Next Article in Journal
An Automated Method to Generate and Evaluate Geochemical Tectonic Discrimination Diagrams Based on Topological Theory
Previous Article in Journal
Effects of Fine Minerals on Pulp Rheology and the Flotation of Diaspore and Pyrite Mixed Ores
Previous Article in Special Issue
Chlorine Isotope Composition of Apatite from the >3.7 Ga Isua Supracrustal Belt, SW Greenland
Open AccessFeature PaperArticle

Multivariate Statistical Analysis of Trace Elements in Pyrite: Prediction, Bias and Artefacts in Defining Mineral Signatures

1
School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide 5005, Australia
2
BHP Olympic Dam, Adelaide 5001, Australia
3
School of Mathematical Sciences, The University of Adelaide, Adelaide 5005, Australia
4
School of Natural Sciences (Earth Sciences), University of Tasmania, Private Bag 79, Hobart 7001, Australia
5
Adelaide Microscopy, The University of Adelaide, Adelaide 5005, Australia
*
Author to whom correspondence should be addressed.
Minerals 2020, 10(1), 61; https://doi.org/10.3390/min10010061
Received: 9 December 2019 / Revised: 29 December 2019 / Accepted: 6 January 2020 / Published: 10 January 2020
(This article belongs to the Special Issue Feature Papers in Mineral Geochemistry and Geochronology 2019)
Pyrite is the most common sulphide in a wide range of ore deposits and well known to host numerous trace elements, with implications for recovery of valuable metals and for generation of clean concentrates. Trace element signatures of pyrite are also widely used to understand ore-forming processes. Pyrite is an important component of the Olympic Dam Cu–U–Au–Ag orebody, South Australia. Using a multivariate statistical approach applied to a large trace element dataset derived from analysis of random pyrite grains, trace element signatures in Olympic Dam pyrite are assessed. Pyrite is characterised by: (i) a Ag–Bi–Pb signature predicting inclusions of tellurides (as PC1); and (ii) highly variable Co–Ni ratios likely representing an oscillatory zonation pattern in pyrite (as PC2). Pyrite is a major host for As, Co and probably also Ni. These three elements do not correlate well at the grain-scale, indicating high variability in zonation patterns. Arsenic is not, however, a good predictor for invisible Au at Olympic Dam. Most pyrites contain only negligible Au, suggesting that invisible gold in pyrite is not commonplace within the deposit. A minority of pyrite grains analysed do, however, contain Au which correlates with Ag, Bi and Te. The results are interpreted to reflect not only primary patterns but also the effects of multi-stage overprinting, including cycles of partial replacement and recrystallisation. The latter may have caused element release from the pyrite lattice and entrapment as mineral inclusions, as widely observed for other ore and gangue minerals within the deposit. Results also show the critical impact on predictive interpretations made from statistical analysis of large datasets containing a large percentage of left-censored values (i.e., those falling below the minimum limits of detection). The treatment of such values in large datasets is critical as the number of these values impacts on the cluster results. Trimming of datasets to eliminate artefacts introduced by left-censored data should be performed with caution lest bias be unintentionally introduced. The practice may, however, reveal meaningful correlations that might be diluted using the complete dataset. View Full-Text
Keywords: pyrite; trace elements; multivariate statistics; left-censored data; Olympic Dam pyrite; trace elements; multivariate statistics; left-censored data; Olympic Dam
Show Figures

Figure 1

MDPI and ACS Style

Dmitrijeva, M.; Cook, N.J.; Ehrig, K.; Ciobanu, C.L.; Metcalfe, A.V.; Kamenetsky, M.; Kamenetsky, V.S.; Gilbert, S. Multivariate Statistical Analysis of Trace Elements in Pyrite: Prediction, Bias and Artefacts in Defining Mineral Signatures. Minerals 2020, 10, 61.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop