Open Access Repository

Multivariate statistical analysis of trace elements in pyrite: prediction, bias and artefacts in defining mineral signatures

Downloads

Downloads per month over past year

Dmitrijeva, M, Cook, NJ, Ehrig, K, Ciobanu, CL, Metcalfe, AV, Kamenetsky, M ORCID: 0000-0002-0417-3975, Kamenetsky, VS ORCID: 0000-0002-2734-8790 and Gilbert, S 2020 , 'Multivariate statistical analysis of trace elements in pyrite: prediction, bias and artefacts in defining mineral signatures' , Minerals, vol. 10, no. 1 , doi: 10.3390/min10010061.

[img]
Preview
PDF (Published version)
137086-multivar...pdf | Download (4MB)

| Preview

Abstract

Pyrite is the most common sulphide in a wide range of ore deposits and well known to hostnumerous trace elements, with implications for recovery of valuable metals and for generation ofclean concentrates. Trace element signatures of pyrite are also widely used to understand ore-formingprocesses. Pyrite is an important component of the Olympic Dam Cu–U–Au–Ag orebody, SouthAustralia. Using a multivariate statistical approach applied to a large trace element dataset derivedfrom 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 wellat the grain-scale, indicating high variability in zonation patterns. Arsenic is not, however, a goodpredictor for invisible Au at Olympic Dam. Most pyrites contain only negligible Au, suggestingthat invisible gold in pyrite is not commonplace within the deposit. A minority of pyrite grainsanalysed do, however, contain Au which correlates with Ag, Bi and Te. The results are interpreted toreflect not only primary patterns but also the eects of multi-stage overprinting, including cyclesof partial replacement and recrystallisation. The latter may have caused element release from thepyrite lattice and entrapment as mineral inclusions, as widely observed for other ore and gangueminerals within the deposit. Results also show the critical impact on predictive interpretations madefrom 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 datasetsis critical as the number of these values impacts on the cluster results. Trimming of datasets toeliminate artefacts introduced by left-censored data should be performed with caution lest bias beunintentionally introduced. The practice may, however, reveal meaningful correlations that might bediluted using the complete dataset.

Item Type: Article
Authors/Creators:Dmitrijeva, M and Cook, NJ and Ehrig, K and Ciobanu, CL and Metcalfe, AV and Kamenetsky, M and Kamenetsky, VS and Gilbert, S
Keywords: pyrite, trace elements, multivariate statistics, left-censored data, Olympic Dam
Journal or Publication Title: Minerals
Publisher: MDPI AG
ISSN: 2075-163X
DOI / ID Number: 10.3390/min10010061
Copyright Information:

© 2020 by the authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) http://creativecommons.org/licenses/by/4.0/

Related URLs:
Item Statistics: View statistics for this item

Actions (login required)

Item Control Page Item Control Page
TOP