Identifying tectonic settings of porphyry copper deposits using zircon trace elements – A semi-supervised machine learning method

Abstract

Porphyry copper deposits (PCDs), as main sources of Cu resources worldwide, have attracted great attention for both scientific research and mining purposes in the past decades. The formation of PCDs is closely related to magmatic-hydrothermal system formed in both (continental/oceanic) arc and non-arc environments. Many low-dimensional geochemistry discriminants were developed for identifying the tectonic environments where ore-related magmas formed. However, long-term geological processes may obscure the geochemistry information recorded in rocks and minerals, which inevitably introduces uncertainties for identifying tectonic environments when using the low-dimensional discriminant diagrams. Here, we proposed a semi-supervised machine learning (ML) approach based on zircon chemistry data (spanning 16 elements) to identify the tectonic environments of PCDs. The results show that the semi-supervised models perform better in identifying the tectonic environments compared to the conventional low-dimensional discriminants. Moreover, as another benefit of big data analysis, ML model can clarify the systematic geochemistry differences of igneous zircons formed in different tectonic environments. The results suggest that (U+Th)/Yb and LREE/HREE are the top two important zircon chemical features for distinguishing PCDs formed in arc or non-arc environments. Specifically, zircons from non-arc show higher (U+Th)/Yb compared to that of continental arc, while zircons from island arc show lowest (U+Th)/Yb and LREE/HREE. Finally, the semi-supervised learning model was applied to the Dexing porphyry copper deposit to approach the longstanding debate regarding the underlying metallogenic setting. The results support the viewpoint that the Dexing porphyry copper deposit was formed in an intracontinental (non-arc) environment related to asthenosphere upwelling.