International Journal of Geosciences, 2012, 3, 62-70 http://dx.doi.org/10.4236/ijg.2012.31008 Published Online February 2012 (http://www.SciRP.org/journal/ijg) 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: *pmnude@ug.edu.gh Received November 29, 2011; revised December 17, 2011; accepted January 19, 2012 ABSTRACT 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. C 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 BURKINA FASO COTE D'IVOIRE Metab asalts, m etaan d esites, Rhyolites 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 INTRUSIVES 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. BIRIM IAN ROCKS Metasedimentary Units BUEM STRUCTURAL 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 TOGO Study Area Bolgatanga VOLT AIAN BASIN 15 0 kilometres 15 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
P. M. NUDE ET AL. 64 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- terns. 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 clusters. 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 analysis. 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 distance. Factor analysis method dates from the start of the 20th Copyright © 2012 SciRes. IJG
P. M. NUDE ET AL. 66 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 similarity. 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- tion). 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- tion). 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
P. M. NUDE ET AL. Copyright © 2012 SciRes. IJG 68 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. REFERENCES [1] E. Arhin and P. M. Nude, “Significance of Regolith Mapping and Its Implication for Gold Exploration in Northern Ghana: A Case Study at Tinga and Kunche,” Geochemistry: Exploration, Environment and Analysis, Vol. 9, 2009, pp. 63-69. doi:10.1144/1467-7873/08-189 [2] G. K. McQueen and D. C. Munro, “Weathering-Con- trolled Fractionation of Ore and Pathfinder Elements at Cobar, NSW,” In: I. C. Roach, Ed., Advances in Regolith, 2003, pp. 296-300. [3] R. R. Anand, J. E. Wildman, Z. S. Varga and C. Phang, “Regolith Evolution and Geochemical Dispersion in Transported and Residual Regolith—Bronzewing Gold Deposit,” Geochemistry : Exploration, Environment, Analy- sis, Vol. 1, No. 12, 2001, pp. 256-276. [4] G. O. Kesse, “The Mineral and Rock Resources of Copyright © 2012 SciRes. IJG
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