T018-0017
Global Systematics of Copper in Arc Magmas Using a Big Data Approach

Wednesday, 9 December 2020
Poster
Nicholas Barber1, Marie Edmonds1, Frances E Jenner2, Helen M. Williams1 and Andreas Audétat3, (1)University of Cambridge, Department of Earth Sciences, Cambridge, United Kingdom, (2)Open University, Milton Keynes, United Kingdom, (3)Bavarian Geoinstitute, University of Bayreuth, Bayreuth, Germany
Abstract:
Copper (Cu) resources are central to the global transition to green energy. However, there remains a lack of agreement regarding which petrological processes play the largest role in shaping the Cu contents of arc magmas. Furthermore, it is an open question whether magmas must be anomalously rich in Cu contents in order to result in porphyry Cu ore fertility. To address these questions, we present a new global database called SlabMetals2: a compilation of arc volcanic rock data, ranging from geochemical and sample-level data from the GeoRoc database, to geophysical, geologic, and tectonic data brought together using Python and QGIS. This database allows us to explore magmatic Cu systematics by interrogating a Big Data framework to elucidate the generic features of Cu’s behavior in active subduction zones.

Our study indicates that high ore potential magmas (inferred by high whole rock Sr/Y) show geochemical evidence for having been generated in hydrous garnet-bearing mantle wedges. Globally, high ore potential magmas show strong depletion of Fe during differentiation (i.e. are calc-alkaline) and are associated with low whole rock mean Cu concentrations and thicker continental crust. These trends are driven by extensive amphibole (+/- garnet) fractionation, which we show lowers the FeO content of the melt and the sulphur concentration at sulphide saturation (SCSS), driving sulphide fractionation and consequent Cu removal. Our global database reveals that extensive amphibole fractionation is a generic feature of ore-producing magmas across many arcs. Our work supports the view that anomalously high magmatic Cu concentrations are not a prerequisite for ore fertility, and a magmatic Cu abundance below the global average is not a detriment to later porphyry formation. Our work is a crucial contribution to the growing appreciation of Big Data in petrology and can help answer fundamental questions about how subduction zone processes impact resource endowment.