Josh Wright Thesis Final 2-1

An analysis of Au-Ag mineralization in the Caribou base-metal VMS deposit, New

Brunswick; examination of nano- to micro-scale inter- and intra-sulphide

distribution and its relation to interpretation of saturation mechanisms and

geometallurgy

by

Joshua Wright

B.Sc., California Polytechnic State University, San Luis Obispo (USA), 2010

A Thesis Submitted in Partial Fulfillment

of the Requirements for the Degree of

Master of Science

in the Graduate Academic Unit of Earth Sciences

Supervisor: David R. Lentz, Ph.D., Department of Earth Sciences

Co-supervisor: Philip P. Garland, Ph.D., Department of Mechanical Engineering

Examining Board: Chris R. McFarlane, Ph.D., Department of Earth Sciences

Gohua Yan, Ph.D., Department of Mathematics and Statistics

This thesis is accepted by the

Dean of Graduate Studies

THE UNIVERSITY OF NEW BRUNSWICK

January, 2016

©Joshua Wright, 2016

ii

ABSTRACT

The Caribou deposit is a volcanogenic massive sulphide (VMS) deposit located in

the Bathurst Mining Camp (BMC; Northeastern New Brunswick). The primary resources

that will be extracted from this deposit are Zn, Pb, and Cu. However, Au and Ag are

important byproduct that will help offset costs. This research project uses mass-balance

methods to document variations in Au and Ag distribution between and within sulphide

minerals, and relates the distributions to saturation mechanisms and geometallurgy.

An image analysis method using Fiji (ImageJ), Weka segmentation, and

Microsoft Excel was developed to compare modal mineralogy results from optical image

analysis (OIA) to results obtained from mineral liberation analysis (MLA); however, the

method was effective but time intensive. Gold and Ag distributions were determined

from MLA and in situ laser ablation coupled plasma-mass spectrometry (LA-ICP-MS)

results. Silver is primarily hosted in galena and tetrahedrite-tennantite. Gold is primarily

hosted in pyrite and arsenopyrite.

iii

DEDICATION

This thesis is dedicated in the memory of John L. Jambor whose work at Caribou

inspired this project. John was an outstanding Canadian scientist who contributed

significantly to the fields of mineralogy, petrology, crystallography, and mineral deposits.

iv

ACKNOWLEDGEMENTS

Firstly, I would like to thank my supervisors Dr. Dave Lentz and Dr. Phillip

Garland for guiding, supporting, and challenging me. Dave is a brilliant scientist, he is

extremely knowledgeable and incredibly ambitious. He has always looked out for my

best interest, and has gone out of his way to provide me with unique opportunities. Dave

has always challenged me, allowing me to excel beyond my ambitions and has brought

my work to the next level. Philip is exceedingly kind and provided me with the

opportunity to further my experience as an engineer. He has been a sounding board and

his praise of my progress and accomplishments has increased my self-confidence

throughout the project. I am grateful to my thesis committee members for guidance and

feedback, which have improved my thesis.

Natural Sciences and Engineering Research Council (NSERC) and the New

Brunswick Innovation Foundation (NBIF) are thanked for their interest and support

funding this project. I would like to thank the Trevali Mining Corporation whom gave

me access to its property, samples, and data.

I would like to thank Dr. David Grant and Dylan Goudie for conducting the MLA

work at Memorial University, Newfoundland, and Brandon Boucher, for helping me

conduct in situ LA-ICP-MS. I am grateful to Azam Dehnavi-Soltani who helped me

learn many techniques, and was always kind enough to answer any question. I would

like to thank Steven Rossiter, my research assistant, who was willing to assist me with

any task, and without whom the project would have been delayed.

I am grateful to my family for their understanding and encouragement throughout

this project. I would also like to thank my fiancée, Holly Seniuk, who has supported me

v

completely throughout this experience, without whom this experience would not have

been possible.

vi

Table of Contents

ABSTRACT ........................................................................................................................ ii

DEDICATION ................................................................................................................... iii

ACKNOWLEDGEMENTS ............................................................................................... iv

Table of Contents ............................................................................................................... vi

List of Tables ..................................................................................................................... ix

List of Figures ................................................................................................................... xv

List of Symbols, Nomenclature or Abbreviations ........................................................... xvi

Chapter 1 Introduction ........................................................................................................ 1 1.1 Caribou deposit background information ................................................................. 1

1.2 Defining geometallurgy ............................................................................................ 2

1.3 Modal mineralogy and gold and silver concentration determinations ...................... 4

1.4 Thesis goals and chapter descriptions ....................................................................... 5

References ....................................................................................................................... 6

Chapter 2 Comparative geometallurgical study of massive sulphide ore characterization

using mineral liberation analysis and optical image analysis ........................................... 10

Abstract ......................................................................................................................... 10

2.1 Introduction ............................................................................................................. 11

2.2 Methodology ........................................................................................................... 14

2.2.1 Microscope Parameters .................................................................................... 16

2.2.2 Image Acquisition ............................................................................................ 16

2.2.3 Computer Hardware Specifications ................................................................. 17

2.2.4 Trainable Weka Segmentation ......................................................................... 17

vii

2.2.5 Preprocessing Software .................................................................................... 21

2.2.6 Phase Maps, Phase Area %, and Grain Size Distributions .............................. 26

2.2.7 Edge Area Redistribution and Mineral Associations ....................................... 26

2.2.8 MLA Conditions .............................................................................................. 29

2.3 Results and Discussion ........................................................................................... 30

2.4 Conclusions ............................................................................................................. 33

References ..................................................................................................................... 34

Chapter 3 An analysis of Au-Ag mineralization in the Caribou base-metal VMS deposit,

New Brunswick; examination of nano- to micro-scale inter- and intra-sulphide

distribution and its relation to geometallurgy ................................................................... 40

Abstract ......................................................................................................................... 40

3.1 Introduction ............................................................................................................. 41

3.2 Background ............................................................................................................. 43

3.2.1 Regional geology ............................................................................................. 43

3.2.2 General characteristics of massive sulphide deposits in BMC ........................ 45

3.2.3 Deposit geology ............................................................................................... 47

3.2.4 Mineral sources of gold and silver ................................................................... 48

3.2.5 Gold in the Bathurst Mining Camp .................................................................. 55

3.2.6 VMS deposit general background information ................................................ 57

3.2.7 Source of ligands.............................................................................................. 58

3.2.8 Source of metals ............................................................................................... 59

viii

3.3 Experimental ........................................................................................................... 60

3.3.1 Sample Selection .............................................................................................. 60

3.3.2 MLA Conditions .............................................................................................. 64

3.3.3 LA-ICP-MS conditions .................................................................................... 65

3.4 Results ..................................................................................................................... 67

3.5 Discussion ............................................................................................................... 71

3.5.1 Distribution of silver and gold ......................................................................... 71

3.5.2 Metallurgical Implications ............................................................................... 79

3.6 Conclusions ............................................................................................................. 80

Chapter 4 Conclusions and recommendations for future work ...................................... 106

Appendix ......................................................................................................................... 110

Curriculum Vitae

ix

List of Tables

Table 2.1 Thin section image 1 results ............................................................................. 29

Table 2.2 Phase area % results of image 1 after horizontal edge redistribution ............... 30

Table 2.3 Phase area % results of image 1 after vertical edge redistribution ................... 30

Table 2.4 Comparison of OIA and MLA results .............................................................. 32

Table 3.1 Lens 3 drill core interval head assay results ..................................................... 61

Table 3.2 Lens 4 drill core interval head assay results ..................................................... 62

Table 3.3 Modal mineralogy results ................................................................................. 67

Table 3.4 Silver grades by lens, polished thin section, and mineral ................................. 68

Table 3.5 Silver distributions by lens, thin section, and mineral ...................................... 69

Table 3.6 Gold grades by lens, thin section, and mineral ................................................. 69

Table 3.7 Gold distributions by lens, polished thin section, and mineral ......................... 70

Table A.1 Average area % results from optical image analysis of ................................. 110






Table A.7 Horizontal area % results from optical image analysis of ............................. 116



Table A.10 Horizontal area % results from optical image analysis of ........................... 119


x


Table A.13 Vertical area % results from optical image analysis of ................................ 122






Table A.19 L4-14-132 thin section non sulphide ........................................................... 128

Table A.20 L4-14-132 weight percent calculations from optical image analysis results 129

Table A.21 L4-14-132 weight percent standard error of the mean calculations ............ 129

Table A.22 L4-14-132 calculated Zn, Pb, and Cu assay values from optical ................. 129

Table A.23 Averaged LA-ICP-MS results of sphalerite grains from 62-119-47.7 polished

thin section ...................................................................................................................... 130

Table A.24 Averaged LA-ICP-MS results for chalcopyrite grains from 62-119-47.7

polished thin section ....................................................................................................... 131

Table A.25 Averaged LA-ICP-MS results for galena grains from 62-119-47.7 polished

thin section ...................................................................................................................... 132

Table A.26 Averaged LA-ICP-MS results for chalcopyrite grains from 62-119-47.7


Table A.27 Averaged LA-ICP-MS results for arsenopyrite grains from 62-119-47.7


Table A.28 Averaged LA-ICP-MS results for pyrite grains from 62-119-47.7 polished

thin section ...................................................................................................................... 135

xi

Table A.29 Averaged LA-ICP-MS results for sphalerite grains from L2-16-67.4 polished

thin section ...................................................................................................................... 136

Table A.30 Averaged LA-ICP-MS results for galena grains from L2-16-67.4 polished

thin section ...................................................................................................................... 137

Table A.31 Averaged LA-ICP-MS results for galena grains from L2-16-67.4 polished

thin section ...................................................................................................................... 138

Table A.32 Averaged LA-ICP-MS results for tetrahedrite-tennantite grains from L2-16-

67.4 polished thin section ............................................................................................... 139

Table A.33 Averaged LA-ICP-MS results for arsenopyrite grains from L2-16-67.4


Table A.34 Averaged LA-ICP-MS results for pyrite grains from L2-16-67.4 polished thin

section ............................................................................................................................. 141

Table A.35 Averaged LA-ICP-MS results for galena grains from L2-16-70 polished thin

section ............................................................................................................................. 142


section ............................................................................................................................. 143


section ............................................................................................................................. 144

Table A.38 Averaged LA-ICP-MS results of chalcopyrite grains from L2-16-70 polished

thin section ...................................................................................................................... 145

Table A.39 Averaged LA-ICP-MS results of arsenopyrite grains from L2-16-70 polished

thin section ...................................................................................................................... 146

xii

Table A.40 Averaged LA-ICP-MS results of pyrite grains from L2-16-70 polished thin

section ............................................................................................................................. 147


section ............................................................................................................................. 148

Table A.42 Averaged LA-ICP-MS results of galena grains from L4-14-132 polished thin

section ............................................................................................................................. 149

Table A.43 Averaged LA-ICP-MS results of chalcopyrite grains from L4-14-132


Table A.44 Averaged LA-ICP-MS results of arsenopyrite grains from L4-14-132



section ............................................................................................................................. 152

Table A.46 Averaged LA-ICP-MS results of sphalerite grains from L4-14-134.5 polished

thin section ...................................................................................................................... 153

Table A.47 Averaged LA-ICP-MS results of galena grains from L4-14-134.5 polished

thin section ...................................................................................................................... 154

Table A.48 Averaged LA-ICP-MS results of chalcopyrite grains from L4-14-134.5


Table A.49 Averaged LA-ICP-MS results of arsenopyrite grains from L4-14-134.5


Table A.50 Averaged LA-ICP-MS results of pyrite grains from L4-14-134.5 polished

thin section ...................................................................................................................... 157

xiii

Table A.51 Averaged LA-ICP-MS results of sphalerite grains from L4-14-134.5 polished

thin section ...................................................................................................................... 158

Table A.52 Averaged LA-ICP-MS results of galena grains from L4-14-134.5 polished

thin section ...................................................................................................................... 159

Table A.53 Averaged LA-ICP-MS results of tetrahedrite-tennantite grains from L4-14-

134.5 polished thin section ............................................................................................. 160

Table A.54 Averaged LA-ICP-MS results of arsenopyrite grains from L4-14-134.5


Table A.55 Averaged LA-ICP-MS results of pyrite grains from L4-14-134.5 polished

thin section ...................................................................................................................... 162

Table A.56 Hypothesis tests comparing lens 3 and 4 thin section LA-ICP-MS results . 163

Table A.57 Hypothesis tests comparing the combined lens 3 thin section LA-ICP-MS

results .............................................................................................................................. 164

Table A.58 Hypothesis tests comparing individual lens 3 thin section LA-ICP-MS results

......................................................................................................................................... 165

Table A.59 Hypothesis tests comparing the combined lens 3 thin section LA-ICP-MS

results .............................................................................................................................. 166


......................................................................................................................................... 167

Table A.61 Hypothesis tests comparing inter grain LA-ICP-MS results from the 62-119-

47 thin section ................................................................................................................. 167

Table A.62 Hypothesis tests comparing inter grain LA-ICP-MS results from the L2-16-

67.4 thin section .............................................................................................................. 168

xiv

Table A.63 Hypothesis tests comparing inter grain LA-ICP-MS results from the L2-16-70

thin section ...................................................................................................................... 169


132 thin section ............................................................................................................... 169


134.5 thin section ............................................................................................................ 170


100.1 thin section ............................................................................................................ 170

xv

List of Figures

Fig. 2.1 Workflow for this study for semi-automated segmentation and geometallurgical

characterization. Figure design based on figure 1 from Asmussen et al. (2015). ............. 15

Fig. 2.2 Image of a polished thin section (reflected light) with simulated target overlay. 18

Fig. 2.3 Weka segmentation interface showing a polished thin section plane polarized

reflected light image 1 prior to segmentation. .................................................................. 18

Fig. 2.4 Training features selected for the Weka Trainable Segmentation plugin. .......... 20

Fig. 2.5 Horizontal edge correction procedure, where the edge phase is represented by the

number 4. .......................................................................................................................... 22

Fig. 2.6 Vertical edge correction procedure, where the edge phase is represented by the

number 4. .......................................................................................................................... 23

Fig. 2.7 Image 1 before (plane polarized reflected light) and after the segmentation and

preprocessing procedures. ................................................................................................. 24


preprocessing procedures. ................................................................................................. 25

Fig. 2.9 Phase maps of image 1. ....................................................................................... 27

Fig. 2.10 Grain size distribution for image 1. ................................................................... 28

Fig. 2.11 Mineral association data for image 1, produced by the edge redistribution

algorithm in Microsoft® Excel. ........................................................................................ 31

Fig. 3.1 Silver versus Pb drill core interval assays: (a) lens 3 (n=35); (b) lens 4 (n =23) 64

xvi

List of Symbols, Nomenclature or Abbreviations

% - percent

° - Degree

°C - Degree Celsius

µm - micrometer

aCl- - Chloride activity

aH2S - Hydrogen sulphide activity

aS2 - Sulfur activity

Au - Gold

Ag - Silver

ANOVA - Analysis of variance

As - Arsenic

Asp - Arsenopyrite

B - Boron

Bi - Bismuth

BMC - Bathurst Mining Camp

Br - Bromine

Bt - Billion tonnes

C - Carbon

Ca - Calcium

Cd - Cadmium

Co - Cobalt

Cl - Chloride

xvii

Cp - Chalcopyrite

Cr - Chromium

Cu - Copper

EM - Electromagnetic survey

EMPA - Electron probe microanalysis

Fe - Iron

fO2 - Oxygen fugacity

g/t - Gram per tonne

Ga - Gallium

Ge - Germanium

GIS - Geographic Information System

Gn - Galena

GSD - Grain size distribution

GUI - graphical user interface

H - Hydrogen

He - Helium

HFW - horizontal frame width

Hg - Mercury

In - Indium

J/cm2 - Joule per square centimeter

kbar - Kilobar

LA-ICP-MS - Laser ablation inductively coupled mass spectrometry

LOD - Limit of detection

xviii

Ma - Million years

Mg - Magnesium

MLA - Mineral liberation analysis

Mn - Manganese

Mt - Million tonnes

NBIF - New Brunswick Innovation Foundation

Ni - Nickel

NIH - National Institute of Health

nm - nanometers

NSERC - Natural Sciences and Engineering Research Council

NSG - Non sulphide gangue

NTS - National Topographical System

O - Oxygen

OIA - Optical image analysis

Pb - Lead

ppm - Part per million

Py - Pyrite

QEMSCAN - Quantitative evaluation of minerals by scanning electron microscopy

r' - Pearson product-moment correlation coefficient

RPM - rotation per minute

S - Sulphur

Sb - Antimony

SEM - Scanning electron microscopy

xix

Sn - Tin

Sp - Sphalerite

Td-Tn - Tetrahedrite-tennantite

Te - Tellurium

Th - Thorium

tif - tagged image file

Tl - Thallium

U - Uranium

V - Vanadium

VMS - Volcanogenic massive sulphide

Weka - Waikato environment for knowledge analysis

Wt. % - Weight percent

Zn - Zinc

1

Chapter 1 Introduction

1.1 Caribou deposit background information

The Caribou deposit is the second largest volcanogenic massive sulphide (VMS)

deposit in the Bathurst Mining Camp (BMC; Goodfellow, 2003), with historical

geological resources of 70 million tonnes (Mt) grading 4.3% Zn, 1.6% Pb, 0.5% Cu, 51.3

g/t Ag, and 1.7 g/t Au (Cavelero, 1993). The deposit is located 50 km west of Bathurst

and lies within the 21O/09 map sheet of the National Topographical System (NTS;

Arseneau, 2013). The massive sulphides are hosted in the Spruce Lake Formation of the

California Lake Group (Goodfellow, 2003) in a steeply plunging synform (Roscoe, 1971,

Cavelero, 1993; Goodfellow, 2003; McClenaghan et al., 2009).

The deposit was discovered in 1955 by Anaconda Mining, during an

electromagnetic (EM) survey (McClenaghan et al., 2009). Exploration of the area

resulted in the discovery of the supergene gossan zone, which was mined between 1970

and 1974, producing 337,400 tonnes of supergene Cu ore grading 3.66% Cu and 61,500

tonnes of gossan (Cavelero, 1993). The gossan was heap-leached between 1982 and

1983, and yielded 106,000 ounces of Ag and 8,300 ounces of Au (Cavelero, 1993).

Exploitation of the massive sulphides at Caribou has been attempted by several

companies including East West Caribou Mining Limited (1967 - 1989), Breakwater

Resources Limited (1990 - 2005), and Blue Note Metals Incorporated (2005 - 2009),

however complications with extraction, due to the fine grained nature of the deposit, and

decreasing metal prices, have led to several mine shutdowns and acquisitions

2

(McClenaghan et al., 2009; Arseneau, 2013). Maple Minerals Incorporated acquired the

property in 2009 and sold it to Trevali Mining Corporation in 2012 (Arseneau, 2013).

Measured and indicated resource estimates of 7.23 million tonnes grading 6.99% Zn,

2.93% Pb, 0.43% Cu, 84.43 g/t Ag, and 0.89 g/t Au were reported in the 43-101 technical

report conducted in 2013. The mine is expected to provide jobs for approximately 250

people (Arseneau, 2013) for 6.3 years.

Currently, the mill at the Caribou deposit is designed to produce Zn, Pb, and Cu

concentrates, which will be shipped to smelters (Arseneau, 2013). During smelting, Au

and Ag will also be recovered, as Au and Ag values are contained within the Zn-, Pb-,

and Cu-bearing minerals. These precious metal byproducts will offset costs, and will

provide a financial buffer particularly during periods of low base-metal prices. The

distribution of Au and Ag at the deposit scale is a result of how the deposit formed, the

environmental conditions present during formation, and the greenschist facies

metamorphism of the deposit associated with deformation (Goodfellow, 2003). Due to

the fine-grained and complex nature of the deposit, a geometallurgical approach is best

suited to characterize the Au and Ag distribution at Caribou.

1.2 Defining geometallurgy

Geometallurgy is a multidisciplinary approach that incorporates information from

engineering, extractive metallurgy, geochemistry, geology, geostatistics, and mineralogy

to enhance a mine’s profits, material recovery, and revenue (Moyeed and Papritz, 2002;

Lozano and Bennet, 2003; Carrasco et al., 2008; Chiles and Delfiner, 2009; Coward et

al., 2009; Everett and Howard, 2011; Keeney et al., 2011; Kuhar et al., 2011; Newton and

3

Graham, 2011; Walters, 2011; Boisver et al., 2013; Powell, 2013; Rossi and Deutsch,

2013; van den Boogaart et al., 2013; Lund et al., 2014; Deutsch et al., 2015). A

geometallurgical approach can be comprised of three discrete components: data

acquisition and interpretation, geometallurgical integration, and spatial modeling (Keeney

and Walters, 2011).

In the data acquisition and interpretation stage of a geometallurgical approach,

samples undergo testing to determine geological and metallurgical characteristics of the

ore (Keeney and Walters, 2011). Geological characteristics determined during testing

often include geochemical determinations, modal mineralogy, mineral textures, and

mineral associations. Metallurgical characteristics determined can include metal

recovery, reagent consumptions, energy consumption (comminution indices), and plant

throughput (Deutsch et al., 2015).

Keeny and Walters (2011) show that in the geometallurgical integration stage, the

data obtained during the acquisition stage is used to develop a geological and

metallurgical database. The authors indicate that multivariate analysis is used to evaluate

the relationships between the geological and metallurgical data, to create a

geometallurgical classification scheme related to metallurgical performance. In the

spatial modeling stage, the deposit is classified into different geometallurgical domains to

better understand how orebody variability will effect resource exploitation (Keeny and

Walters, 2011).

Several studies have focused on developing geometallurgical approaches with

respect to spatial modeling (Keeney and Walters, 2011; Boisvert et al., 2013; Deutsch et

al., 2015), whereas others have focused on determining metallurgical properties at a fine

4

scale (Lund et al., 2014) or relating geological information to processing attributes

(Kuhar et al., 2011). At the Caribou deposit, Au and Ag are not hosted in discrete Au- or

Ag-minerals, but occur within sulphide minerals as inclusions or within solid solution

(Jambor and Laflamme, 1978; McClenaghan et al., 2004). Consequently, a

geometallurgical study must first focus on the Au and Ag distribution at a fine scale,

before a high-resolution geometallurgical spatial approach can be considered. In order to

determine the Au and Ag distribution at the mineral scale, the abundance of each

sulphide phase and the Au and Ag concentrations within each mineral must be

determined.

1.3 Modal mineralogy and Au and Ag concentration determinations

Modal mineralogy for simple mineral assemblages can be determined using

geochemical assays (Lund et al., 2013). However, this approach is less applicable to

trace minerals, such as tetrahedrite-tennantite, due to the major and minor element

overlap of more abundance phases, and the variable composition of tetrahedrite indicated

by Jambor and Laflamme (1978). Determining trace mineral abundance is typically

accomplished using automated methods like mineral liberation analysis (MLA) or

quantitative evaluation of minerals by scanning electron microscopy (QEMSCAN; Reid

et al., 1984; Gottlieb et al., 2000). These methods are expensive and have led to the

development of low-cost optical image analysis (OIA) techniques (Pirard, 2004; Pirard

and Lebichot, 2004; Donskoi and Clout, 2005; Donskoi et al., 2006; Donskoi et al.,

2013). Gold and Ag concentrations are easily determined using in situ laser ablation

5

inductively coupled mass spectrometry (LA-ICP-MS). Finally the distribution of Au and

Ag in each mineral can be determined by mass balance on a per sample basis.

1.4 Thesis goals and chapter descriptions

The overall goal of this project was to characterize the micro- to nano-scale inter-

and intra-sulphide distribution of Au and Ag, relate the distributions to saturation

mechanisms associated with ore formation processes and subsequent metamorphism and

deformation, and use these distributions to evaluate their effect on Au and Ag theoretical

recoveries. Advanced techniques including OIA, MLA, and in situ LA-ICP-MS were

used to provide accurate and rapid methods for characterization. The results from this

study are discussed in two separate chapters.

Chapter 2 compares MLA and OIA conducted on a select polished thin section

from the Caribou deposit, with respect to modal mineralogy, to evaluate the feasibility of

using a low-cost optical-based approach over the more expensive SEM-based image

analysis approaches.

Chapter 3 investigates the Au and Ag distribution within the sulphide minerals

using MLA, LA-ICP-MS, and mass balance techniques. Gold and Ag distributions were

also discussed at the deposit scale and related to saturation mechanisms and metallurgical

recoveries.

Chapter 4 presents the key conclusions and also contains recommendations for

future work.

6

References

Arseneau, G., 2013, Independent technical report for the Caribou massive sulphide

project, Bathurst New Brunswick, Canada, Steffen, Robertson, and Kirsten

Consulting Inc., Report 2CT21.000, p. 1-85.

Boisvert, J.B., Rossi, M.E., Ehrig, K., and Deutsch, C.V., 2013, Geometallurgical

modeling at Olympic Dam Mine, South Australia: Mathematical Geosciences, v.

45, p. 901-925.

Carrasco, P., Chiles, J., and Seguret, S., 2008, Additivity, metallurgical recovery and

grade: Geostats 2008: Santiago, Chile.

Cavelero, R.A., 1993, The Caribou massive-sulphide deposit, Bathurst camp, New

Brunswick, in McCutcheon, S.R., and Lentz, D.R., eds., Guidebook to the

Metallogeny of the Bathurst Camp. Trip #4 of Bathurst'93: 3rd annual field

conference, Canadian Institute of Mining and Metallurgy, p. 115-131.

Chiles, J.-P., and Delfiner, P., 2009, Geostatistics: modeling spatial uncertainty: John

Wiley & Sons.

Deutsch, J. L., Palmer, K., Deutsch, C. V., Szymanski, J., and Etsell, T. H., 2015, Spatial

modeling of geometallurgical properties: Techniques and a case study: Natural

Resources Research, p. 1-21.

Donskoi, E., and Clout, J. M. F., 2005, Automated textural classification of iron ores

using ‘Recognition’ - A specialized software package for studying iron ores: Iron

Ore: Perth, Western Australia, p. 203-211.

7

Donskoi, E., Manuel, J. R., Austin, P., Poliakov, A., Peterson, M. J., and Hapugoda, S.,

2013, Comparative study of iron ore characterisation using a scanning electron

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229.

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industry from pre-extraction data: Applied Earth Science, v. 120, p. 137-147.

Goodfellow, W. D., and McCutcheon, S. R., 2003, Geologic and genetic attributes of

volcanic sediment-hosted massive sulfide deposits of the Bathurst Mining Camp,

Northern New Brunswick-A synthesis, in Goodfellow, W. D., McCutcheon, S. R.,

and Peter, J. M., eds., Massive sulphide deposits of the Bathurst Mining Camp,

New Brunswick, and Northern Maine, Economic Geology Monograph 11, p. 245-

301.

Gottlieb, P., Wilkie, G., Sutherland, D., and Ho-Tun, E., 2000, Using quantitative

electron microscopy for process mineralogy applications: JOM, v. 52, 24-25.

Jambor, J.L., and Laflamme, J.H.G., 1978, The mineral sources of silver and their

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Keeney, L., and Walters, S.G., 2011, A methodology for geometallurgical mapping and

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Kuhar, L.L., Jeffrey, M.I., McFarlane, A.J., Benvie, B., Botsis, N. M., Turner, N., and

Robinson, D.J., 2011, The development of small scale tests to determine

8

hydrometallurgical indices for orebody mapping and domaining: GeoMet 2011:

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Lozano, C., and Bennett, C., 2003, Geometallurgical modeling applied to production

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geometallurgical program: Minerals Engineering, v. 49, p. 7-16.

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textures for geometallurgy: Process Mineralogy 2014: Cape Town, South Africa,

p. 30.

McClenaghan, S.H., Walker, J.A., and Lentz, D.R., 2009, Trace-element contents of base

metal concentrates from the Caribou mine, Bathurst Mining Camp, New

Brunswick: Implications for the recovery of indium from mill products, in Martin,

G. L., ed., Geological investigations in New Brunswick for 2008, New Brunswick

Department of Natural Resources; Minerals, Policy and Planning Division,

Mineral Resource Report 2009-2, p. 1-27.

Moyeed, R.A., and Papritz, A., 2002, An empirical comparison of kriging methods for

nonlinear spatial point prediction: Mathematical Geology, v. 34, p. 365-386.

Newton, M.J., and Graham, J.M., 2011, Spatial modelling and optimisation of

geometallurgical indices, GeoMet 2011: The First AusIMM International


9

Pirard, E., 2004, Multispectral imaging of ore minerals in optical microscopy:

Mineralogical Magazine, v. 68, p. 323-333.

Pirard, E., Lebichot, S., and Krier, W., 2007, Particle texture analysis using polarized

light imaging and grey level intercepts: International Journal of Mineral

Processing, v. 84, p. 299-309.

Powell, M.S., 2013, Utilising orebody knowledge to improve comminution circuit design

and energy utilisation, GeoMet 2013: The Second AusIMM International


Reid, A.F., Gottlieb, P., MacDonald, K.J., and Miller, P.R., 1984, QEM∗SEM image

analysis of ore minerals: volume fraction, liberation and observational variances,

Applied Mineralogy: New York, NY, American Institute of Mining,

Metallurgical, and Petroleum Engineers, p. 191-204.

Roscoe, W.E., 1971, Geology of the Caribou Deposit, Bathurst, New Brunswick:

Canadian Journal of Earth Sciences, v. 8, p. 1125-1136.

Rossi, M.E., and Deutsch, C.V., 2013, Mineral resource estimation: Springer Science &

Business Media.

Coward, S., Vann, J., and Dunham, S., 2009, The primary-response framework for

geometallurgical variables: Seventh International Mining Geology Conference

2009: Queenstown, New Zealand, p. 109-113.

van den Boogaart, K.G., Konsulke, S., and Delgado, R.T., 2013, Non-linear geostatistics

for geometallurgical optimisation: GeoMet 2013: The Second AusIMM

International Geometallurgy Conference, Brisbane, Australia, 2013,, p. 27-35.

10

Chapter 2 Comparative geometallurgical study of massive sulphide ore

characterization using mineral liberation analysis and optical image

analysis

Abstract

Geometallurgical studies aim to maximize the value of an ore body, while

minimizing technical and operational risks. The most critical component of

geometallurgy is ore characterization, which directly affects metallurgical processing

design and recoveries. Manual characterization is no longer practical due to the complex

nature of ores and has been replaced with automated scanning electron microscope

(SEM) based methods such as mineral liberation analysis (MLA) and quantitative

evaluation of minerals by scanning electron microscopy (QEMSCAN). The high cost

and long processing times required for MLA and QEMSCAN have encouraged the

development of low-cost optical image analysis based alternatives. In this paper, we

present a semi-automated reflected light microscope-based technique that uses threshold,

boundary, region, and data mining techniques to characterize the mineralogical properties

of a complex massive sulphide ore. A polished thin section containing drill core from the

Caribou base-metal deposit, located in the Bathurst Mining Camp (BMC), was used in

this study. Modal mineralogy results from optical image analysis (OIA) of 200 images,

that represented approximately 5 % of the thin section area, were compared to MLA of

the entire thin section. Results showed good agreement for most of the sulphide minerals,

with disagreements most likely due to the limited area sampled for optical image

11

analysis. OIA was slower than MLA and provided less detailed information for non-

reflective gangue minerals. Further research will be needed to decrease the processing

time for this approach, in order to make it more competitive with SEM-based methods.

For now, this process offers a cheap and accessible approach for optical image analysis of

opaque minerals.

Key words: geometallurgy, optical image analysis, ImageJ

2.1 Introduction

Geometallurgy is a cross discipline approach that combines information from

sampling, geochemistry, geology, mineralogy, extractive metallurgy, engineering, and

statistics to optimize a mine’s ore mineral recoveries and profits. Relationships between

extractive metallurgy and mineralogy are of particular interest, as mineralogical

properties including hardness, textures, modal mineralogy, grain size distributions,

mineral compositions, and mineral associations strongly affect metallurgical processes

and recoveries (Jones, 1987; Craig and Vaughan, 1994; Clout, 1998; Gottlieb et al., 2000;

Petruk, 2000; Pirard and Bertholet, 2000; Bitencourt et al., 2002; Pirard, 2004; Pirard and

Lebichot, 2004; Donskoi and Clout, 2005; Lane et al., 2008).

Traditionally, ore mineralogical properties were characterized manually using

optical microscopy (Gottlieb et al., 2000). Wherein, a microscope operator would

identify and classify textures, estimate particle and or grain sizes, modal mineralogy, and

mineral associations, making it a time consuming and semi-quantitative process (Donskoi

and Clout, 2005). Due to the complex nature of most ore bodies, the limited application

of manual quantitative microscopy led to development of two automated systems:

12

quantitative evaluations of minerals by scanning electron microscopy (QEMSCAN®)

(Reid et al., 1984; Gottlieb et al., 2000) and mineral liberation analysis (MLA; Gu, 2003).

Both systems use back-scattered electron brightness (average atomic number contrast)

and X-ray data to conduct modal mineralogy and liberation analyses on petrographic

sections or mill products mounted in epoxy. The high cost and long processing times of

these techniques are exacerbated by samples with finer grain and or particle sizes,

typically associated with challenging ore deposits, and have encouraged the development

of low-cost optical image analysis (OIA) based alternatives.

The earliest OIA techniques required manual digitization. Grain boundaries were

traced on a printed photomicrograph and scanned into a digital image (Mukul, 1998), or

traced on a digitizing tablet (Fabbri, 1984; Simigian and Starkey, 1989). The resulting

digital images were processed using software to determine quantitative measurements

such as grain size and shape (Goodchild and Fueten, 1998). Manual digitization

techniques were slow, laborious, and lacked reproducibility between analysts (Gorsevski

et al., 2012), leading to the development of several automated methods.

The first automated methods used thresholding to partition images by combining

pixels with color values from a selected range (Terrible and Fitzpatrick, 1992; Launeau

and Cruden, 1994; Francus, 1998; Higgins, 2000; Perring et al., 2004; Donskoi and

Clout, 2005; Donskoi et al., 2006; Lane et al., 2008; Donskoi et al., 2013; Zhang et al.,

2014). Two alternative techniques shortly followed thresholding: boundary- and region-

based methods (Asmussen et al., 2015). Boundary-based techniques use gradient

operators to identify boundaries or edges by detecting abrupt color changes (Starkey and

Samantaray, 1993; Goodchild and Fueten, 1998; Heilbronner, 2000; van den Berg et al.,

13

2002; Li et al., 2008; Gorveski et al., 2012) and are often based on edge detection

methods such as Canny, Sobel, Laplacian of Gaussian, Prewitt, and Robert’s operator.

Region-based or nearest-neighbor, techniques combine neighboring pixels within a

certain distance that are similar in color (Luumbreras and Serrat, 1996; Barraud, 2006;

Obara, 2007; Tarquini and Favalli, 2010; Mingireanov Filho et al., 2013). Hybrid

techniques, as defined by Asmussen et al. (2015), include a combination of threshold,

boundary-, and region-based techniques (Adams and Bischof, 1994; Bartozzi et al., 2000;

Pirard and Bertholet, 2000; Pirard, 2004; Pirard and Lebichot, 2004; Zhou et al., 2004;

Shih and Cheng, 2005; Choudhury et al., 2006; Fueten and Mason, 2007; Iglesis et al.,

2011; Asmussen et al., 2015).

Most OIA techniques have been developed for processing polarized transmitted

light images. Only a few studies have focused on the application of image analysis to ore

microscopy with most of these focused on its application to iron ore characterization

(Pirard, 2004; Pirard and Lebichot, 2004; Donskoi and Clout, 2005; Donskoi et al., 2006;

Donskoi et al., 2013) and only three studies on sulphide ore (Pirard and Bertholet, 2000;

Pirard, 2004; Lane et al., 2008).

A variety of software has been used to develop these techniques including Adobe

Photoshop (Zhang et al., 2014), C++ (Zhou et al., 2004), Clemex Vision PE (Lane et al.,

2008), DIDACTIM (Launeau and Cruden, 1994), ERDAS (Terrible and Fitzpatrick,

1992), IDRISI (van den Berg, 2000), ImageJ (Abrámoff et al., 2004), JMicrovision

(Roduit, 2008), MATLAB® (Choudhury et al., 2006), NIH Image (Heilbronner, 2000;)

and Geographic Information System (GIS) based software (Barraud, 2006; Li et al..,

2008; Tarquini and Favalli, 2010; Gorveski et al., 2012; Asmussen et al., 2015).

14

In this study, we used a combination of Fiji (Schindelin et al., 2012), a version of

ImageJ bundled with plugins, and Microsoft Excel to characterize massive sulphide ore

(Fig. 2.1). Fiji is a powerful open source image processing program developed by the

National Institutes of Health (NIH) that contains a robust macro environment and the

Trainable Weka Segmentation plugin. The macro capabilities of Fiji allowed most of the

image analysis routines in this study to be automated. Primary image segmentation was

achieved using the Trainable Weka (Waikato Environment for Knowledge Analysis)

Segmentation plugin. The plugin uses a combination of machine learning algorithms,

image analysis techniques, and operator input to produce pixel-based segmentations.

Excel contains a robust programming environment (Visual Basic for Applications) and

provides a powerful graphical user interface (GUI) for storing and reporting data.

Secondary segmentation routines, including edge correction procedures, were achieved

using Excel. Modal mineralogy, grain size distributions, and mineral association results

were reported and stored in Excel. Modal mineralogy results from this study were

compared to MLA results.

2.2 Methodology

A reflected light microscope-based mineral mapping method was developed to

determine modal sulphide mineralogy, mineral grain size distributions, and

mineral associations. A petrographic section from the Caribou base-metal

massive sulphide deposit in the Bathurst Mining Camp (BMC), located in New

Brunswick, Canada, was used to test the OIA method. OIA modal mineralogy

test results were compared to MLA results.

15

Fig. 2.1 Workflow for this study for semi-automated segmentation and geometallurgical

characterization. Figure design based on figure 1 from Asmussen et al. (2015).

Image Acquisition

200 images: 200x magnification, Reflected light

Image Segmentation Iterative classification of mineral phases using the Trainable Weka

Segmentation plugin for Fiji

Edge Correction Addition of missing edge phases especially between lighter phases

Edge Redistribution Redistribution of excessive edge phase regions

Phase A

rea %

Averag

e Grain

Size

Grain

Size D

istributio

n

Phase M

aps

Zeiss A

xio

imager

Micro

scope

Excel

Macro

Fiji

Excel

Macro

16

2.2.1 Microscope Parameters

The reflected light based images were acquired using a Zeiss AxioImager

microscope paired with ZEN Pro software (Blue version). The magnification was

set to 200x using a 10x camera adaptor and 20x objective. Camera exposure and

white balance settings were set to auto balance. Saturation settings were adjusted

manually using a warmer color scheme to maximize contrast between similar light

sulphide phases (Arsenopyrite and Pyrite) and the image histogram was set to the

best fit mode to enhance color contrast.

2.2.2 Image Acquisition

A position array with 200 nodes was created in the ZEN Blue software to

define target photomicrographs for image analysis. A reflected light image of the

thin section with a simulated target grid is presented in Figure 2.2, where each

white “x” represents the center of an image for image analysis. Note that the

image does not show the actual target grid used during the experiment.

The stage was moved to each position manually and a single image was taken.

Many image analysis techniques are multi-spectral with multiple images of the

same area captured with different conditions (polarizer directions and interference

filters) to maximize contrast between minerals (Terrible and FitzPatrick, 1992;

Launeau and Cruden, 1994; Goodchild and Fueten, 1998; Heilbronner, 2000;

Pirard and Bertholet, 2000; Perring et al., 2004; Pirard, 2004; Pirard and Lebichot,

2004; Fueten and Mason, 2007; Iglesias et al 2011; Asmussen et al., 2015).

Although these methods may result in cleaner segmentations, they require more

17

data to be stored and increase the processing time required, especially if the

polarizer directions and interference filters have to be manually adjusted by a

microscope operator. In our method, satisfactory segmentation of opaque

minerals are obtained from a single plane polarized reflected light image.

Each image was exported in tagged image file (TIF), a format that uses a data

compression algorithm that can perfectly reconstruct the original image. Images

were recorded with a spatial resolution of 2,080 horizontal x 1,540 vertical pixels

for a 0.29 mm2 area. The total combined area of the 200 images represented

approximately 5% of the total thin section area.

2.2.3 Computer Hardware Specifications

Processing the large images in this study was computationally demanding. A

high end personal computing system was used to decrease the processing time

required. Image processing was conducted on an ASUS ROG G75JT-CH71

laptop with an Intel Core i7-4710HQ 2.5 GHz processer, 16 gigabytes (GB) of

DDR3 ram, a 7,200 rotations per minute (rpm) hard drive, and Nvidia Gtx 970m 3

GB GDDR5 graphics card.

2.2.4 Trainable Weka Segmentation

Each photomicrograph was segmented into separate phases using the

Weka segmentation plugin for Fiji. Weka segmentation uses an interactive

machine learning algorithm that employs operator feedback, image analysis

techniques, and a data mining method to segment an image. For each picture,

samples of each phase were highlighted and classified by the operator using a

18

Fig. 2.2 Image of a polished thin section (reflected light) with simulated target

overlay.

Fig. 2.3 Weka segmentation interface showing a polished thin section plane polarized

reflected light image 1 prior to segmentation.

19

variety of selection tools (lines, squares, circles, and polygons). A picture of the

Weka segmentation interface containing thin section image 1 is presented in

Figure 2.3. Select training features were used to improve accuracy and decrease

training time and are presented in Figure 2.4.

A total of seven phases were searched for during segmentation: sphalerite

(medium grey), galena (light grey), chalcopyrite (orange with a yellow tint),

arsenopyrite (white), pyrite (creamy yellow), non-sulphide gangue (dark grey and

black), and edges. An edge represents an area within an image where one phase

transitions to another. Small inclusions within grains may be inadvertently

classified as an edge due to the effect of optical smearing (Pirard, 2004). Once a

sample from each phase was selected, the classifier was trained. During the first

training session of each image, basic image statistics were determined and used

along with the operator input to create a classification model. After the model was

developed, Weka classified each pixel and reported the results graphically through

a colored semi-transparent image overlaying the original image (one color for

each phase).

Errors in classification were re-classified by the operator and the classifier was

retrained. During retraining, the classification model was updated to prevent the

misclassifications indicated by the operator.

After the model was updated, the Weka software reclassified the image

creating a new overlay. Iterations of retraining were continued until the operator achieved

a clean segmentation. The time required to process each thin section image varied

between 15 to 30 minutes, depending on the complexity of the image. Finer grain sizes

20

Fig. 2.4 Training features selected for the Weka Trainable Segmentation plugin.

21

and more phases increased processing time. Final classifier models were saved

for each assemblage and reused to shorten training time significantly. Once

segmentation was complete, a classified image and classified image text file were

exported from Fiji for each thin section image. A classified image text file is a

text version of an image, where each pixel is represented by an integer that

represents a specific phase in a tab delimited text file.

2.2.5 Preprocessing Software

The Weka segmentation could accurately classify all phases indicated by the

operator, given enough iterations. However, difficulty was encountered in

obtaining a clean segmentation of the edge phase between two or more lighter

phases (Arsenopyrite, Pyrite, and Galena) with similar reflectivity. The issue was

averted by omitting light grain boundary segmentation by the operator, and

implementing a custom image processing software created in Excel. The software

backed up the original classified image and classified image text files, and then

added missing grain boundaries to the classified image and classified image text

file by parsing the text image first horizontally and then vertically (Figs. 2.5 and

2.6, respectively). The software then exported a fixed classified image and fixed

classified image text file, created a custom macro for Fiji, opened Fiji, executed

the custom macro in Fiji, and then closed Excel. Figures 2.7 and 2.8 show a

photomicrograph prior to and after the classification and preprocessing

procedures.

22

Fig. 2.5 Horizontal edge correction procedure, where the edge phase is represented by the

number 4.

1 1 3 3 3 1 1 3 3 3 1 1 3 3 3

1 1 3 3 3 1 1 3 3 3 1 1 3 3 3

4 4 4 3 3 4 4 4 3 3 4 4 4 3 3

2 2 4 4 4 2 2 4 4 4 2 2 4 4 4

2 2 4 1 1 2 2 4 1 1 2 2 4 1 1

1 1 3 3 3 1 4 4 3 3 1 4 4 3 3

1 1 3 3 3 1 1 3 3 3 1 1 3 3 3

4 4 4 3 3 4 4 4 3 3 4 4 4 3 3

2 2 4 4 4 2 2 4 4 4 2 2 4 4 4

2 2 4 1 1 2 2 4 1 1 2 2 4 1 1

1 4 4 3 3 1 4 4 3 3 1 4 4 3 3

1 1 3 3 3 1 1 3 3 3 1 1 3 3 3

4 4 4 3 3 4 4 4 3 3 4 4 4 3 3

2 2 4 4 4 2 2 4 4 4 2 2 4 4 4

2 2 4 1 1 2 2 4 1 1 2 2 4 1 1

1 4 4 3 3 1 4 4 3 3 1 4 4 3 3

1 1 3 3 3 1 1 3 3 3 1 4 4 3 3

4 4 4 3 3 4 4 4 3 3 4 4 4 3 3

2 2 4 4 4 2 2 4 4 4 2 2 4 4 4

2 2 4 1 1 2 2 4 1 1 2 2 4 1 1

Missing Edge Detected Add Edge

Horizontal Edge Correction


23

Fig. 2.6 Vertical edge correction procedure, where the edge phase is represented by the

number 4.

1 1 4 2 2 1 1 4 2 2 1 1 4 2 2

1 1 4 2 2 1 1 4 2 2 1 1 4 2 2

3 3 4 4 4 3 3 4 4 4 3 3 4 4 4

3 3 3 4 1 3 3 3 4 1 3 3 3 4 1

3 3 3 4 1 3 3 3 4 1 3 3 3 4 1

1 1 4 2 2 1 1 4 2 2 1 1 4 2 2

1 1 4 2 2 4 1 4 2 2 4 1 4 2 2

3 3 4 4 4 4 3 4 4 4 4 3 4 4 4

3 3 3 4 1 3 3 3 4 1 3 3 3 4 1

3 3 3 4 1 3 3 3 4 1 3 3 3 4 1

1 1 4 2 2 1 1 4 2 2 1 1 4 2 2

4 1 4 2 2 4 1 4 2 2 4 1 4 2 2

4 3 4 4 4 4 3 4 4 4 4 3 4 4 4

3 3 3 4 1 3 3 3 4 1 3 3 3 4 1

3 3 3 4 1 3 3 3 4 1 3 3 3 4 1

1 1 4 2 2 1 1 4 2 2 1 1 4 2 2

4 1 4 2 2 4 1 4 2 2 4 4 4 2 2

4 3 4 4 4 4 3 4 4 4 4 4 4 4 4

3 3 3 4 1 3 3 3 4 1 3 3 3 4 1

3 3 3 4 1 3 3 3 4 1 3 3 3 4 1

Vertical Edge Correction



24


preprocessing procedures.

25


preprocessing procedures.

Original Images

Segmented Images

Preprocessed Images

26

2.2.6 Phase Maps, Phase Area %, and Grain Size Distributions

This section describes how phase maps were generated and how phase areas were

obtained. The classified image generated in the previous experiment was an 8 bit RGB

image with 7 channels. Each channel represents a phase. Using Fiji’s threshold tool, each

channel was selected separately and extracted using the analyze particles command. This

command not only creates a separate image with a mask of the selected phase, but also

counts the number of pixels and determines the percentage of total image area covered

and grain size distribution (GSD). Grains on the border of the image were omitted, as

their true dimensions are unknown. Area percentage and GSD results were reported in

Fiji and exported to separate Excel spreadsheets. Grain size distribution results for image

1 were plotted in Excel (Fig. 2.9). Phase maps generated from this image are presented

in Figure 2.10. The results from pixel counting are presented in Table 1. Note that the

edge phase accounts for more than 13.1 % of image area. An edge redistribution

procedure was used to reduce the error associated with the edge phase.

2.2.7 Edge Area Redistribution and Mineral Associations

A Visual Basic program was developed in Excel that horizontally and vertically

parsed the classified images, to correct the area percentage results by re-distributing the

edge phase area equally to the bounding phases. Edges that were bound entirely within a

single phase or on the boundaries of the image were not redistributed. Edge areas

bounded by one phase were reported as inclusions and are either unidentified minerals or

pits in the soft sulphide minerals due to polishing. Edges at the image boundaries were

27

Fig. 2.9 Phase maps of image 1.

Notes: (a) original image (reflected plane polarized light), (b) chalcopyrite phase map, (c)

Arsenopyrite phase map, (d) edge phase map, (e) galena phase map, (f) gangue phase

map, (g) pyrite phase map, (h) sphalerite phase map

a b

c d

e f

h g

28

Fig. 2.10 Grain size distribution for image 1.

not redistributed, as the image boundaries may not represent a grain boundary. The

corrected results are presented in Tables 2 and 3. Edge redistribution data was also used

to generate mineral association bar graphs for each image in Excel. An example of the

bar graphs generated for image 1 are presented in Figure 2.11.

0

20

40

60

80

100

0 100 200 300 400

Cu

m.

Dis

t., %

Grain Size Diameter, µm

Sphalerite

0

20

40

60

80

100

0 5 10 15

Cu

m.

Dis

t., %


Galena

0

20

40

60

80

100

0 20 40 60

Cum

. D

ist.

, %


Chalcopyrite

0

20

40

60

80

100

0 50 100 150

Cum

. D

ist.

, %


Arsenopyrite

0

20

40

60

80

100

0 50 100 150 200

Cum

. D

ist.

, %


Pyrite

0

20

40

60

80

100

0 50 100 150 200

Cum

. D

ist.

, %


Gangue

29

Overall, the edge redistribution procedure decreased the unknown phase area for

image 1 from 13.1 % to around 6 %. The edge redistribution results from the horizontal

Table 2.1 Thin section image 1 results

Phase Pixel Count %Area

Gangue 724442 22.6

Sphalerite 36567 1.1

Arsenopyrite 12345 0.4

Chalcopyrite 54303 1.7

Edge 420239 13.1

Pyrite 1938498 60.5

Galena 16806 0.5

and vertical procedures were nearly the same. The vertical and horizontal edge

correction results for each image were averaged. Detailed results including horizontal,

vertical, and average results for each image are presented in the Appendix Tables A.1

through A.22.

The final phase area percentage results from each polished thin section were averaged

and the average inclusion area was redistributed proportionally to the sulphide phases. It

was assumed the areas classified as inclusions most likely represented pits created in the

soft sulphide minerals during polishing.

2.2.8 MLA Conditions

MLA was conducted on the L4-14-132 thin section at Memorial University of

Newfoundland’s Micro Analysis Facility (MAF-IIC). A FEI 650FEG MLA equipped

with 2 Bruker XFLASH SDD x-ray detectors was used to conduct the analyses using the

following equipment parameters: high voltage of 25 kV, beam current of 10 nA, and a

30

Table 2.2 Phase area % results of image 1 after horizontal edge redistribution

Area, px Total Area, %

Phase Original Redistributed Total Original Corrected

Gangue 724442 88509.5 812951.5 22.6 25.4

Sphalerite 36567 15681 52248 1.1 1.6

Arsenopyrite 12345 1631 13976 0.4 0.4

Chalcopyrite 54303 32785.5 87088.5 1.7 2.7

Pyrite 1938498 93485 2031983 60.5 63.4

Galena 16806 6713 23519 0.5 0.7

Edge

Inclusions 420239 179615 13.1 5.6

Boundaries 1819 0.1

Table 2.3 Phase area % results of image 1 after vertical edge redistribution

Area, px Total Area, %

Phase Original Redistributed Total Original Corrected

Gangue 724442 83901.5 808343.5 22.6 25.2

Sphalerite 36567 14923.5 51490.5 1.1 1.6

Arsenopyrite 12345 1213 13558 0.4 0.4

Chalcopyrite 54303 32807.5 87110.5 1.7 2.7

Pyrite 1938498 88372.5 2026870.5 60.5 63.3

Galena 16806 6376 23182 0.5 0.7

Edge

Inclusions 420239 189247 13.1 5.9

Boundaries 3398 0.1

horizontal frame width (HFW) of 1.5mm. The MLA data was used to generate mineral

maps, calculate modal mineralogy and determine mineral grain size distributions.

2.3 Results and Discussion

OIA and MLA results agreed closely for all phases except pyrite and NSG, most likely

due to both the low sample size for OIA (~5% of the polished thin section area) and the

lower resolution used for MLA (Table 2.4). OIA segmentation of arsenopyrite, pyrite,

and galena was challenging, due to similar grey scale values and colors. Using a warmer

31

Fig. 2.11 Mineral association data for image 1, produced by the edge redistribution

algorithm in Microsoft® Excel.

color saturation scheme improved contrast and selectivity of these minerals significantly,

however a multispectral approach is likely required to significantly improve the contrast

0

20

40

60

80

100P

erce

nt

of

Ed

ges

Sphalerite

0

20

40

60

80

100

Per

cen

t o

f E

dge

s

Arsenopyrite

0

20

40

60

80

100

Per

cen

t o

f E

dge

s

Chalcopyrite

0

20

40

60

80

100

Per

cen

t o

f E

dge

s

Galena

0

20

40

60

80

100

Per

cen

t o

f E

dge

s

Pyrite

0

20

40

60

80

100

Per

cen

t o

f E

dge

s

Gangue

32

between these phases An approach by Pirard (2004) found that selectivity between pyrite

and arsenopyrite was improved using a multi-variate statistical analysis of three images

taken at different wavelengths (438, 498, and 692 nm). Improving selectively would

decrease the training time required during segmentation.

Table 2.4 Comparison of OIA and MLA results

Area %

Method Sphalerite Galena Chalcopyrite Pyrite Arsenopyrite NSG3)

OIA1 16.1 (0.8) 0.4 (0.1) 1.2 (0.1) 56 (1.2) 3.7 (0.4) 22.2 (0.5)

MLA2 19.8 1.2 0.7 65.2 4.2 8.9 1Optical image analysis of 200 images (~5 % of the total thin section area). Standard

error of the mean reported in parenthesis. 2 Mineral liberation analysis 3 Non sulphide gangue

Currently, this approach offers a reasonable solution for processing a small subset of

images with high quality results. Processing an entire thin section (~4300 images at 200x

magnification) with this approach is not practical due to the long processing time

required. Only minor improvements in processing speed would be expected from

computer hardware upgrades. Moderate improvements in processing speed might be

achieved by processing the data within one software program.

Fiji is a powerful image processing program, but currently lacks the advantages of a

fully integrated GIS-based software. GIS software provides several advantages over Fiji

including data management, spatial modeling, geoprocessing, and the ability to stitch the

polished thin section images together (Tarquini and Favalli, 2010; Gorsevski et al., 2012).

The last advantage is critically important in performing accurate grain size analysis.

33

Large grains may be partially contained in several adjacent images and will be excluded

from a grain size analysis, if they are located on the edge of an image. Stitching the

images together so that the large grains are contained within an image mosaic allows

quantification of large grains during grain size analysis. A GIS-based framework should

be developed that provides standard geometallurgical characterizations found in MLA

Dataview software such as; particle properties, grain properties, modal mineralogy,

calculated assays, elemental distributions, mineral grade and elemental grade recovery

modeling, particle and mineral size distributions, mineral associations, mineral locking

data, phase specific surface area, mineral liberation by particle and free surface, and

allow evaluation of grade and recovery models.

2.4 Conclusions

Mineralogical and textural properties of ore directly affect metallurgical processes

and recoveries. Due to increasing ore complexity, manual methods have been replaced

with expensive automated SEM-based alternatives such as MLA and QEMSCAN. This

paper presents a reflected light microscope-based method that uses Fiji and the Weka

Segmentation plugin to characterize mineralogical properties of ore. The operator input

and training time required to segment images is considerable. However, the technique

can successfully segment images with a significant number of sulphide phases.

Comparison of OIA and MLA showed fairly good agreement for all phases except pyrite

and NSG, due to the low sample size used for OIA and the resolution of MLA. Further

research is needed to improve OIA processing speed and reduce operator time so that

OIA-based techniques will be competitive with MLA and QEMSCAN.

34

References

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40

Chapter 3 Analysis of Au-Ag mineralization in the Caribou base-metal

VMS deposit, New Brunswick; examination of nano- to micro-scale

inter- and intra-sulphide distribution and its relation to geometallurgy

Abstract

The Caribou Zn-Pb-Cu-Ag volcanogenic massive sulphide (VMS) deposit located

in northeast New Brunswick represents a significant base-metal resource in the Bathurst

Mining Camp (BMC). Zinc, Pb, and Cu are the primary resources that are being extracted

from this deposit, however Au and Ag are important byproducts that will help offset

costs. This study used mineral liberation analysis (MLA) supported further by in situ

laser ablation inductively coupled plasma-mass spectrometry methods to document

variations in Au and Ag distribution between and within sulphide minerals. The

variations in Ag and Au distribution provide critical inputs to optimization of mineral

processing design. The greatest influence on Au recovery at Caribou is the proportion of

Au hosted in arsenopyrite and pyrite, consequently considerable Au will report to the

tailings. Silver recovery at Caribou is significantly affected by the proportion of Ag

hosted in galena and tetrahedrite-tennantite. Lens 3 Ag values are primarily hosted in

galena and will report mostly to the Pb concentrate; subordinate Ag values associated

with tetrahedrite-tennantite will report mainly to the Cu concentrate. In contrast, lens 4

Ag values are likely primarily associated with tetrahedrite-tennantite and will report

mostly to the Cu concentrate; silver values associated with galena will report to the Pb

concentrate.

Key words: gold; silver; geometallurgy; sulphides

41

3.1 Introduction

The Caribou deposit is a Zn-Pb-Cu-Ag type volcanogenic massive sulphide (VMS)

deposit located in the northwestern part of the Bathurst Mining Camp (BMC), 50 km west

of Bathurst, New Brunswick (McClenaghan et al., 2009b). Since discovery in 1954, the

deposit has been explored, developed, and mined intermittently by several owners.

Historically, complications with extraction have caused several mine shutdowns,

production hiatuses, and acquisitions, due to decreasing metal prices and processing

complications associated with the fine-grained nature of the deposit.

The most recent closure occurred in 2008 when Blue Note Metals Inc. declared

bankruptcy (Arseneau, 2013). The mine was acquired by Maple Minerals Inc. in 2009 and

acquired by Trevali Mining Corp. in 2012 (Arseneau, 2013). Trevali resumed mining and

processing in 2015. The 43-101 technical report conducted in 2013 reported measured and

indicated resource estimates of 7.23 million tonnes grading 6.99% Zn, 2.93% Pb, 0.43%

Cu, 84.43 g/t Ag, and 0.89 g/t Au (Arseneau, 2013).

Although, Zn, Pb, and Cu are the primary resources, Au and Ag are important

byproducts that will help offset costs and decrease financial risk associated with periods of

low base metal prices. Consequently, several studies have addressed the Au and Ag values

in the BMC and at Caribou. The earliest by Jambor and Laflamme (1978), which showed

that Ag was primarily hosted in tetrahedrite and galena with tetrahedrite Ag concentrations

generally significantly higher than galena Ag concentrations. Silver grades and

distributions by lens were shown not to be equal with Ag concentrations highest in the East

Sulphide Body (lens 4) containing an estimated 76% of the Ag in tetrahedrite and 21% in

galena, compared to the South Sulphide Body (lens 3) containing an estimated 75% of the

42

Ag in galena and 19% in tetrahedrite. The study attributed Au values to the presence of

electrum in the ore. Giovanni (2000) conducted a mineralogical study that included

microprobe analyses for Ag on grains of galena and sphalerite. Giovanni (2000) reported

an average galena Ag grade of 0.04 wt. %, which was higher than the galena Ag grade

(0.01 wt. % Ag) reported by Jambor and Laflamme (1978). Microprobe analyses for Ag

were also conducted by Giovanni (2000) on sphalerite, but Ag concentrations were below

detection limits. Goodfellow (2003) determined Ag concentrations for galena, sphalerite,

and pyrite. The study showed that Ag concentrations in these minerals were higher in the

vent complex than in the bedded sulphides.

McClenaghan et al. (2003) showed that Au in the BMC is concentrated in two sulphide

facies with distinct element associations. McClenaghan et al. (2004) reported that arsenian

pyrite was the primary host of Au in the BMC followed by arsenopyrite. McClenaghan et

al. (2009b) addressed the distribution of Au at Brunswick No. 12 attributing the distribution

of Au in the deposit to the differences in Au transport and deposition between high-

temperature (vent-proximal) versus low temperature (vent-distal) conditions.

Despite numerous studies, Au and Ag distribution estimates are semi-quantitative and

do not address Au and Ag values contained in the coexisting sulphide minerals. This study

used mineral liberation analysis (MLA) supported further by in situ laser ablation

inductively coupled plasma-mass spectrometry (LA-ICP-MS) methods to document

variations in Ag and Au intra- and inter-distributions of pyrite, sphalerite, galena,

chalcopyrite, tetrahedrite-tennantite, and arsenopyrite to evaluate their effect on Au and Ag

recoveries during exploitation of the Caribou deposit.

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3.2 Background

3.2.1 Regional geology

The BMC is located in the Miramichi Highlands of northern New Brunswick and

covers an area of about 3,850 km2. The camp hosts 46 volcanogenic massive sulphide

deposits and another 95 mineral occurrences (Goodfellow and McCutcheon, 2003) within

an Ordovician bimodal volcanic and sedimentary sequence (McClenaghan et al., 2009b).

Major deposits include Brunswick No. 6 and No. 12, Caribou, Heath Steele, Half Mile

Lake, Restigouche, Stratmat, and Wedge.

Carboniferous, Devonian, and Silurian-Devonian rocks surround the camp and are

composed of sediments, felsic intrusions, and sedimentary and volcanic rocks, respectively

(McClenaghan et al., 2009b). Rocks within the BMC are Cambro-Ordovician in age and

are divided into five major stratigraphic groups: the Miramichi, Sheephouse Brook,

Tetagouche, California Lake, and Fournier Groups (Goodfellow, 2003; van Staal et al.,

2003). Massive sulphide deposits are hosted in the Bathurst Supergroup, which refers

collectively to the California Lake, Tetagouche, and Sheephouse Brook Groups (van Staal

et al., 2003). Each group in the BMC subdivides further into formations, which are

described in detail by van Staal et al. (2003).

The Cambro-Ordovician Miramichi Group, the oldest rocks, represents a passive

margin sequence comprised of a continentally derived turbidite sequence that was

deposited on the Avalonian platform (van Staal et al., 1998). The sequence consists of

quartz wacke, quartzite, siltstone and black shale Cambrian to early Ordovician in age

(490-478 Ma; Sullivan and van Staal, 1990). Black shale indicates that anoxic marine

44

conditions were widespread during the onset of felsic volcanism and deposition of massive

sulphides (Goodfellow et al., 2003). During the middle to late Arenig, the margin became

active with subduction of the oceanic crust leading to the development of the ensialic

Popelogan volcanic arc (van Staal et al., 1998).

Back-arc rifting of the Popelogan-Victoria arc between 475 Ma and 455 Ma

(Middle Ordovician) formed the volcanic rocks of the California Lake, Tetagouche, and

Sheephouse Brook Groups (van Staal, 1987; van Staal et al., 1991). The rocks were

emplaced in the Tetagouche-Exploits basin, an intra-continental back-arc basin, located

above a southeast-dipping subduction zone inboard of the arc at the eastern margin of the

Iapetus (proto-Atlantic) ocean (van Staal, 1987; van Staal et al., 1991; Goodfellow, 2003;

McClenaghan et al., 2009b). The California Lake Group is comprised of four formations

(in ascending order): Spruce Lake, Mount Brittain, Canoe Landing Lake, and Boucher

Brook (van Staal et al., 2003). The Spruce Lake Formation contains felsic volcanic rocks

and shale (Goodfellow et al., 2003). The Mount Brittain Formation contains felsic

volcaniclastic rocks, minor basalt, and lithic tuff interlayered with black shales and

siltstones (Gower, 1995). The Caribou deposit is hosted in the Spruce Lake and Mount

Brittain formations and is associated with the felsic volcanic rocks (Goodfellow, 2003).

The Canoe Landing Lake Formation consists of mainly pillow basalts (Rogers and van

Staal, 2003). The Boucher Brook Formation contains a basalt and interbedded shale

sequence and a maroon, green shale, and chert sequence (van Staal et al., 2003). The felsic

rocks of the Spruce Lake and Mount Brittain Formations were formed early during rifting

from high-temperature partial melts of a crustal block (van Staal et al., 1991; Lentz, 1999;

Rogers et al., 2003). Extension of the back-arc basin resulted in a progression from

45

enriched, fractionated continental tholeiites to alkali basalts, to more primitive, mantle-

derived mid-ocean ridge, tholeiitic pillow basalts that formed the volcanic rocks of the

Canoe Landing Lake and Boucher Brook formations, and Fournier Group (van Staal et al.

1991).

Collision between Gondwana and Laurentia resulted in closure of the Tetagouche-

Exploits basin by northwest-directed subduction from the Late Ordovician (Caradocian) to

Late Silurian (Ludlovian; van Staal, 1994; van Staal et al., 1998). During closure, thrusting

and imbrication juxtaposed and deformed the Ordovician and Cambro-Ordovician units

(van Staal, 1994; van Staal et al., 1998) with associated lower- to upper- greenschist facies

metamorphism (van Staal and Williams, 1984) and resulted in para-autochthonous and

allochthonous sequences (nappes; van Staal and Fyffe, 1991; van Staal et al., 2003).

3.2.2 General characteristics of massive sulphide deposits in BMC

There are many similarities between the deposits in the BMC. Stringer or feeder zones

underlie most deposits, and have been described in detail for the Brunswick No. 12 (Luff

et al., 1992; Lentz and Goodfellow, 1993a, b), Halfmile Lake (Adair, 1992), and Caribou

deposits (Goodfellow, 2003). Stockwork mineralization is present in the footwall of these

deposits along with large alteration-induced halos formed from the circulation of

hydrothermal fluids during genesis (Goodfellow, 1975; Lentz and Goodfellow, 1993 a, b).

McClenaghan et al. (2004) noted that the massive sulphide bodies in the BMC are hosted

above stockwork mineralization and vent zones. The study reported that the massive

sulphide bodies are composed of fine-grained pyrite, sphalerite, galena, chalcopyrite, and

pyrrhotite with minor arsenopyrite, marcasite, and tetrahedrite. At the base of these lenses,

46

also referred to as the basal sulphide facies, mineralization has been metasomatically

altered by high temperature hydrothermal fluids resulting in an assemblage of pyrite,

pyrrhotite, and chalcopyrite (McClenaghan et al., 2004). Above the basal sulphide facies

are Pb- and Zn-rich lenses that exhibit sulphide layering (McAllister, 1960; Stanton, 1960).

Commonly in the BMC, the layering within bedded sulphides facies formed during

synmetamorphic deformation due to the composition and grain size variations in the

sulphide minerals (McClenaghan et al., 2004), although at Caribou the layering has been

interpreted to have formed by later tectonism, with ductile minerals (sphalerite and galena)

taking up most of the strain (Goodfellow, 2003).

The BMC deposits were subjected to ductile deformation during closure of the

Tetagouche-Exploits arc basin (van Staal and Fyffe, 1991). The grade of metamorphism

varies regionally from lower- to upper-greenschist from northeast to southwest

(Helmstaedt, 1973; van Staal, 1985; van Staal et al., 1992; Lentz, 1999). Peak

metamorphism conditions ranged from 325 to 425 °C and from 4 to 6 kbar (Lentz and

Goodfellow, 1993c; Lentz, 2002; Currie et al., 2003). During deformation and

metamorphism, many of the primary textures were destroyed and recrystallization

increased the grain size of some sulphide grains including porphyroblasts of pyrite,

marcasite, and arsenopyrite (McClenaghan et al., 2004). However, the very fine-grained

nature of most of the sulphides persisted through greenschist metamorphism

(McClenaghan et al., 2004). Deformation also induced linear textures within sphalerite,

galena, and chalcopyrite and a durchbewegung texture in pyrrhotite-rich ores (Craig, 1983;

Marshall and Gilligan, 1987), which are locally present in ores of the BMC, but less so at

the Caribou deposit.

47

3.2.3 Deposit geology

The Caribou deposit is a proximal-autochthonous (Jambor, 1979) volcanic massive

sulphide (VMS) deposit, which formed in a sediment-covered back arc continental rift

during anoxic water-column conditions (Goodfellow et al., 2003). Goodfellow (2003)

reported that the massive sulphides are hosted in sedimentary and felsic volcanoclastic

rocks of the Spruce Lake Formation. He noted that the footwall rocks were composed of

interbedded carbonaceous shale, pale gray phyllite, greywacke, and chloritic schist

interbedded with hydrothermally altered pale green felsic volcanic rocks. Stringer

sulphides overlie the footwall rocks cutting hydrothermally altered sedimentary and felsic

volcanic rocks (Goodfellow, 2003). According to Goodfellow (2003), the massive

sulphides above the stringer zone consist of a vent complex and bedded sulphides. He

reported that above the massive sulphides, chloritic schists are present at the contact

between massive sulphides and the overlying felsic volcanic rocks. Above the felsic

volcanic rocks is a sequence of interbedded sedimentary and felsic volcanic rocks

(Goodfellow, 2003).

Six en échelon lenses extend 1.5 km around the Caribou Synform that plunges 80° -

85° of north (Roscoe, 1971; Cavalero, 1993). Each lens is zoned with Cu-rich vent

complexes near the bottom and western part of each lens, and Pb-Zn-rich bedded sulphides

that overlie the sulphide feeder zones and form a sharp contact with the hanging wall

(Goodfellow, 2003). Mineralization consists primarily of pyrite, sphalerite, galena, and

chalcopyrite with minor tetrahedrite, marcasite, arsenopyrite, electrum, and bournonite

(Jambor, 1981). Quartz, siderite, magnetite, chlorite, muscovite, and stilpnomelane are the

most common gangue minerals (McAllister, 1960, Stanton, 1960; Jambor, 1981).

48

Van Staal et al. (1990) reported that the Caribou deposit has been intensely deformed

with associated greenschist facies metamorphism resulting from multiple collisional events

related to east-dipping subduction. The authors noted that isoclinal folds and a strong

penetrative axial planar schistosity were formed early during the first two deformation

events (D1 and D2). Kink folds postdate the schistosity and were formed before and during

formation of the Caribou Synform (van Staal et al., 1990). A large dextral kink on the

northwest limb of the Tetagouche Antiform produced the Caribou Synform (F4; Jambor

and Laflamme, 1987; van Staal et al., 1990).

3.2.4 Mineral sources of Au and Ag

Sulphide minerals are important sources of Au and Ag. Gold and Ag values can be

hosted in solid solution (Cabri et al., 1989; Cook and Chryssoulis, 1990) or as

submicroscopic inclusions within sulphide minerals (Chyrssoulis et al., 1989). Trace and

minor element concentrations are a function of the abundance of the elements in the system,

the abundance of various host minerals, and the fractionation patterns (element

distributions) between them (Cook et al., 2011). Post-depositional processes, such as

weathering (Reich et al., 2010), high-temperature alteration, and metamorphism can

redistribute Au and Ag concentrations within sulphides (McClenaghan et al., 2004; Reich

et al., 2005).

3.2.4.1 Tetrahedrite-tennantite-freibergite

Jambor and Laflamme (1987) showed that tetrahedrite-group mineral abundance at

Caribou varies by lens with sparse occurrences within the footwall and hanging wall of

lens 1 and 2 (North and Northwest Sulphide bodies), extremely low occurrences in lens 3

49

(South Sulphide Body), and widespread abundance in lens 4 (East Sulphide Body). The

authors noted that tetrahedrite-group minerals primarily occur as disseminated anhedral

grains and rarely as veinlets that cross-cut sulphide layers. Tetrahedrite at the Caribou

deposit is typically associated with galena, sphalerite, and chalcopyrite, however

tetrahedrite textures (disseminated and veinlet) and mineral associations do not correlate

with tetrahedrite composition (Jambor and Laflamme, 1978).

Tetrahedrite and tennantite compositions are highly variable, even in the same layer,

with few analyses that correspond to the ideal end-member formulas of Cu12Sb4S13 and

Cu12As4S13 (Jambor and Laflamme, 1987). These minerals form a Sb-As solid-solution

series with several elements that substitute for Cu. Natural tetrahedrite-tennantite typically

conform to the formula (Cu, Ag)10(Fe, Zn, Hg, Cu*)2(Sb, As)4S13 (Tasuka and Morimoto,

1977), where Cu* is less than 0.2 atoms (Charlat and Levy, 1974). Tetrahedrite-tennantite

contains monovalent (Cu+) and divalent Cu (Cu2+; Peterson and Miller, 1986), with Cu+

trihedrally and Cu2+ tetrahedrally coordinated to sulphur (Riley, 1974). Silver

preferentially substitutes for Cu+ in freibergite rather than Cu2+ atoms, which would require

a coupled substitution to maintain charge balance (Kalbskopf, 1972), and Hg substitutes

for Cu2+ tetrahedrally coordinated with sulphur (Kalbskopf, 1971). Additionally, Bi and Te

can substitute for As and Sb, respectively (Riley, 1974).

According to Riley (1974) Ag-rich tetrahedrite is referred to as argentian tetrahedrite.

He noted that when Ag concentrations exceed 20 wt. %, the mineral undergoes a structural

change and is called freibergite. The Ag-rich end-member has an ideal formula of

Ag12Sb4S13, with an estimated maximum Ag concentration of 51 wt. % (Riley, 1974).

Microprobe analyses by Jambor and Laflamme (1978) indicate that tennantite and

50

freibergite are rare relative to tetrahedrite. The authors showed that Ag concentrations are

highest in Sb rich tetrahedrite-group minerals, however high Sb concentrations do not

imply high Ag concentrations. Jambor and Laflamme (1978) noted that mercurian-

tetrahedrite and freibergite occur mainly on the hanging wall, and estimated that 76% of

the Ag in lens 4 and 21% of the Ag in lens 3 was contained in tetrahedrite. Tetrahedrite-

tennantite-freibergite are not known to host significant concentrations of Au.

3.2.4.2 Galena

Galena is one of the primary hosts of Ag at the Caribou deposit (Jambor and Laflamme,

1987). Jambor (1981) showed that galena abundance varied by lens with low abundances

in lens 1, 2, and 3 (North, Northwest, and South Sulphide bodies, respectively), except in

high grade Pb-Zn zones along hanging wall, and widespread concentrations in lens 4 (East

Sulphide Body). Galena grains are finer grained than pyrite and sphalerite, and occur

mainly as patchy replacements of sphalerite, and as patches, blebs, and veinlets within

pyrite (Jambor, 1981). Galena also occurs as relict replacement textures in pyrite-forming

complex fine-grained inclusions (Giovanni, 2000).

Silver concentrations in natural galena typically correlate to Bi or Sb contents (van

Hook, 1960), and are a result of Ag-Bi (+Pb) and Ag-Sb inclusions and solid solution

(Grant et al., 2015). Ag-Sb and Ag-Bi (+Pb) inclusions in galena were inferred by Amcoff

(1984) and confirmed by Sharp and Buseck (1993) when submicroscopic inclusions of

diaphorite (Pb2Ag3Sb3S8) were observed in galena using transmission electron microscopy

(TEM).

51

Simple substitution of Ag+ for Pb2+ is not significant at temperatures lower than 450°C

(van Hook, 1960). Higher Ag concentrations are achieved through coupled substitution of

Ag+ with Bi3+ or Sb3+ for 2Pb2+(Amcoff, 1976 ; Sharp and Buseck, 1993). Solubility

experiments have shown that AgSbS2 forms a complete solid solution with galena at

temperatures above 350°C (Amcoff, 1976) and that AgBiS2 (matlidite) forms a solid

solution with galena at temperatures above 175°C (van Hook, 1960; Voronin et al., 2013).

However, coupled substitution of Ag and Sb is less stable than coupled substitution of Ag

and Bi (Amcoff, 1976).

Microprobe results from Jambor and Laflamme (1978) showed galena Sb

concentrations in lens 3 and 4 (East and South Body) were low (<0.11 wt. % Sb) or below

detection limits and were erratic with respect to Ag. Bismuth concentrations were much

higher (up to 1.58 wt. %) and were strongly correlated to Ag (Jambor and Laflamme, 1978).

These results suggest that Ag values are likely hosted in solid solution through coupled

substitution with Bi and minor Sb. Jambor and Laflamme (1978) reported average galena

Ag concentration of 1020 ppm Ag. A following study by Giovanni (2000) reported a lower

average Ag concentration of 400 ppm Ag. Galena is not known to host significant

concentrations of Au.

3.2.4.3 Chalcopyrite

Jambor (1981) reported that chalcopyrite abundance at Caribou is low, with the

highest abundance located in lens 2 (Northwest Sulphide Body). He noted that chalcopyrite

mainly occurs as veins along pyrite grain boundaries with patches and minute blebs of in

pyrite and among its interstices. In chalcopyrite-rich zones, chalcopyrite has almost

52

completely replaced pyrite and only small inclusions of pyrite remain (Jambor, 1981).

Chalcopyrite rarely occurs as “chalcopyrite disease”, which is the presence of minute and

abundant inclusions of chalcopyrite distributed fairly evenly within sphalerite grains

(Wiggins and Craig, 1980; Barton and Bethke, 1987). “Chalcopyrite disease” occurrence

is higher in lens 4 (East Sulphide Body) than lens 3 (South Sulphide Body; Jambor, 1981).

Chalcopyrite compositions were not reported by Jambor and Laflamme (1987),

however chalcopyrite can contain significant concentrations of Ag. Experiments by

Prouvost (1966) reported chalcopyrite Ag contents ranging from 5 to 8 wt. % and showed

that Ag was hosted in solid solution. Chen et al. (1980) showed that Ag can occur as

submicroscopic film on the surface of chalcopyrite. A more recent study by Grant et al.

(2015) reported that 6.3% of the total Ag budget at the Hacket River Main Zone was present

mainly as solid solution within chalcopyrite. The study also indicated the presence of

submicroscopic inclusions of Ag and Ag-bearing minerals. Chalcopyrite Ag contributions

to the Ag budget of a deposit can be much higher. Chalcopyrite in the “A” stringer zone of

the Kidd Creek Mine contains 60% of that zones Ag within solid solution (Cabri, 1992).

Harris et al. (1990) reported that solid solution Ag in chalcopyrite accounted for 76% of

the Ag budget for the Izok Lake deposit. Chalcopyrite is a poor host for Au (Cook and

Chryssoulis, 1990), but has been shown to host Au as submicroscopic inclusions (Butler

and Nesbitt, 1999).

3.2.4.4 Sphalerite

Jambor (1981) noted that sphalerite abundance varies by lens with low abundances in

lens 1, 2, and 3 (North, Northwest, and South Sulphide bodies, respectively), except in high

53

grade Pb-Zn zones at the hanging wall, and widespread concentrations in lens 4 (East

Sulphide Body). He reported that sphalerite grains in high-grade Pb-Zn zones occur in

laminar masses, and in low grade Zn zones mainly occurs in pyrite interstices and less

commonly along pyrite grain boundaries. Sphalerite grains were etched and showed

optically homogenous sphalerite consisted of anhedral grains that exhibit lamellar twinning

and are slightly coarser than associated pyrite (Jambor, 1981).

Average sphalerite iron content reported by Jambor (1981) and Giovanni (2000) show

good agreement (less than 6 wt. % and 4.90 wt. %, respectively). Giovanni’s results also

showed a bimodal distribution for both Zn and iron. Sphalerite can also contain minor

concentrations of Ag (Chryssoulis et al., 1985; Cook et al., 2009). Cook et al. (2009)

showed that Ag can be present within sphalerite as inclusions of galena, tetrahedrite-

tennantite, or other sulfosalts or that it can reside in solid solution. The authors suggested

Ag in solid solution may result from coupled substitution between Ag and Sn with 2Ag+

and Sn4+ substituting for 3Zn2+, especially in Sn- and In-rich sphalerite. Sphalerite is not

known to host significant concentrations of Au, but may contain inclusions of electrum

introduced during syn-metamorphic remobilization (Hurley and Crocket, 1985).

3.2.4.5 Arsenopyrite

Jambor (1981) showed that tetrahedrite abundance is widespread. He noted that

arsenopyrite occurs primarily as disseminated euhedral to subhedral grains sometimes

clustered and similar in size with associated pyrite, and occurs less commonly as tiny

anhedral grains 0.5µm in diameter and pyrite overgrowths. Acid etching revealed

arsenopyrite tends to be growth-zoned (Jambor, 1981).

54

Arsenopyrite can host significant concentrations of Au in addition to other trace

elements, such as Co, Cr, Cu, Ni, Mn, Pb, Sb, and V (Liang et al., 2014). Jambor (1981)

reports that arsenopyrite from the Caribou deposit contains variable concentrations of As,

Sb, and Co, with As concentrations ranging from 29.3 to 33.0 atomic % As, Sb

concentrations ranging from 0.5 to 1.3 wt. %, and Co concentrations up to 3.2 wt. %. He

noted that Fe and S contents in arsenopyrite were variable, but did not report concentrations

for these elements. Liang et al. (2014) used high resolution TEM to show that arsenopyrite

grains from the Yangshan ore field containing up to 1 wt. % Au did not contain inclusions

or lattice dislocations on a scale of 5 nm, indicating that Au resides in solid solution. The

authors noted that the arsenopyrite grains were zoned with high concentrations of Au in

As-rich mantle zones and lower concentrations of Au in As-poor cores and rims. Gold

concentrations tend to decrease in arsenopyrite with increasing metamorphic

recrystallization (Wagner et al., 2007) and to preferentially partition in arsenopyrite in

comparison to coexisting pyrite or arsenian pyrite (Liang et al., 2014).

3.2.4.6 Pyrite

Pyrite is the most abundant mineral in the BMC and is arsenian in nature (Sutherland,

1967). Jambor (1981) reported that pyrite grain sizes at Caribou do not vary by lens with

an average of 17µm in diameter, and the pyrite abundance generally increases from

footwall to hanging wall. Pyrite textures vary from euhedral to anhedral to massive with

less common occurrences of framboidal and colloform textures (Jambor, 1981).

Framboidal and colloform textures have been interpreted to represent original textures

(Chen, 1978), however some of the framboidal textures may have formed during

55

deformation and diagenesis (Scott et al., 2009). McClenaghan et al. (2004) reported that

arsenian pyrite is the primary host of Au in the BMC followed by arsenopyrite.

Previous studies have shown that Au can reside in pyrite as a solid solution or as

inclusions and that Au is associated with As (Cook and Chryssoulis, 1990; Fleet et al.,

1993; Palenik et al., 2004; Reich et al., 2005; Morishita et al., 2008; Kovalev et al., 2009;

Deditius et al., 2014; Liang et al., 2014). Reich et al. (2005) defined the solubility of Au

in arsenian pyrite from SIMS and EPMA analyses from Carlin-type and epithermal Au

deposits as a function of As, which is roughly equivalent to an Au and As ratio of 1:200;

above this ratio Au is usually present as native metal (Au0) nanoparticles, and below this

ratio Au resides in solid solution. The dominant form of Au in arsenian pyrite is Au1+,

which likely formed from hydrothermal fluids undersaturated with respect to Au (Reich et

al., 2005). Deditius et al. (2014) noted that the most commonly accepted method of

substitution is that Au substitutes isovalently for Fe in distorted octahedral sites and As

substitutes isovalently for S in structural tetrahedral sites. Gold and As substitution also

may occur coupled with an As2+ or As3+ ion and a Au+ ion for 2Fe2+ ions, in high-sulfidation

environments due to elevated ƒO2 values (Deditius et al., 2014). Silver solubility in pyrite

has not been addressed by many studies. However, Chouinard et al. (2005) suggests that

Ag+ and As3+ could cosubstitute for 2Fe2+.

3.2.5 Gold in the Bathurst Mining Camp

Massive sulphide Au concentrations within the BMC vary by deposit ranging from

0.002 to 6.86 g/t Au with an average of 0.85 g/t Au (McClenaghan et al., 2003). Although

Au is typically a byproduct recovered during smelting, Au has been recovered directly from

56

gossan zones at the Caribou (Cavelero, 1993), Heath Steele deposits (Luff, 1995), and

Murray Brook (Burton, 1993; Boyle, 1995). Luff (1995) showed that gossan deposits

represent a small portion of the Au contained in the BMC, and that the remaining Au and

Ag values are located in the massive sulphide bodies of the BMC. Gold distributions vary

systematically in the feeder zone, basal sulphide, and Pb and Zn lenses of the massive

sulphide deposits, based on distinct geochemical associations (McClenaghan, 2003). Zones

with an Au + Bi + Co ± Cu association display Au concentrations that increase towards the

underlying feeder zones, whereas zones with an Au + Sb + As ± Ag association display Au

concentrations that increase towards the hanging wall (McClenaghan, 2003). The Au + Bi

+ Co ± Cu association is typically observed in the feeder and basal sulphide zones, but

rarely in the Pb and Zn lenses, which usually has an Au + Sb + As ± Ag association

(McClenaghan et al., 2004). The highest Au concentrations are documented in the basal

sulphide facies, but Pb and Zn lenses contain the largest reservoir of Au due to their

predominance in the BMC (McClenaghan et al., 2003).

McClenaghan et al. (2009) reported that pyrite and arsenopyrite contain most of the

Au in the BMC within solid solution or as Au-rich inclusions, and that arsenopyrite

contributes less to the Au-distribution budget, due to low Au concentrations and its limited

presence. In comparison, pyrite is pervasive and tends to have higher concentrations of Au

than arsenopyrite, making it the primary host of Au in the BMC (McClenaghan et al.,

2004). Electrum and native Au has been noted by Leblanc (1989) and Chyssoulis and Agha

(1990), but does not contribute significantly to the Au-distribution budget due to their rare

occurrence. Jambor (1981) and Giovanni (2008) noted very fine electrum grains, 0.1 to

1.5µm in diameter at the Caribou deposit. Other sulphide minerals may host Au including

57

chalcopyrite, pyrrhotite, sphalerite, and tetrahedrite-tennatite-freibergite; however Au

concentrations in these minerals are likely very low and will contribute little to the Au-

distribution budget of the BMC (McClenaghan et al., 2004).

3.2.6 VMS deposit general background information

Volcanogenic massive sulphide deposits (VMS) form at or near the seafloor, where

circulating hydrothermal fluids are mix with cool seawater forming pod-like or sheet-like

sulphide lenses (Shanks and Roland, 2012). Deposits range in size from less than a tonne

to 1.5 billion tonnes (Bt; Galley et al., 2007) and range in age from 3.55 billion years to

zero-age deposits that are actively forming in extensional regimes (Shanks, 2001;

Hannington et al., 2005). The largest and most important deposits include Rio Tinto in

Spain (1.5 Bt), Kholodrina in Russia (300 Mt), Windy Craggy in Canada (300 Mt),

Brunswick No. 12 in Canada (230 Mt), and Ducktown in the United States (163 Mt; Galley

et al., 2007). Mineralization varies but includes significant concentrations (>40%) of

sulphide minerals including chalcopyrite, galena, pyrite, pyrrhotite, chalcopyrite, and

sphalerite with lesser concentrations of non-sulphide gangue minerals including quartz,

barite, anhydrite, iron oxides, chlorite, sericite, talc and their metamorphic equivalents

(Shanks and Roland, 2012).

Shanks and Roland (2012) note that VMS deposits are typically classified by their

resource type (Pb-Zn, Cu-Zn, or Pb-Cu-Zn) and/or by their host rocks (siliciclastic-felsic,

bimodal-felsic, bimodal-mafic, siliciclastic-mafic, and mafic-ultramafic). The authors

report that stringer or feeder zones that consist of sulphide veins and veinlets typically lie

beneath the sulphide lenses in altered host rock. The host rocks typically show alteration

58

zoning including argillic (kaolinite, alunite, illite, and sericite), chloritic (chlorite and

quartz), and propylitic (carbonate, epidote, and chlorite; Bonnet and Corriveau, 2007).

3.2.7 Source of ligands

Shanks and Thurston (2012) noted that fluid inclusions from VMS deposits have

similar salinities to seawater, indicating seawater is the principal source of ligands in VMS

deposits. The authors reported that seawater contains low concentrations of HCO3- (150

mg/L), Br (65 mg/L), and H3BO4 (24 mg/L), and high concentrations of Cl- (19,277 mg/L)

and SO42- (2,600 mg/L). Shanks and Thurston (2012) suggested that “hydrogen sulphide

can be supplied to circulating hydrothermal fluids by (1) hydrothermal or bacterial

reduction of seawater sulfate, (2) hydrolysis reactions with volcanic glass or sulphide

phases in host rocks, or (3) direct injection of magmatic volatiles.” Although it is well

known that magmatic degassing could supply He, CO2, CH4, H2S, or SO2 to hydrothermal

systems (Lilley et al., 1982; Kadko et al., 1995; Glasby et al., 2008), it has been difficult

to prove empirically especially with respect to the hydrothermal vent fluids emanating from

the mid ocean ridge (Shanks et al., 1995; Berndt et al., 1996; Huston, 1999). In comparison,

hydrothermal solutions venting from the eastern Manus Basin, hosted by andesitic to

rhyodacitic rocks, appear to be a mixture of two-thirds seawater and one-third magmatic

water-rich volatiles (Craddock et al., 2007; Bach et al., 2007; Reeves et al., 2011).

Geochemical modeling has demonstrated that metals can be transported in seawater at

temperatures greater than 200°C, and that precipitation of ore minerals in VMS deposits is

mainly due to the temperature change associated with the mixing of hot hydrothermal fluids

with cold seawater (Shanks and Bischoff, 1977; Reed, 1982; Janecky and Seyfried, 1984;

59

Bowers and Taylor, 1985; Janecky and Shanks, 1988; Reed and Palandri, 2006; Shanks

and Thurston, 2012). Chloro- and thio-complexes are the most important ligands involved

in hydrothermal solutions, due to their ability to transport base (Helgeson, 1969; Johnson

et al., 1992; Mountain and Seward, 2003; Reed and Palandri, 2006; ) and precious metals

(Hayashi and Ohmoto, 1991; Seward, 1993; Benning and Seward, 1996; Seward and

Barnes, 1997; Stefannson and Seward, 2004).

3.2.8 Source of metals

One probable source of metals is the surrounding country rocks (Shanks and Thurston,

2012). At high-temperatures (200 to 500°C) basalt and seawater react and produce strongly

acidic solutions (Bischoff and Dickson, 1975; Seyfried and Bischoff, 1977; Mottl and

Holland, 1978) capable of leaching the surrounding rock and producing the metal

concentration signatures observed in hydrothermal fluids venting on the seafloor (Seyfried

et al., 2002, 2004; Seyfried and Shanks, 2004). Oxygen isotopic studies on seafloor

metavolcanic rocks indicate that sub-seafloor metamorphism results from high-

temperature reactions between circulating seawater and oceanic crust, and that such

reactions are widespread (Muehlenbachs and Clayton, 1971, 1972, 1976). Barrie and

Hannington (1999) provided further evidence showing that metals in massive sulphide

deposits are generally proportional to metal concentration ratios of the host rocks.

McClenaghan et al. (2003) reported that Zn-Pb-Cu-type deposits in the BMC are hosted in

felsic volcanic rocks interspersed with sediments, and are underlain by the Miramichi

Group, a continentally derived sedimentary sequence. The authors suggested that the

Miramichi Group is a likely the source of the base and precious metals in the BMC.

60

Magmatic fluids may also contribute to hydrothermal fluid metal concentrations by

injecting metal-rich solutions (Sawkins and Kowalik, 1981; Urabe and Marumo, 1991; de

Ronde, 1995; Lydon, 1996; Yang and Scott, 1996, 2002, 2003, 2006) or by increasing the

acidity of the hydrothermal fluids and thereby increasing the solutions ability to leach

metals from the surrounding rock (Craddock et al., 2007). Shanks and Thurston (2012)

suggested that “pulses of magmatic fluids injected into seawater convective systems may

be important metal contributors in felsic environments, and that water/rock interaction may

dominate metal supply in mafic settings.” Magmatic fluids likely contributed to the metal

budget of the BMC (McClenaghan et al., 2003). Granitoid plutons were emplaced in the

Miramichi Group before and during felsic volcanism (Whalen et al., 1998) and according

to McClenaghan et al. (2003) likely supplied Au to the massive sulphide deposits of the

BMC during degassing of metal-rich magmatic fluids. McClenaghan et al. (2003) suggests

that “more radiogenic Pb-isotope compositions of sulphides from the California Lake

Group and the positive correlation between Au and sulphide tonnage suggest greater

involvement of high-level Ordovician magma bodies as metal sources in the California

Lake block.”

3.3 Experimental Design

3.3.1 Sample Selection

A total of 151 polished thin sections from an earlier testing program (Giovanni, 2000)

were received from the Caribou deposit in New Brunswick. Historical Cu, Zn, Pb, Ag, and

Au drill core assays from those sampled intervals were provided by Trevali Mining and

used to select samples from lens 3 and 4 (Tables 3.1 and 3.2).

61

Table 3.1 Lens 3 drill core interval assay results

Interval g/t Wt. %

Drill Hole ID Depth, m Ag Au Cu Zn Pb Total Cu, Zn, Pb

DDH62-119 47.7 62.0 <1.00 0.82 1.00 2.94 4.76

DDH62-119 44.4 68.5 <1.00 0.37 1.70 6.82 8.89

DDH62-119 41.4 78.8 <1.00 0.35 2.60 7.86 10.81

DDH62-101 43.6 89.1 <1.00 0.41 2.10 6.40 8.91

L2-16 72.5 92.0 <1.00 0.38 2.56 6.98 9.92

L2-05 66.7 103.0 <1.00 0.29 3.64 8.45 12.38

L2-17 117.0 105.0 <1.00 0.80 3.39 7.92 12.11

DDH62-119 49.0 105.6 <1.00 0.66 2.22 8.88 11.76

L2-16 74.6 107.0 <1.00 0.44 3.64 11.01 15.09

L2-17 121.0 108.0 <1.00 0.31 3.69 9.05 13.05

L2-16 70.0 111.0 <1.00 0.36 2.78 6.67 9.81

DDH62-101 46.2 113.8 <1.00 0.31 4.44 10.40 15.15

L2-05 61.7 128.0 <1.00 0.33 4.51 19.81 24.65

L2-17 111.0 128.0 <1.00 0.28 7.42 14.18 21.88

DDH62-101 48.5 133.7 <1.00 0.58 2.82 7.88 11.28

L2-17 114.2 134.0 <1.00 0.29 6.73 13.38 20.40

L2-06 103.7 134.0 <1.00 0.29 3.97 9.71 13.97

L2-05 64.4 135.0 <1.00 0.22 5.85 10.18 16.25

L2-17 108.0 146.0 <1.00 0.42 5.50 10.77 16.69

L2-16 67.4 164.0 <1.00 0.32 4.86 13.45 18.63

L2-06 100.8 169.0 <1.00 0.34 5.60 16.27 22.21

L2-17 105.0 172.0 <1.00 0.28 8.20 21.61 30.09

L2-06 97.8 202.0 <1.00 0.39 1.86 3.22 5.47

Average 121.3 <1.00 0.40 3.96 10.17 14.53

Lens 3 silver assay results ranged from 62.0 to 202.0 g/t Ag. Silver displays a moderate

positive Spearman’s rank correlation coefficient with Pb (rs = 0.59) and Zn (rs = 0.67)

grades (Fig. 3.1). Silver displays a moderately negative Spearman’s rank correlation

coefficient with Cu (rs = -0.42). Gold head assay results showed that these samples

contained less than 1 g/t Au. Copper, Zn, and Pb grades for these samples ranged from

0.22 to 0.82 wt. %, 1.00 to 8.20 wt. %, and 2.94 to 21.61 wt. %, respectively.

62

Table 3.2 Lens 4 drill core interval assay results

Interval g/t Wt. %

Drill Hole ID Depth, m Ag Au Cu Zn Pb Total Cu, Zn, Pb

TH320-02 7.3 41.9 <1.00 0.22 6.34 2.81 9.37

DDH103 358 43.5 <1.00 0.22 9.51 5.85 15.58

TH320-01 6.7 45.2 <1.00 0.18 7.02 4.15 11.35

TH320-01 1.3 51.8 <1.00 0.15 7.63 2.96 10.74

L4-14 137.5 57.3 <1.00 0.47 10.21 1.79 12.47

L4-14 134.5 60.6 <1.00 0.19 6.57 2.91 9.67

L4-02 21.0 68.9 <1.00 0.31 5.53 3.51 9.35

L4-07 108.7 69.9 <1.00 0.20 4.93 3.43 8.56

TH320-01 4 74.9 <1.00 0.15 6.44 4.14 10.73

L4-16 126.3 78.2 <1.00 0.27 10.75 3.57 14.59

L4-07 107.2 80.9 <1.00 0.36 10.11 4.10 14.57

L4-05 82.0 83.0 <1.00 0.35 9.95 5.05 15.35

DDH103 353 90.5 <1.00 0.19 7.66 5.30 13.15

L4-02 19.0 90.9 <1.00 0.34 9.63 3.41 13.38

L4-15 103.2 93.7 <1.00 0.35 8.55 2.53 11.43

L4-14 132.0 99.2 <1.00 0.40 10.22 3.79 14.41

DDH055 76 101.5 1.37 0.40 5.00 2.90 8.30

TH320-02 5.1 104.7 <1.00 0.17 6.48 4.81 11.46

L4-14 128.9 111.3 <1.00 0.24 6.32 5.30 11.86

DDH055 77.5 115.2 1.37 0.25 6.10 3.70 10.05

DDH62-045 33.5 122.4 <1.00 0.00 6.24 3.00 9.24

DDH62-045 38.0 124.4 <1.00 0.00 9.32 6.00 15.32

L4-15 101.6 126.7 <1.00 0.26 10.15 5.23 15.64

L4-02 17.3 140.9 <1.00 0.22 5.63 4.35 10.20

DDH103 350 141.5 <1.00 0.23 6.10 4.50 10.83

L4-05 79.5 148.5 <1.00 0.42 9.05 4.62 14.09

DDH055 80.0 149.4 1.37 0.35 8.20 5.30 13.85

L4-05 76.5 167.0 <1.00 0.28 10.35 8.73 19.36

L4-15 100.1 173.0 <1.00 0.12 8.64 6.60 15.36

L4-16 124.5 175.2 <1.00 0.21 6.80 3.20 10.21

L4-07 105.7 176.9 <1.00 0.28 4.04 3.66 7.98

TH320-02 2.4 178.5 <1.00 0.19 9.87 5.59 15.65

DDH62-045 35.5 188.5 <1.00 <0.00 7.08 3.90 10.98

Average 108.4 <1.03 0.24 7.77 4.26 12.28

63

Lens 4 silver assay results ranged from 41.9 to 188.5 g/t Ag. Silver displays a moderate

positive Spearman’s rank correlation coefficient with Pb grades (rs = 0.41). Gold grades

were less than 1 g/t Au for all samples except DDH055-76m, DDH055-77.5m, and

DDH055-80.0m (1.37 g/t Au). Copper, Zn, and Pb grades for the lens 4 samples ranged

from <0.01 to 0.47 wt. %, 4.0 to 10.8 wt. %, and 1.8% to 8.7 wt. %, respectively.

Comparing the drill core assays by lens shows lens 3 has higher average Ag, Cu, and

Pb concentrations and that lens 4 has higher average Au and Zn concentrations. Samples

for testing were selected based on Ag assay results, as most Au assay results were below

detection limits. Anderson-Darling, Ryan-Joiner, and Kolmogorov-Smirnov normality

tests conducted separately on the lens 3 and lens 4 Ag drill core interval assays resulted in

p-values of 0.264 and 0.585, >0.100 and >0.150, >0.150 and >0.150, respectively,

indicating that the distributions are normal at 5% level of significance. Ninety five percent

confidence intervals for the lens 3 and 4 Ag drill core assays ranged from 106.4 to 136.1

g/t Ag and from 108.4 to 124.0 g/t Ag. Six polished thin sections were selected from the

drill core intervals: one sample below, one between, and one above the confidence intervals

for each lens. Polished thin sections selected from lens 3 were 62-119-47.7, L2-16-67.4,

and L2-16-70 with corresponding Ag drill core assays of 62.0, 111.0, and 164.0 g/t Ag,

respectively. Polished thin sections selected from lens 4 were L4-14-132, L4-14-134.5, and

64

L4-15-100.1 with corresponding Ag drill core assays of 60.6, 132.0, and 173.0 g/t Ag,

respectively.

Fig. 3.1 Silver versus Pb drill core interval assays: (a) lens 3 (n=35); (b) lens 4 (n =23)

3.3.2 MLA Conditions

Mineral liberation analysis (MLA) was conducted on each polished thin section at

Memorial University of Newfoundland’s Micro Analysis Facility (MAF-IIC), to determine

modal mineralogy and mineral grain size distributions. A FEI 650FEG MLA equipped with

2 Bruker XFLASH SDD x-ray detectors was used to conduct the analyses using the

following equipment parameters: high voltage of 25 kV, beam current of 10 nA, and a

horizontal frame width (HFW) of 1.5mm. The MLA data was used to calculate modal

mineralogy and determine mineral grain size distributions. Mineral grain size distributions

65

were used to select a wide variety of mineral grain sizes for LA-ICP-MS, to evaluate grain

size sensitivity with respect to Au and Ag concentrations.

3.3.3 LA-ICP-MS conditions

In situ laser ablation inductively coupled plasma-mass spectrometry (LA ICP-MS) was

conducted on each polished thin section to determine the variation in Au and Ag

concentrations between and within the sulphide minerals. The LA ICP-MS system used a

COMPexPro 102 F 193 nm Excimer laser connected to an Agilent 7700 quadrupole ICP-

MS. The ICP-MS was operated at 1500 W and a torch depth that ranged from 5.0 to 5.5

mm. The system was tuned by rastering across NIST610 glass to achieve ThO+/Th+ <0.2%

(monitor of oxide production), 238U+/232Th+ ~ 1.0 (monitor of effective plasma

temperature), and 22M+/44Ca++ < 0.35% (monitor of double-charged production).

Each polished thin section was loaded into a Laurin Technic sample cell that was

evacuated and backfilled with He to remove traces of air from the cell. Sulphide samples

were ablated in spot mode using 17µm craters, a 3.5 to 4 Hz repetition rate, and a laser

energy (fluency) regulated at ~0.8 J/cm2. When the sulphide grains were large enough,

laser spots were positioned as a line of points across each grain to evaluate elemental

zoning. Each point was ablated for 30s following 40s (60s for arsenopyrite) of gas

background collection. Ablated material was transported out of the cell using 0.3 L/min He

as a carrier gas. This was mixed downstream of the cells with 2.5 mL/min N2 (to enhance

sensitivity) prior to reaching the ICP-MS torch.

The following isotopes were analyzed for all of the sulphide minerals except galena:

33S, 34S, 55Mn, 56Fe, 57Fe, 59Co, 63Cu, 66Zn, 69Ga, 72Ge, 75As, 107Ag, 111Cd, 115In, 121Sb,

66

197Au, 205Tl, 207Pb, 208Pb, and 209Bi. Galena was analyzed for less isotopes to maximize

count times for Au and Ag. The following isotopes were analyzed for galena: 32S, 33S, 34S,

56Fe, 75As, 121Sb, 197Au, 207Pb, and 208Pb. Analyte dwell times were set individually, with

the longest dwell times set for Ag and Au. Total quadrupole sweep time was kept at <0.52s.

After ablation, the laser log file and ICP-MS intensity data file were synchronized using

Iolite running as a plug in for Wave metrics Igor Pro. Each data set was processed using

the Iolite internally-standardized trace-element data reduction scheme, which filtered out

concentrations greater than two standard deviations of the mean. Concentrations in

unknowns were calibrated against sulphide reference material MASS-1 and stoichiometric

concentrations of a single element for each mineral: Zn for sphalerite (67.09%), Pb for

galena (86.6%), Fe for chalcopyrite (30.43%), Sb for tetrahedrite-tennantite (27.33%), Fe

for arsenopyrite (34.3%), and Fe for pyrite (46.54%). Since tetrahedrite-tennantite is a solid

solution and can show a significant range in composition and does not necessarily contain

27.33% Sb, trace element results are considered semi-quantitative. Each ablation time-

series was adjusted when necessary to avoid artifacts related to ablating through thin grains,

large inclusions, or on grain boundaries. Detection limits for Ag, Au, Fe, Cu, and Pb varied.

Limits of detection (LOD) were used in place of concentrations that were reported below

LODs. Analyses with major element concentrations that deviated 10% from the

stoichiometric concentration of that mineral were not used. Gold and Ag concentrations

reported in the mass balance are a grain based average, where the intra-grain average of

each grain was averaged to determine the mineral average for each polished thin section

(Appendix Tables A.23 to A.55). Average Au and Ag concentrations calculated for mass

balance mineral averages that contained LOD are denoted by a less than sign (<), and

67

therefore represent and overestimated concentration for that particular element. The Au

distribution in MASS-1 is somewhat inhomogeneous at the micron scale. Therefore, Au

concentrations reported in this work should be viewed with some caution.

3.4 Results

Overall MLA results (Table 3.3) showed modal mineralogy (converted to wt.

%) varied significantly between each polished thin section. Pyrite concentrations were

highest (64.62 - 90.94 wt. %) and tetrahedrite-tennantite concentrations were generally

lowest (<0.01 - 0.07 wt. %). Arsenopyrite concentrations were highest in the lens 4

polished thin sections (0.92 - 5.28 wt. %) and lowest in the lens 3 polished thin sections

(0.23 - 0.55 wt. %). Chalcopyrite concentrations were highest in the lens 3 polished

thin sections (0.81 - 1.16 wt. %) and lowest in the lens 4 polished thin sections (0.02 -

0.66 wt. %). Galena concentration was highest for the L4-15-100.1 polished thin

section (17.79 wt. %) and lowest for the 62-119-47.7 polished thin section (0.66 wt.

%). Non sulphide gangue (NSG) concentrations ranged from 1.80 to 9.20 wt. % and

consisted of mainly quartz and various carbonates.

Table 3.3 MLA Modal mineralogy results

Polished Wt. %

Lens Thin Section Sp Gn Cp Td-Tn Asp Py NSG1

3 62-119-47.7 4.57 0.66 1.16 <0.01 0.55 85.48 7.58

3 L2-16-67.4 0.07 6.07 0.82 0.07 0.23 90.94 1.80

3 L2-16-70 13.70 5.90 0.81 0.01 0.37 70.01 9.20

4 L4-14-132 16.80 1.87 0.66 <0.01 5.28 69.27 6.12

4 L4-14-134.5 13.93 4.71 0.20 <0.01 4.82 70.07 6.27

4 L4-15-100.1 9.60 17.79 0.02 0.05 0.92 64.62 7.00 1 Non sulphide gangue.

68

Hypothesis testing was conducted on the LA-ICP-MS results to corroborate the

statements presented in the following paragraphs. A Welch’s t-test was performed on data

sets with two samples and a Welch’s analysis of variance (ANOVA) test was performed

on data sets with three or more samples. Both tests were conducted at a 95% confidence

level. A detailed compilation of the statistical results including the test type used and the

null hypothesis is presented in the Appendix Tables A.56 through A.66.

LA-ICP-MS results (Tables 4 and 5) showed that the primary host of Ag in the lens 3

and 4 polished thin sections was galena (51.1 - 88.0% of total Ag). Tetrahedrite-tennantite

when present in higher concentrations (>0.04 %) also contained high proportions of Ag

(18.0 - 38.1% of total Ag). Total Ag host order for the remaining minerals from highest to

lowest was pyrite, sphalerite, arsenopyrite, and chalcopyrite. Pyrite from two of the

polished thin sections (62-119-47.7 and L4-14-134.5) contained almost 25% of the total

Ag, indicating that a significant portion of the Ag values can be locked within pyrite.

Table 3.4 LA-ICP-MS Ag concentrations by lens, polished thin section, and mineral

Polished g/t Ag1

Lens Thin Section Sp Gn Cp Td-Tn2 Asp Py

3 62-119-47.7 32.6 2854.4 5.6 N/A 95.0 8.3

3 L2-16-67.4 255.5 3067.8 <12.9 59878.1 <40.6 8.0

3 L2-16-70 43.8 909.4 51.0 N/A <89.5 0.8

4 L4-14-132 26.9 1172.1 55.1 N/A <44.9 6.5

4 L4-14-134.5 25.1 416.5 40.7 N/A <15.8 12.4

4 L4-15-100.1 57.6 483.3 N/A 138550.0 117.7 18.0 1Non sulphide gangue. 2Semi-quantitative results.

Silver distribution results for the lens 4 polished thin sections did not agree with the

Ag distribution estimates (76% in tetrahedrite and 21% in galena) proposed by Jambor

69

Table 3.5 Silver distributions by lens, thin section, and mineral

Polished Ag, % of Total

Lens Thin Section Sp Gn Cp Td-Tn Asp Py

3 62-119-47.7 5.3 67.3 0.2 N/A 1.9 25.3

3 L2-16-67.4 0.1 78.8 0.0 18.0 0.0 3.1

3 L2-16-70 9.9 88.0 0.7 N/A 0.5 0.9

4 L4-14-132 13.4 65.1 1.1 N/A 7.0 13.4

4 L4-14-134.5 10.7 60.1 0.3 N/A 2.3 26.6

4 L4-15-100.1 3.3 51.1 N/A 38.1 0.6 6.9

and Laflamme (1987). Tetrahedrite was not the primary source of Ag in the polished thin

sections analyzed. Only one of the lens 4 polished thin sections (L4-15-100.1) contained

significant concentrations of tetrahedrite. Tetrahedrite in this sample only contained 38.1%

of the total Ag. Differences in Ag distribution are most likely due to the erratic nature of

tetrahedrite with respect to abundance at the microscale and the limited number of polished

thin sections analyzed. Silver distribution results for the lens 3 polished thin sections were

more agreeable with the Ag distribution estimates reported by Jambor and Laflamme

(1987) for lens 3, 75% in galena and 19% in tetrahedrite, especially when tetrahedrite was

present.

The primary host of Au varied between polished thin sections (Tables 6 and 7). Pyrite

hosted the most Au in the lens 3 polished thin sections (80.7 - 97.6% of total Au) and for

the L4-15-100.1 polished thin section (68.2% of total Au). Arsenopyrite hosted the most

total Au for the L4-14-132 and L4-14-134.5 polished thin sections (88.7% and 71.2% of

the total Au). The remaining minerals did not host a significant proportion of the total Au.

Average galena Ag concentrations were higher in the lens 3 polished thin sections

(2,277.2 g/t Ag) than the lens 4 polished thin sections (690.6 g/t Ag). Average Ag

70

Table 3.6 LA-ICP-MS Au concentrations by lens, polished thin section, and mineral

Polished g/t Au1

Lens Thin Section Sp Gn Cp Td-Tn2 Asp Py

3 62-119-47.7 <0.04 <0.18 <0.15 N/A 21.29 <0.60

3 L2-16-67.4 <0.15 <0.14 <0.21 <0.17 <1.22 <0.60

3 L2-16-70 <0.07 <0.08 <0.18 N/A <7.93 0.48

4 L4-14-132 <0.10 <0.16 <0.99 N/A 15.18 <0.11

4 L4-14-134.5 <0.11 <0.07 <1.11 N/A <5.02 <0.11

4 L4-15-100.1 <0.09 <0.07 N/A <0.50 <13.37 <0.48 1Non sulphide gangue. 2Semi-quantitative results.

Table 3.7 Gold distributions by lens, polished thin section, and mineral

Polished Au, % of Total

Lens Thin Section Sp Gn Cp Td-Tn Asp Py

3 62-119-47.7 0.3 0.2 0.3 N/A 18.5 80.7

3 L2-16-67.4 0.0 1.5 0.3 0.1 0.5 97.6

3 L2-16-70 2.5 1.3 0.4 N/A 7.7 88.1

4 L4-14-132 1.9 0.3 0.7 N/A 88.7 8.4

4 L4-14-134.5 4.5 1.0 0.6 N/A 71.2 22.7

4 L4-15-100.1 1.9 2.7 N/A 0.0 27.2 68.2

concentrations for tetrahedrite-tennantite were higher in the lens 4 polished thin sections

(138,550 g/t Ag) than in the lens 3 polished thin sections (59,878.1 g/t Ag, respectively).

Inter-grain Au and Ag concentrations by mineral rarely showed significant

differences. Significant differences between grains should be interpreted with caution due

to the low sample size. Intra-grain Au and Ag concentrations varied randomly, but not

systematically from the grain averages, which will likely affect metallurgical recoveries in

a complex but not significant manner.

71

3.5 Discussion

3.5.1 Distribution of Ag and Au

Gold concentrations are highest in the basal sulphide facies of the BMC with Au

concentrations that increase towards the underlying feeder zones, and are lowest in the

bedded sulphides facies with Au concentrations that increase toward the hanging wall

(McClenaghan et al., 2003). At the Caribou deposit, Ag concentrations in galena,

sphalerite, and pyrite are highest in the basal sulphide facies and are lowest in the bedded

sulphide facies (Goodfellow, 2003). Goodfellow (2003) reported that chalcopyrite Ag

concentrations (n = 18) were as high as 360 ppm Ag and averaged 150 ppm Ag, however

the location of these grains with respect to facies was not reported by the author.

Chalcopyrite grains analyzed in this study reside in the bedded sulphides and had Ag

concentrations that were much lower (<55.1 ppm Ag), possibly indicating that the

samples analyzed by Goodfellow were located closer to or within the vent complex. If

this is the case, chalcopyrite Ag concentrations will show a similar trend to pyrite,

sphalerite, and galena with higher Ag concentrations in the vent complex than the bedded

sulphides. A similar trend with respect to chalcopyrite Ag concentrations was reported by

Grant et al. (2015) for the Hackett River Main Zone deposit in Nunavut, Canada.

Tetrahedrite-tennantite-freibergite Ag concentrations are lowest in the footwall and

highest in the hanging wall (Jambor and Laflamme, 1978). The distribution of Ag and Au

is likely the result of “zone refining”, the dissolution and replacement of low-temperature

assemblages with high-temperature assemblages by hot hydrothermal fluids in the vent

complex (Eldridge et al., 1983). The distribution of Au and Ag are a function of metal

72

complex stability, redox conditions, temperature, and the abundance of co-substituting

elements.

Einaudi et al. (2003) showed that sulfur activity and redox state directly affect the

carrying capacity of hydrothermal fluids, suggesting that sulphur activity and redox state

are the primary controls for mineral composition within VMS deposits (Grant et al., 2015).

Fe-bearing oxide and sulphide mineral abundances and their ratios relative to each other

can be used to partially constrain the redox conditions present during mineralization (Scott,

1973; Hannington et al., 1999a). Pyrrhotite (FeS) forms in environments with higher sulfur

activities (aS2) and lower oxygen fugacities (fO2), whereas magnetite (Fe3O4) and

pyrrhotite (Fe1-xS) form at higher oxygen fugacities (Large, 1977). Sphalerite iron

concentrations can be compared locally to indicate differences in sulfur activity, with

sphalerite iron concentrations decreasing with decreasing sulfur activity (Barton and

Toulmin, 1966; Large, 1977).

Goodfellow (2003) reported that pyrite is the most abundant Fe-bearing mineral in the

vent complex followed by pyrite, pyrrhotite, chalcopyrite, magnetite, and Fe-rich

sphalerite. The lack of pyrrhotite and lower sphalerite iron concentrations in the bedded

sulphide facies in comparison to the basal sulphide facies (Goodfellow, 2003), suggest

higher fO2 conditions and lower aS2 in the bedded sulphide facies and lower fO2 conditions

and higher aS2 in basal sulphide facies.

3.5.1.1 Galena and Tetrahedrite

Galena and tetrahedrite are the most important hosts of Ag at the Caribou deposit.

The relative abundance of Ag in these minerals is largely controlled by redox conditions

73

and the Bi to Sb ratio of the mineralizing fluid (Huston et al., 1996). Silver partitions into

galena through coupled substitution of Sb in oxidized conditions, and through coupled

substitution of Bi in more reduced conditions (Grant et al., 2015). Limited data is

available for galena located in the vent complex. Microprobe analyses conducted by

Goodfellow in 2003 on 7 grains of galena from the vent complex showed an average Ag

concentration of 1,140 ppm Ag. However, Bi and Sb concentrations were below

detection limits. Considering the BMC vent complex association of Au + Bi + Co ± Cu,

the more reduced conditions in the basal sulphide facies, and that Sb and Ag substitution

is less effective with galena, the high abundance of Ag in galena within the vent complex

is likely a result of coupled substitution between Bi and Ag. Galena in more reduced and

high temperature vent-proximal environments contains more Bi and less Sb than galena

in more oxidized and lower temperature vent-distal environments (Grant et al., 2015).

This trend is clearly present at Caribou, Jambor and Laflamme (1978) showed that galena

in lens 3, which is closer to the vent complex than lens 4, contained more Bi and Ag and

less Sb than galena in lens 4. A similar trend was observed from the in situ LA ICP-MS

analyses conducted on galena, where galena Ag concentrations were generally higher in

the lens 3 polished thin sections than the lens 4 polished thin sections.

Under more oxidized conditions and when the mineralizing fluid contains higher

concentration of Sb, Ag strongly partitions into tetrahedrite-group minerals (Huston et al.,

1996). This trend can be clearly seen at Caribou as Ag concentrations in tetrahedrite tend

to increase from footwall to hanging wall and from proximal to distal lenses (Jambor,

1978). This trend was also observed from the in situ LA-ICP-MS analyses conducted on

tetrahedrite-tennantite, where tetrahedrite-tennantite Ag concentrations were generally

74

higher in the lens 4 polished thin sections than the lens 3 polished thin sections.

Hackbarth and Petersen (1984) suggested that the Ag to Cu ratio in the mineralizing fluid

controls the composition of the tetrahedrite-group minerals, with Ag-poor tetrahedrite-

group minerals crystallizing from Cu-rich fluids and Ag-rich tetrahedrite-group minerals

crystallizing from Cu-poor fluids. Silver-rich tetrahedrite-group minerals at the Caribou

deposit contain low concentrations of Cu, while Ag-poor tetrahedrite-group minerals

contain high concentrations of Cu (Jambor, 1978). Furthermore, Ag-poor tetrahedrite at

Caribou is usually located in the footwall of the deposit where hydrothermal fluids would

be relatively Cu-rich in comparison to mineralizing fluids at the hanging wall. Hackbarth

and Petersen (1984) also suggested that tennantite, the As-rich end member, forms under

more reduced conditions, whereas tetrahedrite, the Sb-rich end member, forms under

more oxidized conditions. The affinity of Ag with Sb-rich tetrahedrite group minerals has

been documented by numerous authors (Sandecki and Amcoff, 1981; Miller and Craig,

1983; Johnson et al., 1986; Sack et al., 2003), including Jambor and Laflamme (1978)

with respect to the Caribou deposit. Arsenic concentrations in tetrahedrite-group minerals

at Caribou are highest in the footwall and are Ag-poor (Jambor and Laflamme, 1978),

suggesting Ag preferentially partitioned into tetrahedrite-group minerals under more

oxidizing conditions. Tetrahedrite and galena are not know to host significant

concentrations of Au. Gold distributions within galena and tetrahedrite are considered

insignificant and will not be discussed.

75

3.5.1.2 Chalcopyrite and Sphalerite

Chalcopyrite and sphalerite generally do not contain significant concentrations of Ag.

Silver partitions into chalcopyrite from Bi-poor solutions under more reduced conditions

(Huston et al., 1996). Hydrothermal fluids in the vent complex at Caribou were likely Bi-

rich resulting in Ag-rich galena and Ag-poor chalcopyrite. Chalcopyrite Ag concentrations

likely decrease laterally and upwards towards the hanging wall as conditions became more

oxidized. Sphalerite tends to host Ag in inclusions rather than solid solution, except in In-

and Sn-rich sphalerite (Cook et al., 2009). Sphalerite Ag concentrations are highest in the

vent complex and lowest in the bedded sulphides (Goodfellow, 2003). Time-resolved depth

profiles from in situ LA ICP-MS analysis of sphalerite grains, show rough Pb signals likely

indicating inclusions of galena. Sphalerite and chalcopyrite don’t host significant

concentrations of Au, hence their distribution with respect to sphalerite and chalcopyrite

will not be discussed. The higher Au concentrations indicated in chalcopyrite in the lens 4

polished thin sections relative to the lens 3 polished thin sections, are a result of higher

limits of detection.

3.5.1.3 Pyrite and Arsenopyrite

Pyrite and arsenopyrite are the primary hosts of Au at the Caribou deposit. The

distribution of Au is mainly due to the difference in Au transport and deposition between

high-temperature (vent-proximal) versus low temperature conditions (vent-distal;

McClenaghan et al., 2003). At high temperatures (>325°C) and low pH conditions (pH =

3), chloro-complexes transport Au (Henley, 1973; Wood et al., 1987; Gammons and

Williams-Jones, 1995; Hannington et al., 1999a; Huston, 2000) and Ag (Wood et al., 1987;

76

Gammons and Williams-Jones, 1995) mainly as AuCl2- and AgCl2

- (Gammons and

Williams-Jones, 1995). Decreases in temperature and aCl- and increases in pH and or fO2,

associated with hydrothermal fluids approaching or mixing with seawater, will result in Au

precipitation (Gammons and William-Jones 1995; Moss and Scott, 2001). Gammons and

William-Jones (1995) showed that Ag precipitation will also occur but to a lesser degree

as chloride complexing with Ag is more stable than with Au. Precipitation of both Au and

Ag could occur at high temperatures from a saturated solution (Gammons and William-

Jones, 1995). Precipitation of Au and Ag from Au and Ag chloride complexes would

proceed the following reactions:

AuCl2-(aq) + 1/2 H2(aq) ↔ Au(s) + 2Cl-

(aq) + H+(aq), (1)

AgCl2-(aq) + 1/2 H2(aq) ↔ Ag(s) + 2Cl-

(aq) + H+(aq), (2)

During zone “refining”, as described by Eldridge et al. (1983), the replacement of lower-

temperature assemblages in the basal sulphides by chalcopyrite would increase pH and

result in Au and Ag precipitation in the basal sulphides (Huston and Large, 1989).

At lower temperatures (<350°C), lower salinities, and near neutral pH, thio complexes

transport Au (Henley, 1973; Seward, 1973; Wood et al., 1987; Renders and Seward, 1989;

Gammons and Williams-Jones, 1995; Hannington et al., 1999a; Huston, 2000) and Ag

(Brown, 1986; Gammons and Barnes, 1989) mainly as Au(HS)2-, Au(HS)0 (Hayashi and

Ohmoto, 1991; Moss and Scott, 2001), Ag(HS)2-, and Ag(HS)0 species (Gammon and

Barnes, 1989). At temperatures below 250°C, thio-complexes can carry more Pb, Zn, and

Ag than chloro- complexes at higher temperatures (Hannington et al., 1999). Decreases in

temperature, aH2S, aS2, fO2 and increases in pH associated with hydrothermal fluids

77

approaching and mixing with sea water result in precipitation of Au and Ag from the

following reactions (Gammon and Barnes, 1989; McClenaghan et al., 2009):

Au(HS)0(aq) + 1/2 H2(aq) ↔ Au(s) + H2S(aq), (3)

Au(HS)2-(aq) + 1/2 H2(aq) + H+ ↔ Au(s) + 2H2S(aq), (4)

Ag(HS)0(aq) + 1/2 H2(aq) ↔ Ag(s) + H2S(aq), (5)

Ag(HS)2-(aq) + 1/2 H2(aq) + H+ ↔ Ag(s) + 2H2S(aq), (6)

In anoxic conditions low oxygen fugacities and elevated H2S concentrations would

have limited precipitation related to changes in fO2 and aH2S (Huston, 2000). During “zone

refining”, as described by Eldridge et al. (1983), increases in temperature within the bedded

sulphides likely resulted in the remobilization of Au and Ag, leading to enrichment of Ag

and Au values in the hanging wall of massive sulphide deposits (Hannington et al., 1986;

Huston and Large, 1987, 1989). The enrichment in the hanging wall is a function of the

prograde-retrograde solubility exhibited by chloro complexes, where decreases in

temperature initially increase thio complex solubility (retrograde) until temperatures

approach the hematite stability boundary and solubility sharply decreases (prograde;

McClenaghan et al., 2003).

Although Au and Ag could directly precipitate and form discrete grains of electrum,

native Ag or native Au, the majority of Au and Ag values at Caribou are contained within

sulphide minerals as a solid-solution or as micro- to nano-inclusions indicating another

mechanism is responsible for the Au and Ag distribution. Jean and Bancroft (1986)

suggested that heavy metal complexes are attracted to charged sulphide mineral surfaces

78

and then are chemisorbed. A similar mechanism is likely responsible for the incorporation

of Au and Ag.

Gold partitions preferentially into As-rich pyrite in comparison to As-poor pyrite, due

to coupled substitution between Au and As (Deditius et al., 2014). Arsenic concentrations

in the BMC are highest in the bedded sulphides and increase towards the hanging wall

(Goodfellow, 2003; McClenaghan, 2004). Consequently, pyrite Au concentrations in the

bedded sulphides likely increase from the footwall to the hanging wall. Arsenopyrite

contains higher concentrations of As than arsenian-pyrite, resulting in a higher Au capacity.

Results from in situ LA ICP-MS show that arsenopyrite Au concentrations are higher than

pyrite Au concentrations. This contrasts to the results reported by the McClenaghan et al.

(2004), which reported that arsenopyrite Au concentrations in the BMC were lower than

arsenian-pyrite Au concentrations.

In situ LA ICP-MS results show that Ag also partitions into arsenopyrite and pyrite,

with higher concentrations in arsenopyrite than in pyrite. Chouinard et al. (2005) suggested

Ag may partition into pyrite through coupled substitution with As, in which Ag+ and As3+

substitute for 2 Fe2+. If this is the case, a similar mechanism may be responsible for Ag

substitution in arsenopyrite. Consequently, Ag distributions in pyrite and arsenopyrite in

the bedded sulphides would increase towards the hanging wall.

Arsenic concentrations are lower in the basal sulphide facies, but Au and Ag

concentrations within pyrite are higher (Goodfellow, 2003). In the vent complex the hottest

hydrothermal fluids contained the most Au. Consequently, pyrite Au concentrations will

increase towards the stringer zone. No data was available for the Au and Ag concentrations

of arsenopyrite located in the vent complex. The high temperature fluids in the vent

79

complex were likely saturated with Au resulting in higher concentrations of Au and Ag as

chloro-complexes in comparison to the lower temperature fluids resulting in more effective

coupled substitution of Au and As into pyrite and the incorporation of Au inclusions.

3.5.2 Metallurgical Implications

Petruk (2000) reported grades and distributions of Ag in products from the Bathurst

Mining and Smelting in September 1977, and showed Ag values hosted in galena were

generally recovered in the Pb concentrate (20.7%) with minor galena Ag recoveries

obtained in the Zn (3.3%), Cu (0.5%), bulk (1.5%), and secondary Zn concentrates (0.5%).

The remaining Ag contained in galena (8.3%) reported to the tailing. Silver hosted in

tetrahedrite-friebergite, chalcopyrite, pyrargyrite, stephanite, and pyrite reported mainly to

the Pb and Cu concentrates (17.7 and 16.3%, respectively) with lesser recoveries in the Zn

concentrate (8.1%), and minor recoveries in the bulk and secondary Zn concentrate (1.9

and 1.0%, respectively). The remaining Ag contained in these minerals (20.2%), reported

to the tailings. Although the mill design is different at the Caribou deposit, these results

can be used to roughly interpret silver recovery behaviour at Caribou with respect to lens

3 and 4.

Lens 3 Ag values are primarily hosted in galena and will mostly report to the Pb

concentrate, whereas Ag values associated with tetrahedrite-tennantite will mostly report

to the Cu concentrate. Lens 4 Ag values are likely primarily associated with tetrahedrite-

tennantite and will mostly report to the Cu concentrate, Ag values associated with galena

will mostly report to the Pb concentrate. Galena and tetrahedrite-tennantite not recovered

during flotation will result in lower Ag recoveries.

80

Silver recoveries will also fluctuate based on the ores origin within the lens. Ore

processed near the footwall will likely result in lower Ag grades and recoveries in the Pb-

and Cu-concentrates, in comparison to ore processed from the hanging wall, since

tetrahedrite-tennatite Ag concentrations tend to increase from the footwall towards the

hanging wall. Base metal ores are typically composited to maintain a specific base metal

ratio, so that mills can operate at near steady state conditions. Consequently, the Ag

recovery behaviour in composited ore is more complex and reflects the combined nature

of the individual composites with respect to origin.

Currently, pyrite and arsenopyrite predominately report to the tailings. Tapley and al.

(2003) showed that selective flotation could be used to create a separate pyrite and

arsenopyrite concentrate. Gold and Ag grades in arsenopyrite may be high enough to

consider separate processing of arsenopyrite for Au and Ag recovery and warrants further

investigation. Intra-grain Au and Ag concentrations varied randomly, but not

systematically from the grain averages, which will likely affect metallurgical recoveries in

a complex but not significant manner.

3.6 Conclusions

Understanding how exhalative volcanic massive sulphide deposits are formed, the

environment in which they were formed, and how the metals are saturated during genesis

are critical to exploration and even exploitation of these resources. Zinc, Pb, and Cu are

the primary resources that will be extracted from this deposit, however Au and Ag are

important byproducts that will help offset costs. The variations in Ag and Au distribution

provide critical inputs to optimization of mineral processing design.

81

Silver is hosted primarily by galena and tetrahedrite in the polished thin sections, with

the highest galena Ag concentrations in the lens 3 polished thin sections and highest

tetrahedrite Ag concentrations in the lens 4 polished thin sections. Gold is generally hosted

in pyrite, and to a lesser degree in arsenopyrite; however, arsenopyrite Au concentrations

are generally higher than pyrite Au concentrations.

Silver recovery at Caribou is highly affected by the proportion of Ag hosted in galena

and tetrahedrite-tennatite. The greatest influence expected on Au recovery at Caribou is the

proportion of Au hosted in arsenopyrite and pyrite. Currently, arsenopyrite and pyrite

report to the mine tailings. However, results from this study indicate arsenopyrite enriched

with Au and Ag, warranting a follow-up study to evaluate the cost and benefits associated

with generating an arsenopyrite concentrate to improve Au recovery.

82

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106

Chapter 4 Conclusions and recommendations for future work

The purpose of this research project was to characterize the micro- to nano-scale

inter- and intra-sulphide distribution of Au and Ag at the Caribou deposit, and relate the

distributions to saturation mechanisms and geometallurgy. This investigation was

conducted using optical microscopy, optical image analysis (OIA), mineral liberation

analysis (MLA), laser ablation ion coupled plasma mass spectrometry (LA-ICP-MS), and

descriptive and inferential statistics. Key conclusions from the investigation are presented

below:

1. OIA is a time intensive process, and is currently not competitive with mineral

liberation analysis (MLA) with respect to time. However, OIA can be used to

process a smaller batch of images with high quality results.

2. Improving mineral selectivity during optical image analysis, would require a

multispectral approach similar to the approach used by Pirard (2004), which

was used to improve discrimination between pyrite and arsenopyrite.

3. Fiji (ImageJ) is a powerful image processing program, but lacks several useful

features that are contained within GIS-based software. These features include

data management, spatial modeling, geoprocessing, and the ability to stich the

polished thin section images together (Tarquini and Favalli, 2010; Gorveski et

al., 2012).

4. Drill core interval Ag concentrations were moderately correlated with Pb

grades for lens 3 and 4 (rs = 0.59 and 0.41, respectively).

5. Average Ag concentrations in the polished thin sections were highest for

tetrahedrite-tennantite and second highest for galena. Average Ag

107

concentration order varied for sphalerite, chalcopyrite and arsenopyrite by thin

section. Pyrite Ag concentrations were generally lowest.

6. Total Ag host order in the polished thin sections from highest to lowest was

generally galena (> 50%), tetrahedrite-tennantite (when present in appreciable

concentrations), pyrite, sphalerite, arsenopyrite and chalcopyrite. Pyrite from

two of the polished thin sections (62-119-47.7 and L4-14-134.5) contained

almost 25% of the total Ag, indicating that a significant portion of the Ag

values can be locked within pyrite. The lens 4 polished thin sections results

did not agree with Jambor and Laflamme (1978) estimated Ag distribution

(80% in tetrahedrite, 20% in galena).

7. Average Au concentrations in the polished thin sections were highest for

arsenopyrite.

8. Pyrite hosted the most total Au in the lens 3 polished thin sections and in one

lens 4 polished thin section (L4-15-100.1). Arsenopyrite hosted the most total

Au for the remaining lens 4 polished thin sections (L4-14-132 and L4-14-

134.5). The remaining minerals did not host a significant proportion of the

total Au.

9. Average galena Ag concentrations were higher in the lens 3 polished thin

sections than the lens 4 polished thin sections.

10. Average Ag concentrations for tetrahedrite-tennantite were higher in the lens

4 polished thin sections than in the lens 3 polished thin sections.

108

11. Inter-grain Au and Ag concentrations by mineral rarely showed significant

differences, in the polished thin sections. Significant differences between

grains should be interpreted with caution due to the low sample size.

12. Polished thin section intra-grain Au and Ag concentrations varied randomly

but not systematically from the grain averages, which will likely affect

metallurgical recoveries in a complex but not significant manner.

Future research is warranted to create an image analysis technique that is a

competitive low-cost alternative to MLA. The technique should be integrated with a

geographic information system (GIS) and use multispectral techniques to improve

discrimination between minerals.

This study focused on the Au and Ag distribution in lens 3 and 4, with limited

samples. A follow-up study would be recommended to look at lens 3 and 4 in more detail

(more samples), to better understand the variations in Ag distribution within these lenses.

Studies should also be conducted to quantify the Ag and Au distributions in the other

lens. Gold concentrations are highest in the basal sulphide facies and vent-proximal

lenses (Goodfellow, 2003). There may be zones in the deposit with Au concentrations

high enough that should be selectively mined (McClenaghan, personal communication,

2015). Finally, arsenopyrite Au and Ag concentrations are high enough to consider

separate processing and warrants further investigation.

References

Goodfellow, W.D., 2003, Geology and genesis of the Caribou deposit, Bathurst mining

camp, New Brunswick, Canada, in Goodfellow, W.D., McCutcheon, S.R., and

109

Peter, J.M., eds., Massive sulphide deposits of the Bathurst Mining Camp, New


Gorsevski, P.V., Onasch, C.M., Farver, J.R., and Ye, X., 2012, Detecting grain

boundaries in deformed rocks using a cellular automata approach: Computers &

Geosciences, v. 42, p. 136-142.

Jambor, J.L., and Laflamme, J.H.G., 1978, The mineral sources of silver and their



1-26.



Tarquini, S., and Favalli, M., 2010, A microscopic information system (MIS) for

petrographic analysis: Computers & Geosciences, v. 36, p. 665-674.

110

Appendix

Table A.1 Average area % results from optical image analysis of

L4-14-132 polished thin section

Area %

Edge

Image Sp Gn Cp Py Asp NSG Inclusions Boundaries

1 1.6 0.7 2.7 63.4 0.4 25.3 5.8 0.1

2 7.4 1.4 0.0 50.4 4.3 31.9 4.5 0.1

3 5.8 0.7 0.0 50.5 3.4 33.7 5.8 0.1

4 11.5 0.1 0.0 56.0 0.8 24.8 6.6 0.1

5 0.4 0.0 0.5 74.4 6.6 13.2 4.7 0.1

6 5.0 0.0 3.4 64.1 1.9 22.5 3.0 0.0

7 4.4 0.0 1.4 63.3 2.5 25.4 2.9 0.0

8 26.6 0.0 0.3 40.7 1.1 25.4 5.8 0.1

9 16.9 0.0 0.8 29.6 13.9 34.8 3.9 0.1

10 26.0 0.0 0.1 49.4 1.7 16.1 6.6 0.1

11 24.4 0.1 0.8 21.4 24.9 24.3 4.0 0.1

12 19.1 0.0 0.0 56.5 0.9 19.4 4.1 0.1

13 19.9 0.0 0.0 57.6 0.0 19.6 2.9 0.1

14 0.6 0.3 0.4 70.7 0.0 22.5 5.4 0.1

15 24.5 0.0 0.0 49.4 0.3 21.5 4.2 0.1

16 18.6 0.0 0.0 53.2 0.0 23.6 4.5 0.1

17 1.9 0.0 0.6 56.2 6.1 27.4 7.7 0.2

18 9.2 0.1 0.3 75.2 1.7 11.4 2.0 0.0

19 32.6 0.3 1.1 36.4 1.7 23.3 4.5 0.1

20 0.5 0.0 1.3 82.6 0.2 11.1 4.3 0.1

21 11.3 0.0 0.7 41.1 15.6 27.9 3.3 0.1

22 12.5 0.0 0.3 67.1 2.6 10.3 7.1 0.1

23 0.4 0.0 2.5 71.7 0.0 21.5 3.9 0.0

24 17.0 0.3 1.9 42.2 0.2 32.7 5.5 0.1

25 26.6 0.0 0.3 34.3 3.7 32.9 2.2 0.0

26 9.4 0.2 0.1 48.8 5.8 32.1 3.5 0.1

27 6.6 0.0 0.2 49.3 12.3 29.9 1.6 0.0

28 2.4 0.0 1.7 70.7 0.0 20.6 4.6 0.1

29 40.7 0.0 0.0 35.1 6.2 14.4 3.5 0.1

30 27.4 0.0 0.4 35.5 16.9 16.0 3.8 0.1

31 3.1 0.0 0.9 81.9 0.0 8.9 5.1 0.1

32 19.4 0.0 0.0 53.1 0.3 23.3 3.9 0.1

111



Area %

Edge


33 2.9 0.2 0.6 71.1 3.8 17.8 3.6 0.1

34 12.6 1.6 0.0 57.5 4.6 14.6 9.0 0.1

35 34.1 0.0 0.3 41.3 2.0 18.3 3.9 0.1

36 8.6 0.0 1.3 58.2 1.8 24.7 5.3 0.1

37 22.2 0.0 0.2 41.7 16.8 14.9 4.2 0.1

38 17.6 0.0 0.4 26.1 22.1 29.7 4.1 0.1

39 12.6 0.0 0.3 68.1 0.0 13.1 5.6 0.2

40 46.2 0.0 0.0 24.1 0.0 27.7 2.0 0.0

41 1.0 1.2 1.7 59.3 3.6 25.4 7.6 0.1

42 15.2 0.5 1.5 41.7 3.4 32.4 5.2 0.1

43 66.4 0.0 0.0 16.6 1.0 14.4 1.6 0.0

44 5.8 0.2 1.8 53.6 15.9 15.0 7.6 0.1

45 5.8 0.2 1.8 53.6 15.9 15.0 7.6 0.1

46 19.7 0.4 1.9 55.6 0.6 15.5 6.2 0.1

47 19.1 0.1 1.0 51.5 0.2 21.7 6.4 0.1

48 19.2 0.3 0.0 50.7 2.5 20.9 6.3 0.1

49 17.5 0.2 2.0 54.5 1.5 17.3 6.9 0.1

50 35.6 0.5 0.0 40.6 1.3 16.5 5.4 0.1

51 22.7 0.5 0.0 46.5 1.4 24.1 4.7 0.1

52 22.8 0.3 3.3 49.5 3.6 12.7 7.7 0.1

53 4.5 0.0 0.8 56.1 0.5 35.9 2.2 0.0

54 53.7 0.0 0.3 23.1 3.3 17.6 2.1 0.0

55 2.6 0.2 1.5 55.0 7.8 26.1 6.8 0.1

56 14.9 0.3 0.0 50.0 0.4 28.9 5.4 0.1

57 10.2 0.9 0.0 67.5 1.8 11.5 8.1 0.1

58 2.1 3.5 1.3 58.0 6.9 20.7 7.6 0.1

59 3.4 0.2 0.9 55.8 3.1 30.1 6.5 0.1

60 21.8 0.0 0.3 61.3 2.2 11.3 3.2 0.0

61 1.3 0.1 1.7 84.2 1.4 5.5 5.8 0.1

62 28.2 0.0 0.4 33.1 17.8 16.6 3.8 0.1

63 15.6 0.0 1.3 53.5 8.2 17.1 4.1 0.1

64 29.2 0.0 0.0 39.5 0.2 28.1 2.9 0.1

65 5.6 1.1 0.6 55.4 0.5 33.2 3.6 0.1

66 1.6 3.4 0.0 67.0 2.5 19.1 6.3 0.1

67 20.9 0.4 0.8 22.0 2.1 50.2 3.5 0.1

112



Area %

Edge


68 0.3 0.1 2.9 79.1 0.1 9.1 8.3 0.1

69 46.3 0.0 0.0 25.7 1.3 24.6 2.0 0.0

70 29.8 0.0 0.4 46.0 1.8 17.9 4.1 0.0

71 6.7 0.3 2.1 65.4 2.0 17.4 6.1 0.1

72 17.3 0.7 1.8 51.7 4.1 16.5 7.8 0.1

73 7.6 1.1 0.0 60.8 1.1 24.4 4.9 0.1

74 16.3 1.7 0.0 60.1 1.1 12.7 8.0 0.1

75 49.5 0.0 0.0 33.6 2.0 12.2 2.7 0.0

76 12.2 0.2 1.8 52.5 3.4 23.6 6.2 0.1

77 11.1 0.3 3.1 54.6 5.6 18.5 6.7 0.1

78 32.2 0.0 0.3 30.7 18.9 13.5 4.4 0.1

79 30.4 0.4 1.6 38.3 2.7 21.2 5.4 0.1

80 22.2 0.6 2.3 39.4 6.8 22.1 6.6 0.1

81 15.0 1.5 0.8 52.0 3.5 22.0 5.1 0.1

82 1.5 0.3 1.2 54.3 0.1 37.9 4.6 0.1

83 3.6 1.7 0.0 60.6 8.8 19.3 5.9 0.1

84 21.9 0.7 0.0 53.0 0.7 16.3 7.3 0.1

85 60.4 0.0 0.1 8.1 13.8 16.2 1.4 0.0

86 19.5 0.5 1.5 50.3 2.3 19.9 5.8 0.1

87 17.8 0.8 2.1 53.8 2.0 16.3 7.1 0.1

88 28.8 0.0 0.0 38.3 1.8 27.2 3.9 0.1

89 11.0 0.5 0.8 46.8 2.4 34.5 4.0 0.1

90 2.2 1.5 0.9 75.9 0.2 12.9 6.2 0.1

91 29.3 0.4 1.5 46.5 0.9 16.7 4.5 0.1

92 15.7 0.0 0.3 60.8 8.6 9.0 5.5 0.1

93 10.8 0.4 3.5 56.1 1.4 21.9 5.8 0.1

94 17.2 0.2 2.2 54.1 2.2 17.1 6.9 0.1

95 37.9 0.0 0.4 41.6 1.6 14.8 3.6 0.1

96 60.9 0.0 0.0 19.0 3.4 14.0 2.6 0.0

97 1.5 0.3 1.8 70.5 0.2 18.9 6.8 0.1

98 18.3 1.1 0.0 58.6 3.0 12.5 6.4 0.1

99 8.9 1.8 0.0 69.7 0.1 9.0 10.4 0.1

100 1.0 0.1 2.8 75.3 0.4 11.9 8.4 0.1

101 22.7 0.0 0.3 55.9 1.8 14.8 4.4 0.0

102 19.9 0.9 1.5 48.6 2.4 21.6 4.9 0.1

113



Area %

Edge


103 15.0 0.4 1.7 49.9 2.3 23.7 7.0 0.1

104 6.5 0.2 2.2 54.5 0.2 29.8 6.4 0.1

105 1.7 0.5 0.8 55.1 10.6 24.0 7.3 0.1

106 4.1 4.0 0.8 58.5 0.0 25.3 7.1 0.1

107 27.3 0.6 2.1 39.8 2.6 23.6 3.9 0.1

108 23.6 0.3 1.1 44.2 2.0 25.0 3.8 0.1

109 21.3 0.3 1.6 44.6 3.2 23.1 5.9 0.1

110 60.5 0.0 0.0 21.8 0.1 15.3 2.2 0.0

111 33.7 0.3 1.8 41.4 1.4 16.8 4.5 0.1

112 12.4 0.0 0.0 58.7 1.4 23.3 4.2 0.0

113 1.0 1.5 2.9 61.6 0.4 27.7 4.8 0.1

114 2.1 1.3 1.2 64.6 0.6 25.8 4.3 0.1

115 5.4 0.1 0.0 84.6 0.0 7.9 1.9 0.0

116 14.2 1.2 2.7 60.0 2.2 12.1 7.5 0.1

117 1.7 0.0 0.4 41.6 0.0 54.6 1.6 0.0

118 0.6 0.0 0.9 90.6 0.0 6.0 1.9 0.0

119 26.8 0.0 0.0 56.5 0.9 11.3 4.4 0.1

120 1.4 0.1 3.4 72.7 0.1 15.0 7.3 0.1

121 0.9 0.3 2.0 56.3 0.7 34.4 5.3 0.1

122 20.0 0.4 2.2 49.6 3.5 17.0 7.1 0.1

123 24.6 1.4 0.0 52.6 3.6 10.4 7.3 0.1

124 0.8 0.1 2.0 67.1 0.0 23.0 6.8 0.1

125 26.1 0.0 0.0 52.7 3.9 13.4 3.9 0.1

126 28.1 0.2 1.7 46.0 0.6 17.7 5.6 0.1

127 10.7 0.3 1.7 55.3 1.9 24.6 5.5 0.1

128 35.5 0.0 0.0 34.8 8.1 14.6 7.0 0.1

129 0.5 0.4 0.9 68.7 0.4 23.8 5.4 0.1

130 0.4 0.3 0.8 68.8 0.4 23.8 5.2 0.1

131 6.8 1.3 0.0 67.5 0.2 15.7 8.2 0.1

132 15.2 0.4 1.3 53.9 4.1 19.1 5.9 0.1

133 18.4 0.7 1.4 45.5 3.5 26.5 3.8 0.1

134 1.7 0.3 3.5 45.1 0.1 43.6 5.6 0.1

135 16.5 0.2 2.5 59.1 0.1 13.5 8.0 0.1

136 18.1 0.0 0.0 61.8 0.5 15.5 4.1 0.1

137 0.6 0.8 1.7 71.3 0.3 18.3 6.8 0.1

114



Area %

Edge


138 7.5 0.2 1.1 62.4 0.9 22.2 5.5 0.1

139 4.4 4.3 0.0 70.1 0.7 12.4 8.0 0.1

140 16.6 0.4 1.8 46.2 0.1 31.4 3.5 0.1

141 0.7 0.2 3.2 59.0 0.2 30.1 6.6 0.1

142 7.2 0.3 2.1 66.9 5.1 10.6 7.8 0.1

143 12.5 0.2 1.9 63.7 0.5 14.3 6.9 0.1

144 11.8 0.0 0.4 70.9 0.6 12.7 3.6 0.0

145 2.9 1.7 1.6 27.2 18.0 41.6 6.9 0.1

146 0.8 0.4 2.6 51.8 6.1 31.7 6.4 0.1

147 11.4 1.4 1.1 62.0 0.1 14.9 9.0 0.1

148 18.6 1.6 1.4 47.7 2.9 23.0 4.8 0.1

149 6.9 0.1 2.1 70.2 2.3 8.9 9.3 0.1

150 36.9 0.0 0.0 36.6 7.6 15.7 3.2 0.0

151 12.4 0.3 2.3 51.8 1.5 25.4 6.3 0.1

152 32.9 0.0 0.0 36.9 4.1 22.0 4.0 0.1

153 0.6 0.2 3.6 21.3 0.4 71.0 2.9 0.1

154 3.8 1.3 2.4 51.7 0.2 36.9 3.7 0.1

155 9.0 0.7 1.5 65.7 1.8 12.2 9.1 0.1

156 36.9 0.7 1.3 35.8 0.5 20.9 3.9 0.1

157 0.0 0.0 6.8 86.8 0.0 4.5 1.9 0.0

158 5.9 0.0 1.5 72.9 3.4 11.6 4.6 0.0

159 4.2 0.4 8.5 47.7 0.3 33.4 5.5 0.1

160 12.3 0.5 0.0 63.3 0.4 16.4 7.1 0.1

161 12.1 0.7 0.0 55.1 8.5 15.8 7.6 0.1

162 1.3 0.6 2.4 73.8 0.6 14.1 7.1 0.1

163 29.4 0.0 0.0 47.5 0.9 17.1 5.0 0.1

164 14.0 1.2 0.0 59.0 3.1 16.2 6.5 0.1

165 8.1 0.0 1.3 56.5 7.3 21.4 5.4 0.1

166 32.4 0.0 1.3 40.8 5.7 14.9 4.8 0.1

167 21.8 0.0 0.2 36.8 5.9 32.1 3.3 0.0

168 19.4 0.0 0.8 46.0 2.2 27.3 4.1 0.1

169 0.8 1.2 0.9 49.7 13.9 25.8 7.6 0.1

170 1.3 0.4 0.0 71.9 1.9 18.9 5.5 0.1

171 5.8 0.7 0.0 66.7 0.5 18.7 7.5 0.1

172 20.5 0.0 0.4 40.7 8.6 25.4 4.4 0.1

115



Area %

Edge


173 14.8 0.3 1.2 49.6 10.3 17.7 6.1 0.1

174 5.4 0.3 1.4 60.9 7.1 16.6 8.1 0.1

175 27.2 0.5 1.8 42.5 6.2 15.7 6.0 0.1

176 12.1 0.2 1.6 45.8 1.4 33.4 5.5 0.1

177 9.5 0.4 1.6 38.2 5.2 39.9 5.1 0.1

178 0.1 0.3 1.4 61.2 7.7 20.6 8.5 0.1

179 4.2 0.1 0.0 48.2 0.0 46.2 1.2 0.0

180 23.5 0.9 0.0 38.8 2.5 29.0 5.2 0.1

181 1.1 0.0 1.3 28.4 3.4 63.4 2.3 0.0

182 19.0 0.3 0.7 33.8 4.9 37.3 3.9 0.1

183 27.6 0.0 1.3 45.5 4.8 15.4 5.4 0.1

184 19.7 0.5 1.1 34.7 8.6 30.0 5.4 0.1

185 9.1 0.5 0.0 55.0 0.3 29.1 5.9 0.1

186 10.3 0.8 1.5 43.2 0.4 37.5 6.1 0.1

187 1.4 0.2 1.3 36.4 0.7 55.9 4.0 0.1

188 0.7 0.4 0.0 37.1 2.4 54.3 5.1 0.1

189 2.2 0.3 0.0 51.7 1.0 41.6 3.1 0.0

190 13.6 0.7 0.0 51.1 0.8 26.4 7.3 0.1

191 18.6 1.2 0.0 33.8 4.0 37.3 5.1 0.1

192 8.1 0.4 0.0 60.8 0.4 20.4 9.8 0.1

193 10.5 0.0 1.6 49.4 11.6 21.0 5.7 0.1

194 6.8 0.0 1.7 65.1 3.2 15.1 8.0 0.1

195 23.1 0.0 1.8 39.6 3.5 24.6 7.3 0.1

196 9.5 0.0 1.8 55.8 4.2 22.7 6.0 0.1

197 20.6 0.0 0.0 53.2 4.4 17.4 4.3 0.1

198 8.8 0.1 0.6 63.6 0.1 19.8 6.8 0.1

199 1.8 0.0 0.0 86.4 0.0 8.3 3.5 0.1

200 13.0 0.0 1.9 55.9 3.5 17.8 7.7 0.1

116

Table A.7 Horizontal area % results from optical image analysis of


Area %

Edge


1 1.6 0.7 2.7 63.4 0.4 25.4 5.6 0.1

2 7.4 1.4 0.0 50.4 4.3 31.9 4.5 0.1

3 5.8 0.7 0.0 50.5 3.4 33.8 5.7 0.1

4 11.5 0.1 0.0 56.1 0.8 24.9 6.5 0.1

5 0.4 0.0 0.5 74.5 6.6 13.2 4.6 0.1

6 5.1 0.0 3.5 64.2 1.8 22.5 2.8 0.0

7 4.4 0.0 1.4 63.4 2.5 25.4 2.9 0.0

8 26.6 0.0 0.3 40.7 1.1 25.4 5.7 0.1

9 16.9 0.0 0.8 29.7 13.9 34.8 3.7 0.0

10 26.0 0.0 0.1 49.4 1.7 16.0 6.7 0.1

11 24.4 0.1 0.8 21.5 24.9 24.4 3.8 0.1

12 19.1 0.0 0.0 56.5 0.9 19.5 4.0 0.1

13 19.9 0.0 0.0 57.6 0.0 19.6 2.8 0.0

14 0.6 0.3 0.4 70.8 0.0 22.5 5.4 0.1

15 24.5 0.0 0.0 49.4 0.3 21.6 4.1 0.0

16 18.5 0.0 0.0 53.2 0.0 23.6 4.6 0.0

17 1.9 0.0 0.6 56.3 6.1 27.5 7.6 0.1

18 9.3 0.1 0.3 75.3 1.7 11.4 1.9 0.0

19 32.7 0.3 1.1 36.5 1.7 23.3 4.3 0.1

20 0.5 0.0 1.3 82.6 0.2 11.1 4.3 0.1

21 11.4 0.0 0.7 41.1 15.6 27.9 3.2 0.1

22 12.6 0.0 0.3 67.3 2.6 10.3 6.9 0.1

23 0.4 0.0 2.5 71.7 0.0 21.5 3.8 0.0

24 17.1 0.3 1.9 42.2 0.2 32.7 5.4 0.1

25 26.6 0.0 0.3 34.3 3.7 32.9 2.1 0.0

26 9.4 0.2 0.1 48.8 5.9 32.1 3.4 0.1

27 6.6 0.0 0.2 49.3 12.4 29.9 1.5 0.0

28 2.4 0.0 1.7 70.7 0.0 20.6 4.6 0.1

29 40.7 0.0 0.0 35.2 6.2 14.5 3.4 0.1

30 27.4 0.0 0.4 35.6 16.9 16.1 3.6 0.1

31 3.1 0.0 0.8 81.9 0.0 8.9 5.2 0.1

32 19.4 0.0 0.0 53.1 0.3 23.3 3.8 0.1

33 3.0 0.2 0.6 71.1 3.8 17.8 3.5 0.0

34 12.6 1.6 0.0 57.6 4.6 14.7 8.9 0.1

35 34.2 0.0 0.3 41.4 2.0 18.4 3.7 0.0

117



Area %

Edge


36 8.6 0.0 1.3 58.3 1.8 24.8 5.2 0.1

37 22.2 0.0 0.2 41.7 16.8 14.9 4.1 0.1

38 17.6 0.0 0.4 26.2 22.1 29.8 3.8 0.0

39 12.7 0.0 0.3 68.1 0.0 13.1 5.7 0.1

40 46.3 0.0 0.0 24.1 0.0 27.7 1.8 0.0

41 1.0 1.2 1.7 59.3 3.6 25.4 7.5 0.1

42 15.2 0.5 1.5 41.7 3.4 32.4 5.1 0.1

43 66.4 0.0 0.0 16.6 1.0 14.5 1.6 0.0

44 5.8 0.2 1.8 53.7 16.0 15.1 7.3 0.1

45 5.8 0.2 1.8 53.7 16.0 15.1 7.3 0.1

46 19.7 0.4 1.8 55.6 0.6 15.5 6.2 0.1

47 19.1 0.1 1.0 51.6 0.2 21.7 6.2 0.1

48 19.3 0.3 0.0 50.8 2.5 21.0 6.1 0.1

49 17.5 0.2 2.0 54.5 1.5 17.4 6.8 0.1

50 35.7 0.5 0.0 40.7 1.3 16.6 5.3 0.1

51 22.7 0.5 0.0 46.5 1.5 24.2 4.6 0.1

52 22.8 0.3 3.4 49.6 3.6 12.8 7.4 0.1

53 4.5 0.0 0.8 56.1 0.5 35.9 2.2 0.0

54 53.8 0.0 0.3 23.1 3.3 17.6 1.9 0.0

55 2.6 0.2 1.5 55.0 7.9 26.1 6.6 0.1

56 15.0 0.3 0.0 50.0 0.4 28.9 5.3 0.1

57 10.2 0.9 0.0 67.5 1.8 11.5 8.0 0.1

58 2.1 3.6 1.3 58.0 6.9 20.7 7.5 0.1

59 3.4 0.2 0.9 55.8 3.1 30.2 6.4 0.1

60 21.8 0.0 0.3 61.4 2.2 11.4 3.0 0.0

61 1.3 0.1 1.7 84.2 1.4 5.6 5.7 0.1

62 28.3 0.0 0.4 33.1 17.8 16.6 3.7 0.1

63 15.7 0.0 1.4 53.5 8.2 17.1 4.1 0.1

64 29.2 0.0 0.0 39.5 0.2 28.1 2.9 0.1

65 5.6 1.1 0.6 55.4 0.5 33.2 3.5 0.1

66 1.7 3.4 0.0 67.1 2.5 19.1 6.2 0.1

67 21.0 0.4 0.8 22.0 2.1 50.3 3.3 0.1

68 0.3 0.1 3.0 79.2 0.1 9.1 8.2 0.1

69 46.3 0.0 0.0 25.7 1.3 24.7 2.0 0.0

70 29.8 0.0 0.4 46.2 1.8 17.9 3.9 0.0

118



Area %

Edge


71 6.7 0.3 2.1 65.5 2.0 17.5 5.9 0.1

72 17.3 0.7 1.8 51.7 4.2 16.5 7.6 0.1

73 7.6 1.1 0.0 60.7 1.1 24.4 5.0 0.1

74 16.3 1.7 0.0 60.1 1.1 12.8 7.8 0.1

75 49.5 0.0 0.0 33.6 2.0 12.2 2.6 0.0

76 12.2 0.2 1.8 52.6 3.4 23.7 6.1 0.1

77 11.1 0.3 3.1 54.6 5.6 18.6 6.6 0.1

78 32.2 0.0 0.3 30.7 18.9 13.5 4.3 0.1

79 30.4 0.4 1.6 38.4 2.7 21.2 5.1 0.1

80 22.2 0.6 2.3 39.4 6.9 22.1 6.4 0.1

81 15.0 1.5 0.8 52.0 3.6 22.0 4.9 0.1

82 1.5 0.3 1.2 54.3 0.1 37.9 4.6 0.1

83 3.6 1.7 0.0 60.7 8.9 19.3 5.7 0.1

84 22.0 0.7 0.0 53.1 0.7 16.3 7.1 0.1

85 60.4 0.0 0.1 8.1 13.8 16.2 1.3 0.0

86 19.5 0.5 1.5 50.4 2.3 19.9 5.7 0.1

87 17.8 0.8 2.1 53.9 2.0 16.4 6.9 0.1

88 28.8 0.0 0.0 38.3 1.8 27.3 3.8 0.0

89 11.0 0.5 0.8 46.8 2.4 34.5 4.0 0.1

90 2.2 1.5 0.9 76.0 0.2 12.9 6.2 0.1

91 29.3 0.4 1.5 46.6 0.9 16.8 4.4 0.1

92 15.8 0.0 0.3 60.9 8.7 9.0 5.3 0.1

93 10.8 0.4 3.5 56.1 1.4 21.9 5.7 0.1

94 17.2 0.2 2.2 54.2 2.2 17.1 6.8 0.1

95 37.9 0.0 0.4 41.6 1.6 14.9 3.5 0.1

96 61.0 0.0 0.0 19.0 3.4 14.0 2.5 0.0

97 1.5 0.3 1.8 70.6 0.2 18.9 6.7 0.1

98 18.3 1.1 0.0 58.7 3.0 12.6 6.2 0.1

99 8.9 1.8 0.0 69.7 0.1 9.0 10.4 0.1

100 1.0 0.1 2.9 75.3 0.4 11.9 8.3 0.1

101 22.8 0.0 0.3 56.0 1.8 14.8 4.2 0.0

102 20.0 0.9 1.5 48.7 2.4 21.7 4.8 0.1

103 15.0 0.4 1.7 50.0 2.3 23.8 6.9 0.1

104 6.6 0.2 2.2 54.6 0.2 29.8 6.3 0.1

105 1.7 0.5 0.8 55.1 10.6 24.0 7.2 0.1

119



Area %

Edge


106 4.1 4.0 0.8 58.5 0.0 25.3 7.0 0.1

107 27.3 0.6 2.0 39.8 2.7 23.7 3.7 0.1

108 23.6 0.3 1.1 44.2 2.0 25.0 3.8 0.0

109 21.3 0.3 1.6 44.7 3.2 23.1 5.7 0.1

110 60.5 0.0 0.0 21.8 0.1 15.4 2.1 0.0

111 33.7 0.3 1.8 41.5 1.4 16.8 4.4 0.1

112 12.4 0.0 0.0 58.7 1.4 23.3 4.1 0.0

113 1.0 1.6 2.9 61.7 0.4 27.7 4.7 0.1

114 2.2 1.3 1.2 64.7 0.6 25.9 4.1 0.1

115 5.4 0.1 0.0 84.7 0.0 8.0 1.8 0.0

116 14.2 1.2 2.7 60.0 2.2 12.2 7.4 0.1

117 1.7 0.0 0.4 41.6 0.0 54.6 1.6 0.0

118 0.6 0.0 0.9 90.6 0.0 6.0 1.8 0.0

119 26.8 0.0 0.0 56.5 0.9 11.3 4.4 0.0

120 1.4 0.1 3.4 72.7 0.1 15.0 7.3 0.1

121 0.9 0.4 2.0 56.3 0.7 34.4 5.2 0.1

122 20.1 0.4 2.2 49.7 3.5 17.0 7.0 0.1

123 24.6 1.4 0.0 52.7 3.7 10.4 7.1 0.1

124 0.8 0.1 1.9 67.2 0.0 23.1 6.7 0.1

125 26.1 0.0 0.0 52.7 3.9 13.4 3.8 0.1

126 28.1 0.2 1.7 46.0 0.6 17.7 5.5 0.1

127 10.7 0.3 1.7 55.3 1.9 24.6 5.4 0.1

128 35.5 0.0 0.0 34.7 8.1 14.6 7.0 0.1

129 0.4 0.4 0.9 68.7 0.4 23.8 5.4 0.1

130 0.4 0.4 0.8 68.8 0.4 23.8 5.2 0.1

131 6.9 1.3 0.0 67.5 0.2 15.8 8.2 0.1

132 15.2 0.5 1.3 53.9 4.1 19.1 5.9 0.1

133 18.5 0.7 1.5 45.5 3.6 26.6 3.7 0.1

134 1.7 0.3 3.5 45.2 0.1 43.6 5.4 0.1

135 16.6 0.2 2.5 59.2 0.1 13.5 7.9 0.1

136 18.0 0.0 0.0 61.8 0.5 15.6 4.1 0.1

137 0.6 0.9 1.8 71.3 0.3 18.3 6.8 0.1

138 7.5 0.2 1.0 62.5 1.0 22.2 5.4 0.1

139 4.4 4.4 0.0 70.1 0.7 12.5 7.9 0.1

140 16.6 0.4 1.8 46.3 0.1 31.5 3.4 0.1

120



Area %

Edge


141 0.7 0.2 3.2 59.0 0.2 30.1 6.5 0.1

142 7.2 0.3 2.1 66.9 5.1 10.6 7.7 0.1

143 12.5 0.2 1.9 63.7 0.5 14.4 6.8 0.1

144 11.8 0.0 0.4 70.9 0.6 12.7 3.6 0.0

145 2.9 1.7 1.6 27.3 18.1 41.6 6.7 0.1

146 0.8 0.5 2.6 51.9 6.2 31.8 6.3 0.1

147 11.4 1.4 1.1 62.0 0.1 15.0 8.9 0.1

148 18.6 1.6 1.4 47.7 2.9 23.0 4.7 0.1

149 6.9 0.1 2.2 70.2 2.3 8.9 9.2 0.1

150 36.9 0.0 0.0 36.6 7.6 15.7 3.2 0.0

151 12.4 0.3 2.3 51.9 1.5 25.4 6.1 0.1

152 33.0 0.0 0.0 37.0 4.1 22.1 3.7 0.1

153 0.5 0.2 3.7 21.3 0.4 71.0 2.9 0.1

154 3.8 1.3 2.4 51.7 0.2 36.9 3.7 0.0

155 9.0 0.7 1.5 65.7 1.8 12.2 9.1 0.1

156 36.9 0.7 1.3 35.9 0.5 20.9 3.8 0.1

157 0.0 0.0 6.9 86.9 0.0 4.5 1.7 0.0

158 5.9 0.0 1.5 73.1 3.4 11.6 4.4 0.1

159 4.2 0.4 8.5 47.7 0.3 33.4 5.4 0.1

160 12.3 0.5 0.0 63.3 0.4 16.4 7.0 0.1

161 12.2 0.7 0.0 55.2 8.6 15.9 7.4 0.1

162 1.3 0.6 2.4 73.9 0.6 14.1 7.0 0.1

163 29.4 0.0 0.0 47.5 0.9 17.2 5.0 0.1

164 14.0 1.2 0.0 59.1 3.1 16.2 6.3 0.1

165 8.1 0.0 1.3 56.7 7.3 21.5 5.1 0.1

166 32.5 0.0 1.3 41.0 5.7 15.0 4.4 0.1

167 21.8 0.0 0.2 36.9 5.9 32.1 3.1 0.0

168 19.5 0.0 0.8 46.1 2.2 27.3 4.1 0.1

169 0.8 1.2 0.9 49.9 14.0 25.9 7.4 0.1

170 1.3 0.4 0.0 72.0 1.9 18.9 5.4 0.1

171 5.8 0.7 0.0 66.7 0.5 18.7 7.4 0.1

172 20.5 0.0 0.4 40.8 8.6 25.5 4.2 0.1

173 14.8 0.3 1.2 49.7 10.3 17.8 5.9 0.1

174 5.4 0.3 1.4 61.0 7.1 16.7 8.0 0.1

175 27.2 0.5 1.8 42.4 6.2 15.8 6.0 0.1

121



Area %

Edge


176 12.1 0.2 1.6 45.8 1.4 33.4 5.5 0.1

177 9.5 0.4 1.6 38.2 5.3 40.0 4.9 0.1

178 0.1 0.3 1.4 61.3 7.8 20.6 8.3 0.1

179 4.3 0.1 0.0 48.3 0.0 46.2 1.1 0.0

180 23.6 0.9 0.0 38.9 2.5 29.0 5.1 0.1

181 1.1 0.0 1.3 28.5 3.4 63.5 2.2 0.0

182 19.0 0.3 0.7 33.8 4.9 37.4 3.8 0.1

183 27.6 0.0 1.2 45.5 4.8 15.4 5.3 0.1

184 19.7 0.5 1.1 34.7 8.6 30.0 5.3 0.1

185 9.1 0.5 0.0 55.0 0.3 29.2 5.8 0.1

186 10.3 0.8 1.5 43.2 0.5 37.6 6.0 0.1

187 1.4 0.3 1.3 36.4 0.7 55.9 3.9 0.1

188 0.7 0.4 0.0 37.2 2.4 54.4 4.9 0.0

189 2.3 0.3 0.0 51.8 1.0 41.6 3.1 0.0

190 13.6 0.7 0.0 51.2 0.8 26.5 7.2 0.1

191 18.6 1.2 0.0 33.9 4.0 37.3 4.9 0.1

192 8.1 0.4 0.0 60.8 0.4 20.4 9.8 0.1

193 10.5 0.0 1.5 49.6 11.6 21.1 5.6 0.1

194 6.8 0.0 1.6 65.2 3.2 15.2 7.9 0.1

195 23.2 0.0 1.8 39.6 3.5 24.6 7.1 0.1

196 9.5 0.0 1.7 55.8 4.2 22.7 5.9 0.1

197 20.6 0.0 0.0 53.2 4.4 17.4 4.3 0.1

198 8.8 0.1 0.6 63.6 0.1 19.8 6.8 0.1

199 1.7 0.0 0.0 86.4 0.0 8.3 3.4 0.1

200 13.1 0.0 1.9 56.0 3.5 17.8 7.6 0.1

122

Table A.13 Vertical area % results from optical image analysis of


Area %

Edge


1 1.6 0.7 2.7 63.3 0.4 25.2 5.9 0.1

2 7.4 1.4 0.0 50.4 4.3 31.9 4.5 0.1

3 5.8 0.7 0.0 50.4 3.4 33.7 5.9 0.1

4 11.5 0.1 0.0 55.9 0.8 24.8 6.7 0.2

5 0.4 0.0 0.5 74.4 6.5 13.2 4.9 0.1

6 5.0 0.0 3.4 64.1 1.9 22.5 3.1 0.0

7 4.4 0.0 1.3 63.3 2.5 25.4 3.0 0.0

8 26.6 0.0 0.3 40.7 1.1 25.4 5.8 0.1

9 16.9 0.0 0.8 29.6 13.8 34.7 4.1 0.1

10 26.0 0.0 0.1 49.4 1.6 16.1 6.6 0.2

11 24.4 0.1 0.7 21.4 24.8 24.2 4.3 0.1

12 19.0 0.0 0.0 56.4 0.9 19.4 4.1 0.1

13 19.8 0.0 0.0 57.5 0.0 19.6 3.0 0.1

14 0.6 0.3 0.4 70.7 0.0 22.4 5.5 0.1

15 24.5 0.0 0.0 49.4 0.3 21.4 4.3 0.1

16 18.6 0.0 0.0 53.3 0.0 23.6 4.5 0.1

17 1.9 0.0 0.6 56.2 6.0 27.4 7.8 0.2

18 9.2 0.1 0.3 75.2 1.7 11.4 2.0 0.0

19 32.5 0.3 1.1 36.4 1.7 23.3 4.7 0.1

20 0.5 0.0 1.3 82.6 0.2 11.1 4.2 0.1

21 11.3 0.0 0.7 41.1 15.6 27.9 3.4 0.1

22 12.4 0.0 0.3 67.0 2.7 10.3 7.3 0.1

23 0.4 0.0 2.5 71.6 0.0 21.4 3.9 0.0

24 17.0 0.3 1.9 42.2 0.2 32.6 5.6 0.1

25 26.5 0.0 0.3 34.2 3.7 32.9 2.4 0.0

26 9.3 0.2 0.1 48.8 5.8 32.1 3.6 0.1

27 6.6 0.0 0.2 49.3 12.3 29.9 1.6 0.0

28 2.4 0.0 1.7 70.7 0.0 20.5 4.6 0.1

29 40.6 0.0 0.0 35.1 6.2 14.4 3.7 0.1

30 27.3 0.0 0.4 35.4 16.9 16.0 4.0 0.1

31 3.1 0.0 0.9 81.9 0.0 9.0 5.0 0.1

32 19.4 0.0 0.0 53.1 0.3 23.2 4.0 0.1

33 2.9 0.2 0.6 71.1 3.8 17.8 3.6 0.1

34 12.6 1.6 0.0 57.4 4.5 14.6 9.2 0.1

35 34.0 0.0 0.3 41.2 2.0 18.3 4.0 0.1

123



Area %

Edge


36 8.6 0.0 1.3 58.2 1.8 24.6 5.4 0.1

37 22.2 0.0 0.2 41.6 16.7 14.9 4.3 0.1

38 17.5 0.0 0.4 26.1 22.0 29.6 4.3 0.1

39 12.6 0.0 0.3 68.0 0.0 13.1 5.6 0.2

40 46.1 0.0 0.0 24.1 0.0 27.6 2.1 0.0

41 1.0 1.2 1.7 59.3 3.6 25.4 7.6 0.2

42 15.2 0.5 1.5 41.6 3.3 32.4 5.3 0.1

43 66.4 0.0 0.0 16.6 1.0 14.4 1.7 0.0

44 5.7 0.2 1.8 53.4 15.8 14.9 8.0 0.1

45 5.7 0.2 1.8 53.4 15.8 14.9 8.0 0.1

46 19.7 0.3 1.9 55.6 0.6 15.5 6.3 0.1

47 19.0 0.1 1.0 51.5 0.2 21.6 6.5 0.1

48 19.2 0.3 0.0 50.6 2.5 20.8 6.5 0.1

49 17.4 0.2 2.0 54.5 1.5 17.3 7.0 0.1

50 35.6 0.5 0.0 40.6 1.2 16.4 5.6 0.1

51 22.6 0.5 0.0 46.4 1.4 24.1 4.9 0.1

52 22.8 0.3 3.3 49.4 3.5 12.6 8.0 0.1

53 4.5 0.0 0.7 56.1 0.5 35.9 2.3 0.0

54 53.7 0.0 0.3 23.0 3.3 17.5 2.2 0.0

55 2.6 0.2 1.5 54.9 7.7 26.0 7.0 0.1

56 14.9 0.3 0.0 49.9 0.4 28.8 5.5 0.1

57 10.2 0.9 0.0 67.5 1.8 11.4 8.1 0.1

58 2.1 3.5 1.3 57.9 6.8 20.6 7.7 0.1

59 3.4 0.1 0.9 55.7 3.1 30.1 6.6 0.1

60 21.7 0.0 0.3 61.2 2.2 11.3 3.3 0.0

61 1.3 0.1 1.7 84.1 1.4 5.5 5.9 0.1

62 28.2 0.0 0.5 33.1 17.8 16.5 3.9 0.1

63 15.5 0.0 1.3 53.5 8.2 17.1 4.2 0.1

64 29.2 0.0 0.0 39.5 0.2 28.0 2.9 0.1

65 5.6 1.0 0.6 55.4 0.4 33.2 3.7 0.1

66 1.6 3.4 0.0 67.0 2.5 19.1 6.4 0.1

67 20.9 0.4 0.8 21.9 2.1 50.2 3.7 0.1

68 0.3 0.1 2.9 79.1 0.1 9.1 8.4 0.1

69 46.3 0.0 0.0 25.8 1.3 24.6 2.1 0.0

70 29.8 0.0 0.4 45.9 1.8 17.8 4.3 0.1

124



Area %

Edge


71 6.6 0.3 2.1 65.3 1.9 17.4 6.3 0.1

72 17.3 0.7 1.8 51.6 4.1 16.4 8.0 0.1

73 7.6 1.1 0.0 60.8 1.1 24.4 4.9 0.1

74 16.2 1.7 0.0 60.0 1.1 12.7 8.1 0.1

75 49.4 0.0 0.0 33.5 2.0 12.1 2.9 0.0

76 12.2 0.2 1.8 52.4 3.3 23.6 6.3 0.1

77 11.1 0.2 3.1 54.5 5.6 18.5 6.8 0.1

78 32.2 0.0 0.3 30.6 18.8 13.5 4.5 0.1

79 30.4 0.4 1.6 38.2 2.6 21.1 5.6 0.1

80 22.2 0.5 2.3 39.3 6.7 22.0 6.8 0.1

81 14.9 1.5 0.8 51.9 3.5 22.0 5.3 0.1

82 1.5 0.3 1.2 54.3 0.1 38.0 4.6 0.1

83 3.6 1.7 0.0 60.5 8.8 19.3 6.1 0.1

84 21.8 0.7 0.0 53.0 0.7 16.3 7.4 0.1

85 60.3 0.0 0.1 8.1 13.8 16.1 1.5 0.0

86 19.5 0.5 1.5 50.3 2.2 19.9 5.9 0.1

87 17.7 0.8 2.1 53.7 1.9 16.3 7.2 0.2

88 28.8 0.0 0.0 38.2 1.8 27.2 3.9 0.1

89 11.0 0.4 0.8 46.8 2.3 34.5 4.0 0.1

90 2.2 1.4 0.9 75.9 0.2 12.9 6.3 0.1

91 29.2 0.4 1.6 46.5 0.9 16.7 4.6 0.1

92 15.7 0.0 0.3 60.7 8.6 8.9 5.7 0.1

93 10.8 0.4 3.5 56.1 1.4 21.9 5.8 0.1

94 17.2 0.2 2.2 54.1 2.1 17.1 6.9 0.1

95 37.8 0.0 0.4 41.6 1.6 14.8 3.7 0.1

96 60.8 0.0 0.0 18.9 3.4 13.9 2.8 0.0

97 1.4 0.3 1.7 70.5 0.2 18.9 6.9 0.1

98 18.2 1.2 0.0 58.6 2.9 12.4 6.6 0.1

99 8.9 1.8 0.0 69.7 0.1 9.0 10.4 0.1

100 1.0 0.1 2.8 75.3 0.4 11.9 8.4 0.1

101 22.7 0.0 0.3 55.9 1.7 14.8 4.5 0.0

102 19.9 0.9 1.5 48.6 2.3 21.6 5.1 0.1

103 14.9 0.4 1.7 49.9 2.3 23.7 7.1 0.1

104 6.5 0.2 2.2 54.5 0.2 29.8 6.5 0.1

105 1.7 0.5 0.8 55.0 10.5 23.9 7.4 0.1

125



Area %

Edge


106 4.1 4.0 0.8 58.4 0.0 25.3 7.1 0.2

107 27.3 0.6 2.1 39.8 2.6 23.6 4.0 0.1

108 23.6 0.3 1.1 44.2 2.0 24.9 3.8 0.1

109 21.2 0.3 1.6 44.5 3.2 23.0 6.1 0.1

110 60.4 0.0 0.0 21.8 0.1 15.3 2.4 0.0

111 33.7 0.3 1.8 41.4 1.4 16.8 4.6 0.1

112 12.4 0.0 0.0 58.7 1.4 23.2 4.3 0.1

113 1.0 1.5 2.9 61.6 0.4 27.7 4.8 0.1

114 2.1 1.3 1.2 64.5 0.6 25.8 4.5 0.1

115 5.4 0.1 0.0 84.6 0.0 7.9 2.0 0.0

116 14.2 1.2 2.7 59.9 2.2 12.1 7.6 0.1

117 1.7 0.0 0.4 41.6 0.0 54.6 1.6 0.0

118 0.6 0.0 0.9 90.5 0.0 5.9 1.9 0.0

119 26.8 0.0 0.0 56.5 0.9 11.2 4.4 0.1

120 1.4 0.1 3.3 72.6 0.1 15.0 7.3 0.1

121 0.9 0.3 2.0 56.2 0.7 34.3 5.5 0.1

122 20.0 0.4 2.3 49.6 3.5 16.9 7.1 0.2

123 24.6 1.4 0.0 52.5 3.6 10.3 7.5 0.1

124 0.8 0.1 2.0 67.1 0.0 23.0 6.8 0.1

125 26.1 0.0 0.0 52.7 3.9 13.3 4.0 0.1

126 28.1 0.2 1.7 46.0 0.6 17.6 5.6 0.1

127 10.6 0.3 1.7 55.2 1.9 24.5 5.6 0.1

128 35.5 0.0 0.0 34.8 8.1 14.5 7.1 0.1

129 0.5 0.4 0.9 68.7 0.4 23.8 5.4 0.1

130 0.4 0.3 0.9 68.8 0.4 23.8 5.2 0.1

131 6.8 1.3 0.0 67.5 0.2 15.7 8.3 0.1

132 15.2 0.4 1.3 53.9 4.1 19.0 6.0 0.1

133 18.4 0.7 1.4 45.4 3.5 26.5 3.9 0.1

134 1.7 0.3 3.5 45.0 0.1 43.5 5.7 0.1

135 16.5 0.2 2.4 59.1 0.1 13.4 8.1 0.2

136 18.2 0.0 0.0 61.7 0.5 15.4 4.2 0.1

137 0.7 0.8 1.7 71.2 0.3 18.3 6.9 0.1

138 7.5 0.2 1.1 62.4 0.9 22.1 5.7 0.1

139 4.3 4.3 0.0 70.1 0.7 12.4 8.1 0.1

140 16.6 0.4 1.8 46.2 0.0 31.4 3.6 0.1

126



Area %

Edge


141 0.7 0.2 3.2 58.9 0.2 30.0 6.7 0.2

142 7.2 0.3 2.1 66.8 5.0 10.6 7.9 0.1

143 12.4 0.2 1.9 63.6 0.4 14.3 7.0 0.1

144 11.8 0.0 0.4 70.9 0.6 12.7 3.5 0.0

145 2.9 1.7 1.6 27.2 17.9 41.6 7.0 0.1

146 0.8 0.4 2.6 51.7 6.1 31.6 6.6 0.1

147 11.4 1.4 1.1 61.9 0.1 14.9 9.1 0.1

148 18.5 1.6 1.4 47.7 2.9 22.9 4.9 0.1

149 7.0 0.1 2.1 70.1 2.3 8.8 9.4 0.1

150 36.9 0.0 0.0 36.6 7.6 15.6 3.3 0.0

151 12.4 0.3 2.3 51.8 1.4 25.3 6.4 0.1

152 32.9 0.0 0.0 36.7 4.1 21.9 4.2 0.0

153 0.6 0.2 3.6 21.3 0.4 71.0 3.0 0.0

154 3.8 1.3 2.4 51.6 0.2 36.9 3.7 0.1

155 9.0 0.7 1.5 65.7 1.8 12.2 9.1 0.1

156 36.8 0.7 1.3 35.8 0.4 20.9 4.0 0.1

157 0.0 0.0 6.7 86.7 0.0 4.5 2.1 0.0

158 5.9 0.0 1.5 72.8 3.4 11.5 4.8 0.0

159 4.2 0.4 8.5 47.6 0.3 33.3 5.6 0.1

160 12.3 0.5 0.0 63.2 0.4 16.3 7.2 0.1

161 12.1 0.7 0.0 55.0 8.5 15.8 7.8 0.1

162 1.3 0.6 2.4 73.7 0.6 14.1 7.3 0.2

163 29.4 0.0 0.0 47.5 0.9 17.1 5.1 0.1

164 13.9 1.2 0.0 58.9 3.1 16.1 6.7 0.1

165 8.0 0.0 1.3 56.2 7.4 21.3 5.7 0.1

166 32.4 0.0 1.3 40.6 5.8 14.9 5.1 0.1

167 21.7 0.0 0.2 36.8 5.9 32.0 3.4 0.0

168 19.4 0.0 0.8 46.0 2.2 27.3 4.2 0.1

169 0.8 1.2 0.9 49.6 13.8 25.7 7.9 0.1

170 1.3 0.4 0.0 71.8 1.9 18.9 5.6 0.1

171 5.8 0.7 0.0 66.7 0.5 18.7 7.6 0.1

172 20.4 0.0 0.4 40.6 8.7 25.3 4.5 0.0

173 14.8 0.3 1.2 49.6 10.2 17.7 6.2 0.1

174 5.4 0.3 1.4 60.9 7.1 16.6 8.3 0.1

175 27.2 0.5 1.8 42.5 6.2 15.7 6.1 0.1

127



Area %

Edge


176 12.1 0.2 1.6 45.8 1.3 33.4 5.5 0.2

177 9.5 0.4 1.6 38.1 5.2 39.9 5.2 0.1

178 0.1 0.3 1.4 61.2 7.7 20.6 8.6 0.1

179 4.2 0.1 0.0 48.2 0.0 46.2 1.3 0.0

180 23.5 0.9 0.0 38.8 2.5 28.9 5.3 0.1

181 1.1 0.0 1.3 28.3 3.4 63.3 2.5 0.0

182 18.9 0.3 0.7 33.8 4.9 37.3 4.0 0.1

183 27.6 0.0 1.3 45.5 4.8 15.3 5.4 0.1

184 19.7 0.4 1.1 34.6 8.5 29.9 5.5 0.1

185 9.1 0.5 0.0 55.0 0.3 29.0 6.0 0.1

186 10.3 0.8 1.5 43.2 0.4 37.5 6.2 0.1

187 1.4 0.2 1.3 36.4 0.7 55.8 4.1 0.1

188 0.7 0.4 0.0 37.0 2.3 54.2 5.2 0.1

189 2.2 0.3 0.0 51.7 1.0 41.6 3.2 0.1

190 13.5 0.7 0.0 51.1 0.8 26.4 7.4 0.1

191 18.5 1.2 0.0 33.8 4.0 37.2 5.2 0.1

192 8.1 0.4 0.0 60.8 0.4 20.4 9.8 0.1

193 10.5 0.0 1.6 49.2 11.7 21.0 5.9 0.1

194 6.7 0.0 1.7 65.1 3.2 15.1 8.1 0.1

195 23.0 0.0 1.8 39.5 3.4 24.5 7.5 0.1

196 9.4 0.0 1.8 55.7 4.2 22.6 6.1 0.1

197 20.6 0.0 0.0 53.2 4.4 17.4 4.4 0.1

198 8.9 0.1 0.6 63.5 0.1 19.7 6.9 0.1

199 1.8 0.0 0.0 86.4 0.0 8.3 3.5 0.1

200 13.0 0.0 2.0 55.9 3.5 17.8 7.7 0.1

128

Table A.19 L4-14-132 thin section non sulphide

gangue weighted average density

Phase Weight % Density

Stilpnomelane 0.00163 2.90

Quartz 0.91171 2.65

Chlorite-Fe 0.00827 3.00

Chlorite-Mg 2.59879 2.90

Muscovite 0.00000 2.83

Ilmenite 0.62161 4.72

Calcite 0.00022 2.71

Dolomite 0.00000 2.85

Epidote 0.00000 3.40

Talc 0.00005 2.75

Chamosite (+Mg) 0.00011 3.00

Altered Fe

sulphide 0.07549 3.00

Magnetite-Cr10 0.03107 5.18

Ankerite 0.07794 3.10

Siderite_Mg 0.00022 3.70

Cassiterite 0.00581 6.90

Siderite 0.06257 3.92

Siderite (+Mn) 1.71926 3.83

Weighted Average 3.34

129

Table A.20 L4-14-132 weight percent calculations from optical image analysis results

Sp Gn Cp Py Asp NSG1

Phase Area, % 16.1 0.4 1.2 56.4 3.7 22.2

Density, g/cm3 4.00 7.57 4.20 5.01 6.00 3.342)

Phase Area * Density 64.28 3.20 4.92 282.52 22.44

74.15

75

Weight % 14.2 0.7 1.1 62.6 5.0 16.4

1Non sulphide gangue 2A density of 3.34 g/cm3 was used for the NSG, the weighted average of the

NSG minerals

Table A.21 L4-14-132 weight percent standard error of the mean calculations

Sp Gn Cp Py Asp NSG1)

Phase Area, % 1.8 0.1 0.2 2.0 0.6 1.4

Conversion 1.1 0.6 1.1 0.9 0.8 1.4

Weight % 1.6 0.2 0.2 2.3 0.8 1.1

1Non sulphide gangue 2A density of 3.34 g/cm3 was used for the NSG, the weighted average of the NSG

minerals

Table A.22 L4-14-132 calculated Zn, Pb, and Cu assay values from optical

image analysis

Phase Average Composition, wt %

Type Wt. % Zn Pb Cu

Sphalerite 14.2 67.1 0.0 0.0

Galena 0.7 0.0 86.6 0.0

Chalcopyrite 1.1 0.0 0.0 34.6

Pyrite 62.6 N.A N.A N.A

Arsenopyrite 5.0 N.A N.A N.A

NSG1) 16.4 N.A N.A N.A

Total Wt. % 100.0 9.6 0.6 0.4

1Non sulphide gangue

130

Table A.23 Averaged LA-ICP-MS results of sphalerite grains from 62-119-47.7 polished thin section

Concentration2

Grain Size1, % g/mt

ID µm Fe Cu Pb Ag Au

Sp-5 641 4.9 ± 0.1 0.151 ± 0.026 2.084 ± 0.0108 5.7 ± 0.6 0.02 ± N.A.

Sp-8 164 2.5 ± 0.1 1.752 ± 0.063 0.006 ± 0.001 48.6 ± 2.1 0.05 ± 0.03

Sp-4 83 3.5 ± 0.1 1.5 ± 0.1 0.012 ± 0.004 32.1 ± 1.7 <0.03 ± N.A.

Sp-3 60 4.8 ± 0.1 0.154 ± 0.018 <0.0003 ± N.A. 6.1 ± 0.5 <0.02 ± N.A.

Sp-1 43 3.4 ± 0.5 2.004 ± 0.063 1.2750 ± 0.2800 86.3 ± 6.6 <0.03 ± N.A.

Sp-2 28 3.7 ± 0.2 1.324 ± 0.041 0.0031 ± 0.0004 16.6 ± 0.8 0.06 ± 0.032

Avg. 3.8 ± 0.2 1.146 ± 0.045 <0.5634 ± 0.0593 32.6 ± 2.0 <0.04 ± N.A.

1Grain size based on the diameter of the smallest circle that can encompass the grain. 2Concentration values reported include the mean value and the standard error of the mean at 95% confidence.

131

Table A.24 Averaged LA-ICP-MS results for chalcopyrite grains from 62-119-47.7 polished thin section

Concentration2

Grain Size1, % g/mt


Sp-5 641 4.9 ± 0.1 0.151 ± 0.026 2.084 ± 0.0108 5.7 ± 0.6 0.02 ± N.A.

Sp-8 164 2.5 ± 0.1 1.752 ± 0.063 0.006 ± 0.001 48.6 ± 2.1 0.05 ± 0.03

Sp-4 83 3.5 ± 0.1 1.5 ± 0.1 0.012 ± 0.004 32.1 ± 1.7 <0.03 ± N.A.

Sp-3 60 4.8 ± 0.1 0.154 ± 0.018 <0.0003 ± N.A. 6.1 ± 0.5 <0.02 ± N.A.

Sp-1 43 3.4 ± 0.5 2.004 ± 0.063 1.2750 ± 0.2800 86.3 ± 6.6 <0.03 ± N.A.

Sp-2 28 3.7 ± 0.2 1.324 ± 0.041 0.0031 ± 0.0004 16.6 ± 0.8 0.06 ± 0.03

Avg. 3.8 ± 0.2 1.146 ± 0.045 <0.5634 ± 0.0593 32.6 ± 2.0 <0.04 ± N.A.


132

Table A.25 Averaged LA-ICP-MS results for galena grains from 62-119-47.7 polished thin section

Concentration2

Grain Size1, % g/mt

ID µm Fe Pb Ag Au

Gn-3 212 2.702 ± 1.900 90.4 ± 1.0 3218.0 ± 0.0 <0.23 ± N.A.

Gn-1 61 <0.142 ± N.A. 89.5 ± 1.8 2640.0 ± 0.0 <0.18 ± N.A.

Gn-2 44 0.013 ± 0.004 88.5 ± 1.2 2637.0 ± 49.0 <0.25 ± N.A.

Gn-7 43 0.047 ± 0.019 89.0 ± 1.3 3011.0 ± 65.0 <0.20 ± N.A.

Gn-8 42 1.510 ± 0.190 91.3 ± 3.1 2642.0 ± 82.0 <0.12 ± N.A.

Gn-10 39 1.320 ± 0.610 89.0 ± 1.6 2929.0 ± 51.0 <0.11 ± N.A.

Gn-6 34 0.093 ± 0.019 92.9 ± 2.5 2904.0 ± 58.0 <0.18 ± N.A.

Avg. <0.832 ± N.A. 90.1 ± 1.8 2854.4 ± 43.6 <0.18 ± N.A.


133

Table A.26 Averaged LA-ICP-MS results for chalcopyrite grains from 62-119-47.7 polished thin section

Concentration2

Grain Size1, % g/mt

ID µm Cu Zn Fe Pb Ag Au

Cp-5 123 33.4 ± 0.7 0.022 ± 0.006 30.4 ± 0.6 0.0010 ± 0.0005 6.1 ± 1.1 <0.15 ± N.A.

Cp-4 71 34.2 ± 0.6 1.410 ± 0.280 30.9 ± 0.6 0.0049 ± 0.0017 5.1 ± 0.9 <0.15 ± N.A.

Cp-1 45 31.0 ± 0.7 0.004 ± 0.002 30.7 ± 0.7 0.0011 ± 0.0004 5.1 ± 1.2 <0.08 ± N.A.

Cp-2 43 35.0 ± 0.6 0.019 ± 0.002 30.6 ± 0.5 0.0017 ± 0.0006 4.9 ± 0.7 <0.25 ± N.A.

Cp-8 41 34.5 ± 0.9 0.003 ± 0.001 30.2 ± 0.5 0.0004 ± 0.0002 3.9 ± 0.8 <0.20 ± N.A.

Cp-9 40 31.0 ± 0.7 6.500 ± 1.100 29.8 ± 0.4 0.0048 ± 0.0010 8.4 ± 1.1 <0.06 ± N.A.

Avg. 33.2 ± 0.7 1.326 ± 0.232 30.4 ± 0.5 0.0023 ± 0.0007 5.6 ± 1.0 <0.15 ± N.A.


134

Table A.27 Averaged LA-ICP-MS results for arsenopyrite grains from 62-119-47.7 polished thin section

Concentration2

Grain Size1, % g/mt


Asp-2 80 0.131 ± 0.054 <0.024 ± N.A. 33.0 ± 1.9 2.30 ± 0.31 247.0 ± 0.3 13.95 ± 0.37

Asp-1 58 0.080 ± 0.041 0.010 ± 0.004 32.9 ± 1.8 3.28 ± 0.25 40.8 ± 0.2 4.00 ± 0.01

Asp-5 41 0.043 ± 0.013 <0.006 ± N.A. 35.1 ± 1.0 0.10 ± 0.03 5.2 ± 4.0 55.00 ± 20.00

Asp-4 27 0.042 ± 0.009 0.046 ± 0.019 35.3 ± 2.9 1.54 ± 0.28 87.0 ± 18.0 12.20 ± 4.80

Avg. 0.074 ± 0.029 <0.022 ± N.A. 34.1 ± 1.9 1.80 ± 0.22 95.0 ± 5.6 21.29 ± 6.30

1Grain size based on the diameter of the smallest circle that can encompass the grain.

2Concentration values reported include the mean value and the standard error of the mean at 95% confidence.

135

Table A.28 Averaged LA-ICP-MS results for pyrite grains from 62-119-47.7 polished thin section

Concentration2

Grain Size1, % g/mt


Py-1 1700 0.0390 ± 0.0051 0.0019 ± N.A. 46.8 ± 2.0 0.0264 ± 0.0040 11.3 ± 0.9 1.30 ± 0.12

Py-2 1022 <0.0400 ± N.A. <0.0037 ± N.A. 45.0 ± 3.9 <0.0152 ± N.A. <5.7 ± N.A. <0.22 ± N.A.

Py-5 500 <0.0385 ± N.A. 0.0023 ± 0.0004 45.6 ± 2.6 0.0379 ± 0.0071 <4.7 ± N.A. <0.51 ± N.A.

Py-6 250 <0.0737 ± N.A. 0.3034 ± 0.0603 46.1 ± 3.9 <0.0847 ± N.A. 13.5 ± 2.0 0.40 ± 0.12

Py-8 100 <0.0034 ± N.A. <0.0006 ± N.A. 45.0 ± 3.8 <0.0301 ± N.A. 3.8 ± 0.9 <0.56 ± N.A.

Py-7 50 <0.0265 ± N.A. 0.0141 ± 0.0045 45.4 ± 3.8 <0.1293 ± N.A. 9.2 ± 1.8 <0.51 ± N.A.

Py-9 25 0.0415 ± 0.0050 0.0026 ± 0.0012 48.2 ± 4.3 0.0362 ± 0.0031 9.8 ± 1.3 0.72 ± 0.12

Avg. <0.0375 ± N.A. 0.0469 ± N.A. 46.0 ± 3.5 <0.0514 ± N.A. <8.3 ± N.A. <0.60 ± N.A.


136

Table A.29 Averaged LA-ICP-MS results for sphalerite grains from L2-16-67.4 polished thin section

Concentration2

Grain Size1, % ppm

ID µm Cu Fe Pb Ag Au

Sp-3 42 <1.1 ± N.A. 11.2 ± 1.3 0.08 ± 0.01 464 ± 48 <0.19 ± N.A.

Sp-10 40 <0.2 ± N.A. 5.8 ± 1.0 0.09 ± 0.02 46.9 ± 2.2 <0.12 ± N.A.

Avg. <0.6 ± N.A. 8.5 ± 1.1 0.08 ± 0.01 255.5 ± 25.1 <0.15 ± N.A. 1Grain size based on the diameter of the smallest circle that can encompass the grain. 2Concentration values reported include the mean value and the standard error of the mean at 95% confidence.

137

Table A.30 Averaged LA-ICP-MS results for galena grains from L2-16-67.4 polished thin section

Concentration2

Grain Size1, % ppm

ID µm Fe Pb Ag Au

Gn-10 250 <0.713 ± N.A. 90.4 ± 1.7 3787.3 ± 71.0 <0.14 ± N.A.

Gn-9 88 <3.702 ± N.A. 90.2 ± 1.7 868.5 ± 27.0 <0.12 ± N.A.

Gn-7 53 1.070 ± 0.2 90.8 ± 1.1 1991.0 ± 24.0 <0.13 ± N.A.

Gn-2 45 4.900 ± 1.5 90.1 ± 1.7 4289.0 ± 91.0 <0.14 ± N.A.

Gn-8 43 3.600 ± 1.2 90.6 ± 1.8 1951.0 ± 37.0 <0.14 ± N.A.

Gn-4 41 6.800 ± 1.8 90.7 ± 1.0 4040.0 ± 49.0 <0.15 ± N.A.

Avg. <4.0925 ± N.A. 90.5 ± 1.4 3067.8 ± 50.3 <0.14 ± N.A.


138

Table A.31 Averaged LA-ICP-MS results for galena grains from L2-16-67.4 polished thin section

Concentration2

Grain Size1, % ppm


Cp -2 250 26.1 ± 4.9 <0.048 ± N.A. 30.0 ± 4.1 <0.867 ± N.A. <21.1 ± N.A. <0.32 ± N.A.

Cp -6 100 29.5 ± 0.7 0.019 ± 0.004 29.0 ± 1.0 <0.003 ± N.A. <4.6 ± N.A. <0.09 ± N.A.

Cp -1 72 28.1 ± 4.0 0.021 ± 0.007 27.7 ± 1.5 <0.008 ± N.A. <5.3 ± N.A. <0.29 ± N.A.

Cp -4 34 27.1 ± 2.9 <0.003 ± N.A. 29.4 ± 1.4 <0.027 ± N.A. <20.5 ± N.A. <0.15 ± N.A.

Avg. 27.7 ± 3.1 <0.023 ± N.A. 29.0 ± 2.0 <0.226 ± N.A. <12.9 ± N.A. <0.21 ± N.A.


139

Table A.32 Averaged LA-ICP-MS results for tetrahedrite-tennantite grains from L2-16-67.4 polished thin section

Concentration2

Grain Size1, % ppm


Td-Tn-7 150 19.8 ± 0.5 5.42 ± 0.09 <3.4 ± N.A. <2.172 ± N.A. 56846.7 ± 986.7 <0.09 ± N.A.

Td-Tn-8 77 19.2 ± 0.5 4.26 ± 0.08 15.8 ± 10.5 0.016 ± 0.016 60500.0 ± 1500.0 <0.30 ± 0.05

Td-Tn-4 57 18.9 ± 0.8 4.61 ± 0.11 10.6 ± 4.2 <0.742 ± N.A. 58000.0 ± 1300.0 0.24 ± 0.15

Td-Tn-5 49 17.8 ± 0.5

4.08 ± 0.06 3.3 ± 0.2 <0.110 ± N.A. 68900.0 ± 1400.0 <0.09 ± 0.032

Td-Tn-6 41 21.3 ± 0.5

6.45 ± 0.09 12.4 ± 2.4 0.785 ± 0.785 63600.0 ± 1700.0 <0.27 ± 0.07

Td-Tn-1 38 17.7 ± 0.5

4.47 ± 0.08

3.5 ± 0.2

0.002 ± 0.002

64900.0 ± 1900.0

<0.11 ± 0.01

Td-Tn-10 32 18.6 ± 0.6

5.80 ± 0.18

14.5 ± 4.2

0.044 ± 0.044

46400.0 ± 1100.0

<0.10 ± 0.07

Average 19.1 ± 0.5 5.01 ± 0.10 <9.0 ± N.A. <0.553 ± N.A. 59878.1 ± 1412.4 <0.17 ± N.A.


140

Table A.33 Averaged LA-ICP-MS results for arsenopyrite grains from L2-16-67.4 polished thin section

Concentration2

Grain Size1, % ppm


Asp-3 150 0.018 ± 0.007 <0.010 ± N.A. 34.1 ± 2.2 0.181 ± 0.031 48.1 ± 11.9 <0.13 ± N.A.

Asp-1 88 <0.007 ± N.A. <0.012 ± N.A. 32.5 ± 1.4 0.012 ± 0.003 13.7 ± 6.2 <1.37 ± N.A.

Asp-9 68a <0.008 ± N.A. <0.004 ± N.A. 38.5 ± 4.1 4.648 ± 0.598 81.0 ± 14.0 <1.29 ± N.A.

Asp-11 68b 0.008 ± 0.007 <0.005 ± N.A. 33.9 ± 1.3 0.485 ± 0.135 8.5 ± 2.6 <0.63 ± N.A.

Asp-2 56 0.085 ± 0.022 <0.014 ± N.A. 33.4 ± 2.3 0.614 ± 0.137 40.8 ± 10.2 <1.07 ± N.A.

Asp-6 43 <0.006 ± N.A. <0.008 ± N.A. 34.4 ± 1.7 0.038 ± 0.016 <13.6 ± N.A. <2.47 ± N.A.

Asp-4 40 <0.004 ± N.A. <0.006 ± N.A. 35.0 ± 1.3 0.028 ± 0.006 14.4 ± 5.6 <0.56 ± N.A.

Asp-7 38 <0.004 ± N.A. <0.006 ± N.A. 34.9 ± 1.0 0.425 ± 0.185 <2.8 ± N.A. <1.09 ± N.A.

Asp-5 27 0.033 ± 0.008 <0.006 ± N.A. 33.4 ± 2.9 0.039 ± 0.008 52.0 ± 11.0 1.31 ± 0.81

Asp-10 19 0.030 ± 0.005 0.005 ± 0.003 32.6 ± 2.8 2.685 ± 0.875 152.0 ± 23.0 1.37 ± 0.50

Avg. <0.024 ± N.A. <0.007 ± N.A. 34.0 ± 1.9 0.616 ± 0.195 <40.6 ± N.A. <1.22 ± N.A.


141

Table A.34 Averaged LA-ICP-MS results for pyrite grains from L2-16-67.4 polished thin section

Concentration2

Grain

Size1, % ppm

ID

µm Cu Zn Fe Pb Ag Au

Py-5

1200 <0.0421 ± N.A. <0.0002 ± N.A. 48.1 ± 2.7 0.026 ± N.A. 9.6 ± 3.4 <0.04 ± N.A.

Py-2

200 <0.0084 ± N.A. <1.7391 ± N.A. 47.0 ± 4.2 0.066 ± 0.010 3.1 ± 0.5 1.57 ± 0.24

Py-1

100 <0.0494 ± N.A. 0.0038 ± 0.0013 46.8 ± 5.0 1.564 ± 0.698 13.8 ± 4.0 <0.31 ± N.A.

Py-3

50 0.0056 ± 0.0026

0.0003 ± 0.0002 46.4 ± 3.8 0.057 ± 0.0 2.0 ± 0.43 0.62 ± 0.13

Py-1

25 <0.0095 ± N.A.

0.0340 ± 0.0220 44.2 ± 3.3 0.440 ± 0.1 11.5 ± 1.4 0.48 ± 0.095

Avg.

<0.0230 ± N.A.

<0.3555 ± N.A.

46.5 ± 3.8

<0.430 ± N.A.

8.0 ± 2.0

<0.60 ± N.A.



142

Table A.35 Averaged LA-ICP-MS results for galena grains from L2-16-70 polished thin section

Concentration2

Grain

Size1

, % ppm


Sp-4 368 5.9 ± 0.1 0.470 ± 0.085 <1.559 ± N.A. 75.1 ± 3.3 <0.11 ± N.A.

Sp-6 231 5.9 ± 0.2 0.276 ± 0.023 <2.024 ± N.A. 48.4 ± 3.5 <0.05 ± N.A.

Sp-5 141 6.0 ± 0.1 0.035 ± 0.004 0.0027 ± 0.0004 19.2 ± 1.6 0.072 ± 0.04

Sp-3 92 6.7 ± 0.1 0.104 ± 0.087 0.0514 ± 0.0295 38.6 ± 0.0 <0.08 ± N.A.

Sp-1 59 6.2 ± 0.1 0.134 ± 0.008 <0.0035 ± N.A. 23.9 ± 1.0 <0.05 ± N.A.

Sp-2 37 5.8 ± 0.1 0.333 ± 0.024 0.0042 ± 0.0004 57.7 ± 1.9 0.072 ± 0.032

Avg. 6.1 ± 0.1 0.225 ± 0.038 0.607 ± 0.0101 43.8 ± 1.9 <0.07 ± N.A.

1Grain size based on the diameter of the smallest circle that can encompass the grain. 2Concentration values reported include the mean value and the standard error of the mean at 95%

confidence.

143


Concentration2

Grain

Size1

, % ppm


Sp-4 368 5.9 ± 0.1 0.470 ± 0.085 <1.559 ± N.A. 75.1 ± 3.3 <0.11 ± N.A.

Sp-6 231 5.9 ± 0.2 0.276 ± 0.023 <2.024 ± N.A. 48.4 ± 3.5 <0.05 ± N.A.

Sp-5 141 6.0 ± 0.1 0.035 ± 0.004 0.0027 ± 0.0004 19.2 ± 1.6 0.072 ± 0.04

Sp-3 92 6.7 ± 0.1 0.104 ± 0.087 0.0514 ± 0.0295 38.6 ± 0.0 <0.08 ± N.A.

Sp-1 59 6.2 ± 0.1 0.134 ± 0.008 <0.0035 ± N.A. 23.9 ± 1.0 <0.05 ± N.A.

Sp-2 37 5.8 ± 0.1 0.333 ± 0.024 0.0042 ± 0.0004 57.7 ± 1.9 0.072 ± 0.032

Avg. 6.1 ± 0.1 0.225 ± 0.038 0.607 ± 0.0101 43.8 ± 1.9 <0.07 ± N.A.


confidence.

144


Concentration2

Grain Size1, % ppm

ID µm Fe Pb Ag Au

Gn-6 117 0.179 ± 0.064 90.1 ± 2.0 749.5 ± 15.8 0.09 ± N.A.

Gn-3 85 1.400 ± 0.600 90.3 ± 1.7 1004.0 ± 23.0 0.02 ± N.A.

Gn-2 54 6.850 ± 2.100 90.1 ± 1.1 940.0 ± 23.0 <0.07 ± N.A.

Gn-1 46 8.800 ± 2.500 90.0 ± 1.1 944.0 ± 24.0 <0.15 ± N.A.

Avg. 4.307 ± 1.316 90.1 ± 1.5 909.4 ± 21.4 <0.08 ± N.A.



145

Table A.38 Averaged LA-ICP-MS results of chalcopyrite grains from L2-16-70 polished thin section

Concentration2

Grain Size1, % ppm


Cp-8 123 31.9 ± 1.6 0.008 ± 0.001 30.2 ± 1.0 0.148 ± 0.096 155.2 ± 11.7 0.21 ± 0.10

Cp-2 79 34.7 ± 1.0 0.011 ± 0.001 30.5 ± 0.6 0.013 ± 0.003 15.9 ± 1.8 0.24 ± 0.10

Cp-5 61 28.5 ± 7.1 0.023 ± 0.007 30.6 ± 1.4 0.030 ± 0.007 91.0 ± 23.0 <0.05 ± N.A.

Cp-9 52 32.9 ± 1.2 0.071 ± 0.011 30.4 ± 0.8 0.090 ± 0.075 9.3 ± 2.9 0.30 ± 0.18

Cp-3 44 37.1 ± 1.2 0.020 ± 0.002 30.9 ± 0.6 0.002 ± 0.001 7.3 ± 1.3 <0.11 ± N.A.

Cp-1 30 30.0 ± 1.8 0.149 ± 0.037 30.5 ± 0.9 0.027 ± 0.004 27.6 ± 3.2 0.16 ± 0.08

Avg. 32.5 ± 2.3 0.047 ± 0.010 30.5 ± 0.9 0.052 ± 0.031 51.0 ± 7.3 <0.18 ± N.A.


146

Table A.39 Averaged LA-ICP-MS results of arsenopyrite grains from L2-16-70 polished thin section

Concentration2

Grain Size1, % ppm


Asp-10 150 0.031 ± 0.005 0.001 ± 0.000 34.1 ± 2.3 0.150 ± 0.017 134.1 ± 21.9 0.70 ± 0.11

Asp-8 85 <0.010 ± N.A. <0.013 ± N.A. 34.6 ± 2.2 0.029 ± 0.011 3.7 ± N.A. 2.02 ± N.A.

Asp-1 64 0.032 ± 0.011 1.166 ± 0.499 32.7 ± 1.9 2.029 ± 0.331 102.0 ± 24.5 20.10 ± 4.75

Asp-7 46 0.024 ± 0.002 0.003 ± 0.001 32.3 ± 2.2 1.615 ± 0.575 187.0 ± 14.0 0.52 ± 0.08

Asp-6 42 0.006 ± 0.003 0.142 ± 0.056 33.7 ± 1.1 0.470 ± 0.075 20.8 ± 5.2 16.30 ± 3.60

Avg. <0.021 ± N.A. <0.265 ± N.A. 33.5 ± 1.9 0.858 ± 0.202 <89.5 ± N.A. <7.93 ± N.A.


147

Table A.40 Averaged LA-ICP-MS results of pyrite grains from L2-16-70 polished thin section

Concentration2

Grain Size1, % ppm


Py-2 882 0.002 ± 0.001 <0.5 ± N.A. 44.6 ± 4.2 0.0054 ± 0.0007 0.7 ± 0.2 0.72 ± 0.13

Py-1 310 0.002 ± 0.002 <0.7 ± N.A. 47.2 ± 4.7 0.0121 ± 0.0024 1.0 ± 0.3 0.23 ± 0.07

Avg. 0.002 ± 0.001 <0.6 ± N.A. 45.9 ± 4.4 0.00874 ± N.A. 0.8 ± 0.2 0.48 ± 0.10


148


Concentration2

Grain

Size1

, % ppm

ID µm Cu Zn Pb Ag Au

Sp-2 298 0.107 ± 0.010 68.0 ± 1.3 0.0018 ± 0.0003 18.457 ± 1.59 <0.10 ± N.A.

Sp-3 144a 0.062 ± 0.005 68.5 ± 1.5 0.0128 ± 0.0024 32.54 ± 1.32 <0.09 ± N.A.

Sp-6 144b 0.058 ± 0.003 68.2 ± 1.9 0.0029 ± 0.0003 23.23 ± 1.23 <0.10 ± N.A.

Sp-4 98 0.158 ± 0.011 68.4 ± 1.4 0.0029 ± 0.0002 36.100 ± 1.77 <0.09 ± N.A.

Sp-5 62 0.096 ± 0.004 67.2 ± 4.7 0.0041 ± 0.0004 24.350 ± 1.70 <0.09 ± N.A.

Avg. 0.007 68.1 ± 2.1 0.0049 ± 0.0007 26.935 ± 1.52 <0.10 ± N.A.


149

Table A.42 Averaged LA-ICP-MS results of galena grains from L4-14-132 polished thin section

Concentration2

Grain Size1, % ppm

ID µm Fe Pb Ag Au

Gn-10 250 <0.596 ± N.A. 91.0 ± 0.6 1388.3 ± 40.7 <0.16 ± N.A.

Gn-1 81 0.408 ± 0.061 89.0 ± 0.5 1275.5 ± 14.0 <0.13 ± N.A.

Gn-8 60 0.047 ± 0.006 90.3 ± 1.0 1068.1 ± 10.7 <0.14 ± N.A.

Gn-5 49 0.380 ± 0.110 89.7 ± 0.9 953.0 ± 11.0 <0.16 ± N.A.

Gn-4 42 5.300 ± 1.800 89.4 ± 0.5 1419.0 ± 14.0 <0.12 ± N.A.

Gn-7 38 7.800 ± 1.200 90.9 ± 1.0 783.0 ± 14.0 <0.25 ± N.A.

Gn-2 27 1.360 ± 0.260 90.0 ± 0.6 1318.0 ± 18.0 <0.17 ± N.A.

Avg. <2.270 ± N.A. 90.0 ± 0.7 1172.1 ± 17.5 <0.16 ± N.A.


150

Table A.43 Averaged LA-ICP-MS results of chalcopyrite grains from L4-14-132 polished thin section

Concentration2

Grain Size1, % ppm


Cp-10 212 33.45 ± 0.77 2.02 ± 0.29 25.3 ± 0.5 0.005 ± 0.001 60.8 ± 6.6 <0.49 ± N.A.

Cp-1 80 33.90 ± 1.10 0.19 ± 0.04 28.7 ± 0.7 0.007 ± 0.001 66.6 ± 8.0 <0.88 ± N.A.

Cp-7 61 32.40 ± 1.10 0.03 ± 0.01 25.9 ± 0.7 0.007 ± 0.001 53.3 ± 5.0 <0.98 ± N.A.

Cp-2 51 33.77 ± 0.87 0.12 ± 0.02 26.3 ± 0.6 0.040 ± 0.009 43.7 ± 6.4 <1.01 ± N.A.

Cp-6 41 36.00 ± 3.80 7.42 ± 0.46 36.2 ± 1.1 0.010 ± 0.002 46.0 ± 6.3 <1.59 ± N.A.

Cp-4 38 34.60 ± 2.00 0.04 ± 0.01 40.3 ± 1.1 0.006 ± 0.001 72.7 ± 7.7 <1.14 ± N.A.

Cp-3 26 33.80 ± 1.00 0.11 ± 0.01 29.6 ± 0.7 0.030 ± 0.005 42.6 ± 6.6 <0.81 ± N.A.

Avg. 33.99 ± 1.52 1.42 ± 0.12 30.3 ± 0.8 0.015 ± 0.003 55.1 ± 6.7 <0.99 ± N.A.


151

Table A.44 Averaged LA-ICP-MS results of arsenopyrite grains from L4-14-132 polished thin section

Concentration2

Grain Size1, % ppm


Asp-4 300 0.020 ± 0.007 <0.844 ± N.A. 25.8 ± 1.3 0.990 ± 0.233 <34.8 ± N.A. 39.0 ± 7.0

Asp-1 161 <0.003 ± N.A. <0.008 ± N.A. 35.5 ± 2.9 3.790 ± 0.450 31.0 ± 13.0 2.9 ± 2.7

Asp-2 124 0.950 ± 0.160 <0.009 ± N.A. 30.3 ± 1.0 0.165 ± 0.029 69.0 ± 18.0 3.6 ± 2.5

Avg. <0.324 ± N.A. <0.287 ± N.A. 30.5 ± 1.7 1.648 ± 0.237 <44.9 ± N.A. 15.2 ± 4.1



152


Concentration2

Grain Size1, % ppm


Py-1 412 0.064 ± 0.023 0.3949 ± 0.0684 46.3 ± 3.8 0.104 ± 0.032 6.7 ± 1.2 <0.12 ± N.A

Py-4 200 0.008 ± 0.002 <0.0003 ± N.A. 48.9 ± 4.3 0.163 ± 0.032 9.0 ± 1.3 0.07 ± N.A

Py-2 100a 0.006 ± 0.002 <0.0004 ± N.A. 44.8 ± 3.6 0.026 ± 0.003 3.6 ± 0.5 0.09 ± 0.05

Py-3 100b 0.040 ± 0.006 0.2610 ± 0.0450 48.1 ± 5.2 0.234 ± 0.053 6.7 ± 0.8 <0.17 ± N.A.

Avg. 0.030 ± 0.008 0.1641 ± N.A. 47.0 ± 4.2 0.132 ± 0.030 6.5 ± 1.0 <0.11 ± N.A



153

Table A.46 Averaged LA-ICP-MS results of sphalerite grains from L4-14-134.5 polished thin section

Concentration2

Grain

Size1

, % ppm

ID µm Cu Zn Pb Ag Au

Sp-4 1200 0.008 ± 0.001 67.4 ± 2.2 0.0028 ± 0.0003 17.5 ± 1.2 <0.12 ± N.A.

Sp-10 792 0.036 ± 0.003 67.5 ± 1.6 0.0031 ± 0.0002 20.6 ± 1.0 <0.14 ± N.A.

Sp-6 544 0.026 ± 0.004 65.5 ± 1.9 0.0063 ± 0.0020 15.4 ± 1.1 <0.11 ± N.A.

Sp-2 352 0.014 ± 0.001 69.8 ± 2.4 0.0051 ± 0.0008 24.5 ± 1.4 <0.11 ± N.A.

Sp-3 227 0.055 ± 0.009 68.5 ± 2.6 0.0023 ± 0.0003 15.0 ± 1.2 <0.10 ± N.A.

Sp-5 156 0.062 ± 0.008 65.6 ± 1.5 0.0159 ± 0.0093 21.8 ± 1.2 <0.09 ± N.A.

Sp-9 108 0.094 ± 0.007 69.8 ± 2.5 0.0069 ± 0.0003 43.7 ± 2.5 <0.14 ± N.A.

Sp-8 74 0.039 ± 0.003 65.4 ± 3.5 0.0074 ± 0.0013 33.0 ± 3.6 <0.11 ± N.A.

Sp-7 30 0.035 ± 0.008 75.0 ± 12.0 0.0215 ± 0.0077 34.2 ± 5.9 <0.11 ± N.A.

Avg. 0.041 ± 0.005 68.3 ± 3.3 0.0079 ± 0.0025 25.1 ± 2.1 <0.11 N.A.


confidence.

154

Table A.47 Averaged LA-ICP-MS results of galena grains from L4-14-134.5 polished thin section

Concentration2

Grain Size1, % ppm

ID µm Fe Pb Ag Au

Gn-6 355 <0.227 ± N.A. 86.6 ± 0.6 414.8 ± 6.4 <0.15 ± N.A.

Gn-9 94 1.320 ± 0.640 83.8 ± 0.4 455.3 ± 4.7 <0.04 ± N.A.

Gn-7 70 0.240 ± 0.064 84.4 ± 0.8 425.0 ± 11.0 <0.04 ± N.A.

Gn-8 56 <0.004 ± N.A 84.9 ± 0.7 371.1 ± 5.6 <0.04 ± N.A.

Avg. <0.448 N.A. 84.9 ± 0.6 416.5 ± 6.9 <0.07 ± N.A.


155

Table A.48 Averaged LA-ICP-MS results of chalcopyrite grains from L4-14-134.5 polished thin section

Concentration2

Grain Size1, % ppm


Cp-1 212 37.9 ± 1.5 0.033 ± 0.006 29.8 ± 0.8 0.3 ± 0.1 42.4 ± 6.2 <1.09 ± N.A.

Cp-2 78 35.3 ± 1.6 0.026 ± 0.006 29.5 ± 1.1 0.087 ± 0.009 20.0 ± 5.1 <1.19 ± N.A.

Cp-10 48 25.3 ± 3.0 2.700 ± 1.800 31.2 ± 2.2 5.150 ± 1.250 67.0 ± 11.0 <0.77 ± N.A.

Cp-8 44 39.1 ± 1.1 1.300 ± 0.220 30.5 ± 0.9 0.455 ± 0.165 19.8 ± 4.1 <1.22 ± N.A.

Cp-7 40 31.3 ± 1.4 0.011 ± 0.004 31.2 ± 1.4 0.815 ± 0.285 53.2 ± 6.5 1.67 ± 0.78

Cp-9 38 25.1 ± 4.1 0.028 ± 0.006 29.4 ± 2.0 4.450 ± 2.900 42.0 ± 12.0 <0.73 ± N.A.

Avg. 32.3 ± 2.1 0.683 ± 0.340 30.3 ± 1.4 1.874 ± 0.779 40.7 ± 7.5 <1.11 ± N.A.


156

Table A.49 Averaged LA-ICP-MS results of arsenopyrite grains from L4-14-134.5 polished thin section

Concentration2

Grain Size1, % ppm


Asp-7 264 0.021 ± 0.006 <0.116 ± N.A. 33.7 ± 2.2 0.197 ± 0.0316 <12.8 ± N.A. <1.72 ± N.A.

Asp-1 177 0.035 ± 0.009 <0.019 ± N.A. 33.5 ± 1.5 0.241 ± 0.032 <17.7 ± N.A. <5.48 ± N.A.

Asp-8 132 0.024 ± 0.013 0.041 ± 0.034 35.4 ± 2.9 0.247 ± 0.043 <21.1 ± N.A. <4.57 ± N.A.

Asp-4 97 0.015 ± 0.004 <0.056 ± N.A. 34.0 ± 1.3 0.176 ± 0.020 <17.0 ± N.A. <5.22 ± N.A.

Asp-2 71 0.016 ± 0.004 <0.011 ± N.A. 32.8 ± 1.1 0.088 ± 0.017 <10.5 ± N.A. 8.10 ± 2.40

Avg. 0.022 ± 0.007 <0.049 ± N.A. 33.9 ± 1.8 0.190 ± 0.029 <15.8 ± N.A. <5.02 ± N.A.


157

Table A.50 Averaged LA-ICP-MS results of pyrite grains from L4-14-134.5 polished thin section

Concentration2

Grain Size1, % ppm


Py-1 533 0.071 ± 0.018 <0.0257 ± N.A. 48.0 ± 4.9 0.32 ± 0.08 12.3 ± 2.3 <0.11 ± N.A.

Py-2 250 0.160 ± 0.031 <0.0287 ± N.A. 47.6 ± 5.1 0.09 ± 0.01 7.4 ± 1.1 <0.12 ± N.A.

Py-3 125 0.030 ± 0.006 <0.1654 ± N.A. 44.8 ± 3.1 0.59 ± 0.14 20.8 ± 3.2 <0.14 ± N.A.

Py-4 50 0.018 ± 0.005 <0.0005 ± N.A. 47.3 ± 4.2 1.46 ± 0.38 18.1 ± 4.2 <0.08 ± N.A.

Py-5 25 0.002 ± 0.000 0.0011 ± 0.000 41.0 ± 4.4 0.74 ± 0.16 3.2 ± 1.1 <0.10 ± N.A.

Avg. 0.056 ± 0.012 <0.0443 ± N.A. 45.7 ± 4.3 0.64 ± 0.15 12.4 ± 2.4 <0.11 ± N.A


158

Table A.51 Averaged LA-ICP-MS results of sphalerite grains from L4-14-134.5 polished thin section

Concentration2

Grain Size1, % ppm

ID µm Cu Fe Pb Ag Au

Sp-2 261 0.0072 ± 0.0005 2.7 ± 0.1 0.18 ± 0.04 42.8 ± 2.3 <0.10 ± N.A.

Sp-3 173 0.0993 ± 0.0086 3.2 ± 0.1 12.9 ± 2.5 21.3 ± 4.5 0.08 ± 0.05

Sp-4 131 0.0044 ± 0.0008 4.1 ± 0.4 3.9 ± 0.3 17.7 ± 5.0 <0.08 ± N.A.

Sp-6 82 0.0034 ± 0.0003 3.1 ± 0.1 3.0 ± 0.1 15.5 ± 0.7 <0.08 ± N.A.

Sp-7 65 0.0047 ± 0.0005 8.7 ± 1.7 8.40 ± 1.40 32.0 ± 3.5 0.13 ± 0.086

Sp-8 55 0.0423 ± 0.0043 3.2 ± 0.1 3.29 ± 0.08 99.0 ± 12.0 <0.07 ± N.A.

Sp-9 40 0.0090 ± 0.0011 4.8 ± 0.8 4.60 ± 0.62 175.0 ± 50.0 <0.08 ± N.A.

Avg. 0.0243 ± 0.0023 4.3 ± 0.5 5.18 ± 0.70 57.6 ± 11.1 <0.09 ± N.A.


159

Table A.52 Averaged LA-ICP-MS results of galena grains from L4-14-134.5 polished thin section

Concentration2

Grain Size1, % ppm

ID µm Fe Pb Ag Au

Gn-1 1000 <2.121 ± N.A. 93.1 ± 2.1 596.3 ± 14.9 <0.08 ± N.A.

Gn-2 250 <0.040 ± N.A. 93.7 ± 2.0 593.9 ± 13.4 <0.08 ± N.A.

Gn-3 170 <0.351 ± N.A. 92.3 ± 2.4 577.4 ± 14.6 <0.06 ± N.A.

Gn-4 125 <0.716 ± N.A. 90.5 ± 2.1 639.8 ± 16.4 <0.07 ± N.A.

Gn-5 94 1.111 ± N.A. 91.7 ± 2.4 571.2 ± 14.7 <0.06 ± N.A.

Gn-6 87 <0.002 ± N.A. 93.5 ± 3.0 670 ± 16 <0.06 ± N.A.

Gn-7 57 <0.002 ± N.A. 90.2 ± 2.8 423 ± 14 <0.06 ± N.A.

Gn-9 34 2.670 ± 0.5900 91.5 ± 1.0 356.8 ± 7.3 <0.07 ± N.A.

Avg. <0.891 ± N.A. 91.7 ± 2.3 483.3 ± 12.4 <0.07 ± N.A.


160

Table A.53 Averaged LA-ICP-MS results of tetrahedrite-tennantite grains from L4-14-134.5 polished thin section

Concentration2

Grain Size1, % ppm


Td-Tn-1 125 19.1 ± 0.8 2.6 ± 0.051 20.2 ± 4.4 0.3 ± 0.1 135000.0 ± 3200.0 0.59 ± 0.29

Td-Tn-2 92 15.8 ± 1.3 2.8 ± 0.057 7.0 ± 1.9 10.0 ± 3.5 141400.0 ± 4700.0 <0.44 ± N.A.

Td-Tn-3 72 14.9 ± 0.8

2.6 ± 0.062 9.8 ± 0.6 1.2 ± 0.4 138000.0 ± 3200.0 <0.62 ± N.A.

Td-Tn-5 50 17.1 ± 1.0

2.7 ± 0.063 3.0 ± 0.2 1.1 ± 0.2 139800.0 ± 3800.0 <0.34 ± N.A.

Avg. 16.7 ± 1.0

2.7 ± 0.058

10.0 ± 1.8

3.1 ± 1.1

138550.0 ± 3725.0

<0.50 ± 0.29



161

Table A.54 Averaged LA-ICP-MS results of arsenopyrite grains from L4-14-134.5 polished thin section

Concentration2

Grain Size1, % ppm


Asp-1 200 0.026 ± 0.003 1.643 ± 0.451 34.2 ± 2.7 4.8 ± 0.8 125.0 ± 12.0 3.88 ± 0.56

Asp-2b 75 0.011 ± 0.003 <0.863 ± N.A. 34.3 ± 1.2 0.9 ± 0.2 37.7 ± 8.6 12.05 ± 2.10

Asp-2a 72 0.028 ± 0.004 <0.006 ± N.A. 33.3 ± 2.0 8.8 ± 1.4 163.0 ± 24.0 29.20 ± 4.40

Asp-3 58 0.054 ± 0.007 4.74 ± 0.570 32.7 ± 2.8 8.9 ± 2.3 201.0 ± 20.0 4.14 ± 0.97

Asp-4 49 0.004 ± 0.003 <0.009 ± 0.006 33.9 ± 1.4 1.6 ± 0.3 46.0 ± 11.0 17.00 ± 3.40

Asp-5 45 0.021 ± 0.006 1.45 ± 0.680 33.5 ± 2.2 4.2 ± 0.7 115.0 ± 16.0 6.60 ± 1.90

Asp-7b 35 0.092 ± 0.014 <0.002 ± 0.002 32.2 ± 2.2 0.7 ± 0.2 136.0 ± 26.0 20.70 ± 2.90

Avg. 0.034 ± 0.005 <1.245 ± N.A. 33.4 ± 2.1 4.3 ± 0.8 117.7 ± 16.8 13.37 ± 2.32


162

Table A.55 Averaged LA-ICP-MS results of pyrite grains from L4-14-134.5 polished thin section

Concentration2

Grain Size1, % ppm


Py-1 400 0.0178 ± 0.0041 <0.0005 ± N.A. 48.2 ± 3.3 25.7 ± 2.3 18.7 ± 2.0 <0.72 ± N.A.

Py-2 175 0.0110 ± 0.0010 0.0024 ± 0.0005 48.8 ± 3.1 25.9 ± 2.0 31.7 ± 4.4 0.69 ± 0.08

Py-4 50 0.0014 ± 0.0002 0.0045 ± 0.0020 47.9 ± 4.7 0.3 ± 0.1 3.7 ± 0.78 <0.03 ± N.A.

Avg. 0.0100 ± 0.0018 <0.0025 ± N.A. 48.3 ± 3.7 17.3 ± 1.5 18.0 ± 2.4 <0.48 ± N.A.



163

Table A.56 Hypothesis tests comparing lens 3 and 4 thin section LA-ICP-MS results

Test Null Hypothesis P-Value Result

2- Sample t Test for Sp(g/t Ag) µ3 = µ4 0.323 No difference in means at the 0.05 level of significance

2- Sample t Test for Sp(g/t Au) µ3 = µ4 0.019 Significant difference in means at 0.05 level of significance

2- Sample t Test for Gn(g/t Ag) µ3 = µ4 <0.001 Significant difference in means at 0.05 level of significance

2- Sample t Test for Gn(g/t Au) µ3 = µ4 0.046 Significant difference in means at 0.05 level of significance

2- Sample t Test for Cp(g/t Ag) µ3 = µ4 0.044 Significant difference in means at 0.05 level of significance

2- Sample t Test for Cp(g/t Au) µ3 = µ4 <0.001 Significant difference in means at 0.05 level of significance

2- Sample t Test for Td-Tn(g/t Ag) µ3 = µ4 <0.001 Significant difference in means at 0.05 level of significance

2- Sample t Test for Td-Tn(g/t Au) µ3 = µ4 0.012 Significant difference in means at 0.05 level of significance

2- Sample t Test for Asp(g/t Ag) µ3 = µ4 0.891 No difference in means at the 0.05 level of significance

2- Sample t Test for Asp(g/t Au) µ3 = µ4 0.367 No difference in means at the 0.05 level of significance

2- Sample t Test for Py(g/t Ag) µ3 = µ4 0.115 No difference in means at the 0.05 level of significance

2- Sample t Test for Py(g/t Au) µ3 = µ4 0.008 Significant difference in means at 0.05 level of significance

2 - Sample t Test for Sp P80 Grain Size µ3 = µ4 0.974 No difference in means at the 0.05 level of significance

2 - Sample t Test for Gn P80 Grain Size µ3 = µ4 0.324 No difference in means at the 0.05 level of significance

2 - Sample t Test for Cp P80 Grain Size µ3 = µ4 0.712 No difference in means at the 0.05 level of significance

2 - Sample t Test for Td-Tn P80 Grain Size µ3 = µ4 N/A N/A

2 - Sample t Test for Asp P80 Grain Size µ3 = µ4 0.436 No difference in means at the 0.05 level of significance

2 - Sample t Test for Py P80 Grain Size µ3 = µ4 0.224 No difference in means at the 0.05 level of significance

164

Table A.57 Hypothesis tests comparing the combined lens 3 thin section LA-ICP-MS results


One-Way ANOVA for Ag, % of Total by Mineral µ1 = µ2 = µ3 = µ4 = µ51 0.003 Significant difference in means at 0.05 level of significance

One-Way ANOVA for Au, % of Total by Mineral µ1 = µ2 = µ3 = µ4 = µ51 0.002 Significant difference in means at 0.05 level of significance

One-Way ANOVA for Grade, g/t Agby Mineral µ1 = µ2 = µ3 = µ4 = µ51 0.071 No difference in means at the 0.05 level of significance

One-Way ANOVA for Grade, g/t Au by Mineral µ1 = µ2 = µ3 = µ4 = µ51 0.005 Significant difference in means at 0.05 level of significance

One-Way ANOVA for Sp (g/t Ag) by Thin Section µ1 = µ2 = µ32 0.064 No difference in means at the 0.05 level of significance

One-Way ANOVA for Sp (g/t Au) by Thin Section µ1 = µ2 = µ32 0.087 No difference in means at the 0.05 level of significance

One-Way ANOVA for Gn (g/t Ag) by Thin Section µ1 = µ2 = µ32 <0.001 Significant difference in means at 0.05 level of significance

One-Way ANOVA for Gn (g/t Au) by Thin Section µ1 = µ2 = µ32 0.078 No difference in means at the 0.05 level of significance

One-Way ANOVA for Cp (g/t Ag) by Thin Section µ1 = µ2 = µ32 0.164 No difference in means at the 0.05 level of significance

One-Way ANOVA for Cp (g/t Au) by Thin Section µ1 = µ2 = µ32 0.625 No difference in means at the 0.05 level of significance

One-Way ANOVA for Asp (g/t Ag) by Thin Section µ1 = µ2 = µ32 0.425 No difference in means at the 0.05 level of significance

One-Way ANOVA for Asp (g/t Au) by Thin Section µ1 = µ2 = µ32 0.187 No difference in means at the 0.05 level of significance

One-Way ANOVA for Py (g/t Ag) by Thin Section µ1 = µ2 = µ32 0.002 Significant difference in means at 0.05 level of significance

One-Way ANOVA for Py (g/t Au) by Thin Section µ1 = µ2 = µ32 0.919 No difference in means at the 0.05 level of significance

1Comparing sphalerite, galena, chalcopyrite, arsenopyrite and pyrite.

2Comparing thin sections 62-119-47.4, L2-16-67.4, and L2-16-70.

165


Thin Section Test Null Hypothesis P-Value Result

62-119-47.7 One-Way ANOVA for g/t Ag by Mineral µ1 = µ2 = µ3 = µ4 = µ51 <0.001 Significant difference in means at 0.05 level of significance

62-119-47.7 One-Way ANOVA for g/t Au by Mineral µ1 = µ2 = µ3 = µ4 = µ51 <0.001 Significant difference in means at 0.05 level of significance

L2-16-67.4 One-Way ANOVA for g/t Ag by Mineral µ1 = µ2 = µ3 = µ4 = µ5 = µ62 <0.001 Significant difference in means at 0.05 level of significance

L2-16-67.4 One-Way ANOVA for g/t Au by Mineral µ1 = µ2 = µ3 = µ4 = µ5 = µ62 0.045 Significant difference in means at 0.05 level of significance

L2-16-70 One-Way ANOVA for g/t Ag by Mineral µ1 = µ2 = µ3 = µ4 = µ51 <0.001 Significant difference in means at 0.05 level of significance

L2-16-70 One-Way ANOVA for g/t Au by Mineral µ1 = µ2 = µ3 = µ4 = µ51 0.173 No difference in means at the 0.05 level of significance


2Comparing sphalerite, galena, chalcopyrite, tetrahedrite-tennantite, arsenopyrite, and pyrite.

166

Table A.59 Hypothesis tests comparing the combined lens 3 thin section LA-ICP-MS results


One-Way ANOVA for Ag, % of Total by Mineral µ1 = µ2 = µ3 = µ4 = µ51 0.002 Significant difference in means at 0.05 level of significance

One-Way ANOVA for Au, % of Total by Mineral µ1 = µ2 = µ3 = µ4 = µ51 0.131 No difference in means at the 0.05 level of significance

One-Way ANOVA for Grade, g/t Agby Mineral µ1 = µ2 = µ3 = µ4 = µ51 0.076 No difference in means at the 0.05 level of significance

One-Way ANOVA for Grade, g/t Au by Mineral µ1 = µ2 = µ3 = µ4 = µ51 0.005 Significant difference in means at 0.05 level of significance

One-Way ANOVA for Sp (g/t Ag) by Thin Section µ1 = µ2 = µ32 0.400 No difference in means at the 0.05 level of significance

One-Way ANOVA for Sp (g/t Au) by Thin Section µ1 = µ2 = µ32 0.044 Significant difference in means at 0.05 level of significance

One-Way ANOVA for Gn (g/t Ag) by Thin Section µ1 = µ2 = µ32 <0.001 Significant difference in means at 0.05 level of significance

One-Way ANOVA for Gn (g/t Au) by Thin Section µ1 = µ2 = µ32 0.006 No difference in means at the 0.05 level of significance

2-Sample t Test for Cp (g/t Ag) by Thin Section µ1 = µ23 0.141 No difference in means at the 0.05 level of significance

2-Sample t Test for Cp (g/t Au) by Thin Section µ1 = µ23 0.519 No difference in means at the 0.05 level of significance

One-Way ANOVA for Asp (g/t Ag) by Thin Section µ1 = µ2 = µ32 0.021 No difference in means at the 0.05 level of significance

One-Way ANOVA for Asp (g/t Au) by Thin Section µ1 = µ2 = µ32 0.199 No difference in means at the 0.05 level of significance

One-Way ANOVA for Py (g/t Ag) by Thin Section µ1 = µ2 = µ32 0.252 No difference in means at the 0.05 level of significance

One-Way ANOVA for Py (g/t Au) by Thin Section µ1 = µ2 = µ32 0.420 No difference in means at the 0.05 level of significance


2Comparing thin sections L4-14-132, L4-14-134.5, L4-15-100.1

3Comparing thin sections L4-14-132 and L4-14-134.5

167


Thin Section Test Null Hypothesis P-Value Result

L4-14-132 One-Way ANOVA for g/t Ag by Mineral µ1 = µ2 = µ3 = µ4 = µ51 <0.001 Significant difference in means at 0.05 level of significance

L4-14-132 One-Way ANOVA for g/t Au by Mineral µ1 = µ2 = µ3 = µ4 = µ51 0.002 Significant difference in means at 0.05 level of significance

L4-14-134.5 One-Way ANOVA for g/t Ag by Mineral µ1 = µ2 = µ3 = µ4 = µ51 <0.001 Significant difference in means at 0.05 level of significance

L4-14-134.5 One-Way ANOVA for g/t Au by Mineral µ1 = µ2 = µ3 = µ4 = µ51 0.045 Significant difference in means at 0.05 level of significance

L4-15-100.1 One-Way ANOVA for g/t Ag by Mineral µ1 = µ2 = µ3 = µ4 = µ5 = µ62 <0.001 Significant difference in means at 0.05 level of significance

L4-15-100.1 One-Way ANOVA for g/t Au by Mineral µ1 = µ2 = µ3 = µ4 = µ5 = µ62 0.173 No difference in means at the 0.05 level of significance


2Comparing sphalerite, galena, chalcopyrite, tetrahedrite-tennantite, arsenopyrite, and pyrite.

Table A.61 Hypothesis tests comparing inter grain LA-ICP-MS results from the 62-119-47 thin section

Mineral Test Grains Null Hypothesis P-Value Result

Sp One-Way ANOVA for g/t Ag 641, 164, and 83 µm µ1 = µ2 = µ3 0.052 No difference in means at the 0.05 level of significance

Sp One-Way ANOVA for g/t Au 641, 164, and 83 µm µ1 = µ2 = µ3 0.409 No difference in means at the 0.05 level of significance

Gn 2-Sample t Test for g/t Ag 212 and 61 µm µ1 = µ2 0.061 Significant difference in means at 0.05 level of significance

Gn 2-Sample t Test for g/t Au 212 and 61 µm µ1 = µ2 0.695 No difference in means at the 0.05 level of significance

Asp 2-Sample t Test for g/t Ag 80 and 58 µm µ1 = µ2 0.164 No difference in means at the 0.05 level of significance

Asp 2-Sample t Test for g/t Au 80 and 58 µm µ1 = µ2 0.326 No difference in means at the 0.05 level of significance

168

Table A.62 Hypothesis tests comparing inter grain LA-ICP-MS results from the L2-16-67.4 thin section


Gn 2-Sample t Test for g/t Ag 250 and 88 µm µ1 = µ2 <0.001 Significant difference in means at 0.05 level of significance


Asp One-Way ANOVA for g/t Ag 150, 68, 56, and 43 µm µ1 = µ2 = µ3= µ4 0.056 No difference in means at the 0.05 level of significance

Asp One-Way ANOVA for g/t Au 150, 68, 56, and 43 µm µ1 = µ2 = µ3= µ4 0.382 No difference in means at the 0.05 level of significance

Py One-Way ANOVA for g/t Ag 1200, 200, and 100 µm µ1 = µ2 = µ3 0.012 Significant difference in means at 0.05 level of significance

Py One-Way ANOVA for g/t Au 1200, 200, and 100 µm µ1 = µ2 = µ3 0.003 Significant difference in means at 0.05 level of significance

169

Table A.63 Hypothesis tests comparing inter grain LA-ICP-MS results from the L2-16-70 thin section


Sp One-Way ANOVA for g/t Ag 368, 231, and 92 µm µ1 = µ2 = µ3 0.215 No difference in means at the 0.05 level of significance

Sp One-Way ANOVA for g/t Au 368, 231, and 92 µm µ1 = µ2 = µ3 0.185 No difference in means at the 0.05 level of significance

Gn 2-Sample t Test for g/t Ag 117 and 54 µm µ1 = µ2 0.107 No difference in means at the 0.05 level of significance


Cp One-Way ANOVA for g/t Ag 123, 79, and 52 µm µ1 = µ2 = µ3 0.053 No difference in means at the 0.05 level of significance

Cp One-Way ANOVA for g/t Au 123, 79, and 52 µm µ1 = µ2 = µ3 0.425 No difference in means at the 0.05 level of significance

Asp One-Way ANOVA for g/t Ag 150, 85, and 64 µm µ1 = µ2 = µ3 0.184 No difference in means at the 0.05 level of significance

Asp One-Way ANOVA for g/t Au 150, 85, and 64 µm µ1 = µ2 = µ3 0.250 No difference in means at the 0.05 level of significance

Py 2-Sample t Test for g/t Ag 882 and 310 µm µ1 = µ2 0.309 No difference in means at the 0.05 level of significance

Py 2-Sample t Test for g/t Au 882 and 310 µm µ1 = µ2 0.152 No difference in means at the 0.05 level of significance

Table A.64 Hypothesis tests comparing inter grain LA-ICP-MS results from the L4-14-132 thin section


Sp One-Way ANOVA for g/t Ag 298, 144, 98, and 62 µm µ1 = µ2 = µ3= µ4 0.141 No difference in means at the 0.05 level of significance

Sp One-Way ANOVA for g/t Au 298, 144, 98, and 62 µm µ1 = µ2 = µ3= µ4 0.110 No difference in means at the 0.05 level of significance

Gn One-Way ANOVA for g/t Ag 250, 81, and 60 µm µ1 = µ2 = µ3 0.123 No difference in means at the 0.05 level of significance

Gn One-Way ANOVA for g/t Au 250, 81, and 60 µm µ1 = µ2 = µ3 0.892 No difference in means at the 0.05 level of significance

Py One-Way ANOVA for g/t Ag 412, 200, and 100 µm µ1 = µ2 = µ3 0.698 No difference in means at the 0.05 level of significance

Py One-Way ANOVA for g/t Au 412, 200, and 100 µm µ1 = µ2 = µ3 0.804 No difference in means at the 0.05 level of significance

170



Sp One-Way ANOVA for g/t Ag 1200, 792, 544, 352, 227, and 156 µm µ1 = µ2 = µ3= µ4 = µ5 = µ6 0.642 No difference in means at the 0.05 level of significance

Sp One-Way ANOVA for g/t Au 1200, 792, 544, 352, 227, and 156 µm µ1 = µ2 = µ3= µ4 = µ5 = µ6 0.110 No difference in means at the 0.05 level of significance

Asp One-Way ANOVA for g/t Ag 264, 177, 132, and 97 µm µ1 = µ2 = µ3= µ4 0.123 No difference in means at the 0.05 level of significance

Asp One-Way ANOVA for g/t Au 264, 177, 132, and 97 µm µ1 = µ2 = µ3= µ4 0.502 No difference in means at the 0.05 level of significance

Py One-Way ANOVA for g/t Ag 533, 250, and 125 µm µ1 = µ2 = µ3 0.076 No difference in means at the 0.05 level of significance

Py One-Way ANOVA for g/t Au 533, 250, and 125 µm µ1 = µ2 = µ3 0.584 No difference in means at the 0.05 level of significance



Gn One-Way ANOVA for g/t Au 1000, 250, 170, 125, and 94 µm µ1 = µ2 = µ3= µ4 = µ5 0.485 No difference in means at the 0.05 level of significance

Gn One-Way ANOVA for g/t Ag 1000, 250, 170, 125, and 94 µm µ1 = µ2 = µ3= µ4 = µ5 0.015 Significant difference in means at 0.05 level of significance

Asp 2-Sample t Test for g/t Ag 200 and 75µm µ1 = µ2 0.318 No difference in means at the 0.05 level of significance

Asp 2-Sample t Test for g/t Au 200 and 75µm µ1 = µ2 0.370 No difference in means at the 0.05 level of significance

Py 2-Sample t Test for g/t Ag 400 and 175 µm µ1 = µ2 0.908 No difference in means at the 0.05 level of significance

Py 2-Sample t Test for g/t Au 400 and 175 µm µ1 = µ2 0.121 No difference in means at the 0.05 level of significance

171

Curriculum Vitae

Candidate’s full name:

Joshua Wright

Universities attended:

M.Sc. (Earth Sciences), 2013-2015

B.Sc. (Materials Engineering), 2006-2010

Publications:

Seniuk, H. A., Larry, W., Reed, D. D., and Wright, J. W., 2015, An examination of

matching with multiple response alternatives in professional hockey: Behavior

Analysis: Research and Practice.

Conference Presentations:

Wright, J.W., Lentz, D.R., and Philip, P.G., 2015, Comparative geometallurgical study of

massive sulphide ore characterization using mineral liberation analysis and optical

image analysis: NB CIM Mini Convention 2015: Bathurst, New Brunswick.

Wright, J.W., Lentz, D.R., and Philip, P.G., 2015, A geometallurgical analysis of Au-Ag

mineralization in the Caribou base-metal VMS deposit, New Brunswick:

Examination of nano- to micro-scale inter- and intra-sulphide distribution and its

relation to interpretation of saturation mechanisms: EMP 2015: Fredericton, New

Brunswick, Abstracts, p. 80.

Documents

Josh Wright Thesis Final 2-1