International Journal of Geosciences, 2012, 3, 62-70 Published Online February 2012 (
Identifying Pathfinder Elements for Gold in Multi-Element
Soil Geochemical Data from the Wa-Lawra Belt, Northwest
Ghana: A Multivariate Statistical Approach
Prosper Mackenzie Nude1*, John Mahfouz Asigri1, Sandow Mark Yidana1,
Emmanuel Arhin2, Gordon Foli3, Jacob Mawuko Kutu1
1Department of Eart h Science, University of Ghana, Legon, Ghana
2Geology Department, University of Leicester, Leicester, UK
3Department of Geologic a l Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
Email: *
Received November 29, 2011; revised December 17, 2011; accepted January 19, 2012
A multivariate statistical analysis was performed on multi-element soil geochemical data from the Koda Hill-Bulenga
gold prospects in the Wa-Lawra gold belt, northwest Ghana. The objectives of the study were to define gold relation-
ships with other trace elements to determine possible pathfinder elements for gold from the soil geochemical data. The
study focused on seven elements, namely, Au, Fe, Pb, Mn, Ag, As and Cu. Factor analysis and hierarchical cluster
analysis were performed on the analyzed samples. Factor analysis explained 79.093% of the total variance of the data
through three factors. This had the gold factor being factor 3, having associations of copper, iron, lead and manganese
and accounting for 20 .903% of the total variance. From hierarchical clustering, gold was also observed to be clustering
with lead, copper, arsenic and silver. There was further indication that, gold concentrations were lower than that of its
associations. It can be inferred from the results that, the occurrence of gold and its associated elements can be linked to
both primary dispersion from underlying rocks and secondary processes such as lateritization. This data shows that Fe
and Mn strongly associated with gold, and alongside Pb, Ag, As and Cu, these elements can be used as pathfinders for
gold in the area, with ferruginous zones as targets.
Keywords: Multivariate Analyses; Multi-Elements; Soil Geochemical Data; Pathfinder Elements; Gold;
Northwest Gha na
1. Introduction
The Wa-Lawra greenstone belt marks the eastern margin
of the larger Proterozoic Birimian greenstone belt which
trends through sou thern and central Burkina Faso to nor-
thern Ghana. The Birimian greenstone belt is known to
host a number of significant gold (Au) and base metal
deposits including the famous AngloGold-Ashanti mine
in Ghana. Although the Wa-Lawra belt shares similar li-
thological and structural characteristics to the greenstone
belts located in southwestern Ghana, which host a num-
ber of “World Class” gold deposits, discovery of substan-
tial gold deposits from the Wa-Lawra belt in Ghana has
been elusive, with Azumah Resources and Castle Miner-
als being the only operating exploration companies in the
area. The lack of success in the discovery of potential gold
deposits of commercial quantity has been partly attrib-
uted to the complex regolith structur e in the area [1]. For
effective interpretation of soil geochemical data and lo-
cation of economic gold, better knowledge of metal path-
ways in the regolith are needed . McQueen an d Munro [2 ]
have shown that the geochemical dispersion of gold and
their pathfinder elements are strongly dependent on the
pr eservation of trace ele ments in the rego lith. So the id en-
tification of relationship among trace elements with spe-
cific minerals and mineralogical control structures such
as cutans and concretions in the regolith may be a better
tool to use to identify and rank gold anomalies [3]. Thus
despite the problems of regolith complexities, the estab-
lishment of pathfinder elements can aid in the identifica-
tion of element-host mineral associations which may pro-
vide a consistent sampling medium, reduce geochemical
noise and fine-tune exploration techniques for success.
In this research, multivariate statistical methods were
used for the evaluation of multi-element soil geochemical
data from the Koda Hill-Bulenga gold prospects in the
Wa-Lawra gold belt. These statistical methods, which are
the first of its kind performed on these data, facilitated an
understanding of the variations in Au and th e relatio nship
*Corresponding author.
opyright © 2012 SciRes. IJG
P. M. NUDE ET AL. 63
between Au concentrations and the concentrations of other
elements in the soil samples. The method appears useful
in the determination of possible pathfinder elements which
can guide exploration activities.
2. Location and Geology of Study Area
The study area falls within the Birimian gold bearing
belts of northern Ghana [4,5]. Figure 1 is a regional geo-
logical map of northern Ghana showing the lithological
distributions. Insert is the Koda Hill-Bulenga areas loca-
ted at the southeastern end of the Wa-Lawra belt where
this study was done. The geology of the Wa-Lawra belt
has been described by several workers including Leube
et al. [6], Taylor et al. [7], Hirdes et al. [8], whiles the de-
tails of the geology of the Koda Hill-Bulenga area can be
found in Nude and Arhin [9]. The area is underlain by
metavolcanic, pyroclastic and metasedimentary rocks.
The metavolcanic rocks are of basaltic and gabbroic in
compositions and most of them have been altered into
various schist. The metasedimentary rocks consist of phyl-
lites, tuffaceous and carbonaceous phyllites, sandstones,
siltstones, tuff, cherts and manganeferous sediments. In-
truding the metavolcanic and metasedimentary rocks are
magmatic bodies and porphyritic g ranitoids that hav e ge-
nerally been classified into two broad categories. These
are 1) hornblende-rich varieties that are closely associa-
ted with the volcanic rocks and known locally as “Dix-
cove” or “belt” type and 2) mica-rich varieties which tend
to border the volcanic belt or greenstones and are found
in the metasedimen t units, and referred to as “Cape Coas t”
or “basin” type granitoids. The Birimian units of the area
feature most of the same lithologies observed in the green-
stone belts found in southern Ghana. On a regional scale
the Wa-Lawra belt can be traced northwards for several
hu ndred kilometers into northwestern Burkin a Faso where
the belt is known to host several major gold and base me-
tal deposits.
3. Physiography and Regolith
The landscape of northern Ghana is gently undulating at
the moderate elevated areas or low pediment areas. The
upland areas are generally marked by scree that decrea-
ses in fragment size down-slope. Thin layers of colluvium,
which is interspersed with alluvial plains, cover the low-
lying areas. In northern Ghana, most areas retain relicts
of lateritic weathering profiles. The upper surficial pro-
files generally have a thin veneer of pisoliths and sheet-
wash deposit cover in the low lying areas. The area is the
continuation of th e ex ten siv e wooded savannah of central
600000 m N
1050000 m N
750000 m N
1200000 m N
Metab asalts, m etaan d esites,
Phy l l ites , s la tes, grey wackes ,
ar illaceous r ock s w ith som e
tu ffaceou s sch ists
San d ston es, siltston es, m u d ston es
C alcar eou s, ar gillaceou s, sand y
and fer rigenous shales, arkose,
s an ds tones, grey wacke,
conglom er ate an d p h yllites.
Metasedimentary Units
Belt In tr usives
Sheared hornblende-biotite
granod iorite, d ior ite, ton alite
Mu scovite-b iotite gr an ite,
gn eiss migmatites
Basin In tr u sives
NMetavolcani c U n its
Figure 1. Geological map of northern Ghana showing the lithological distributions. Insert is the Koda Hill-Bulenga area
where the study w as done.
Copyright © 2012 SciRes. IJG
Ghana with the annual rainfall typically in the range 1000 -
1250 mm/yr [10].
The spatial distributions of th e regolith materials in the
Koda Hill-Bulenga areas [1] consist of residual and trans-
ported regolith. The residual regolith materials are com-
monly preserved at ridge tops and high pediments, while
proximal transported materials or colluv iums are found at
the base of ridges and often at moderate elevated terrains
and are preserved on the landscapes generally as colluvial
soils, screes/talus. The transported reg oliths are found ge-
nerally at low pediments and low lying areas and in drai-
nage catchment areas. There are also widespread residual
laterites or duricrust and ferricretes or t ransported laterites.
4. History of Gold Exploration
The occurrence of gold in Northern Ghana has been re-
ported since 1935 [11]. Prior to this however, galamsey
(small scale artisanal gold mining) activities in the area
were quite prevalent. Pilot systematic conventional gold
exploration started in this area in 1960 after a collabora-
tive geological mapping and prospecting by Ghana Geo-
logical Survey and their Soviet counterpart identified and
confirmed the gold occurrence reported by Junner. In 1990
BHP-Minerals undertook a regional stream sediment sur-
vey using the BLEG technique in order to cover the en-
tire area of the Wa-Lawra belt. The stream and soil sam-
ples collected did produce some anomalies, but in com-
parison to southern and western Ghana, they were not
considered economically viable. There also existed the
likelihood that, the anomalies were entirely not even re-
lated to mineralization . Carter [12] reported that between
the years of 1997 and 2000, an extensive geochemical sur-
vey was carried out encompassing the entire Wa-Lawra
belt. During this period, Ashanti-AGEM Limited held pro-
specting rights over the entire Wa-Lawra belt. The com-
pany carried out a wide spaced reconnaissance survey in-
volving soils, termite mounds, laterite, stream sediment
and lithological grab sampling alongside geological map-
ping and Landsat-TM imagery studies [13]. Over 4500
soil samples were collected and analyzed. The AGEM
survey defined several anomalous sub-areas, each incur-
porating a number of anomalous trends and clusters, most-
ly soil, but often supported by other sample data. Follow-
up work on the anomalous sub-areas resulted in the defi-
nition of four contiguous priority areas that include Ba-
bile, Boiri, and Chereponi South and North. Later in 1999,
AGEM farmed out the areas to the south to SEMAFO
Ghana Limited. Rather interestingly, no commercial mine
has been operational in the area until in 2006 when Azu-
mah Resources commenced a new geochemical sampling
program and exploration re-assessment. The company has
since delineated many prospective geochemical targets
with mineable reso urces. The Kod a Hill-Bulen ga areas are
currently being explored by Castle Minerals Limited.
Gold Mineralization
Quartz veins occur in almost all the lithologic units of th e
area. However, gold-bearing quartz veins are observed in
association with shear and fault zones along the contact
zones of the boundaries of the metavolcanic and meta-
sedimentary rocks, and also in the chemical sediments.
The chemical sediments are of particular interest as a
source of gold. According to Melcher and Stumpfl [14],
the widespread manganiferous phyllites of the chemical
sediments carry high background gold contents as are the
gondites in the greenstone succession. The gold quartz
veins reveal a secondary mineral assemblage characteris-
tic of hydrothermal alt e rat ion i.e., chlorite, carbonate, mus-
covite, graphite, epidote, and sulphides.
5. The Application of Mul tivariate Statist ical
Methods to Geochem ical Data
The multivariate and regionalized character of geochemi-
cal variables makes them an interesting candidate for nu-
merical analysis using both geostatistics [15,16] and data
analysis methods [17] in order to identify geochemical
anomalies. The development of low-cost, rapid multi-ele-
ment analytical techniques has generated large geoche-
mical databases in many exploration programs. When a
sampling program consists of several thousand samples,
the resulting data matrix is enormou s and effective inter-
pretation using all of the elements individually becomes
burdensome. However, the application of multivariate sta-
tistical techniques can extrac t geochemical patterns related
to the underlying geology, weathering, alteration and mi-
neralizat ion which enhance the in terpretation of these pat-
Statistical methods have been widely applied to inter-
pret geochemical data sets and define anomalies. These
methods need to be used cautiously because of the parti-
cular characteristics of geochemical data. Geochemical
data sets seldom represent a single population or distribu-
tio n; the data are typically spatially dependent and at each
sample site, a range of different processes have influen-
ced the element abundances measured. The data are also
imprecise due to unavoid able variability in sampling me-
thods and media and the lev el of analytical precision. As
a result no single universally applicable statistical test has
been developed for identifying anomalies. Statistical in-
vestigation should use a range of techniques to explore
the nature of geochemical data before selecting anomalous
values e.g. [18].
Factor analysis (FA) and Hierarchical Cluster Analysis
(HCA) were applied to a multivariate geochemical data-
set in this study. Factor analysis is an appropriate method
for establishing element associations. When this technique
is applied to a geochemical data set, it is possible to ob-
tain several factors, as linear functions of the original che-
mical elements. Some of these factors can be used for
Copyright © 2012 SciRes. IJG
P. M. NUDE ET AL. 65
studying a specific group of variables, giving conclusions
about an association of elements, which is geochemically
more significant than the study of individual variables.
These techniques use the probabilistic and spatial beha-
vior of geochemical variables, giving a tool for identify-
ing potential anomalous areas to locate mineralization.
The use of multivariate analysis also permits the study of
the spatial structure intrinsic to geochemical data an d the
identification and refinement of significant anomalies re-
lated to Au-bearing mineral deposits. Factor analysis can
simplify a complex data set by identifying one or more
underlying “factors” or processes that might explain the
dimensions associated with dat a variability [19]. The “load-
ing” of each factor, i.e. the degree of association between
each variable and each factor, allows the recognition of
Hierarchical Cluster Analysis (HCA), as the most com-
mon cluster analysis method applied for geological/hydro-
logical analysis, looks for groups of samples according to
their similarities. HCA is a powerful tool for analyzing
data sets for expected or unexpected clusters including
the presence of outliers. In HCA, each point forms, ini-
tially, one cluster, and the preliminary matrix is analyzed.
The most similar points are grouped forming one cluster
and the process is repeated until all points belong to one
cluster [17]. HCA examines distances between samples
and datasets. The result obtained could be presented in a
two-dimensional plot called dendogram which illustrates
the fusions or divisions made at each successive stage of
6. Methodology
For the purpose of this project, historical data from multi-
element soil geochemical survey conducted in the area
were used. The data included over 2000 sample sites
(data can be obtained from author on request) which
were reduced to 249 samples after data cleaning. The soil
samples were taken at depths of between 40 - 60 cm with
their respective coordinates taken and recorded. Other
parameters which were recorded during sampling were
the landscape, regolith and vegetation. The samples were
then prepared and analyzed for Au by conventional fire
assay-atomic absorption spectrometry (FA-AAS) [20], as
FA-AAS is generally accepted as dependable analytical
method for gold [21].
Generally, the basic procedure for fire assay involves
the mixing of a powdered sample (10 g - 50 g) with so-
dium carbonate (ash), borax (sodium borate), litharge,
flour and silica. A foil of Pb or Ag is usually added as a
collector. The mixture is then fired at a temperature rang-
ing from 1000˚C - 1200˚C. The obtained lead button is
then removed by cupellation at 950˚C. The resultant gold
prill is digested with aqua regia mixture and the solution
analysed by atomic absorption spectrometer using gold
standards. The other trace elements, namely As, Ag, Pb,
Fe, and Cu were analyzed using routine Inductively Cou-
pled Plasma Mass Spectrometry (ICP-MS).
On the analyzed historical data, descriptive statistics
including mean, minimum, maximum and standard devia-
tion were calculated for the respective elements. These
indicated a significant departure of the datasets from nor-
mality and as such, the need to normalize it via logarith-
mic transformation. The very nature of geochemical data
makes them rather spatially dependent and as such inhe-
rently non-normal. Additionally, the prime assumption un-
derlying the application of the multivariate methods of
FA and HCA is for the data to follow normal distribution.
To identify the relationship among trace elements and
gold and their possible sources, multivariate statistical
analyses, such as factor analysis and hierarchical cluster
analysis, were performed using statistical software pack-
age SPSS [22].
The results of HCA are presented in the form of a den-
drogram where procedures in the hierarchical clustering
solution and values of the distances between clusters
(squared Euclidean distance) are represen ted [23]. The pro-
cess starts by calculating the similarity/dissimilarity be-
tween the N objects. Then two objects which when clus-
tered together minimize a given agglomeration criterion,
are clustered together thus creating a class comprising
these two objects. Then the dissimilarity between this class
and the N-2 other objects is calculated using the agglo-
meration criterion. The two objects or classes of objects
whose clustering together minimizes the agglomeration
criterion are then clustered together. This process conti-
nues until all the objects have been clustered. These suc-
cessive clustering operations produce a binary clustering
tree (dendrogram), whose root is the class that contains
all the observations. This d endrogram represents a hierar-
chy of partitions. It is then possible to choose a partition
by truncating the tree at a given level, the level depend-
ing upon either user-defined constraints (the user knows
how many classes are to be obtained) or more objective
criteria. In this study, a phenon line was draw n across the
dendrogram so developed for the determination of the
most optimal clusters to define the dataset. To calculate
the dissimilarity between the various variables, different
methods are possible but the Wards method was consi-
dered for this work. Earlier, squared Euclidean distances
were used to determine measures of similarities/dissimi-
larities amongst the parameters for the distinguishing of
initial clusters. This method aggregates two groups so
that within-group inertia increases as little as possible to
keep the clusters homogeneous. This criterion, proposed
by Ward [24], can only be used in cases with quadratic
distances, i.e. cases of Euclidian distance and Chi-square
Factor analysis method dates from the start of the 20th
Copyright © 2012 SciRes. IJG
century [25] and has undergone a number of develop-
ments, several calculation methods having been put for-
ward. This method was initially used by psychometric-
cians, but its field of application has little by little spread
into many other areas, for example, geology. Factor analy-
sis, involves the extraction of principal components from
the initial dataset. Each principal component is expected
to represent a process or set of processes which influence
the spatial variation of the values of the parameters. The
Kaiser [26] criterion was used to determine the number
of components to extract. This method suggests that only
those factors with associated eigenvalues which are stric-
tly greater than or equal to 1 should be kept. The scree
plot can also be used to determine the number of factors
which represent unique sources of variation in the dataset.
In that respect, the number of factors to be kept corre-
sponds to the first tu rning point fou nd on the curv e of the
scree plot [27]. “Principal components” was used as the
extraction method. The method of principal components
can be seen as a projection method which projects ob-
servations from a p-dimensional space with p variables to
a k-dimensional space (where k < p) so as to conserve the
maximum amount of information (information is meas-
ured here through the total variance of the scatter plots)
from the initial dimensions. If the information associated
with the first 2 or 3 axes represents a sufficient percent-
age of the total variability of th e scatter plot, the ob serva-
tions will be able to be represented on a 2 - 3-dimen si on al
chart, thus making interpretation much easier. This method
of extraction enabled the calculation of matrices to pro-
ject the variables in a new space using a new matrix
which shows the degree of similarity between the vari-
ables. The covariance matrix was used as the index of
7. Results and Discussion
7.1. Summary Statistics
Summary statistics of multi-element analysis of Au, As,
Ag, Pb, Cu, Fe and Mn analytical results are displayed in
Table 1. Table 2 also displays statistics for the same set
of data after a logarithmic transformation was applied to
the dataset. A brief comparison of the two tables is made
using Fe as an example.
There is a very large disparity between the median and
maximu m value in Figure 2. Iron has a median value of
31,100 mg/kg and a maximum of 155,900 mg/kg with an
even lower mean of 43,186 mg/kg. The result is a longer
whisker above the mean and a shorter one below it. This
implies that, most of the Fe values greatly depart from
the mean which is also an indication of the extreme
variability of geo chemical data. This trend in any dataset
makes it rather difficult to be used in any multivariate
analysis since the data is obviously non-normal. A log
Table 1. Summary statistics of multi-element analysis results.
VariableObservationsMinimum Maximum Mean SD
Au_mg/kg253 0.001 0.174 0.006 0.013
Ag_mg/kg253 2.000 60.000 7.012 7.443
As_mg/kg253 5.000 203.000 27.621 35.050
Cu_mg/kg253 3.000 139.000 34.557 27.777
Fe_mg/kg253 7100.000155900.000 43186.95733063.880
Pb_mg/kg253 0.001 0.043 0.005 0.006
Mn_mg/kg253 40.000 4210.000 712.945 768.442
Table 2. Summary statistics of log transformed data.
VariableObservationsMinimum Maximum MeanSD
log_Au253 –3.000 –0.759 –2.5550.500
log_Ag253 0.301 1.778 0.7030.323
log_AS253 0.699 2.307 1.1270.512
log_Cu253 0.477 2.143 1.4160.332
Log_Fe253 3.851 5.193 4.5320.293
Log_Pb253 –3.000 –1.367 –2.5000.436
Log_Mn253 1.602 3.624 2.6600.407
Figure 2. Box plot of Fe using Table 1 (before transforma-
transformation as shown in Figure 3 gives a more refined
dataset with both the maximum and minimum values
evenly distributed about the mean value. This trend indi-
cates a more uniform dataset with a smaller and more
stable variance which aids greatly in data analysis.
7.2. Cluster Analysis
An R-mode clustering schedule produced the dendrogram
in Figure 4. The resultant clustering has Au, Pb and As
Copyright © 2012 SciRes. IJG
P. M. NUDE ET AL. 67
Figure 3. Box plot of Fe using Table 2 (after transforma-
Figure 4. Dendrogram displaying clusters of multi-elemen-
tal analysis results.
in the first cluster. As can be deduced from their dissimi-
larity index, this group happens to be the most homoge-
neous pair arising from the fact that they contribute the
le as t co ncen trat ions w ith in th e soil sa mples an alyzed. They
have average concentrations of 0.006 and 0.005 mg/kg
respectively. This concentration of Au is however not
completely dissatisfying since native gold appears in ra-
ther small concentrations. The second cluster has Ag, Fe,
Mn, and Cu a s its me mbers with their respective average
concentrations being 7.012, 27.621 and 34.557 mg/kg.
This cluster has As and Cu being the most homogeneous
pair. Iron and manganese are the most abundant amongst
the analyzed samples. Gold is known to be fixed after
dispersion in secondary min eral hosts such as Fe-Mn and
Al-Fe hydroxides. Besides, volcanicalstic rocks are abun-
dant in the study area and are known to contain high
amounts of ferromagnesian minerals. Additionally, gold
occurrence in the study area is also of the arsenopy-
rite/porphyry copper nature. Evidence of this is the por-
phyry copper deposits disco vered in neighbor ing Burkin a
Faso which is of the same geological formation.
However, the fairly distributed concentrations of silver,
lead, and arsenic are not completely dissatisfying since
aiding in gold pro specting is the pr ime objective. Althou-
gh performing HCA on variables rather than on cases is
preferred in most research studies [28,29], HCA was de-
veloped, in the present study, on soil samples, in order to
identify similarities in Au contents and that of the trace
elements. This approach was selected instead of trying to
discriminate between the different sources of metals,
which would be accounted for by FA. Thus, the aim in
performing HCA was to identify the samples which rep-
resented different areas where Au content followed a si-
milar pattern. This different approach was preferred since,
in that sense, the results provided by Q-mode HCA and
R-mode FA, in this work, is complementary, although
they are not quite different methods. FA helped to group
the elements according to their underlying geological fac-
tors. Once this information is known, HCA allowed clus-
tering the areas with high Au content and its associated
trace element concentration.
Three main clusters can be distinguished in the den-
drogram shown in Figure 5. This method is distinct from
all other methods because it uses an analysis of variance
approach to evaluate the distances between clusters. Clus-
ter one includes about 107 samples which has associated
with it concentrations of, As, Ag, Mn & Fe with rather
low concentrations of Pb, Cu and particularly Au. The
second cluster, comprising of the least number of sam-
ples, clusters samples with the highest Au concentration
with a rather predictable high As concentration. This is
so because, disseminated sulphide type of ore has been
documented in the area and according to Leube et al. [6],
sericite- and pyrite/arsenopyrite-rich selvages frequently
carry gold in the structure controlled deposit types of the
Wa-Lawra belt. The third cluster which has the largest
number of samples also appears to be highly concen-
trated in Cu, Pb, Fe and Mn. Its Au content is slightly
higher in relation to that of the first cluster but however,
considerably lower to the second cluster. The respective
Au and trace element concentration of the various sam-
ples within each of the three clusters was arrived at by
computing the arithmetic mean of each elemental con-
centration. Due to the extent of the data, and also because
of the unequal number of samples in each cluster, an av-
erage of 45 samples each was considered. Au had an av-
erage concentration of 0.091 mg/Kg in cluster two repre-
senting the highest. Clusters one and three had concen-
trations of 0.0010 mg/kg and 0.0014 mg/kg respectively.
Besides, it can be generally observed that, clusters with
Copyright © 2012 SciRes. IJG
Copyright © 2012 SciRes. IJG
Figure 5. Dendrogram from Q-mode HCA.
Table 4. Component matrix.
higher concentrations of Au have an associated fair con-
centration of As, Cu and Ag. The above clustering crite-
rion can alternately be described as “dissimilarity clus-
tering” with the phenon line chosen at dissimilarity index
of 20. Reducing the height of the phenon line would re-
sult in having more clusters which are closely related.
variable Component
1 2 3
Log_Au –0.405 0.065 0.901
Log_Ag 0.447 0.099 –0.237
Log_As –0.091 0.979 –0.086
Log_Cu 0.837 –0.177 0.107
Log_Fe 0.862 0.031 0.215
Log_Pb 0.722 0.295 0.213
Log_Mn 0.785 –0.008 0.346
7.3. Factor Analysis
“Principal components” was the method of extraction used.
The analysis indicated three factors in the data account-
ing for 79.093% of the to tal variability. Table 3 indicates
the variance explained for each of the factors extracted.
The factor model showing the loadings of the various
variables under each factor is presented in Table 4.
plot of the factor loadings of factors 1 and 2 on the vari-
ous elements is shown in Figure 6. The geology of the
area particularly the rock formations present in the study
area can be associated with factor 1. It is known that, the
area is underlain by volcanic rocks which are particularly
rich in ferromagnesium minerals. This can also be attrib-
uted to the presence of chemical sediments and magne-
sium-rich rocks known as gondites. These chemical sedi-
ments inter flow the rock formations carrying in its path
other constituents contributing to the loadings observed
with factor one. Also, co pper tran sp ort is kno wn to b e via
volcanic activity and thus, the vast nature of volcaniclas-
tic rock formations present in the area would have an as-
sociated copper content. Additionally, copper is found in
association with other metals such as Pb which is also
generally associated with Ag.
It is obvious from Table 3 that the first factor which
accounts for 34.73% of total variance is dominated by
copper, iron, lead, and manganese associated with some
contribution of silver, while the second is the As factor
with some positive loading with lead, which explained
23.46%, and the third though having Au as the dominant
factor, also has some positive associations with Fe, Pb
and Mn which explained 20.90% of the total variance. A
Table 3. Total variance explained.
Component Total % of
Variance Cumulative
% Total % of
Variance Cumulative
1 0.406 34.729 34.729 0.406 34.729 34.729
2 0.274 23.462 58.19 0.274 23.462 58.19
3 0.244 20.903 79.093 0.244 20.903 79.093
4 0.109 9.311 88.404
5 0.08 6.835 95.239
6 0.042 3.618 98.857
7 0.013 1.143 100
The association of copper, iron, lead and manganese
with factor 1 is shown in Figure 6. The association of As
with sulphide in th e area is also made evident in factor 2.
Factor 3 being the Au factor with some Cu, Fe, Pb, and
Mn association can be attributed to the hydrothermal pro-
cesses responsible for their emplacement. This process is
P. M. NUDE ET AL. 69
Figure 6. Plot of factor loadings of factors 1 and 2.
chiefly responsible for Au mineralization in the area. Hy-
drothermal deposits are generally associated with some
Pb, Cu and Ag enrichment particularly the disseminated
sulphide type which occurs in the area. The association
between gold and these elements [30] stems from the fact
that, the gold in the Lawra belt is concentrated by vol-
canic related processes, principally chemical precipita-
tion in exhalative sediments. The gold and associated mi-
nerals were remobilized from these chemical sediments
by metamorphogenic processes with the auriferous fluid
transported and deposited in structurally favorable sites.
Futhermore, during weathering trace elements such as Cu
and Au can be preferentially absorbed and trapped in
Al-Fe and Fe-Mn hydroxides. Gold co-exists with As, Cu,
Pb and Fe released from arsenopyrite, chalcopyrite, ga-
lena and sphalerite among others, in the oxidized envi-
ronment during weathering and adsorbed on to the sur-
faces of Fe-hydroxides such as goethite and hematite or
trapped in kaolinite in the regolith. Most tropical soils are
rich in Fe-hydroxides which are able to fix weighted ele-
ments such as gold and have affinity for As [2].
8. Implications for Gold Exploration in the
Wa-Lawra Belt
In the Birimian of southwestern Ghana where major gold
deposits have been found, arsenopyrite (FeAsS), chalco-
pyrite (CuFeS2) and pyrite (FeS2) are noted to be the
major host minerals of gold [4,5,31]. These sulphide mi-
nerals may host trace elements such as arsenic (As), Cu,
Zn, Ni, Pb and Au etc. [5] as pathfinder elements which
have led to exploration success in the Birimian of south-
western Ghana. However, from the present study Fe, Pb,
Mn, Ag, As and Cu appear to be associated with Au and
therefore suitable as pathfinder elements. Thus despite
similarities in geology and structural setting of the Wa-
Lawra belt with the belts in southwestern Ghana, differ-
ences in pathfinder elements appear to exist. This is pro-
bably due to the nature of the regolith resulting from wea-
thering and landscape processes. The association of Fe
and Mn with Au in this study is unique and appears to
differ from what pertains in southern Ghana. The Wa-
Lawra belt is largely lateriric and the regolith is domi-
nated by Fe-oxides/oxyhydroxides. It is possible that Au
mineralization is strongly asso ciated with ferruginization ;
Fe-Mn oxides being secondary phases are capable of gold
encrustation and therefore possible hosts to Au minerali-
zation. They should therefore be considered as targets for
Au exploration in the area.
9. Conclusion
The application of both factor analysis and hierarchical
cluster analysis to historical multi-element soil geochemi-
cal data from the Koda Hill-Bulenga area in Wa-Lawra
belt of Ghana showed that, gold was associated with cop-
per, iron, lead and manganese. Factor analysis also show-
ed that gold and these element associations occurred in
tandem, which can be explained via the same underlying
geological factors. The results of factor analysis made it
possible for the initial seven variables and 253 samples to
be reduced to three factors representing 79% of the total
variance explained. From hierarchical clustering, gold was
also observed to be clustering with lead, copper, arsenic
and silver. There was further indication that, gold concen-
trations were lower than that of its associations (Fe, Pb,
Mn, Ag, As and Cu). It can be inferred from these results
that, the occurrence of gold and its associated elements
was due to both primary dispersion from underlying rocks
and secondary processes such as lateritization. Iron and
Mn alongside Pb, Ag, As and Cu can be used as pathfin-
ders for gold in the area with ferruginous zones as targets.
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