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

Minerals ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 61 ◽  
Author(s):  
Marija Dmitrijeva ◽  
Nigel J. Cook ◽  
Kathy Ehrig ◽  
Cristiana L. Ciobanu ◽  
Andrew V. Metcalfe ◽  
...  
Keyword(s):  
Trace Elements ◽  
Statistical Analysis ◽  
Trace Element ◽  
South Australia ◽  
Large Datasets ◽  
Ore Deposits ◽  
Partial Replacement ◽  
Multivariate Statistical ◽  
Wide Range ◽  
Olympic Dam

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.

Laser ablation ICP MS trace element composition of native gold from the Abitibi Greenstone belt, Timmins Ontario.

2021 ◽  
Author(s):  
John D. Greenough ◽  
Alejandro Velasquez ◽  
Mohamed Shaheen ◽  
Joel Gagnon ◽  
Brian J. Fryer ◽  
...  
Keyword(s):  
Trace Elements ◽  
Laser Ablation ◽  
Trace Element ◽  
Greenstone Belt ◽  
Native Gold ◽  
Internal Standard ◽  
Gold Deposits ◽  
Ore Deposits ◽  
Abitibi Greenstone Belt ◽  
Icp Ms

Trace elements in native gold provide a “fingerprint” that tends to be unique to individual gold deposits. Fingerprinting can distinguish gold sources and potentially yield insights into geochemical processes operating during gold deposit formation. Native gold grains come from three historical gold ore deposits; Hollinger, McIntyre (quartz-vein ore), and Aunor near Timmins, Ontario, at the western end of the Porcupine gold camp and the south-western part of the Abitibi greenstone belt. Laser-ablation, inductively-coupled plasma mass spectrometry (LA ICP MS) trace element concentrations were determined on 20 to 25 µm wide, 300 µm long rastor trails in ~ 60 native gold grains. Analyses used Ag as an internal standard with Ag and Au determined by a scanning electron microscope with an energy dispersive spectrometer. The London Bullion Market AuRM2 reference material served as the external standard for 21 trace element analytes (Al, As, Bi, Ca, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Pd, Pt, Rh, Sb, Se, Si, Sn, Te, Ti, Zn; Se generally below detection in samples). Trace elements in native gold associate according to Goldschmidt’s classification of elements strongly suggesting that element behavior in native Au is not random. Such element behavior suggests that samples from each Timmins deposit formed under similar but slightly variable geochemical conditions. Chalcophile and siderophile elements provide the most compelling fingerprints of the three ore deposits and appear to be mostly in solid solution in Au. Lithophile elements are not very useful for distinguishing these deposits and element ABSTRACT CUT OFF BY SOFTWARE

OPENING THE MAGMATIC-HYDROTHERMAL WINDOW: HIGH-PRECISION U-Pb GEOCHRONOLOGY OF THE MESOPROTEROZOIC OLYMPIC DAM Cu-U-Au-Ag DEPOSIT, SOUTH AUSTRALIA

2020 ◽  
Vol 115 (8) ◽  
pp. 1855-1870 ◽  
Author(s):  
Liam Courtney-Davies ◽  
Cristiana L. Ciobanu ◽  
Simon R. Tapster ◽  
Nigel J. Cook ◽  
Kathy Ehrig ◽  
...  
Keyword(s):  
High Precision ◽  
Volcanic Rocks ◽  
South Australia ◽  
Ore Deposits ◽  
Magmatic Zircon ◽  
Ionization Mass ◽  
Temporal Window ◽  
Precision Data ◽  
Iocg Deposit ◽  
Olympic Dam

Abstract Establishing timescales for iron oxide copper-gold (IOCG) deposit formation and the temporal relationships between ores and the magmatic rocks from which hydrothermal, metal-rich fluids are sourced is often dependent on low-precision data, particularly for deposits that formed during the Proterozoic. Unlike accessory minerals routinely used to track hydrothermal mineralization, iron oxides are dominant components of IOCG systems and are therefore pivotal to understanding deposit evolution. The presence of ubiquitous, magmatic-hydrothermal U-(Pb)-W-Sn-Mo–bearing zoned hematite resolves a range of geochronological issues concerning formation of the ~1.6 Ga Olympic Dam IOCG deposit, South Australia, at up to ~0.05% precision (207Pb/206Pb weighted mean; 2σ) using isotope dilution-thermal ionization mass spectrometry (ID-TIMS). Coupled with chemical abrasion-ID-TIMS zircon dates from host granite and volcanic rocks within and enclosing the ore-body, a confident magmatic-hydrothermal chronology is defined. The youngest zircon date from the granite intrusion hosting Olympic Dam indicates magmatism was occurring up until 1593.28 ± 0.26 Ma. The orebody was principally formed during a major mineralizing event following granite uplift and during cupola collapse, whereby the hematite with the oldest age is recorded in the outer shell of the deposit at 1591.27 ± 0.89 Ma, ~2 m.y. later than the youngest documented magmatic zircon. Hematite dates captured throughout major lithologies, different ore zones, and the ~2-km vertical extent of the deposit support ~2 m.y. of hydrothermal activity. New age constraints on the spatial-temporal evolution of the formation of Olympic Dam are considered with respect to a mantle to crustal continuum model. Cyclical tapping of magma reservoirs to maintain crystal mushes for extended time periods and incremental building of batholiths on the million-year scale prior to main mineralization pulses can explain the ~2-m.y. temporal window temporal window inferred from the data. Despite the challenge of reconciling such an extended window with contemporary models for porphyry deposits (≤1 m.y.), formation of Proterozoic ore deposits has been addressed at high-precision and supports the case that giant IOCG deposits may form over millions of years.

Deconvolution of the composition of fine-grained pyrite in sedimentary matrix by regression of time-resolved LA-ICP-MS data

2020 ◽  
Vol 105 (6) ◽  
pp. 820-832 ◽  
Author(s):  
Aleksandr S. Stepanov ◽  
Leonid V. Danyushevsky ◽  
Ross R. Large ◽  
Indrani Mukherjee ◽  
Irina A. Zhukova
Keyword(s):  
Trace Elements ◽  
Regression Analysis ◽  
Trace Element ◽  
Sedimentary Rocks ◽  
Trace Element Composition ◽  
Element Composition ◽  
Silicate Matrix ◽  
Time Resolved ◽  
Icp Ms ◽  
Wide Range

Abstract Pyrite is a common mineral in sedimentary rocks and is the major host for many chalcophile trace elements utilized as important tracers of the evolution of the ancient hydrosphere. Measurement of trace element composition of pyrite in sedimentary rocks is challenging due to fine-grain size and intergrowth with silicate matrix and other sulfide minerals. In this contribution, we describe a method for calculation of trace element composition of sedimentary pyrite from time-resolved LA-ICP-MS data. The method involves an analysis of both pyrite and pyrite-free sediment matrix, segmentation of LA-ICP-MS spectra, normalization to total, regression analysis of dependencies between the elements, and calculation of normalized composition of the mineral. Sulfur is chosen as an explanatory variable, relative to which all regressions are calculated. The S content value used for calculation of element concentrations from the regressions is calculated from the total, eliminating the need for independent constraints. The algorithm allows efficient measurement of concentrations of multiple chalcophile trace elements in pyrite in a wide range of samples, including quantification of detection limits and uncertainties while excluding operator bias. The data suggest that the main sources of uncertainties in pyrite composition are sample heterogeneity and counting statistics for elements of low abundance. The analysis of regression data of time-resolved LA-ICP-MS measurements could provide new insights into the geochemistry of the sedimentary rocks and minerals. It allows quantification of ratios of elements that do not have reference material available (such as Hg) and provides estimates on the content of non-sulfidic Fe in the silicate matrix. Regression analysis of the mixed LA-ICP-MS signal could be a powerful technique for deconvolution of phase compositions in complex multicomponent samples.

scholarly journals Multivariate Statistical Analysis of Particle X-ray Spectra

1998 ◽  
Vol 4 (S2) ◽  
pp. 202-203
Author(s):  
Ian M. Anderson ◽  
John A. Small
Keyword(s):  
Statistical Analysis ◽  
Trace Element ◽  
Multivariate Statistical Analysis ◽  
A Priori ◽  
Detectable Level ◽  
Multivariate Statistical ◽  
Raw Data ◽  
X Ray ◽  
Carbon Substrates ◽  
Particle Spectra

Multivariate statistical analysis (MSA) is a powerful tool for the analysis of series of spectra. This paper explores an application of MSA to a series of energy dispersive X-ray (EDX) spectra acquired in the scanning electron microscope (SEM) from a series of particles. The raw data were series of spectra previously acquired to test analytical procedures for trace element detection. This paper explores the possibility of performing the trace element detection with MSA components that have been extracted from the raw data without any a priori assumptions about the information content of the particle spectra. Particles were prepared from two analytical glasses, dispersed onto carbon substrates and coated with carbon. The compositions of the two glasses are substantially similar, except that one glass (K-3106) contains 0.7 wt.% Fe, whereas the other glass (K-3069) does not contain Fe at a detectable level.

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