Trace Element Composition of Chalcopyrite from Volcanogenic Massive Sulfide Deposits: Variation and Implications for Provenance Recognition

Author:

Caraballo Enzo1,Beaudoin Georges1,Dare Sarah2,Genna Dominique23,Petersen Sven4,Relvas Jorge M.R.S.5,Piercey Stephen J.6

Affiliation:

1. 1 Département de géologie et génie géologique, Université Laval, 1065 avenue de la Médecine, Quebec, Quebec G1V 0A6, Canada

2. 2 Département des Sciences Appliquées, Université du Québec à Chicoutimi, 555, boul. de l’Université, Saguenay, Quebec G7H 2B1, Canada

3. 3 CONSOREM, 555, boul. de l’Université, Saguenay, Quebec G7H 2B1, Canada

4. 4 Helmholtz Centre for Ocean Research Kiel, Wischhofstr 1-3, 24148 Kiel, Germany

5. 5 Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal

6. 6 Memorial University of Newfoundland, 300 Prince Philip Drive, St. John’s, Newfoundland and Labrador A1B 3X5, Canada

Abstract

Abstract Chalcopyrite from 51 volcanogenic massive sulfide (VMS) and sea-floor massive sulfide (SMS) deposits from six lithostratigraphic settings was analyzed for trace elements by laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to evaluate its potential as an indicator mineral for exploration. Partial least squares discriminant analysis (PLS-DA) results reveal that chalcopyrite from different lithostratigraphic settings has different compositions reflecting host-rock assemblages and fluid composition. Three random forest (RF) classifiers were developed to distinguish chalcopyrite from the six lithostratigraphic settings with a divisive approach. This method, which primarily classifies according to the major host-rock affinity and subsequently according to VMS settings, yielded an overall accuracy higher than 0.96 on test data. The model validation with literature data having the same elements required by the models yielded the highest accuracies (>0.90). In validation using published data with missing elements, the accuracy is moderate to high (0.60–1); however, the performances decrease significantly (<0.50) when the most important elements are missing. Similarly, RF regression models developed using all sets of analyzed elements to determine ccp/(ccp + sp) ratio (ccp = chalcopyrite; sp = sphalerite) in chalcopyrite within a single VMS setting reported high performances, thus showing a potential to predict the Cu/Zn ratio (Cu-rich vs. Zn-rich) of the mineralization based on chalcopyrite composition. This study demonstrates that trace element concentrations in chalcopyrite are primarily controlled by lithotectonic setting and can be used as predictors in an RF classifier to distinguish the different VMS subtypes.

Publisher

Society of Economic Geologists, Inc.

Subject

Economic Geology,Geochemistry and Petrology,Geology,Geophysics

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