Zircon is a common mineral in igneous rocks, which is resistant to both chemical weathering and physical abrasion. Its chemistry can potentially be used to distinguish ore-forming porphyry magmas from barren magma systems. This study compiles >23,000 zircon analyses from >30 porphyry deposits, barren intrusions, and rivers to determine the principal geochemical characteristics of fertile zircons using predictive modeling, and compares them with traditional geochemical thresholds. The results show that the Eu/Eu* and Dy/Yb ratios, P content, and the curvature at the end of rare earth element (REE) patterns (λ3) are the most diagnostic characteristics of fertile zircons. The use of geochemical thresholds, as Boolean conditions, reach their maximum performance for Eu/Eu* and Dy/Yb (sensitivity [sens] = 0.73, specificity [spec] = 0.90), but it is outperformed by the random forest model (sens = 0.91, spec = 0.93) in the testing set. Explanatory analysis of the models shows that the fertility signal in zircons becomes stronger as the porphyry system evolves and is accompanied by an overall decrease in the middle to light REE and P content, characteristics that are absent in barren zircons. We attribute the observed difference in λ3 to the co-crystallization of other accessory phases, suggesting that the changes in the zircon Ce anomaly is controlled by the depletion of light and middle REE. The low P content in fertile zircons is caused by extensive crystallization of apatite. Fertile zircons have an excess of (REE + Y)3+, which we attribute to charge-balance by H+ in hydrous magmas. Simple machine learning algorithms outperform the traditional geochemical discriminators in their predictions and provide insights into characteristics that have not previously been considered for evaluating porphyry copper fertility using zircon geochemistry. We propose simplified methods that can be easily incorporated into exploration workflows.

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