Geochemistry Exploration, Environment, Analysis 2016 Gray 100 15

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Geochemistry: Exploration, Environment, Analysis

Published online February 8, 2016 doi:10.1144/geochem2014-333 | Vol. 16 | 2016 | pp. 100–115

© 2016 The Author(s). Published by The Geological Society of London for GSL and AAG. All rights reserved. For permissions: http://www.geolsoc.org.uk/ permissions. Publishing disclaimer: www.geolsoc.org.uk/pub_ethics

Mineral exploration is becoming increasingly expensive in Western

Australia and throughout the world. Exploration targets are becom-

ing more difficult to find and greater emphasis is being placed on

exploring through deep (>30 m) transported cover and into basin

terrains. Groundwater interacting with mineralized rocks can create

a geochemical signature that may be much greater in size than iden-

tified from rock geochemistry. This can reduce the required sam-

pling density, assisting cost effective exploration in covered

terrains. Exploration hydrogeochemistry has been advocated formany areas of the world (e.g. Leybourne & Cameron 2010). The

authors have been developing this technology in Western Australia

(Gray 2001; Gray & Noble 2006; Gray et al . 2011, 2014), while

Giblin (1994), Kirste et al . (2003), Pirlo & Giblin (2004) and de

Caritat et al . (2005) have also pursued these methods in Australia.

This northern Yilgarn study was a test of concept for broad

scale hydrogeochemistry, with potential for lithological mapping,

establishment of environmental background and mineral explora-

tion in other areas, especially outside recognized mineralization

belts. The area selected within the Archaean-aged Yilgarn Craton,

hosts numerous Ni, Au and U ore systems, as well as several

VHMS Zn (Cu, Ag) deposits. Hydrogeochemical studies also pro-

vide information on rock weathering to benefit exploration effec-

tiveness in regolith-dominated terrains.

The sampling is relatively unbiased with regards to geology and

or mineralization. This enabled various other testing to be done,

including comparison of pumped v. ‘stagnant’ wells and bores,

how to measure ‘contamination’ in ‘stagnant’ wells, and which

elements are affected. Such metrics may have value when examin-

ing other databases based on similar sampling methodologies.

Specific applications of the use of groundwater chemistry in

exploring for particular commodities in Australia are discussed in

other papers (e.g. U by Noble et al . 2011, VHMS by Gray et al .

2014) with initial developments described in Gray et al . (2009,

2014). This paper discusses the methods used to develop and inter-

pret large scale regional hydrogeochemical databases. We also briefly discuss the utility of groundwater for lithogeochemical

mapping, and the various statistical and mathematical approaches

utilized to develop robust groundwater applications that can be

relatively easily applied elsewhere in Australia or the world.

Geological and groundwater characteristics of

the Yilgarn Craton

The >2500 Ma Yilgarn Craton is located within the SW of Australia

(Fig. 1). The geology of the northern Yilgarn Craton is comprised

of variably distributed Archaean-aged granites and greenstones

(dominantly mafic volcanic rocks, Fig. 2). The greenstones occur

in a series of belts including the Norseman-Wiluna, Yandal,

Duketon, Dingo Range, Meekatharra, Windimurra, and Gum

Creek Belts (Myers & Hocking 1998; Morris & Sanders 2001).

Cassidy et al . (2006) summarize the tectonic evolution of the

Yilgarn Craton. The northern Yilgarn is split by a continental

Regional scale hydrogeochemical mapping of the northern

Yilgarn Craton, Western Australia: a new technology for

exploration in arid Australia

David J. Gray1*, Ryan R.P. Noble1, Nathan Reid1, Gordon J. Sutton2 & Mark C. Pirlo3

1 CSIRO Mineral Resources, 26 Dick Perry Ave, Kensington, WA 6151, Australia2 School of Chemistry, University of New South Wales, Sydney, NSW 2052, Australia3 Exploration & Environmental Geochemist: www.pirlo.com.au* Correspondence: [email protected]

Abstract: The northern Yilgarn Craton, with an extensive mineral exploration history and relatively fresh and neutralgroundwaters, was selected to test the utility of regional hydrogeochemical mapping in Australia. The assembled data of2509 groundwater samples (generally at 4–8 km spacing) are relatively unbiased, allowing robust statistical analysis such as

testing sample types (flowing v. ‘stagnant’), contamination, and lithological controls on groundwater characteristics. Litho-logical indicators were developed to map underlying bedrock through cover. Areas with discrepancies between groundwaterresults and previous geological mapping were identified. Where these are areas previously discounted as prospective formineral commodities, they may now be re-considered on this basis. Even in well explored parts of this region, this studyidentified new areas which may have prospective rocks overlain with a thin (


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Hydrogeochemistry of the northern Yilgarn Craton 101

divide; to the east is the Southern Cross Domain and to the west is

the Murchison Domain, both part of the Youanmi Terrane, whichin turn is part of the Eastern Goldfields Superterrane. Both

Domains in this area have granitic rocks and granitic gneiss associ-

ated with extensive NE trending, elongate greenstone belts

(Williams 1975; Myers 1997). The granitic rocks comprise grano-

diorite-monzogranite and deformed and metamorphosed monzo-

granites (Cornelius et al . 2008). The greenstones generally

comprise mafic and ultramafic volcanic rocks underlain by quartz-

ite, banded iron formation and minor felsic volcanics. The

Windimurra Belt (between Mt Magnet and Sandstone; Fig. 2) is

different from the other greenstone belts in the sampling area. It is

a layered gabbroic intrusive containing vanadiferous and titanifer-

ous magnetite bands.

The Yilgarn Craton is commonly deeply weathered (20–100 m).

Surficial aquifers are mainly unconfined, with the water-tablecommonly 10–60 m below surface in the southern half and 2–35 m

in the north. Numerous researchers have discussed the Menzies

Line (Fig. 3), a groundwater/soil/botanical divide running approx-

imately EW at 29.5°S (e.g. Butt et al . 1977). South of this line, the

dominant flora are generally Eucalyptus (gum) species (often in

mallee form in more arid areas), and soils commonly contain cal-

cite (CaCO3; Chen et al . 2002). To the north, Acacia (wattle) or

Triodia (spinifex) species commonly dominate and soils are car-

bonate-poor (Anand & Butt 2010).

This surface differentiation is also consistent with major

groundwater differentiation: south of the Menzies line groundwa-

ters are commonly acidic and saline to hypersaline (Fig. 3;

Commander 1989); whereas to the north groundwaters are neutral

to alkaline and generally fresh, trending to saline in the main valley

floors (Gray 2001). This northern region was selected for the

hydrogeochemical mapping study, as it has generally fresh waters,

without the extreme variation in acidity (pH 3–8) and high salinity

(commonly >30 000 mg/L) of the southern Yilgarn Craton.

Additionally, the north Yilgarn has ubiquitous access to farm bores, as well as similarities to other central Australian environ-

ments. Within the northern Yilgarn Craton there is some salinity

differentiation: the thin blue lines in Figure 2 show the boundaries

of the major water catchments: most samples from the upstream,

higher elevation, parts of the catchment areas are fresh; compared

with the higher salinity along the valley floors. The NS broad blue

line at approximately 119°E represents the continental divide

between drainage to the Indian Ocean, and, to the east, internal

drainage to the Officer and Eucla Basins.

Sampling and field treatments

Shallow groundwater samples (n = 2509) were collected across the

northern Yilgarn Craton (Fig. 4) from wells and bores used forlivestock and human consumption, with the majority of samples

coming from groundwater with water tables within 10 m of the sur-

face. The most effective collection method (1457 samples), was

directly from actively pumping farm (windmill, solar) bores, as

close to the pumping stem as possible, to avoid any possible in-

train contamination or precipitation. Another 1052 groundwater

samples were collected using a flow-through bailer (fitted with

one-way valves) due to abandonment or access difficulties. Where

possible, bailed samples were collected c. 5 m below the water

table, but on occasion there was less than 5 m of water available,

and water was sampled from shallower depths.

The water sample is being brought upwards from depth and will

interact with the atmosphere. Addition of atmospheric O2 will

commonly cause precipitation of colloidal Fe hydroxide, which

could then lead to co-precipitation of metals of interest. Degassing

of CO2 will raise pH and also effect element solubilities.

These issues are minor for these surficial groundwaters which are

Fig. 1. (a) Simplified geology map of the

Yilgarn Craton, SW Australia (GSWA

2014), with continental divide between

Indian Ocean and internal drainage

shown; (b) Rainfall (mm) isohyets

(Australian Bureau of Meteorology,

1961–1990).

Fig. 2. Geology (GSWA 2014), main

towns, mines and water catchments of

the northern Yilgarn Craton. The broad

blue line denotes the continental divide

between Indian Ocean and internal

drainage, and the thin blue lines are the

main drainage systems. Greyed areas are

the main saline playas.

at Instituto Geologico Minero y Metalurgico on April 6, 2016http://geea.lyellcollection.org/ Downloaded from

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D.J. Gray et al.102

generally neutral pH and moderate Eh (>0 mV), low (commonly


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endpoint of pH = 4.3 at CSIRO Laboratories in Kensington,

Western Australia. Direct comparisons of field and laboratory

alkalinity analyses for 44 sites showed a direct linear correlation

between analyses for these shallow groundwaters. Major anions

(Cl, SO4, Br, F and NO3) were analysed using the filtered sample

by Ion Chromatography (IC) at CSIRO Laboratories in Kensington,

Western Australia. The IC equipment used was a Metrohm modu-

lar IC using an acid re-generated suppressor, MetroSep A Supp5

column, a carbonate/bicarbonate eluent (32 mM Na2CO3 and

10 mM NaHCO3) and a conductivity detector. A sample split was

sent to CSIRO Land and Water Laboratory in Adelaide for analy-

sis of Dissolved Organic Carbon (DOC) and PO4.

Major elements Al, B, Ca, Cr, Cu, Fe, K, Li, Mg, Mn, Na, P, S,

Si, Sr and Zn) were analysed by Inductively Coupled Plasma

Optical Emission Spectroscopy (ICP-OES) at CSIRO Land and

Water Laboratory in Adelaide, SA. Trace elements (Ag, As, Ba,

Cd, Ce, Co, Cr, Cu, Dy, Er, Eu, Ga, Hf, Ho, La, Lu, Mo, Nb, Nd,

Ni, Pb, Pr, Rb, Sb, Sc, Sm, Sn, Sr, Ta, Th, U, V, W, Y, Yb, Zn and

Zr) were analysed by Inductively Coupled Plasma Mass

Spectrometry (ICP-MS): the first quarter of the samples were ana-

lysed at Geoscience Australia, Canberra, and the rest at CSIRO

Land and Water in Adelaide, SA. Many samples were still belowdetection for REE, Ag, Cd and Ni. Detection limits for ICP analy-

ses are affected by salinity, with more saline samples having

higher detection limits due to increased dilution requirements

(Table 1). Initial batch effects were removed with increases of

reported detection limits (see 4.3 and 4.4 below).

Dissolved Au, Ag and PGE were below detection in groundwa-

ter samples, hence the use of an activated carbon sachet to pre-

concentrate these critical pathfinders. The carbon analysis was

performed by a commercial laboratory (Bureau Veritas in

Canningvale, WA). The activated charcoal was ashed, and the

residue dissolved in aqua-regia. The solution was analysed by

ICP-MS for Au, Pt, Pd, and Ag. Laboratory investigations have

indicated that using this preconcentration system will enable suc-cessful analyses of waters for Au, Ag and PGE at low concentra-

tions, though at a lessened accuracy than via standard analyses.

Calibration of the method was obtained by shaking standards of

varying concentrations, and in varying salinities, with activated

carbon.

Solution modelling

Equilibrium activity diagrams were derived using The

Geochemist’s Workbench® (Bethke 2007). Solution chemical spe-

ciation and degree of mineral saturation are determined as

Saturation Indices [SI; log10[(Ion Activity Product)/(Solubility

Constant)] from the solution compositions using the program

PHREEQE (Parkhurst et al . 1980). If the SI for a mineral is withinthe zero range the water is in equilibrium with that mineral, under

the conditions specified. The zero range is estimated for every

mineral based on stoichiometry, thermodynamic accuracy and

analytical issues; ranging from −0.2 to 0.2 for major element min-

erals such as halite or gypsum to (for example) −1.5 to 1.5 for a

complex minor element mineral such as carnotite (KUO2VO4).

Where the SI is below the zero range, the solution is under-satu-

rated with respect to that mineral, so that, if present, the phase may

dissolve. If the SI is greater than zero the solution is over-saturated

with respect to this mineral, which could potentially precipitate

from solution.

Such determinations only specify possible reactions, as kinetic

constraints may rule out reactions in shallow environments at low

temperature and pressure (i.e. approximately 25°C/1 atm). For

example, waters are commonly in equilibrium with calcite, but

may become over-saturated with respect to dolomite, due to the

slow rate of precipitation of this mineral (Drever 1982). However,

this method still provides understanding of the thermodynamic

constraints on solution processes at a site. This assists in determin-

ing whether the spatial distribution of an element is correlated with

geological properties such as lithology or mineralization, or

whether they are related to weathering/environmental effects. For

example, if Ca distribution is controlled by equilibrium with gyp-

sum in all samples, then the spatial distribution of dissolved Ca

will reflect SO4 concentration alone and have no direct exploration

significance.

Quality control

The quality control of analyses was monitored by analysing labora-

tory standards (1 in 20 samples), blanks (1 in 20) and duplicate

samples (1 in 15 samples for anion and alkalinity analyses, 1 in

20 samples for ICP-MS and ICP-OES). Both the Geoscience

Australia (Canberra) and CSIRO (Adelaide) laboratories were used

over this time for ICP-MS analyses, as well as having some of the

equipment change due to upgrades. Therefore, special care was taken

in the cross laboratory QA/QC. In addition to the duplicates within

the north Yilgarn samples, duplicates from previous projects were

submitted as another check. Using duplicates, blanks and standardsresults, the errors for each element were calculated as % difference

errors, half relative difference errors and 95% confidence errors on

the batch (Stanley & Lawie 2007). The errors are defined as:

% difference error =

assay1 assay2 /maximum assay1or assay2−( ) ( ) ×

( )−

100

Half relative difference =

assay1 assay2 / assay1+ assay22 100

95% confidence = 1.96

( ) ×

±

σ

n

Where 1.96 is the 95th percentile of a normal distribution with a

mean value of 1, σ is the standard deviation of all assay1-assay2

values, and n is the number of duplicates. Differences were calcu-

lated for each duplicate pair then a 95% confidence error was calcu-

lated on these differences, as with Gray et al . (2009). The standard

errors were the 95% confidence of all replicates. The standard

errors were used to determine analytical precision and, the dupli-

cate errors determined sample heterogeneity. Elements with errors

less than 10% were acceptable, whereas those greater than 10% and

with greater than 2 times laboratory detection limit were investi-

gated in more detail to ensure a reliable detection limit (as below).

Determination of sample concentration errors

The errors determined from the duplicate analyses, standards and blanks were used to determine the upper confidence limit for data

interpretation for each element (Table 1), on the principle that all

values below the greatest error value are noise in the data. Filtering

the data to these higher values reduced the noise component and

smoothed the data. For example, the laboratory quoted detection

limit for As was 0.01 µg/L; however, from the various batches and

laboratories the duplicate, standard and blank errors were ±0.76,

0.14, 0.19, 0.42 and 0.34 µg/L. Therefore, the maximum error of

0.76 µg/L was rounded to a 1 µg/L confidence limit, to ensure robust

interpretation of any concentration differences between sites. This

determination was done for all minor and trace elements (Table 1).

Data treatment and development

Pumping v. ‘stagnant’ wells or bores

For consistency and comparability, groundwater sampling and

field treatment protocols need to be defined and documented.


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D.J. Gray et al.104

Samples from routinely pumping windmills, solar or other pump-

ing bores were distinguished as ‘flowing’, whereas non-pumping

wells or bores were defined as ‘stagnant’. Presumably, continu-

ally pumping windmills provide a better representative sample

than ‘stagnant’ wells. However, it may still be a compromised

sub-sample - for example, in this survey the observed Zn concen-

trations consistently greater than 10 µg/L could be partially due to

minor contamination from flow through galvanized piping. A big-

ger issue is the bailing of water from ‘stagnant’ wells and bores.

Not using these water samples causes broad gaps in the mapping

area. Testing to see if bailed samples are useable and clarifying

how to robustly incorporate these samples is critical for this sur-

vey, and additionally may have value in dealing with historical

data.

Table 1. Summary statistics and percentiles for groundwaters of the north Yilgarn Craton. Below detection: Dy, Er, Eu, Gd, Hf, Ho, Lu, Nb, Pr, Sc, Sm,Ta, Tb, Te, Th, Ti, Tl, Yb and Zr

Name Units Method No. Values Confidence Limit Median Std Dev# Min Max 25th Per. 75th Per. 95th Per.

TDS mg/L 2463 0 1576 2910 59 80145 940 2809 6392

pH Field meter 1965 0.02 7.6 0.5 5.5 10.9 7.4 8.0 8.5

Eh mV Field meter 1828 20 354 57 −54 572 317 385 436

HCO3 mg/L Titration 1672 2 200 95 4 775 140 260 360

F mg/L IC 2387 0.14 0.8 2.6 0.07 73.8 0.4 1.2 2.2

Cl mg/L IC 2459 10 640 1477 5 38310 340 1260 3200

Br mg/L IC 1884 0.1 2.4 3.2 0.1 47.2 1.4 4.2 8.2

NO3 mg/L IC 1453 1 59 45 0.5 1106 42 81 129

SO4 mg/L IC 1669 8 192 452 4 12200 112 312 717

PO4 mg/L Auto Analyser 1647 0.04 0.05 0.1 0.02 1.02 0.02 0.07 0.20

DOC mg/L TP 1647 1 2.5 2.2 0.5 48.5 1.2 3.4 5.7

Ca mg/L ICP-OES 1979 2 73 89 3 868 47 115 261

K mg/L ICP-OES 1979 2 20 46 1 1660 12 34 77

Mg mg/L ICP-OES 1979 2 59 111 1 2887 36 103 234

Na mg/L ICP-OES 2210 5 378 853 9 24004 212 714 1730

Al mg/L ICP-OES 612 0.01 0.005 0.030 0.005 0.5 0.005 0.005 0.04

B mg/L ICP-OES 1668 0.1 0.9 1.0 0.05 26 0.6 1.3 2.5

Fe mg/L ICP-OES 1072 0.01 0.005 0.15 0.005 4.3 0.005 0.02 0.13

Si mg/L ICP-OES 1673 1 35 9 0.5 57 28 40 46Sr mg/L ICP-OES 2458 0.05 0.8 1.2 0.025 16.8 0.5 1.3 3.0

Mn mg/L ICP-OES 1284 0.005 0.0025 0.05 0.0025 0.99 0.0025 0.005 0.03

Li µg/L ICP-OES 2158 2 2 19 1 280 1 12 42

V µg/L ICP-MS 1658 5 15 23 2.5 290 10 30 60

Cr µg/L ICP-MS 1624 1 0.5 9 0.5 310 0.5 1 7

Co µg/L ICP-MS 1635 0.5 0.25 0.3 0.25 8 0.25 0.25 0.25

Ni µg/L ICP-MS 1642 3 1.5 5 1.5 120 1.5 1.5 3

Cu µg/L ICP-MS 1660 5 10 56 2.5 925 5 25 80

Zn µg/L ICP-MS 1660 10 30 212 5 4970 20 60 200

Ga µg/L ICP-MS 494 0.2 0.1 0.0 0.1 0.4 0.1 0.1 0.1

Ge µg/L ICP-MS 465 1 0.5 0.1 0.5 2 0.5 0.5 0.5

As µg/L ICP-MS 2450 1.5 0.75 58 0.75 2795 0.75 3 7.5

Se µg/L ICP-MS 441 2 4 2.5 1 24 2 4 8

Rb µg/L ICP-MS 2440 5 15 25 2.5 375 10 30 65Y µg/L ICP-MS 1244 0.2 0.1 0.1 0.1 2.8 0.1 0.1 0.2

Zr µg/L ICP-MS 1406 1 0.5 0.1 0.5 2 0.5 0.5 0.5

Mo µg/L ICP-MS 1966 2 4 16 1 318 2 10 28

Cd µg/L ICP-MS 1577 0.5 0.25 0.1 0.25 3.5 0.25 0.25 0.25

In µg/L ICP-MS 505 0.2 0.1 0.0 0.1 0.2 0.1 0.1 0.1

Sn µg/L ICP-MS 1661 1 0.5 0.5 0.5 14 0.5 0.5 0.5

Sb µg/L ICP-MS 1212 0.8 0.4 1.3 0.4 44 0.4 0.4 0.4

Ba µg/L ICP-MS 1671 2 52 70 1 2542 38 72 126

La µg/L ICP-MS 1412 0.2 0.1 0.2 0.1 5.2 0.1 0.1 0.1

Ce µg/L ICP-MS 1429 0.2 0.1 0.3 0.1 12 0.1 0.1 0.1

Pr µg/L ICP-MS 947 0.2 0.1 0.0 0.1 0.4 0.1 0.1 0.1

Nd µg/L ICP-MS 949 0.2 0.1 0.2 0.1 7.2 0.1 0.1 0.1

Hf µg/L ICP-MS 1300 1 0.5 0.0 0.5 1 0.5 0.5 0.5

Ta µg/L ICP-MS 1413 0.5 0.25 0.0 0.25 0.5 0.25 0.25 0.25W µg/L ICP-MS 2147 2 1 1.0 1 26 1 1 1

Tl µg/L ICP-MS 413 0.5 0.25 0.0 0.25 0.5 0.25 0.25 0.25

Pb µg/L ICP-MS 1587 1.2 0.6 2.2 0.6 82.8 0.6 0.6 1.2

Th µg/L ICP-MS 993 1 0.5 0 0.5 0.5 0.5 0.5 0.5

U µg/L ICP-MS 1966 2 4 35 1 696 1 14 64

Au ng/L ICP-MS/C 2368 3 1.5 9 1.5 357 1.5 1.5 6

Pt ng/L ICP-MS/C 2411 4 2 3 2 160 2 2 2

Pd ng/L ICP-MS/C 2312 4 2 1.2 2 48 2 2 2

Ag ng/L ICP-MS/C 2264 50 25 237 25 8200 25 25 150




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There are major time and logistic issues in purging remote bores

and wells. Additionally, purging of monitoring wells can signifi-

cantly affect sample quality and may itself lead to non-representa-

tive groundwater samples (Barcelona et al . 1994; Nielsen &

Nielsen 2006). A traditional purging strategy is the removal of a

fixed, but arbitrarily defined, number of well volumes. This typi-

cally involves purging 3–5 well volumes but does not account for

variation in site-specific hydrogeology, well response, purging rateor provide independent chemically-based confirmation of when a

‘representative’ groundwater sample enters the well (Barcelona

et al . 1994; Nielsen & Nielsen 2006). The well may be hydrauli-

cally over-purged and dewatered, causing aeration of the forma-

tion and increased sample turbidity. Such issues will be particularly

marked in purging 3+ well volumes of a well several metres diam-

eter and 10’s of metres deep.

The north Yilgarn terrain is primary an uncovered, deeply

weathered environment and the surficial aquifer being sampled in

this study is un- or weakly- pressurized. With predominantly hori-

zontal groundwater flow and uncased well walls, there will be a

significant component of lateral flow through the well. Use of a

bailing system, preferably to 5 m below the water table, may stillderive a useful sample indicative of the surrounding groundwater.

This procedure has been used for ‘stagnant’ wells and bores in this

study, though (as discussed below) with statistical comparisons

against regularly pumping sources.

A number of processes within a ‘stagnant’ well or bore may

modify the original signal, including degassing, interaction with

well linings or metal infrastructure, or organic contamination. It

may be difficult to totally separate these effects, and for the pur-

poses of this discussion, these are all included as ‘contamination’.

Intuitively, we expect that severely contaminated samples (e.g.

dead animals in wells) should be used with extreme caution,

whereas wells that have been routinely pumped until the week

before sampling may offer good results for many elements. This

leads to the contention that ‘stagnant’ samples cannot be treated asa single sample set, and if ‘stagnant’ samples are used it is critical

to find a way to characterize and group these samples – i.e. a ‘con-

tamination index’. As described below, combining empirical evi-

dence with an understanding of the chemical changes occurring in

such wells and bores enabled us to develop metrics for ‘contamina-

tion’ in ‘stagnant’ samples, which when interpreted conservatively

gives a data-set for which the sampling errors are minor relative to

the differences in original chemistry.

Visual logging of the condition of the site and visual and smell

testing gives important information and we integrated this into a

field logging scheme. Comparative concentration distributions of

flowing and ‘stagnant’ sample sets were examined by plotting the

ranked concentration (or, where appropriate, log concentration) v.of each element v. the N-score values (i.e. z-scores from a standard

normal distribution (mean = 0 and a standard deviation = 1) using in

IoGas® (IoGlobal 2011). Some specific elements, such as Mn or P

(measured as PO4; Fig. 5), differed greatly between flowing and

‘stagnant’ samples (and were used as ‘contamination’ parameters).

For example, PO4 is significantly higher in ‘stagnant’, relative to

flowing, samples (Fig. 5), with 45% of the ‘stagnant’ samples hav-

ing PO4 contents greater than the 98th percentile level for flowing

samples.

The most strongly (orders of magnitude) enriched solutes in

‘stagnant’ samples (particularly when visibly discoloured or

smelly) were DOC, PO4 (expected to be from biological sources),Mn (possibly reductive leaching), Fe and Zn (possibly from vari-

ous sources including metallic materials). Not surprisingly, there

are inter-relationships between their chemistries in ‘contaminated’

samples (Figs 6 and 7). Dissolved Fe and Mn can be used in this

manner for this sample set as they are commonly low in these

surficial groundwaters (for flowing samples: Fe – median

0.004 mg/L, mean 0.037 mg/L; Mn – median 0.002 mg/L, mean

0.010 mg/L).

Samples observed in the field to be contaminated (based on the

state of the well and/or the colour and smell of the samples) had a

number of clear differences:

- Significantly higher in at least one of DOC, PO4, Mn, Fe and

Zn;- Significantly lower in various elements expected to be sensitive

to reduction, such as U and Cr, and NO3 and SO4 (when plotted

relative to Cl).

Other parameters also showed variation, such as Eh which was

commonly lower in highly ‘contaminated’ samples, and bicarbo-

nate which trended towards higher values in ‘contaminated sam-

ples’ (and weakly correlated with DOC). However, these two

parameters were not useful in robustly determining ‘contamina-

tion’, as the ‘contamination’ effects were less than the background

variation.

Determination of ‘contamination’ values and

factors

DOC, PO4, Mn, Fe and Zn were used to create a ‘contamination

value’ (CV):

CV = Mean log P

log OC

log Fe

log Mn

10 10 10 100 277 6 94 0 234 0 05.

,.

,.

,. 99 0 546

10,.

(all elements ).

log Zn

in mg/L

The denominators used for each of the variables are the 98 th per-

centile for each variable in the flowing bore dataset. As shown

below, the CV was used to split the samples into 6 ‘contamination

factor’ (CF) classes (% shown is the proportion of each CF class of

the entire dataset):

CF 1: Flowing bores (all other CF groups are bailed samples) 58%

CF 1.8: CV < −0.8 (bailed, ‘uncontaminated’) 9%

CF 2.2: −0.8 < CV < −0.417 (bailed, very slightly ‘contaminated’)

12%

CF3: −0.417 < CV < −0.1 (bailed, slightly ‘contaminated’) 9%

Fig. 5. Probability plot comparison

of ranked concentration v. n score

for flowing and ‘stagnant’ samples,

showing distinct population differences.

Separation of the lines indicatesdifferences in the types of samples. Both

Mn and PO4 have different distributions

in the ‘stagnant’ water samples compared

to the flowing water samples.




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D.J. Gray et al.106

Fig. 6. Dissolved PO4-P v. organic

carbon for flowing and ‘stagnant’ samples

of northern Yilgarn groundwaters,

showing distinct population differences.

‘Stagnant’ samples are distinguished

based on available field logging. Dashed

lines represent 98 percentile lines for the

flowing samples.

Fig. 7. Dissolved Mn v. Fe for flowing

and ‘stagnant’ samples of northern

Yilgarn groundwaters, showing distinct

population differences. ‘Stagnant’

samples are distinguished based on

available field logging. Dashed lines

represent 98 percentile lines for the

flowing samples.

CF4: −0.1 < CV < 0.3 (bailed, ‘contaminated’) 10%

CF5: 0.3 < CV (bailed, highly ‘contaminated’) 2%

The CF 1 and 1.8 Groups had very similar probability distributions

for almost all elements (e.g. Figs 8 and 9). The CV cut-off of


http://geea.lyellcollection.org/http://geea.lyellcollection.org/http://geea.lyellcollection.org/


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Table 2. Influence of contamination on each measured component, i.e.those with only CF Class 1 kept are the most influenced by contamination,whereas up to CF Class 5 kept indicates the whole data set was used

Element CF Kept Element CF Kept Element CF Kept

DOC 1 Si 1.8 F 4

Al 1.8 SO4 1.8 K 4

B 1.8 V 1.8 Mg 4

Ba 1.8 Zn 1.8 Na 4

Co 1.8 Fe 2.2 Pd 4

Cr 1.8 pH 2.2 Rb 4

Cu 1.8 U 2.2 Sr 4

Eh 1.8 Mo 3 TDS 4

HCO3 1.8 REE 3. W 4Mn 1.8 Sn 3 Ag 5

Ni 1.8 Au 4 As 5

NO3 1.8 Br 4 Li 5

Pb 1.8 Ca 4 Pt 5

PO4 1.8 Cd 4

Sb 1.8 Cl 4

Fig. 8. Contamination factor

probability plot spli t of groundwater U

concentrations. CF classes 1, 1.8, and 2.2

have similar probability populations and

therefore unaffected by contamination,

whereas classes 3, 4 and 5 are culled

from the U data set. Data is also

represented as boxplots: median (black

line), mean (black dot), quartiles (1st

and 4th whiskers), outliers (circles) and

extreme outliers (triangles).

Fig. 9. Contamination factor

probability plot spli t of groundwater As

concentrations. The various CF classes

were not statistically distinguished.

Arsenic data is also represented as

boxplots as for Figure 8.

−0.417 between CF Classes 2.2 and 3, is equal to the 98% percen-

tile value for CV in the flowing bore dataset.

Probability plots were prepared for all elements, coloured by CF

(e.g. Figs 8 and 9). For each element, the probability line for each

CF group were compared with the CF 1 line and culled if the vari-

ation was visually significant. Thus, for example, the CF 3, 4 and

5 groups showed probability lines well below that for CF 1 for

dissolved U (Fig. 8) and U data was deleted for these CF groups.

Data for each element that was significantly different from the CF1

population were culled (Table 2). Although this technique should

work well in most instances, some of the high values removed –

particularly for Zn, Mn and Fe – could be natural. In essence, we

have taken the conservative decision to risk some loss of the high-

est anomalous Fe, Mn etc values in the ‘stagnant’ samples, rather

than include any erroneous results caused by contamination. As

more than half the sample set is the flowing samples (CF1), this

will have little effect on the threshold for anomalous values. Such

differentiation is possible as this data is relatively unbiased (not

just sampling over ore deposits). This methodology can be adapted

for other hydrogeochemical surveys.

Element excess and depletion

Using element ratios (compared to Cl or Na), some data were

observed to be in excess or deficit. The ratio distance away from a

standard sea water (SW) line (i.e. the compositions of the ions if

sea water were diluted or concentrated by evaporation) was deter-

mined, and provided a numerical measurement of the excess or

depletion. Element ratios discussed in this paper are Sr with respect

to Ca (Fig. 10), Rb with respect to K, K with respect to Na, and

SO4 with respect to Cl.

The formulas listed below (all in mg/L, except Rb in µg/L) were

derived so as to robustly measure deviation from the sea water line

for the specific ion pair. Thus, for KNaSW, the 0.0363 value is the

K:Na ratio in sea water. Sea water ratios are used, as there is a

major input of sea water salt into the Yilgarn Craton, presumably

as aerosols (McArthur et al . 1989). Using the different calculation

methods for lower ion concentrations minimizes skewing the cal-

culated indices due to analytical errors close to detection limits. At

higher concentrations these become a ratio difference (Fig. 10):




9/19

D.J. Gray et al.108

KNaSW = [2 x (K – 0.0363 x Na)]/[0.0363 x (Na + 500)] … Na <

500 mg/L

= [K – 0.0363 x Na]/[0.0363 x Na] … Na ≥ 500 mg/L

SO 4ClSW = [2 x (SO4 – 0.1396 x Cl)]/[0.1396 x (Cl + 500)] … Cl

< 500 mg/L

= [SO4 – 0.1396 x Cl]/[0.1396 x Cl] … Cl ≥ 500 mg/L

SrCaSW = [2 x (Sr – 0.0195 x Ca)]/[0.0195 x (Ca + 20)] … Ca <

20 mg/L= [Sr – 0.0195 x Ca]/[0.0195 x Ca] … Ca ≥ 20 mg/L

RbKSW = [2 x (Rb – 0.306 x K)]/[0.306 x (K + 20)] … K <

20 mg/L

= [Rb – 0.306 x K]/[0.306 x K] … K ≥ 20 mg/L

e.g. for Ca > 20 mg/L

- SrCaSW = 2 means the Sr/Ca sample ratio is 3 x sea water

- SrCaSW = 1 means the Sr/Ca sample ratio is 2 x sea water

- SrCaSW = 0 means the Sr/Ca sample ratio is equal to that of sea

water

- SrCaSW = −0.5 means the Sr/Ca sample ratio is half that of sea

water

- SrCaSW = −0.75 means the Sr/Ca sample ratio is one quarter

that of sea water

- SrCaSW = −0.95 means the Sr/Ca sample ratio is one twentieth

that of sea water

This interpretation is visually indicated in a plot of Sr v. Ca (Fig.

10), with the data points classified according to the SrCaSW index.

Red dots are at and above the Sr:Ca sea water line and are consid-

ered to have a high Sr:Ca ratio. In contrast the blue triangles are

very low in Sr, relative to Ca, and have a high Ca:Sr ratio.

5.4 Elemental indices

Each element concentration was scaled from 0 to 1 based on the0.1th and 99.9th percentile of the data (if normal distribution) or of

the logged data (if log normal distribution, Fig. 11). There are

some detection issues with this process as some elements are

below detection for many of the samples. In this instance the detec-

tion limit was set as the zero value for the index. This will skew the

normal (or log normal) distribution at the low end.

Single element and multi-element indices

A number of elements dissolved in groundwater are useful in dis-

criminating lithologies in the area. For example, dissolved U is higher

over granites, and V and Cr concentrations higher on basic rocks.

However, use of multielement indices gives better lithological dis-

crimination. Given the observed V, Cr enrichment in basic lithologiesin greenstone belts, and of U in granites a lithological index was cal-

culated to test discrimination of greenstone from granitic lithologies:

Lithol 1 = V + Cr 2 U− ×

Fig. 10. Dissolved Sr v. Ca for North

Yilgarn Craton groundwaters, coloured

by SrCaSW range.

Fig. 11. Example of elemental index

generation for dissolved U. The base10

logarithm of uncontaminated classes 1,

1.8 and 2.2 (Fig. 8) was incorporated and

then scaled between 0 and 1.




10/19


Other elemental indices have been developed as mineral explo-

ration tools. These are used to show the presence of weathering

sulphides, Fe-rich sulphides, Ni-rich sulphides and other commod-

ities such as Au and U (Gray & Noble 2006; Gray et al . 2009,

2014; Noble et al . 2011).

Mapping of data and lithological differentiation

Following the data treatment described above, the various element

concentrations, ion ratio indices, and saturation indices were plot-

ted overlain on the geological mapping of the Yilgarn Craton (e.g.

Fig. 12). As will be described, a number of differing parameters

closely matched the underlying geology and/or were effected by

faults and geological contacts. In addition, specific areas showed

anomalous groundwater parameters, and these will be highlighted

(Fig. 4), and discussed further in the text. These anomalous areas

were commonly identified using multiple parameters, strengthen-

ing the interpretation.

Statistical methods

The groundwater measurements together with the specified rock

types were used to fit various models that identified the geology

using the groundwater measurement data. There were 11 rock

types as mapped by Geological Survey of Western Australia

(GSWA 2014), which were split into two broad classes, referred to

as Greenstone and Granite (using the GSWA nomenclature), as

follows:

• Greenstone rock types: mafic intrusive, mafic volcanic,

ultramafic volcanic, mafic volcanic, meta mafic, sedimen-

tary siliciclastic.

• Granite rock types: felsic volcanic, granitic, meta felsic

intrusive, protolith unknown, mixed (Felsic rocks weregrouped with the Granite rock types.)

This was a very broad distinction, but is a useful concept. If all 11

categories were treated separately, some categories contained too

few sites for model identification. Details of the datasets, methods

and statistical programming are found in Sutton et al . (2010). To

create a model that distinguished between the two classes, bino-

mial Generalized Linear Models (GLM) were used. The response

variable is a zero-one variable, with Granite denoted 0 and

Greenstone denoted 1. The model provides a fitted value between

zero and one, which estimates the probability that a particular site

is Greenstone, based on the analyte groundwater chemistry at that

site. Initially, a binomial GLM was fitted using all sites and 35

analytes. This gave a baseline on the expected error rate that could

be achieved. A model was then fitted using a forward-backward

selector to select the analytes to include in the binomial GLM,

based on minimising the Akaike information criterion (Sutton

et al . 2010). By this method all samples were allocated to one of

the two geological groups.

Slope analysis

Geology (GSWA 2014), magnetics and gravity (GeoscienceAustralia 2009), radiometric (Geoscience Australia 2009), drain-

age (GSWA 2014) and Multi-resolution Valley Bottom Flatness

(MrVBF; Gallant & Dowling 2003) data sets were used for maps

and interpretation. The MrVBF index was used to map the palaeo-

channels: it provides a ‘flatness’ index between 0 and 8, with

scores of 8 being the flattest regions, which commonly includes

areas with the thickest regolith cover.

Results

The analytical and statistical tests, new detection limits and ‘con-

tamination’ filtering provided a robust dataset. The dataset gener-

ated in this study is available with the original report (Gray et al .

2014) or by contacting the authors.

Major geochemical parameters

Groundwaters of the northern Yilgarn are generally fresh, but

become more saline approaching palaeo-drainage channels/valley

floors, where water evaporates and salt lakes occur (Gray 2001).

However, there are a number of areas of saline fluids in upland

areas, which can be recognized when lowland groundwaters are

removed from the mapping (using MrVBF cut-off of 5.6; ‘Slope

analysis’ section). Thus, in Figure 12 the highlighted areas denote

upland regions with anomalously high salinities. These areas are

generally associated with other anomalies:

• A single sample at Area A (51 000 mg/L TDS; 1.5 x seawater) is extremely saline for an uplands area. As expected,

it is at gypsum saturation, and additionally is anomalously

high in SO4, relative to Cl;

• Area B (>5000 mg/L) is an area which is anomalous in a

number of elements. It is potentially unrecognized green-

stone assemblages under granite that is prospective for Au

and Ni (Gray et al . 2014);

• Area F (>10 000 mg/L) sits along a granite greenstone con-

tact. This is high in the landscape as it is the continental

watershed divide, and these salinities are highly anomalous.

Groundwater is generally neutral to weakly alkaline and oxidized

(Fig. 13). Although pH can be indicative of Fe sulphide weathering

at a local scale, these effects are not readily distinguished in the

regional dataset. In broad terms, granitic groundwater tends to be

weakly acidic to neutral, whereas groundwaters above greenstones

tend to be neutral to alkaline, a result of the increased abundance

Fig. 12. Groundwater salinities (TDS)

in the northern Yilgarn Craton in upland

areas only (MrVBF cutoff of 5.6). The

highlighted areas denote upland zones

with significant saline groundwaters.




11/19

D.J. Gray et al.110

of olivine and orthopyroxene in mafic and ultramafic rocks.

Relatively reduced groundwater (Eh below 260 mV) is a response

to weathering of ultramafic rocks or nearby sources, such as

reduced palaeochannel sediments, rock sulphides, or even reduced

water sources from deep fault systems. Such effects have com-

monly been observed close to ore bodies and other sulphide sys-

tems, but are rarely observed in these regional shallow

groundwaters.

Lithological indicators

Several elements (particularly when combined as indices) are

effective lithological indicators. Note that this requires the element

in question to be generally soluble in these environments; thus rare

earth elements (elevated in granites for example) have little utility

in these fresh neutral groundwaters due to being commonly below

detection.Gray (2003) demonstrated that dissolved Cr (as Cr 6+) is a con-

sistent indicator of greenstone lithology, particularly ultramafic

rocks at the prospect (2 µg/L separate

greenstones from granites (the two major lithological groups in the

Yilgarn Craton). However, there are significant areas of high Cr in

the SE granite system surrounding the Leonora – Leinster green-

stones (Areas B–E; Fig. 14). These are areas of deep weathering

and quite possibly significant transported cover, given the broad

drainage systems (>100 km wide). These results suggest signifi-

cant, unmapped or hidden, greenstone ‘slivers’ within mapped

granite areas. In general, geological mapping of underlying lithol-

ogy may be difficult where significant transported cover masks the

bedrock and inference of unit continuity linked with geophysics

contributes to large sections of the geological map. Future research

will move from the relatively exposed rocks of the northern

Yilgarn Craton to more covered terrains to test the potential of

these methods to map through cover.

The distribution of dissolved V also shows broad-scale varia-

tion spatially related to lithology (Gray et al . 2014), with green-

stones having greater V concentrations than granites. Although the

spatial correlation with geology is not as close as for dissolved Cr

(Fig. 14), dissolved V is consistently >20 µg/L for groundwaters

contacting mafic lithologies. It also tends to be high in most of the

specific granite areas with anomalously high dissolved Cr. High

dissolved V in areas dominated by granite (particularly observed

in the west of the sampling area) may indicate V-rich granites,

potentially due to substitution in minerals such as magnetite (Dare

et al . 2014; Nadoll et al . 2014), biotite and muscovite (Tischendorf

et al . 2001, 2007), and tourmaline (Trumbull et al . 2008). The

availability of dissolved V from granites as well as mafic rocksmay be highly relevant to the location of secondary U (as carnotite;

K 2(UO2)2(VO4)23H2O; Noble et al . 2011) as this represents addi-

tional sources for the V in this secondary mineral.

Whereas Cr and V occur in greater concentrations over green-

stones compared to granites, the opposite is true for U. Uranium is

significantly higher in granites and is an additional lithological

indicator (Fig. 15). However, there are clear differences between

different granite areas: much of the SE granite area (121°40'E/28°S)

and, to a lesser degree, granites immediately east of Cue

(118°10'E/27°45'S) are low in dissolved U. Area F, with high dis-

solved U along the main NS drainage divide spine of the Yilgarn,

indicates a geochemical anomaly to the west of the Youanmi

greenstone/granite contact. Analogous variation in granite chemis-

try occurs with dissolved F (Gray et al . 2014).

Lithological discrimination can be improved by combining

element data. The Lithol1 index (V + Cr − 2 × U; Fig. 16)

more clearly separates greenstones (triangles; Lithol1 > 0.5) from

Fig. 13. Groundwater pH and Eh

conditions in the northern Yilgarn Craton.Grey areas are pH/Eh zones where Fe

is dominantly present as the relevant

solid phase. Created using Geochemists

Workbench®, [25°C, 1.013 bars, [Fe] =

0.001 M, [SO4] = 0.001 M, and [HCO3]

= 0.01 M, troilite (FeS) suppressed].

Modelling used the thermo.com.v8.r6+.

dat database, with additional data from

Warner et al . (1996).




12/19


granites (dots; Lithol1 < −0.3) than single element abundances.

Potentially, such indices could be used to map lithology in basinal

regions, such as east or north of the Yilgarn Craton margin. Similar

anomalous areas are observed in the Lithol1 index data as for sin-

gle element results (e.g. Figs 14 and 15).

There is also potential to use ratios between major elements for

lithological discrimination. These parameters are expected to be

highly robust, as they use elements that are conservative in these

environments, and, if useful, will expand the utility of historical

groundwater data which commonly includes only major element

data. One useful parameter is Ca relative to Sr (Fig. 10): although

these elements are highly differentiated in rocks they have similar

reactivity in the regolith. Areas of high Ca:Sr are spatially corre-

lated with greenstone belts (Fig. 17). As observed using the other

parameters discussed above, Ca-enrichment in the western part of

Area F is consistent with greenstone characteristics, although

anomalism is less apparent for Areas D, G and H. Rubidium related

to K (Fig. 18), also discriminates lithology, and does pick up Areas

B-E as all having mafic characteristics, although this index does

appear more ‘noisy’. For both indices, the values in Area J are

highly variable: this area to the NW is the older Narryer Domain

(Cassidy et al . 2006), which is highly altered and has been exten-

sively structurally reworked, potentially leading to many lithologi-

cal ‘fragments’. The K:Na index also partially discriminates

lithologies, but with more noise still.

This lithological discrimination can be recognized through sta-

tistical analysis. In Figure 19, overlays of the GLM statistical anal-

ysis (for the NE Yilgarn only) are presented on a map of the

region’s geology. For the majority of sites the modelled geology

agrees with the mapped geology. Circles calculate as granites or

felsics, in agreement with geological mapping. Squares denote

groundwaters which calculate as granites or felsics, contrary to

Fig. 14. Groundwater Cr distribution for

the northern Yilgarn Craton.

Fig. 15. Groundwater U distribution for

the northern Yilgarn Craton.

Fig. 16. Lithol1 Index (V+Cr-2xU)

partially discriminates granites (dots) and

greenstones (triangles). Areas of granite

with significant Cr+V excess are circled.




13/19

D.J. Gray et al.112

map. Triangles calculate as greenstones, in agreement with mapped

geology. Pentagons calculate as greenstone, though presentlymapped on this large scale geological map as granites/felsics.

Many of these, not surprisingly, occur near boundaries. However,

Areas B, C, E and I all show up as being spatially anomalous, with

the statistical analysis characterising samples within the areas as

‘greenstone’ groundwaters, though the geological mapping is as

granite. These are interpreted as unmapped ‘rafts’ of greenstones,

or thin (


14/19


under cover. This study demonstrates the validity of this technique,

and here we also identified several areas of significant anomalism.

In particular, Areas B–E and, to a lesser degree, H–K indicate

‘rafts’ of prospective greenstone slivers or altered granites unrec-

ognized previously because they mainly occur in highly weathered

terrains with playas and exotic cover. Analysis of the broad scale

gravity geophysical measurements indicates Area B to have denser

rocks, supporting the hypothesis of incorrect lithological assign-

ment. In Areas A, D, H and G there is little indication of higher

density rocks in the regional gravity, possibly due to insufficient

data density and/or obscuring of the gravity measurements by

thick overburden.

Groundwater results from this study encouraged exploration in

Area F by Resource Mining Corporation Ltd (RMC), who observed

komatiitic rocks in drill spoil (RMC 2010), validating the ground-

water-based interpretation.

Conclusions

In contrast to southern regions of the Yilgarn Craton (Gray 2001),

the waters in the northern Yilgarn Craton are fairly homogeneous

and dominantly fresh and neutral, though with increased ground-

water salinity in the base of palaeo-drainages and close to

salt lakes. The groundwaters also have relatively low dissolved

Fig. 19. Lithological differentiation

from GLM analyses of groundwater

geochemistry (using Rb, Cr, U, B, Ca, Cl,

Sr, Ba, F, Mg, Co, HCO3, Br, V, Na) for

NE Yilgarn Craton.

Fig. 20. SO4ClSW Index distribution in

northern Yilgarn Craton groundwaters.

Fig. 21. Dissolved Ba in northern Yilgarn

Craton groundwaters.




15/19

D.J. Gray et al.114

concentrations of metals compared with groundwater from the

southern Yilgarn. Understanding of water chemistry, along with

new approaches to measurement and filtering for ‘contamination’

has allowed the development of a number of interpretative outputs.

These outputs include:

• Metrics for ‘contamination’, and statistical analysis of ele-

mental sensitivity to contamination, providing greater con-

fidence in the data.

• Lithological indicators (Cr, V, U and other elements,

Lithol1 index, Sr:Ca, Rb:K etc.), separating greenstones

from granites and mapping lithology through cover at the

sample spacing of 4-10 km used in this study.

• Identification of previously unrecognized zones for poten-

tial mineral occurrences where interpretations from

groundwater differ from previous geological mapping.

• Areas of S enrichment that are related to varying geologi-

cal areas and/or fault structures, the latter indicating that

these structures still have active input into the surface envi-

ronment.

• Anion excess and depletion methods have been developed

and are a valuable for detecting sulphides. This coupled

with multi-element indices (Gray et al . 2014) provides a

solid platform for the use of hydrogeochemistry in explora-

tion for sulphide occurrences in relatively fresh and neutral

groundwater environments.

Groundwater chemistry determined from samples collected from

bores and wells is useful for defining lithological changes, hydro-

thermal alteration and mineralization. The developed methods and

interpretation from this study of more than one thousand waters

sampled from the shallow aquifer of the northern Yilgarn Craton

can be applied to extend exploration into other covered areas with

similar groundwater characteristics. Similar groundwater environ-

ments include much of the northern two thirds of Australia, par-ticularly the underexplored basins and Craton margins. Provided

that access to groundwater exists, hydrogeochemical exploration

provides a tool to evaluate prospectivity and improve exploration

efficiency in areas of cover and difficult terrain.Acknowledgements

and Funding

Acknowledgements and Funding

This project was supported by CSIRO, GSWA and by MERIWA (Projects M402

and M414) and the specific industry sponsors. Richard Jarrett provided exten-

sive advice in the development of generalized linear models for lithological

discrimination.

Financial support was provided by the following industry sponsors: Agnew/

Goldfields, AMF, Anglo-American, AngloGold Ashanti, Aragon Resources,

Areva, Resources, Avoca Resources, Barrick, BHP-Billiton, Cameco, Crescent

Gold L, Cullen Resources, Drake Resources/Aura Energy, Echo, EncounterResources, Enterprise Metals, Geotech, Heron Resources, Image Resources/

Emu Nickel, Independence Group, Jindalee Resources, Maximus Resources,

Mega Redport, Mindax, Minjar Gold, New Era, Newmont, Norilsk, Oz Minerals,

Regalpoint, Rio Tinto Exploration, RMC, Spark Energy, Thundelarra, Toro

Energy (Nova Energy), Troy Resources, Venture Minerals, Venus Resources,

Windy Knob Resources. Further in-kind and financial support from MERIWA,

Geological Survey of Western Australia, Geoscience Australia, CRC LEME,

DET CRC, and Department of Water made this project possible.

Additional, in-kind support for accommodation was provided by Apex

Minerals, Darlot Mine site (Barrick), Echo Resources, BHPB and Troy

Resources.

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