyry Mo Reserve [PMR] model) were also developed using XGBoost and LightGBM, respectively, to give prediction of resource size in unexplored area. F1‐scores for the models are 0.97, 0.91, and 0.82. The model‐built feature importance and Shapley Additive exPlanations values imply that (EuN/EuN*)/Y, Th/U, Th/U and Ce are important in the PDT model, Ti, T (°C), U, and Hf are important for the PCR model, and Hf, U, Th/U, and EuN/EuN* are important for the PMR model. From a mineral system perspective, the three models imply that water, temperature, and magma evolution are pivotal to the type of deposits that forms. Temperature and magma evolution in particular are important in prediction of Cu and Mo resource size. Application of models to the Wunugetushan deposit gives ore type and resource predictions that are consistent with known deposit occurrence and geochemistry. These findings suggest that machine learning models may not only assist in understanding the main geological processes linked to porphyry mineralization, but also have application in reducing exploration risk.

Keywords

• zircon trace element
• machine learning
• porphyry deposit type
• resource size
• Wunugetushan deposit