Supplementary Material
Establishing a High Quality Baseline of Geochemical Data
In total, this project selected and analysed 1,319 samples from the Abitibi greenstone belt (AGB) from 2007 to 2009.

a)	In early 2007, 172 samples from the OGS LGC legacy rock powder stockpile were chosen and submitted to OGS GeoLabs for new major, minor and rare earth element analysis. 
b)	In summer 2007, 114 new samples were collected from outcrop and drillcore, and a further 519 were selected from the OGS LGC legacy stockpile, and submitted to OGS GeoLabs in late 2007 for new major, minor and rare earth element analysis.
c)	In 2008, a further 101 samples were selected from the OGS legacy stockpile and submitted to the GSC Labs for new major, minor and rare earth element analysis.
d)	In 2009, 413 legacy samples from the collection at the GSC Québec office were sent to ACME Labs in Vancouver for new minor and rare earth element analysis.

As part of this study, a number of legacy lithogeochemical analysis from Quebec (SIGEOEM database) and Ontario (LGC database) were re-analysed for major elements, trace elements, rare earth elements, CO2, H2O and S. Table 1 provides a summary of these analyses.

Table 1 – Reanalysed pulps and new samples			
Re-analyses	Area	No. Analyses	
LGC 2006	Ontario	172	
LGC 2007	Ontario	519	
SIGEOM	Quebec	413	
2007 Field Work	Ontario	114	
GSC Labs	Ontario	101	
Total		1319	

In total 1319 samples were re-analysed throughout the Blake River Group and Kidd-Munro areas of Quebec and Ontario.

Despite the shortcomings of these databases, the contents represents a large store of potentially useful information that can reveal important information about the chemostratigraphy, alteration and potential base- and previous-metal mineralization that exists within the AGB.

The samples were analysed for (Si, Al, Fe+3, Mg, Ca, Na, K, Ti, P, Mn) using X-ray Fluorescence (XRF) on fused pellets. Fe+2, CO2, H2O+, S rare earth elements (La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Yb, Lu) and additional trace elements (Ba, Be, Bi, Cd, Co, Cr, Cs, Cu, Ga, Hf, Li, Mo, Nb, Ni, Pb, Rb, Sb, Sc, Sn, Sr, Ta, Th, Ti, Tl, Tm, U, V, W, Y, Zn, Zr). 

Hand samples and pulps that were submitted for re-analyses were subject to quality control procedures. Samples analysed by GeoLabs used internal standard reference materials, laboratory duplicates and blanks for quality control. The results of all analyses were within acceptable limits. Details on the analyses including the results of the quality control are available in Hilary & Grunsky (2010).

Samples submitted to the GSC laboratory were analysed using XRF and ICP-MS. The major elements oxides were determined using XRF and ICP-MS was used to determine a range of trace and rare earth elements. Details on the quality control are provided in Hilary & Grunsky (2010). H2O+ was not analysed for this group of samples

Rock powders were obtained from Le ministère des Ressources naturelles et de la Faune, Quebec (MRNF) and were subsequently analysed by ACME Laboratories in Vancouver, British Columbia. The order of pulps was randomized with certified reference materials and pulp duplicates inserted at regular intervals. FeO was determined by titration Rare earth and refractory elements were determined by ICP mass spectrometry following a Lithium metaborate / tetraborate fusion and nitric acid digestion of a 0.2 g sample (same decomposition as Group 4A). In addition a separate 0.5 g split is digested in aqua regia and analysed by ICP Mass Spectrometry to report the precious and base metals. CO2, S and H2O+ were determined by combustion and infrared spectroscopy. 

For both the LGC and SIGEOM databases, major elements derived by XRF and ICP were merged under the assumption that for the vast majority of samples, there would little difference between the two methods within the range of values being considered for study. For the rare earth elements, only samples determined from ICP-MS were considered for further evaluation.

A significant issue in the use of legacy datasets is the recognition of how Fe is reported. In many cases Fe was reported as total Fe2O3 although this may not have been explicitly stated in any accompanying metadata. As well, FeO may accompany the Fe2O3 result. Some assumptions and adjustments were made to the Fe data on a dataset-by-dataset basis.
Where FeO was reported as zero, it was assumed that Fe2O3 was reported as total Fe2O3 (Fe2O3T).
For analyses derived from the SIGEOM database in Quebec, Fe2O3 was reported as total Fe2O3 (Fe2O3T) and true (Fe2O3V). Table 3 lists a sequence of adjustments that were made to the data.

A total of 35 datasets were used in this study; assembled from sources in Ontario and Quebec. The data were merged into a common set of major element oxides, unique sample number, geographic coordinates, rock name and associated tectonic assemblage (if known). Inconsistencies with the various datasets included, missing values for FeO, CO2, H2O+ and S. Many samples had missing major element data (e.g. SiO2, Al2O3, etc.) and these were eliminated. One issue that emerged in the data merge process was the correct allocation of Fe. In many cases, only total Fe2O3 was reported and no distinction was made on the amount of Fe+3 or Fe+2 abundances in the rock samples, Each dataset was scanned for the way Fe was reported. Samples that reported both Fe2O3 and FeO were left as is and Fe2O3T was determined by the formula: Fe2O3T = Fe2O3 + FeO/0.8998. Other datasets reported FeO only. This value was converted to Fe2O3T by dividing the FeO value by 0.8998. Samples in which only Fe2O3 were reported were treated as Fe2O3T. Samples for which there was ambiguity in the determination of Fe, were discarded. A decision tree for handling Fe data is shown in Table 2.

Similarly, the volatile components, LOI, CO2 and H2O+ required a specific ordered sequence for processing missing values. The sequence is listed in Table 3.

The merged datasets was also screened for non-zero values of SiO2, Al2O3, Fe2O3T, MgO, CaO, Na2O, K2O, TiO2, MnO and P2O5, and Loss On Ignition (LOI = CO2 + S + H2O+) for a total of 28,508 samples. There were 25,858 samples with LOI > 0 and 4,830 samples with CO2 and H2O+ > 0. 

Table 2. Decision tree for Fe correction in whole rock analyses

Case of: Fe2O3t == 0 & Fe2O3v == 0 & FeO == 0
Drop these analyses

Case of: Fe2O3t == 0 & Fe2O3v > 0 & FeO > 0
Fe2O3t=Fe2O3v + FeO/0.8998

Case of: Fe2O3t == 0 & Fe2O3v == 0 & FeO > 0
Fe2O3t=FeO/0.8998

Case of: Fe2O3t == 0 & Fe2O3v >0 & FeO == 0
Fe2O3t=Fe2O3v; 
Fe2O3v=0

Case of: Fe2O3t > 0 & Fe2O3v >0 & FeO > 0
Leave as reported

Case of: Fe2O3t > 0 & Fe2O3v == 0 & FeO > )
Case of: Fe2O3t < FeO/0.8998
Fe2O3v=Fe2O3t
Fe2O3t=Fe2O3v + FeO/0.8998

Case of: Fe2O3t >= FeO/0.8998
Fe2O3v=Fe2O3t - FeO/0.8998

Case of: Fe2O3t > 0 & Fe2O3v > 0 & FeO == 0
Case of: Fe2O3t == Fe2O3v
Fe2O3t left as reported
Fe2O3v=0

Case of:Fe2O3t < Fe2O3v
Fe2O3t=Fe2O3v
Fe2O3v=0

Case of: Fe2O3t > Fe2O3v
Leave as reported

Case of: Fe2O3t > 0 & Fe2O3v == 0 & FeO == 0
Leave as reported

Table 3. Decision tree for volatile replacement strategy in whole rock analyses

Case 1:
CO2 = 0 & S = 0 & H2O+ = 0 & LOI = 0

H2O+, CO2 and LOI were estimated using the regression methods described above. S was arbitrarily set to a value 0.01 weight %. LOI, H2O+ and CO2 are estimated in order. H2O+, CO2 and S are then summed and plotted against the estimated LOI. Estimates with significant discrepancies (< 0 or > 30%) are removed from the population.
 
Case 2:
CO2 = 0 & S = 0 & H2O+ = 0 & LOI > 0
H2O+ and CO2 are estimated along with a preset value of 0.01% for S. The sums of the estimated components are compared against the actual LOI. Based on the ratios of H2O+ and CO2 with LOI, these two components are adjusted so that they sum to LOI – 0.01%

Case 3
CO2 = 0 & S > 0 & H2O+ > 0
Where LOI is available and is greater than the sum of CO2, S and H2O+, the estimate of CO2 is calculated to be LOI – H2O+ - S.
Where LOI is available and less than the sum of CO2, S and H2O+, the estimate of CO2 is calculated using the regression method described above. LOI is computed as the sum of the three components. The original LOI is discarded.

Case 4
CO2 > 0 & S = 0 & H2O+ >0 & LOI > 0
Where S=0 and LOI > sum of CO2, S and H2O+, S is computed as: LOI – (CO2 + H2O+).
Where S=0 and LOI < sum of CO2, S and H2O+, S cannot be estimated and is set 0.01%

Case 5
CO2 > 0 & S > 0 & H2O+ = 0
Where H2O+=0 and LOI > sum of CO2, and S,  H2O+ is computed as: LOI – (CO2 + S).
Where H2O+=0 and LOI < sum of CO2, and S,  H2O+ is predicted using the regression method described above and LOI is adjusted as the sum of . CO2, S and the estimated  H2O+.

Table 3 (continued). Decision tree for volatile replacement strategy in whole rock analyses

Case 6
CO2 > 0 & S = 0 & H2O+ = 0
Where LOI > sum of CO2, S and H2O+, set S=0.01
H2O+ = LOI - CO2 - S
Where LOI <= sum of CO2, S and H2O+
H2O+ is estimated using the regression described above and LOI is set as the sum of CO2, S and the estimated H2O+. Negative estimates of H2O+ are set to 0.01

Case 7
CO2 = 0 & S = 0 & H2O+ > 0
Where LOI > sum of CO2, S and H2O+, set S=0.01
CO2 = LOI - H2O+- S
Where LOI <= sum of CO2, S and H2O+
CO2is estimated using the regression described above and LOI is set as the sum of the estimated CO2, S and H2O+. Negative estimates of CO2 are set to 0.01

Case 8
CO2 == 0 & S > 0 & H2O+ == 0
Where LOI is not equal to the  sum of CO2, S and H2O+
H2O+ and CO2 are predicted using the regression methods described above.
The values of H2O+ and CO2 are adjusted to sum to LOI – S based on the ratios of the predicted H2O+ and CO2.

Predictive Relationships in Lithogeochemical Data – Estimating 
Missing Values

Irvine & Baragar (1971) suggested that iron correction be based on the amount of TiO2 and defined the following formula:

if (FeO>(1.5+TiO2) then
	Fe2O3=Fe2O3-(Fe2O3-(TiO2+1.5))
	FeO=FeO+(Fe2O3-(TiO2+1.5))*.8998

LeMaitre (1976) used modern volcanic sequences to adjust iron ratios by a regression of these ratios on the alkalis. Middlemost (1989) published a diagram of Fe+3/Fe+2 ratios for modern volcanic rocks based on the TAS diagram, which is assumes that there has been no major element cation mobility. The use of a regression method to estimate  Fe+3/Fe+2 ratios as employed by LeMaitre (1976) and the TAS diagram of Middlemost (1989) would be unreasonable to apply to Archean rocks because of the mobility of the alkali elements.

For this study, the Fe2O3 was estimated using a regression approach based on 1069 samples that were re-analysed (Table 4). The R statistical programming environment was used for the processing of the data (R-Project 2011). Because the rocks of the Blake River and Kidd-Munro volcanic assemblages are not considered to be alkaline, the detailed approach suggested by Middlemost (1989) is unnecessary and a regression using robust estimators was considered an effective approach. A plot of Fe2O3/SiO2 versus Fe2O3T/SiO2 is shown in Figure S1a. The presence of outliers based on low Fe2O3 values relative to Fe2O3T results in a dispersed pattern. The ratios of Fe2O3/SiO2 to Fe2O3T/SiO2 were examined through the use of quantile-quantile plots. Outliers were trimmed based on log10[(Fe2O3/SiO2)/(Fe2O3T/SiO2)] greater than -0.2 and less than -0.8. The resulting population of 883 samples is shown in Figure S1b. Note that the dispersion of the relationship between the two variables increases with increasing ratios. The figure shows a distinct positive trend although it is heteroscedastic, that is, the variance increases with increasing ratios of (Fe2O3/SiO2) and (Fe2O3T/SiO2). Despite this variable uncertainty, a linear regression was applied to test the prediction of Fe2O3. Figure S1c shows a plot of actual Fe2O3/SiO2 values versus predicted Fe2O3/SiO2 values. The pattern, while not ideal, could be used as a basis for estimating missing values. The regression equation was subsequently used to estimate Fe2O3 values for analyses in which Fe2O3T is reported and Fe2O3 is missing. Figure S2 shows a plot of Fe2O3 predicted versus Fe2O3 the results of the regression applied to the assembled datasets used in the study. The fit of the points is considered acceptable although there are obvious linear features in the data that reflect assumptions that were made for missing values and are likely related to the problem of closure. For samples where Fe is reported as Fe2O3T or FeOT, this regression approach was used to first estimate Fe2O3 and then determine FeO by the molecular weight adjustment of 0.8998*(Fe2O3T – Fe2O3).

Table 4 – Fe2O3 Regression

Call:
lm(formula = Fe2O3/SiO2 ~ (Fe2O3T/SiO2))

Residuals:
      Min        1Q    Median        3Q       Max 
-0.109060 -0.017354 -0.003435  0.010861  0.229588 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  4.612e-04  2.720e-03   0.170    0.865    
Fe2O3T       1.342e-02  7.830e-04  17.140   <2e-16 ***
Fe2O3T:SiO2 -1.474e-04  1.757e-05  -8.388   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 0.0312 on 1123 degrees of freedom
Multiple R-squared: 0.5293,     Adjusted R-squared: 0.5285 
F-statistic: 631.5 on 2 and 1123 DF,  p-value: < 2.2e-16

Figures S1a, S1b, S1c here

Figure S2 here

Estimation of H2O+, CO2 and LOI
A robust regression method (rlm) from the R statistical package was used to estimate H2O+, CO2 and S based on the set of reanalysed data. These methods were applied using a cross-validation technique of sampling the data 25 times with groups 50 samples. The resulting residuals were compared at the end of the 25 runs. Quantile-quantile plots of the residuals were plotted for each of the regression for H2O+ and CO2. These plots demonstrated the uniformity of the sample population for providing estimates of regression.

Lattice water (H2O+) was estimated using the following robust regression formula (see Table 5):

H2O+/CaO= f(SiO2/CaO, Al2O3/CaO, Fe2O3T/CaO, MgO/CaO, K2O/CaO, Na2O/CaO, TiO2/CaO, MnO/CaO, P2O5/CaO).

Lattice water content is controlled by the hydration of igneous minerals within volcanic rocks by the conversion of olivines to serpentinite, pyroxene to chlorite/pumpellyite/actinolite/tremolite, alkali feldspars to sericite and plagioclase feldspars to prehnite/zoisite/epidote. Thus, the inclusion of lattice water into low-grade regionally metamorphosed volcanic rocks is stoichiometrically controlled. Figure S3 displays the actual versus predicted values of H2O+. These results appear to be reasonable for use in estimating values of H2O+ where it is unknown, based on the assumption that the regional metamorphic grade is the same.

Table 5: Robust Regression for H2O+
Call: rlm(formula = H2Op/CaO ~ SiO2/CaO + Al2O3/CaO + Fe2O3T/CaO + MgO/CaO + K2O/CaO + Na2O/CaO + TiO2/CaO + MnO/CaO + P2O5/CaO) Residuals:
       Min         1Q     Median         3Q        Max 
 -1.367902  -0.067023  -0.004909   0.069787 103.107597 

Coefficients:
            Value    Std. Error t value 
(Intercept)   1.5170   0.1177    12.8872
SiO2         -0.0032   0.0013    -2.4999
Al2O3         0.0158   0.0037     4.2661
Fe2O3T        0.0051   0.0055     0.9265
MgO           0.0555   0.0030    18.6678
K2O          -0.0559   0.0084    -6.6293
Na2O         -0.0879   0.0064   -13.6997
TiO2         -0.0914   0.0385    -2.3758
MnO          -1.0561   0.1822    -5.7978
P2O5          0.1495   0.0925     1.6151
SiO2:CaO     -0.0019   0.0001   -18.4477
CaO:Al2O3     0.0010   0.0004     2.3885
CaO:Fe2O3T   -0.0021   0.0007    -2.9677
CaO:MgO      -0.0063   0.0004   -16.8144
CaO:K2O      -0.0102   0.0014    -7.0855
CaO:Na2O     -0.0032   0.0009    -3.6250
CaO:TiO2      0.0047   0.0048     0.9732
CaO:MnO       0.1701   0.0248     6.8557
CaO:P2O5     -0.0022   0.0095    -0.2289

Residual standard error: 0.1004 on 953 degrees of freedom

Figure S3 here

The estimation of CO2 is complicated by the fact that the introduction of carbonate into a rock is a process that is not related to its initial formation. Therefore, the balance between CO2 and the geochemistry is not in equilibrium with the fractionation of a volcanic rock. Pervasive carbonatization of a volcanic rock is stoichiometrically controlled mostly by the presence of plagioclase. It is common that calcite veins may be present in varying minor quantities in volcanic rocks. As a result, the correlation between volcanic composition and pervasive carbonatization is confounded by the presence of these veinlets. Thus, the measure of predictability of CO2 as a function of volcanic rock composition may be difficult to predict with a high degree of accuracy. As a result, at low levels of CO2, the correlation with volcanic composition is poor. In order to minimize the influence of this effect, analyses with less than 1% by weight CO2 were dropped from the regression. CO2.

The following ratio-based robust regression model was used to estimate CO2 content of a rock:

CO2/CaO= f(SiO2/CaO, Al2O3/CaO, Fe2O3T/CaO, MgO/CaO,  K2O/CaO, Na2O/CaO, TiO2/CaO, MnO/CaO, P2O5/CaO).

Figure S4 shows a plot of predicted CO2 values versus actual CO2 values for the reanalysed data.

Table 6: Robust Regression for CO2
Call: rlm(formula = CO2/CaO ~ SiO2/CaO + Al2O3/CaO + Fe2O3T/CaO + MgO/CaO +     K2O/CaO + Na2O/CaO + TiO2/CaO + MnO/CaO + P2O5/CaO)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.37462 -0.15647 -0.01435  0.18789  0.70268 

Coefficients:
            Value   Std. Error t value
(Intercept)  7.9929  1.2756     6.2660
SiO2        -0.0743  0.0143    -5.2086
Al2O3       -0.0383  0.0438    -0.8734
Fe2O3T      -0.1449  0.0392    -3.6971
MgO         -0.1572  0.0584    -2.6918
K2O         -0.0500  0.0837    -0.5977
Na2O        -0.0380  0.0559    -0.6790
TiO2         0.0993  0.2795     0.3552
MnO          0.3925  1.7731     0.2213
P2O5         0.3442  1.0160     0.3388
SiO2:CaO    -0.0005  0.0014    -0.3670
CaO:Al2O3   -0.0116  0.0067    -1.7418
CaO:Fe2O3T   0.0104  0.0071     1.4771
CaO:MgO      0.0125  0.0089     1.4023
CaO:K2O      0.0130  0.0197     0.6595
CaO:Na2O    -0.0020  0.0102    -0.1957
CaO:TiO2    -0.0219  0.0436    -0.5016
CaO:MnO     -0.3640  0.2313    -1.5736
CaO:P2O5    -0.1211  0.1714    -0.7068

Residual standard error: 0.2688 on 50 degrees of freedom (181 observations deleted due to missing values)

Figure S4 here

Attempts to estimate S based on the reanalysed data were not successful. This is expected as the presence of S is not associated with primary igneous fractionation for calc-alkaline volcanic rocks and where S is present is due to secondary processes and/or mineralization events. However, sulphur is ubiquitous throughout volcanic rocks in both primary and secondary forms. Where S was not available, a default value of 0.01 was set.

In addition to the individual estimation of the volatile components, an estimate of LOI prediction was carried out on 1069 re-analysed samples using the following robust regression equation:
LOI/CaO ~ SiO2/CaO + Al2O3/CaO + Fe2O3T/CaO + MgO/CaO + K2O/CaO + Na2O/CaO + TiO2/CaO + MnO/CaO + P2O5/CaO

The regression prediction is based on the ratio of LOI to CaO and samples were selected within the range:0.4 < LOI/CaO < 3.0 and LOI < 12 and CaO < 12.
Figure S5 shows a plot of the actual versus calculated values of LOI within the above ranges. The observed correlation is considered acceptable as a predictive tool.

Table 7: Robust Regression for LOI

Call: rlm(formula = LOI/CaO ~ SiO2/CaO + Al2O3/CaO + Fe2O3T/CaO + MgO/CaO +     K2O/CaO + Na2O/CaO + TiO2/CaO + MnO/CaO + P2O5/CaO)

Residuals:
      Min        1Q    Median        3Q       Max 
 -1.56827  -0.11609  -0.01206   0.11802 100.23619 

Coefficients:
            Value    Std. Error t value 
(Intercept)  10.7215   0.1871    57.3004
SiO2         -0.0876   0.0021   -42.5430
Al2O3        -0.1148   0.0055   -20.7192
Fe2O3T       -0.1037   0.0090   -11.5061
MgO          -0.0414   0.0047    -8.8267
K2O           0.0660   0.0136     4.8582
Na2O         -0.1274   0.0101   -12.5507
TiO2         -0.2652   0.0622    -4.2630
MnO          -0.7766   0.2652    -2.9280
P2O5          0.0715   0.1465     0.4881
SiO2:CaO     -0.0038   0.0002   -22.6402
CaO:Al2O3     0.0020   0.0006     3.1513
CaO:Fe2O3T    0.0001   0.0011     0.0704
CaO:MgO      -0.0087   0.0006   -14.8603
CaO:K2O      -0.0273   0.0023   -11.9383
CaO:Na2O     -0.0043   0.0014    -3.0445
CaO:TiO2      0.0096   0.0078     1.2303
CaO:MnO       0.0405   0.0349     1.1589
CaO:P2O5     -0.0162   0.0154    -1.0493

Residual standard error: 0.1743 on 1049 degrees of freedom

Figure S5 here

Supplementary Material References
Hillary, E.M., Grunsky, E.C. & Adcock, S.W., 2008, Compilation of lithogeochemistry - Abitibi greenstone belt - Ontario portion. Geological Survey of Canada Open File 5510, 1 CD-ROM.
Hillary, E.M. & Grunsky, E.C., 2010, Compilation of reanalyses and new analyses of lithogeochemistry - Abitibi greenstone belt, Ontario and Quebec. Geological Survey of Canada Open File 6623, 1 CD-ROM.
Grunsky,E.C., 2010,  The interpretation of geochemical survey data. Geochemistry: Exploration, Environment and Analysis 10, 27-74. 
Irvine, T.N. & Baragar, W.R.A., 1971, A Guide to the chemical classification of the Common Volcanic Rocks. Canadian Journal of Earth Sciences, 8, 523-546.
LeMaitre, R.W., 1976, Some Problems of Projection of Chemical Data into Mineralogical Classifications. Contributions to Mineralogy and Petrology, 56, 181-189.
Middlemost, E.A.K., 1989, Iron oxidation ratios and the classification of volcanic rocks. Chemical Geology, 77, 9-26.
R-Project 2011.  The R Project for Statistical Computing.  http://www.r-project.org/.

List of Figures

Figure 1. Volcanic assemblages with the locations of Cu-bearing prospects and deposits, Abitibi greenstone belt. Mineral deposit locations comprise a merged set of prospects, and past and present producers, all containing Cu and/or Zn.

Figure 2. Sample locations and databases compiled from Ontario and Quebec. Samples that were re-analysed and/or collected for this study are shown in red. A total of 30,055 samples were initially selected for study.

Figure 3. A. Ordered plot of eigenvalues (‘Screeplot’) derived from the principal component analysis of the major element lithogeochemical data.B. Biplot of the first two principal components (clr transform) derived from the major element data of the Abitibi greenstone belt (26,465 samples). All rock types are included in this analysis. Observations are coloured according to a k-means clustering procedure based on 8 groups. See the text for details. C. Biplot of principal components 2 and 5. The plot shows data that are relatively enriched in S along the positive PC5 axis and are relatively enriched of CO2 along the positive PC2 axis. Relative enrichment in H2O+ is shown along the negative PC5 axis.

Figure 4. A. Map of the second principal component derived from major element data using a log-centred transform. Mineral prospects and deposits are overlain on the image. The image was created using a kriged interpolation with 250 m grid cell size, a 1000 m search radius and an exponential variogram model for the kriged estimates. The image of the second principal component has been screened to 50% transparency to enhance the locations of the mineral deposits/occurrences.  B. Map of the fifth principal component derived from major element data using a logcentred transform. Mineral prospects and deposits are overlain on the image. The image was created using a kriged interpolation with 250 m grid cell size, a 1000 m search radius and an exponential variogram model for the kriged estimates. The image of the fifth principal component has been screened to 50% transparency to enhance the locations of the mineral deposits/occurrences.

Figure 5. A. Ordered eigenvalues (’Screeplot’) for the principal component analysis of the rare earth elements data. The first two eigenvalues account for > 92 % of the data variability. B. Biplot of  PC1 versus PC2 for REE elements based on a log-centred transform. Positive PC1 scores represent samples with relative enrichment in heavy rare earth elements. Negative PC1 scores represent samples with relative enrichment in light rare earth elements. Colors indicate the slope between La and Lu. Symbol size indicates the degree of Eu enrichment/depletion. Round symbols indicate Eu depletion and triangles indicate Eu enrichment. See the text for additional details.

Figure 6. A. Map of principal component 1 derived from log-centred rare earth elements data (re-analysed data only). Positive scores and red colored symbols indicate relative heavy rare earth elements enrichment. Negative scores (purple colors) highlight samples with relative light rare earth elements enrichment. See the text for additional details. B. Map of principal component 1 derived from log-centred rare earth elements data (re-analysed data only). A slope proxy defined as [La-Lu] is used to describe the slope of the light to heavy rare earth elements profile. Positive scores and red colored symbols indicate La >  Lu values whereas negative scores (purple symbols) indicate Lu > La See the text for additional details. C. Map of principal component 2 derived from log-centred rare earth elements data (re-analysed data only). Positive scores highlight samples with relative Eu enrichment. Negative scores highlight samples with relative Eu depletion. The upper Blake River Group shows a distinctive Eu depletion trend whereas the lower Blake River assemblages and the Kidd-Munro assemblages show mostly Eu enrichment.

Figure 7. Map of Cu+Zn values. The mineral deposits databases from the Ontario and Quebec provincial geological surveys and MDI were merged and developed prospects, past producers and current producers were combined into a database containing massive sulfide deposits only. 

Figure 8. Map of the ACNK [Al2O3/(Na2O+K2O+CaO)] alteration index. The larger the ratio, the greater the likelihood that alkali elements (Na, K, Ca) have been removed through hydrothermal alteration. Areas where this increased ratio is spatially coherent may be indicative of VMS footwall alteration.

Figure 9. Map of the Ishikawa alteration index (Ishikawa et al. 1976: (Na2O+K2O)/[(K2O+MgO)+(Na2O+CaO) where areas of increased K2O and MgO, may indicate VMS alteration. 

Figure 10. Map of normative corundum. Notable increase in the abundance of corundum occurs in the Noranda, Kidd Creek and Kamskotia camps. As well, there is a significant increase in corundum in the Roquemaure and Kewagama areas

Figure 11. Map of accumulated indices based on selected thresholds for Cu+Zn, ACNK index, Ishikawa index, normative corundum, principal component 2 and principal component 5. See Table 2 for the threshold values that were used to create the map.

Tables
Table 1. Eigenvalues/vectors/loadings/scores of a principal component analysis applied to the log-centred major element data.
Table 2.  Threshold values for indicators used to predict VMS potential from the Abitibi greenstone belt lithogeochemical data.
Table 3. Sequence of data checking and creation of additive alteration indices.

Supplement
Table 1.  Reanalysed Samples
Table 2. Decision tree for Fe correction in whole rock analyses
Table 3. Decision tree for volatile replacement strategy in whole rock analyses
Table 4.  Fe2O3 Regression
Table 5. Robust Regression for H2O+
Table 6. Robust Regression for CO2
Table 7. Robust Regression for LOI
Figures for Supplement
Fig. 1a. Fe2O3/SiO2 versus Fe2O3T/SiO2 from 1319 re-analysed samples.
Fig. 1b. Fe2O3 versus FeO, after trimming samples with 0.1 < (Fe2O3/SiO2) < 1.0.
Fig. 1c. Actual Fe2O3/SiO2 values versus predicted Fe2O3/SiO2 values. Values were predicted using a standard linear regression method in R (lm).
Fig. 2 Predicted Fe2O3 versus Actual Fe2O3 
Fig. 3.  Plot of H2O+ predicted versus H2O+ observed from the re-analysed lithogeochemical data. Fitted values were derived using a robust linear regression model (rlm) in R.
Fig. 4 Plot of actual CO2 predicted versus CO2 observed from the re-analysed lithogeochemical data using a standard linear regression method in R (lm).
Fig. 5 Plot of actual versus calculated values of LOI using a standard linear regression method in R (lm).