Lower Quartile Solutions

Transcription

1 Lower Quartile Solutions FINAL REPORT Salt River Base Mineral Project Estimation Model July 14 th, 2006 A report on the estimation of the mineral resource model within the Salt River Base Mineral Project Dexter S. Ferreira

2 Table of Contents 1. Introduction Database Naïve Statistics Compositing Sample Spacing Cutting Limits Bivariate Statistics Geological Modeling Methodology Cross Validation Bias Trends Classification Specific Gravities Results Discussion Qualifications, Experience and Independence List of Tables Table 1.0 Naive Statistics - Uncut 1m Composites per Metal All Zones... 4 Table 2.0 Naive Statistics - Uncut 1m Composites per Metal Cu... 4 Table 3.0 Naive Statistics - Uncut 1m Composites per Metal Pb... 4 Table 4.0 Naive Statistics - Uncut 1m Composites per Metal Zn... 4 Table 5.0 Naive Statistics - Uncut 1m Composites per Metal Ag... 5 Table 6.0 Naive Statistics - Uncut 1m Composites per Metal Au... 5 Table 7.0 Euclidean Spacing - 1m Composites... 6 Table 8.0 Cutting Limits 1m Composites... 6 Table 9.0 Naive Statistics - Cut 1m Composites per Metal All Zones... 7 Table 10.0 Naive Statistics - Uncut 1m Composites per Metal Cu... 7 Table 11.0 Naive Statistics - Uncut 1m Composites per Metal Pb... 7 Table 12.0 Naive Statistics - Uncut 1m Composites per Metal Zn... 7 Table 13.0 Naive Statistics - Uncut 1m Composites per Metal Ag... 8 Table 14.0 Naive Statistics - Uncut 1m Composites per Metal Au... 8 Table 15.0 Estimation Search Strategy Table 16.0 Comparison of Actual 1 and Estimated Means per Metal Table 17.0 Residual Values - All Estimates Table 18.0 Mineral Resources Upper Body Pb% Table 19.0 Mineral Resources Middle Body Pb% Table 20.0 Mineral Resources Lower Body Pb% Table 21.0 Mineral Resources All Zones Pb% Table 22.0 Mineral Resources Upper Body Ag (g/t) Table 23.0 Mineral Resources Middle Body Ag (g/t) Table 24.0 Mineral Resources Lower Body Ag (g/t) Table 25.0 Mineral Resources All Zones Ag (g/t) Table 26.0 Mineral Resources Upper Body Au (g/t) Table 27.0 Mineral Resources Middle Body Au (g/t) Table 28.0 Mineral Resources Lower Body Au (g/t) Table 29.0 Mineral Resources All Zones Au (g/t) Table 30.0 Mineral Resources Upper Body Table 31.0 Mineral Resources Middle Body Table 32.0 Mineral Resources Lower Body Lower Quartile Solutions 1

3 Table 33.0 Mineral Resources All Zones Table 34.0 Metal Prices Current (June 14 th 2006) Table 35.0 Metal Prices April 4 th, Table 36.0 US Dollar per Tonne - Current Prices (June 14 th 2006) Table 37.0 US Dollar per Tonne - April 4th, 2006 Prices Table 38.0 Insitu Value of Salt River Base Mineral Project List of Figures Figure 1.0 Plan View of Mineralized Zones and Drillholes... 5 Figure 2.0 Bivariate Statistics - 1m Composites/Cut... 9 Figure 3.0 Grade Trend Plot - Cu (%) Figure 4.0 Grade Trend Plot - Pb (%)...13 Figure 5.0 Grade Trend Plot - Zn (%)...13 Figure 6.0 Grade Trend Plot - Ag (g/t) Figure 7.0 Grade Trend Plot - Au (g/t) List of Appendices Appendix I Data Boxplots Cut and Uncut 1m Composites Appendix II Cut Stats Appendix III Down the Hole Variography Appendix IV Directional Variography 38 Lower Quartile Solutions 2

4 1. Introduction The mineral resources of the Salt River Base Mineral Project in South Africa were visited with the objective of estimating a global mineral resource. The author of this report, visited the property on April 24 to the 25 th, 2006 accompanied by Mr. Marius Welthagen, Chief Executive of Thabex Exploration Ltd., and Mr. Deon le Roux, project geologist. The material reviewed by the author included reports, geological plans and administrative documents made possible by Thabex staff. 2. Database The database consists of drillhole assays for Cu (%), Pb (%), Zn (%), Ag (g/t) and Au (g/t) presented in DBase and Excel format. Also received were drillhole collars, survey values and lithological tags. A number of plots of the mineralized zones were also received. The assaying protocol carried out included standards for every 30 to 40 samples. At least one Certified Reference Material QC sample was prepared with every tray of one hundred samples so that there was control from pressed disc preparation through to XRF analysis. Therefore, all areas of XRF analysis were covered. At the end of assaying, random samples (2-3 samples per tray of 100 samples) were selected to be re-read and these results were compared to the original result to ensure that the sample order had not been mixed up. The standards monitor instrument accuracy as well as drift. These duplicate readings were read at the end of the batch (loaded in new positions) as a totally different batch file the original batch file is not set up to re-read a same sample that it read before to ensure that a same error was not duplicated. There are 40 drillholes within the project area and 3 mineralized zones; namely the Upper Zone, the Middle Zone and the Lower Zone. A wireframe had to be constructed for this estimation. 3. Naïve Statistics A complete set of naïve statistics was performed on the drillhole database that was contained within the following limits: N to N, E to E and -600 El to 1000 El. The statistics looked into the characteristics of metal grade values of uncut samples (raw), as well as composites code by mineralized zone (as defined by the project geologist). This latter dataset examined coding obtained from the original drillhole tagging. Due to data paucity, lucid frequency distribution plots were difficult to achieve. Populations seem to display typical normal distributions with one peak. Frequency distribution and cumulative log probability plot for uncut Cu, Pb, Zn, Ag and Au samples were plotted. The data was separated by mineralization zone type in order to determine whether differences could be observed between datasets. Frequency distribution and logarithmic probability plots of all metals on a per zone basis were plotted. The probability plots of each metal were superimposed on each other (all three zones) in order to see if the plots were similar. Despite data paucity, the plots reveal that the populations are fairly similar; thus the decision to concatenate all zones into one dataset was made. 4. Compositing Sample data was composited into regular 1m composites within the intersection of the mineralized zone in question for each drillhole. Composites were created using a minimum gap of 0.001m (gap to be ignored) and a maximum gap of 0m; the minimum composite length allowed to be created was 0.5m. Sample lengths varied from 0.02m to 17.9m with the geometric mean being about 1.2m. The composite length of 1m was thus deemed appropriate for this study. Table 1 displays the statistics of all the composites for each metal within the geology model at the noted composite length for all mineralized zones. Note that the rocktype tagging has been derived from the geology model itself. The rocktypes are discussed later but please refer to Geological Modeling section for the appropriate coding. Boxplots for the data below is shown in Appendix I. Cu displays the highest coefficient of variation, 1.30, followed by Au at 1.14, which Lower Quartile Solutions 3

5 indicates a moderate degree of variability; whilst Pb, Zn and Ag show lower values. These values are affected by extreme values and drop once outliers are cut. Table 1.0 Naive Statistics - Uncut 1m Composites per Metal All Zones Statistic Cu Pb Zn Ag Au Number of Data Mean (g/t) Std. Deviation Coeff. Of Var Maximum (g/t) Upp. Qrtle (g/t) Median (g/t) Low. Qrtle (g/t) Minimum (g/t) Number of Holes The statistics were further broken down into mineralized zones. Table 2 through to Table 6 shows the naïve statistics for each uncut metal on a per mineralized zone basis. Table 2.0 Naive Statistics - Uncut 1m Composites per Metal Cu Statistic Mineralized Zone Upper Middle Lower Number of Data Mean (g/t) Std. Deviation Coeff. Of Var Maximum (g/t) Upp. Qrtle (g/t) Median (g/t) Low. Qrtle (g/t) Minimum (g/t) Number of Holes Table 3.0 Naive Statistics - Uncut 1m Composites per Metal Pb Statistic Mineralized Zone Upper Middle Lower Number of Data Mean (g/t) Std. Deviation Coeff. Of Var Maximum (g/t) Upp. Qrtle (g/t) Median (g/t) Low. Qrtle (g/t) Minimum (g/t) Number of Holes Table 4.0 Naive Statistics - Uncut 1m Composites per Metal Zn Statistic Mineralized Zone Upper Middle Lower Number of Data Mean (g/t) Std. Deviation Coeff. Of Var Maximum (g/t) Upp. Qrtle (g/t) Median (g/t) Low. Qrtle (g/t) Minimum (g/t) Number of Holes Lower Quartile Solutions 4

6 Table 5.0 Naive Statistics - Uncut 1m Composites per Metal Ag Statistic Mineralized Zone Upper Middle Lower Number of Data Mean (g/t) Std. Deviation Coeff. Of Var Maximum (g/t) Upp. Qrtle (g/t) Median (g/t) Low. Qrtle (g/t) Minimum (g/t) Number of Holes Table 6.0 Naive Statistics - Uncut 1m Composites per Metal Au Statistic Mineralized Zone Upper Middle Lower Number of Data Mean (g/t) Std. Deviation Coeff. Of Var Maximum (g/t) Upp. Qrtle (g/t) Median (g/t) Low. Qrtle (g/t) Minimum (g/t) Number of Holes Cu shows higher means in the middle zone followed by the upper zone and lastly the lower zone. If median values are considered to lessen the impact of outliers, the upper body has higher values followed by the middle and then the lower. Pb values indicate the opposite as does Zn and Ag. Recall that the datasets haven t been cut and that the influence of outliers will ultimately change the characteristics of the final statistics. Note also the differing numbers of data for each mineralized zone. This is due to the different continuity of each zone as defined by the project geologist. In this project, the largest and most continuous zone is the upper body. This zone almost covers the entire project area. The second most continuous zone is the lower body; which is mainly restricted in the western side of the project area. The last and smallest zone is the middle body, which is restricted to the southeastern flank of the deposit (see Figure 1). Figure 1.0 Plan View of Mineralized Zones and Drillholes Lower Quartile Solutions 5

7 5. Sample Spacing The Euclidean spacing between composites was examined by zone type. Overall, the distances range from 140.2m to 304.6; the figure quoted is the median of the respective datatype. Table 7 shows the spacing between samples in three-dimensional space for surface composited drillholes. Table 7.0 Euclidean Spacing - 1m Composites Dataset Average Minimum Maximum Lower Quart. Median Upper Quart. Metres Metres Metres Metres Metres Metres Upper Middle Lower All Sample spacing calculations were performed in order to aid in assessing what block dimensions would be appropriate in blockmodeling. With typical median values being about 150m (all data), a block size of 40m x 40m x 1m (XYZ) was deemed appropriate since this would imply that between any two drillholes, there would be no more than four unsupported blocks in between. That is assuming that the two drillholes are piercing the end blocks. Therefore, in the ideal case, there would be four unsupported blocks in between two supported blocks. 6. Cutting Limits Cutting statistics were performed with the help of cumulative log probability plots, indicator correlation for lag 1 plots, coefficient of variation plots and finally percent metal contained plots. It should be noted that these are merely guidelines and that ultimately, the cutting limit is at the discretion of the participating geostatistician. The indicator correlation for lag 1 plots show the correlation between samples for the first lag set. Plotting this indicator against increasing minimum thresholds for Cu, Pb, Zn, Ag and Au grades leads to a line tending closer towards zero. In other words, at ever increasing thresholds of Au grade, there are fewer and fewer samples of similar grade. At this point, it indicates a lack of correlation between samples within the first lag set, and suggests an ideal cutting limit for assay values. The coefficient of variation plots shows the change in this coefficient with increasing Cu, Pb, Zn, Ag and Au values. A rapid change in this coefficient indicates a rapid change in the standard deviation and/or a change in the mean. This suggests an ideal cutting limit for Cu, Pb, Zn, Ag and Au grades. Kinks, plateaus and/or changes in the cumulative log probability plots also suggest changes in populations (perhaps subpopulations) and serve as a good indicator of cutting limits for Cu, Pb, Zn, Ag and Au values. A slightly different plot is the percent of contained metal in samples versus increasing trimming levels for metal grades. This plot enables one to check how much metal is being lost to cutting at a certain Cu, Pb, Zn, Ag and Au grade thresholds. Table 8 lists the all the cutting thresholds suggested from all methodologies described. The reasons for choosing one methodology over another is primarily based on how well defined that limit is depicted on the appropriate plot(s). The plots for each rocktype can be found in Appendix II. Table 8.0 Cutting Limits 1m Composites Statistic Cu Pb Zn Ag Au % % % g/t g/t Grade Limit No. of Samples in Database No. of Samples Cut % Cut 1.52% 3.30% 4.13% 7.08% 9.10% Additional statistics were performed on the cut composited drillhole datasets and are presented in Table 9 below. Data shown below is also in Appendix I as boxplots. Lower Quartile Solutions 6

8 Table 9.0 Naive Statistics - Cut 1m Composites per Metal All Zones Statistic Cu Pb Zn Ag Au Number of Data Mean (g/t) Std. Deviation Coeff. Of Var Maximum (g/t) Upp. Qrtle (g/t) Median (g/t) Low. Qrtle (g/t) Minimum (g/t) Number of Holes Notice that when the 1m composite assays were cut, the coefficients of variations drop; well below the 1.0 threshold. Also, notice that these coefficients are fairly low depicting a relatively low degree of variability. The statistics were further broken down into mineralized zones. Table 10 through to Table 14 shows the naïve statistics for each uncut metal on a per mineralized zone basis. Table 10.0 Naive Statistics - Uncut 1m Composites per Metal Cu Statistic Mineralized Zone Upper Middle Lower Number of Data Mean (g/t) Std. Deviation Coeff. Of Var Maximum (g/t) Upp. Qrtle (g/t) Median (g/t) Low. Qrtle (g/t) Minimum (g/t) Number of Holes Table 11.0 Naive Statistics - Uncut 1m Composites per Metal Pb Statistic Mineralized Zone Upper Middle Lower Number of Data Mean (g/t) Std. Deviation Coeff. Of Var Maximum (g/t) Upp. Qrtle (g/t) Median (g/t) Low. Qrtle (g/t) Minimum (g/t) Number of Holes Table 12.0 Naive Statistics - Uncut 1m Composites per Metal Zn Statistic Mineralized Zone Upper Middle Lower Number of Data Mean (g/t) Std. Deviation Coeff. Of Var Maximum (g/t) Upp. Qrtle (g/t) Median (g/t) Low. Qrtle (g/t) Minimum (g/t) Number of Holes Lower Quartile Solutions 7

9 Table 13.0 Naive Statistics - Uncut 1m Composites per Metal Ag Statistic Mineralized Zone Upper Middle Lower Number of Data Mean (g/t) Std. Deviation Coeff. Of Var Maximum (g/t) Upp. Qrtle (g/t) Median (g/t) Low. Qrtle (g/t) Minimum (g/t) Number of Holes Table 14.0 Naive Statistics - Uncut 1m Composites per Metal Au 7. Bivariate Statistics Statistic Mineralized Zone Upper Middle Lower Number of Data Mean (g/t) Std. Deviation Coeff. Of Var Maximum (g/t) Upp. Qrtle (g/t) Median (g/t) Low. Qrtle (g/t) Minimum (g/t) Number of Holes The cut 1m composites were then used to do comparative statistic between each metal. Cu was plotted against Pb yielding 121 pairings but demonstrating no correlation. Cu was then plotted against Zn and again, for 121 pairs, showing no correlation. Cu was plotted against Au values yielding 77 pairs (fewer Au assays in database compared to Cu assays) and showed a weak correlation of 0.52 (correlation coefficient). The strongest correlation was that of Zn versus Ag yielding a coefficient of 0.69 for 113 pairs. The scattergrams are shown in Figure 2 below. Lower Quartile Solutions 8

10 Figure 2.0 Bivariate Statistics - 1m Composites/Cut 8. Geological Modeling For this project, a geological model needed to be built. The drillhole file was imported into Datamine/Guide and the assays and lithological tagging, if available, were switched on. The zone definition, as defined by the project geologist, was also switched on. Strings were then clicked at the tops and bottoms of each drillhole depicting the top and bottom of each mineralized zone. These zones were dipping at 25 northeast and striking at approximately 120. The down dip extension was then exaggerated into the NE in order to account for any possible estimated Lower Quartile Solutions 9

11 block spreading. The higher elevation portion of the zones were truncated by topography. Generating a DTM using drillhole collars generated this surface. Once all the strings were drawn, they were closed thus representing closed polygons. These strings were then linked to one another to generate a wireframe for each mineralized zone. Each zone was given its own color and discretized with blocks measuring 40m x 40m x 1m (xyz) in size. Statistics were then compiled using this tagging and compared to the statistics from each drillhole as demarked in their lithological tagging field. It was found that due to the narrowness of each zone, it would be more representative to not use the lithological tagging from the geological model from GSLIB. A narrow model, once discretized in coarse blocks would lead to certain amount of misclassification; meaning that drillholes intersecting a rockcoded block in GSLIB would pick up that code. However, few drillholes, coarse blocks and narrow zones would allow for either an assayed intersection to be coded as waste in GSLIB. A different strategy was adopted and is described in detail in the Methodology section. 9. Methodology Variography was performed on the 1m cut composite datasets on a per mineralized zone basis. No preferred direction was identifiable due to small amounts of data. It was then decided to concatenate all the mineralized zones data together and perform variography on the resultant dataset. With this set, anisotropy was somewhat visible in the 110 to 140 azimuth utilizing 100m lags and angle tolerances ranging from +/ to +/ for Cu, Pb and Ag. However, it appears to be mainly due to data availability. Zn demonstrated some anisotropism at 140, slightly different than the other metals. Au demonstrated no obvious anisotropy and therefore omni directional variography was done. Therefore, a direction of 120º was chosen for Cu, Pb and Ag, whilst 140 was chosen for Zn and no particular direction was chosen for Au. The directional variogram is shown in Appendix IV. The nugget contribution was taken from down the hole variograms (Appendix III). In order to test whether the creation of several composites of the same value, which would change the spatial variance of the data, would result in anisotropical differences in the generated variograms, variograms were regenerated using uncut and sample composited data for each mineralized zone. The variograms revealed exactly the same anisotropy in the same directions as revealed by the cut 1m composited datasets. Ordinary kriging was selected as the estimation method of interpolating gold grades into a threedimensional blockmodel. The block size chosen was identical to that discretizing the geological model for both models, 40m x 40m x 1m (Northing x Easting x Elevation). Within the project area there were 135 blocks in the X direction, 62 blocks in the Y direction and 1600 blocks in the Z direction, for a total of blocks. The project area consists of an area from N to N, E to E and -600 El to 1000 El. A minimum of 3 and a maximum of 10 samples were utilized for an estimate. Tests were performed in this study to investigate the effects of certain interpolation parameters on the variability of the estimates. Firstly, the maximum number of samples utilized for an estimate was examined. In this estimation model, the maximum is set to 10 composites, with an average of 6 composites used. A number of kriging runs with various maximum sample values were done, and the average kriging variance of each run was compared to the maximum number of samples utilized. As the maximum number of samples is increased, the change in the kriging variance decreased. The maximum number of samples is then selected from the area where a change in slope (becomes flatter) occurs, which is in this case, between 8 and 12 samples. At this point, the addition of more samples does not significantly change the kriging variance at all. No restrictions were placed on the minimum number of drillholes since this, in areas of lower drillhole Lower Quartile Solutions 10

12 density, would create estimates between drillholes but not at the actual drillhole locations themselves. Due to the narrowness of the mineralized zones, the geological coding could not be successfully obtained from the geological model due to misclassification; some drillhole values could be omitted due to non-intersection with a discretizing block. It was then decided to volumetrically select all composites within each mineralized zone using Datamine/Guide and recode then as 1 for the Upper Body, 2 for the Middle Body and 3 for the Lower Body. Once complete, the centroids of the discretizing blocks within the geology model were exported into GSLIB. Since these zones were modeled in three dimensions, it was decided to use these centroids and estimate these points in space. Effectively, this means that composite values that were appropriately tagged were allowed to estimate the geological modeled blocks only; not into host rock. No boundary conditions were set given the paucity of the dataset. The search strategy utilized in the kriging runs is listed in Table 15. Table 15.0 Estimation Search Strategy Principal Direction Minor Direction Vertical Direction Metal Radius Metres Azimuth/Plunge Degrees Radius Metres Azimuth/Plunge Degrees Radius Metres Azimuth/Plunge Degrees Cu / / /65 Pb / / /65 Zn / / /65 Ag / / /65 Au / / /0 Only one interpolation run was carried. Although there are some unestimated blocks left within the geological model, no attempt was made to fill in these blocks with estimates. The ranges selected represent the first range of the double spherical variogram modeled for each metal. The first range of the variograms modeled is roughly two-thirds of the modeled variance. It was decided to be somewhat conservative by restricting the ellipsoid dimensions to this first range. 10. Cross Validation Numerous cross validation tests were performed on the final blockmodel; one of them being naïve cross-validation. This technique consists of removing one sample and using the kriging parameters to estimate it, and then comparing it to the original sample. This was done systematically for all samples with a final correlation, comparing estimates to actual values, being reported. This allows for the testing of the kriging parameters utilized in the estimation process. Using this process, the base case kriging run revealed a correlation coefficient 0.93 for Cu, 0.91 for Pb, 0.73 for Zn, 0.78 for Ag and 0.77 for Au which is are high values for these deposits. Another test was to take all the samples that actually intersected an estimated block, weighting them by length and then comparing them to the estimated block. This test is one, which is done in order to verify the amount of variability and/or smoothing in the estimate. Obviously, samples occurring within a certain estimated block should reveal a grade very similar to the estimated grade itself. This test was done for all mineralized zones together. The results were plotted on a scattergram and a correlation coefficient and other statistics performed on the data. Cu demonstrates a correlation of 0.95, Pb showed 0.97, Zn showed 0.98, Ag showed 0.96 and Au showed 0.98, which represents a sufficient amount of local variability and/or smoothing. A comparison between the average grades of the cut composites and the average estimated grades of the mineralized zones were made. The comparison was made by first polygonally declustering the 1m cut composites in three-dimensional space. This declustered dataset was then compared to the mean of the estimated blockmodel of that metal only. The results indicate that the resource is slightly biased low; the estimated kriged mean is lower than the datasets declustered mean; the result are shown in Table 16. Lower Quartile Solutions 11

13 Table 16.0 Comparison of Actual 1 and Estimated Means per Metal Metal Composites Estimates Cu Pb Zn Ag Au Note 1. The Actual refers to 1m cut composites. In conclusion, the generation of higher block estimates is not in anomalous proportions, and thus we can preclude the possibility of overestimation of all metals; conversely, Ag is slightly overestimated showing a slightly higher estimated mean of 16.91g/t versus the 1m cut composite mean of 16.35g/t. 11. Bias The amount of bias was also studied by determining the difference between the actual grade and the estimated grade; a test done via naïve cross-validation. These differences are then plotted on a frequency distribution plot and the mean established. In a perfectly unbiased estimate, the mean should be zero. Table 17 below shows the amount of bias for the project. Table 17.0 Residual Values - All Estimates Metal Number of Estimates Mean Grade Cu Pb Zn Ag Au As can be seen from the table above, there is very little in the way of bias; Ag demonstrating a slight bias high. 12. Trends The trend of the composites used for the estimates were compared to those contained within each estimation run for each blockmodel. Figure 3 to Figure 7 shows the trends in two planes, Easting versus the average grade and Elevation versus the average grade. Figure 3.0 Grade Trend Plot - Cu (%) Lower Quartile Solutions 12

14 Figure 4.0 Grade Trend Plot - Pb (%) Figure 5.0 Grade Trend Plot - Zn (%) Figure 6.0 Grade Trend Plot - Ag (g/t) Figure 7.0 Grade Trend Plot - Au (g/t) Lower Quartile Solutions 13

15 Overall, the trend plots show that the final blockmodel mimics the composited drillholes very well. The blockmodels have retained and mimicked the trends as held by the composites and thus one can preclude the argument that the blockmodels are not representative of composite grade trends, or that the blockmodels have created data trends not representative of the composites themselves. 13. Classification The resources estimated at the Salt River Base Mineral Project were classified as indicated for this study. Given the paucity of drillhole information, especially down dip, the mineralization can only be considered indicative. The search ellipsoid dimensions were restricted to the first range of the modeled variograms roughly two-thirds of the total variance. The results and statistics presented within reflect the authors (Competent Person) view of the deposit. 14. Specific Gravities The specific gravity utilized for the Salt River Base Mineral Project resource estimate was assumed to be 3.05; as received by field personnel. 15. Results Table 18 to Table 29 lists the resources at various cut-off grades for various metals within the three mineralized zones of the Salt River Base Mineral Project respectively. Grade thresholds were chosen arbitrarily to break down the table equally. Table 18.0 Mineral Resources Upper Body Pb% Cut-off Upper Body Pb (%) Tonnes Pb (%) ,248, ,442, ,836, ,525, ,851, ,738, ,601, , , , Table 19.0 Mineral Resources Middle Body Pb% Cut-off Middle Body Pb (%) Tonnes Pb (%) ,209, ,477, ,622, ,914, ,933, ,810, , , , Table 20.0 Mineral Resources Lower Body Pb% Cut-off Lower Body Pb (%) Tonnes Pb (%) ,140, ,286, ,427, ,217, ,978, ,582, ,518, , , Lower Quartile Solutions 14

16 Table 21.0 Mineral Resources All Zones Pb% Cut-off All Bodies Pb (%) Tonnes Pb (%) ,597, ,205, ,885, ,656, ,762, ,130, ,012, , , , Table 22.0 Mineral Resources Upper Body Ag (g/t) Cut-off Upper Body Ag (g/t) Tonnes Ag (g/t) ,309, ,191, ,133, ,021, ,796, ,084, ,723, ,049, ,427, ,207, ,183, Table 23.0 Mineral Resources Middle Body Ag (g/t) Cut-off Middle Body Ag (g/t) Tonnes Ag (g/t) ,511, ,959, ,861, ,666, ,388, ,168, ,871, ,602, ,582, ,455, ,235, Table 24.0 Mineral Resources Lower Body Ag (g/t) Cut-off Lower Body Ag (g/t) Tonnes Ag (g/t) ,051, ,051, ,051, ,051, ,022, ,446, ,002, ,246, ,240, ,474, ,947, Lower Quartile Solutions 15

17 Table 25.0 Mineral Resources All Zones Ag (g/t) Cut-off All Bodies Ag (g/t) Tonnes Ag (g/t) ,871, ,201, ,045, ,738, ,206, ,698, ,596, ,897, ,249, ,136, ,365, Table 26.0 Mineral Resources Upper Body Au (g/t) Cut-off Upper Body Au (g/t) Tonnes Au (g/t) ,706, ,433, ,276, ,130, ,061, ,274, ,987, ,425, ,011, , , Table 27.0 Mineral Resources Middle Body Au (g/t) Cut-off Middle Body Au (g/t) Tonnes Au (g/t) ,163, ,046, ,875, ,792, ,753, ,420, ,318, ,713, ,366, , , Table 28.0 Mineral Resources Lower Body Au (g/t) Cut-off Lower Body Au (g/t) Tonnes Au (g/t) ,528, ,996, , , , Table 29.0 Mineral Resources All Zones Au (g/t) Cut-off All Bodies Au (g/t) Tonnes Au (g/t) ,397, ,475, ,883, ,576, ,414, ,694, ,305, ,138, ,377, ,015, , Lower Quartile Solutions 16

18 The final blockmodels were then re-assembled to reflect Zn as the primary metal. In this case, all estimated blocks contain a Zn estimate, but not necessarily another metal. If another metal value is not estimated (missing), it will bias the metal low. The results are shown for each mineralized zone, Table 30 to Table 32, and then for the totals in Table 33 for various Zn% thresholds. Table 30.0 Mineral Resources Upper Body Cut-off Upper Body Zn (%) Tonnes Zn (%) Cu (%) Pb (%) Ag (g/t) Au (g/t) ,219, ,054, ,910, ,576, , Table 31.0 Mineral Resources Middle Body Cut-off Middle Body Zn (%) Tonnes Zn (%) Cu (%) Pb (%) Ag (g/t) Au (g/t) ,653, ,027, ,167, , , Table 32.0 Mineral Resources Lower Body Cut-off Lower Body Zn (%) Tonnes Zn (%) Cu (%) Pb (%) Ag (g/t) Au (g/t) 0.0 8,677, ,037, ,226, ,342, , Table 33.0 Mineral Resources All Zones Cut-off All Bodies Zn (%) Tonnes Zn (%) Cu (%) Pb (%) Ag (g/t) Au (g/t) ,549, ,118, ,303, ,587, , Note that values are higher than the previous table mainly because Cu estimate had the largest search ellipsoid in the estimate runs whilst Zn had a smaller one (475m vs. 250m; see Methodology section). The above amounts were converted to US Dollar equivalents on a per tonne basis for each zone. Two different pricing levels were used, the metal prices as per the issuing date of this report, July 14 th, 2006 (Table 36), and the metal prices as per Thabex s press release of April 4 th, 2006 (Table 37). Dollar per tonne equivalents are calculated in $US and have assumed 2205lbs per metric tonne. The pricing levels utilized are shown in Table 34 and Table 35 below. Table 34.0 Metal Prices Current (July 14 th 2006) Metal Unit $US Zn Per lb 1.59 Cu Per lb 3.65 Pb Per lb 0.47 Ag Per oz Au Per oz Lower Quartile Solutions 17

19 Table 35.0 Metal Prices April 4 th, 2006 Metal Unit $US Zn Per lb 1.23 Cu Per lb 2.53 Pb Per lb 0.52 Ag Per oz Au Per oz Table 36.0 US Dollar per Tonne - Current Prices (July 14 th 2006) At Individual Metal s 0.0 $/Tonne Cutoff Grade 0.0 $ Table 37.0 US Dollar per Tonne - April 4th, 2006 Prices At Individual Metal s 0.0 $/Tonne Cutoff Grade 0.0 $92.52 If the property is valued using an evaluation similar to that published earlier this year at the JSE for a similar base metal resource in South Africa, an insitu value of US$359M is arrived at (see Table 38); using April 4 th, 2006 commodity prices. Table 38.0 Insitu Value of Salt River Base Mineral Project Metal US$ Value Zn $1,695,215, Cu $2,408,971, Pb $147,102, Ag $197,226, Au $143,553, TOTAL $4,592,069, Value $358,593, Discussion The mineral resource for the Salt River Deposit is now complete. The methodology applied was ordinary kriging and the resultant blockmodel has proven to be somewhat robust for all metals, especially Cu. Cross validation values indicate that there is an adequate level of confidence in the final blockmodel. Drillhole values indicate that there is good continuity of the mineralization zones, especially the upper zone. This indicates that once more drilling is completed; the geology model should be remodeled in order to test the continuity and to acquire sufficient data to adequately test the individual characteristics of the separate zones. At a Zn cutoff grade of 0.0%, resource calculations yield an Indicated Mineral Resource of 42.5Mt at an insitu grade of 1.47% for Zn, 0.65% for Cu, and 0.28% for Pb, 12.27g/t for Ag and 0.18g/t for Au within the Salt River Base Mineral Project. These Mineral Resource figures are reported in accordance to the SAMREC code of This study attempted to generate a blockmodel with metal values that are representative of the insitu resources of the Salt River Base Mineral Project. Given the amount and quality of data available for resource estimation, and the data received, it is believed that the resultant model is representative of the insitu resources. The results and statistics presented within reflect the authors (Competent Person) view of the deposit. Lower Quartile Solutions 18

20 17. Qualifications, Experience and Independence The author of the Competent Person s Report, Mr. Dexter Ferreira, is a senior geostatistician with over 15 years experience in project evaluation internationally, including extensive involvement with mineral projects throughout South America and Africa. He is a member of South African Council for Natural Scientific Professions, and qualifies as an Expert, Competent Person and Qualified Person as defined in National Instrument and the JORC Code. Lower Quartile Solutions 19

21 Appendix I Data Boxplots Cut and Uncut 1m Composites Lower Quartile Solutions 20

22 Lower Quartile Solutions 21

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27 Appendix II Cut Stats Lower Quartile Solutions 26

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33 Appendix III Down the Hole Variography Lower Quartile Solutions 32

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39 Appendix IV Directional Variography Lower Quartile Solutions 38

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