17.0 mineral resource 43-101.pdf

Pincock, Allen & Holt 17.1 80530 January 18, 2008

17.0 MINERAL RESOURCE

17.1 Resource Model

The mineral resource estimate was prepared for the Nkamouna area using a three-dimensional block model to estimate cobalt, nickel, and manganese grade for individual 10 by 10 meters horizontal by 1 meter vertical blocks. In addition, lithology codes and resource classification codes were defined for each block. This estimate updated the prefeasibility model with additional data, including: 162 deepened pits, five additional pits, and revised topography data. The 2007 assay database contains 4 percent greater assay intervals than the 2005 database. The updated resource estimation was done with Datamine Studio 3.0 geologic modeling software, although the methodology remains essentially the same.

Because the deposit is very thin (averaging 13.5 meters including ore and overburden) compared to the horizontal extent (over 4,000 meters), a 10X to 30X vertical exaggeration is required to view the entire deposit in cross-section. With greater than 10X vertical exaggeration, however, cross-sections become unacceptably tall, so basic interpretation and modeling were done using a flattened coordinate system. Thus, in the flattened coordinate system, an elevation of zero is the topographic surface and an elevation of minus 10 is ten meters below topography.

The general procedure used for resource estimation was as follows:

1. In the prefeasibility estimate, the depth to the bottom of Upper Laterite (granular), breccias, and the Lower Limonite (ferrilite) were extracted from the geologic logs of pits and drill holes and edited to correct for partial-depth pits and holes. The prefeasibility model depths were used as the starting point for this estimate.

2. A triangulated DTM model was created to represent the depth from surface to the bottom of each geologic unit. The depths to the bottom of each surface were edited interactively in Datamine to add data for the new pits and to adjust for deepened pits. In addition, estimated depths below pits and drill holes that did not penetrate the bottom of some horizons were entered as needed. An improvement to the prefeasibility model is that extrapolation outside the area of pits/drill holes was limited to 150 meters using a 3-dimensional polygon that also controlled the limiting depth of the surface.

3. Based on cross-section plots of cobalt grade in the flattened model, it was observed that cobalt grade could be correlated parallel to the top of mineralization. The depth to the top of mineralized cobalt from the prefeasibility estimate was used as the starting point for the current model.

4. A top-of-mineralization, or TOMI, model was created such that the top of mineralization in each drill hole was at a constant elevation. The advantage of this model is that the optimum correlation between the metal grades is horizontal and the shape and continuity of the


mineralization can be viewed directly on plan maps. This model also went through several iterations of editing and remodeling to remove inconsistencies in the data from shallow holes that did not penetrate the top of mineralization and from multiple pits and drill holes within a few meters of each other.

5. Basic statistics, using the TOMI coordinate system, showed that there are three cobalt grade populations, including low-grade (poorly mineralized), mid-grade (mineralized), and high-grade (strongly mineralized). Manganese was found to have grade distributions similar in shape, but higher grade than cobalt, consistent with the strong correlation between cobalt, manganese, and asbolane. Nickel appears to be much more evenly distributed than cobalt and manganese and was found to only have two grade zones, mid-grade, and high-grade.

6. Grade zones were defined for each metal as closed shapes in plan maps in the TOMI model coordinate system.

7. Basic statistics were run within the grade zones to confirm the grade distributions and variograms were run to confirm continuity of grades within the zones.

8. Block grades were estimated for cobalt, nickel, and manganese using inverse-distance-power (IDP) estimation with grade-zoning controls. IDP estimation parameters were adjusted so the estimated block distributions adequately reflected mining selectivity.

9. A sample spacing model was prepared in TOMI model coordinates that measured the spacing of samples around each block. This model was used to classify the resources into measured, indicated, and inferred resource classes based on pit/drill hole spacing.

10. The individual 1-meter thick blocks from the flat model were composited into vertical stacks of blocks using a cutoff grade and minimum thickness criteria. This process created a gridded-seam model with 10x10m horizontal blocks. The gridded-seam model was then accumulated into 50x50m blocks for mine planning and scheduling with XPAC.

17.2 Modeling Coordinate System

The model was constructed using UTM Zone 33/WGS84 coordinates. The Datamine project used single precision data files, which results in an XY coordinate precision of approximately 0.02 meters. Extended precision is recommended for future models, however, to eliminate problems from the limited range of the Datamine IJK parameter in future models.

17.3 Block-Model Location and Size Parameters

Three block models were used for resource estimation in the prefeasibility model, including the flat model, the unfolded model, and a gridded-seam model with minable seams for pit design. The unfolded model is referred to in this report as the top-of-mineralization-indexed, or TOMI model.


The current model combines the flat model, the TOMI model, and a true elevation model into a single Datamine block model by storing a Z-coordinate for each model, as a separate column in the Datamine model file. It is very simple to convert the model from one type to the other by replacing the elevation of the block center (ZC) with the Z-Coordinate from the desired coordinate system. Because of limitations on the Datamine IJK parameter, each XY location in the model was implemented as a single block, and the thickness of the block is equal to the thickness of the model. Individual blocks are created as 1-meter high Datamine sub-blocks.

The true-elevation coordinate space is simply the true elevation of the block. The flat-model coordinate space defines elevation zero (0) as the current topographic surface. Thus, an elevation of minus 12 meters (-12m) in this system is equivalent to 12 meters below surface. The size and location parameters for this model are shown in Table 17-1. The TOMI model coordinate space defines zero elevation as the top of mineralization. Thus, the top of mineralization, which is an irregular, undulating surface in flat and true elevation coordinates is a flat, horizontal plane in the TOMI coordinates. The TOMI model may be visualized as a seam model with sub-blocks running parallel to the top of mineralization.

The minable resource model is a composited model in which the vertical stacks of blocks have been combined into a gridded-seam model that summarizes the thicknesses of overburden, interburden waste, and ore based on cutoff and mining selectivity criteria. Model parameters for the minable resource model are the same as those for the other models, except that the block height is variable.

The scheduling model consolidates the seam model into 50x50m wide blocks that summarize the average thickness, tonnage and grade for the Runge XPAC scheduling software.

TABLE 17-1 Geovic Mining Corp. Nkamouna Project, Cameroon Block Model Size and Location Parameters

Number Blocks Block Size

(meters)

Minimum Value

(meters)

Maximum Value

(meters) Length

(meters)

Easting (Columns) 470 10 368,500 373,200 4,700

Northing (Rows) 450 10 359,500 364,000 4,500

Elevation(Levels) 500 (Sub-blocks) 1 500 1,000 500

17.4 Pit and Drill-Hole Data

Pit and drill-hole data were provided by Geovic as three EXCEL files containing collar coordinates, lithologic codes, and assay data. Each of these files was edited slightly for data checking and/or to facilitate later use of the data. The resulting data were saved as comma delimited ASCII files importing into Datamine (collar.csv, lithology.csv and assay.csv).


17.4.1 Collar Data

Collar data included a unique database ID number, an area code (NKM, KON), sample type (Pit, UN Core Hole, Core Hole, Reverse Circulation, SG sample, bulk sample), collar coordinates (Easting, Northing, and Elevation). The Geovic collar data spreadsheet was edited to add a depth-of-hole field (extracted from the assay data), and two fields indicating whether the hole should be used for the resource estimate and the reason that it was not used.

None of the UN core holes were used for the resource estimated because they were considered unreliable. In addition, pits were not used in the model if they were extremely shallow sample pits and if information from adjacent holes indicated that the mineralized zone was much deeper.

Other holes were not used because several samples (pits or reverse circulation (RC) drill holes) were present at approximately the same location. Some pits were re-sampled up to four times as part of various sampling studies; in addition, some RC Air drill holes were twinned with a pit and some of the pit twins had more than one sample. Only one of these twinned holes, was used at each location, because the short range variability created severe anomalies in lithologic contact and the top of mineralization surface models. The sample used for resource estimation was based on the following selection criteria: If one of the drill holes or pit samples was deeper, or had more assayed intervals, that hole was used; otherwise, the original hole or pit was used. Summaries of the number of holes used and holes excluded are shown in Tables 17-2 and 17-3.

TABLE 17-2 Geovic Mining Corp. Nkamouna Project, Cameroon Summary of Samples Used for Resource Estimation

Sample Type

PrefeasibilityNumber

Pits/Holes Prefeasibility

Meters

Current Number

Pits/Holes Current Meters

Bulk 2 29.0 1 16

Core Drill Hole 23 600.5 23 601

Pit 1,043 13,457.2 1,047 14,162.46

Pit Original Pit of Twin 19 306.1 21 343.55

Pit East Side Twin 11 167.3 10 155.7

Pit North Side Twin 1 20.0 1 20.0

RC Air Hole 164 3,458.5 164 3,458.5 RC Air Hole Original RC of Twin 11 197.0 10 183.0

Total 1,272 18,235.5 1,277 18,940.31


TABLE 17-3 Geovic Mining Corp. Nkamouna Project, Cameroon Summary of Drill Hole Data Not Used for Resource Estimation

Hole Type Reason Not Used Number of Holes

Pit No Collar 12

Pit Pit not dug 1

Pit Outside Model 3

Bulk Shallow 1

Pit Shallow 58

Bulk Twin 3

Pit Twin 79

Pit SG Twin 17

RC Air Hole Twin 2

UN Core Hole UN Core Hole 11

Total 188

17.4.2 Lithologic Data

Lithologic data was assigned a unique database ID number, an area code (NKM, KON), an alphanumeric lithologic code, and intervals of down-hole depth for which the lithologic code applied. A numeric code was assigned to each lithologic unit as shown in Table 17-4. The numeric codes were assigned so that the lithologic units were ordered from surface downward.

The data for the formation DTM models were prepared for the prefeasibility model by scanning the drill holes for the last or lowermost occurrence of GR (unit 1), which defined the bottom of the Upper Laterite and top of the ferricrete breccia. The last occurrence of any of the ferricrete breccia codes (UB, FB, LB) defined the bottom for the ferricrete breccia and the top of the ferralite. The last or lowermost occurrence of FL or LFL was used to define the bottom of the ferralite unit. In addition to the formation depths extracted from the lithology data, manual estimates of lithology depths were introduced where drill holes were too shallow to intersect the formation or were otherwise inconsistent. The formation depth data were further edited in Datamine to improve the depth picks for the current model. The method for estimation of the manual picks is discussed in Section 17.7, Lithologic Surface Models.


TABLE 17-4 Geovic Mining Corp. Nkamouna Project, Cameroon Summary Lithologic Codes Used For Estimation Numeric

Code Geovic Code

Number Intervals Meters Geovic Description PAH Description

1 GR 4,360 4,114.60 Granular Upper Laterite

2 UB 1,452 1,170.25 Upper Breccia Upper Ferricrete Breccia

3 FB 3,054 2,547.20 Ferricrete Breccia Hardpan Ferricrete Breccia

4 LB 4,135 3,412.05 Lower Breccia Lower Ferricrete Breccia

5 FL 7,356 6,802.56 Ferralite Lower Limonite

6 SP 298 257.25 Saprolite Saprolite

7 SE 45 18.30 Serpentine Serpentinite

8 QS 101 83.85 Quartz Sand Silcrete

9 SH 367 318.45 Schist Schist

10 QT 145 124.10 Quartz Silcrete

11 LFL 82 75.00 Lower Ferralite Ferralite

12 CB 12 5.25 Carbonate Alteration

13 Unknown 28 11.45

Total 21,435 18,940.31

17.4.3 Assay Data

The assay data file contained the same database ID and area codes as the collar and lithology files, plus the date and report number for each assay. Assay data was entered as from, to interval depths, plus assays for cobalt, nickel, and manganese. Additional data was contained in the data file for up to four additional assays of cobalt, nickel and/or manganese, but the additional assays were not used for this resource estimate. Intervals with no assay were coded with negative numbers that showed the reason for the missing assay.

17.4.4 Conversion of Collar, Lithology, and Assay Data for Resource Estimation

The assay data were processed for entry into the resource estimation model, as follows:

1. Added spaces and redundant alphanumeric codes such as SG-A, SG, -A, and -D were removed from the database ID field. The Database ID and Area codes were combined to create drill hole names for Datamine.


2. The collar coordinates data were imported to Datamine, then updated with survey data for the new pits and those pits that had been resurveyed since the previous estimate.

3. The lithology depth data was joined to the collar coordinates. The collar coordinate data were updated for 152 pits and drill holes using the Datamine JOIN process.

4. The initial value for the depth of the top of mineralization was determined for the prefeasibility based on the top of the first two consecutive assays above 0.06 percent cobalt. If two consecutive intervals above 0.06% cobalt were not found, the top of the ferralite was used as the top of mineralization. The depth to the top of mineralization, called ZINDEX in Datamine, was joined to the collar file, then checked and updated interactively in Datamine.

5. Drill hole files were created using actual elevation, the depth to the top of mineralization and zero (0) as the collar elevation.

6. Assays below detection limit and missing assays were modified as summarized in Table 17-5. Where cobalt was assayed and manganese was not assayed, manganese was estimated using the power-curve regression formulae that are summarized in Table 17-6 in Section 17.4.5, Estimation of Missing Manganese Assays from Cobalt. (Manganese was usually only missing where cobalt was less than 0.1% Co.).

TABLE 17-5 Geovic Mining Corp. Nkamouna Project, Cameroon Modification of Assay Data

Assay after Modification Original Value

Co Ni Mn


17.4.5 Estimation of Missing Manganese Assays From Cobalt

Approximately 10 percent (2,197) of the intervals in the assay data were not assayed for manganese. These values, which were identified in the Geovic assay data by an Issue 7" flag were estimated by Geovic using the cobalt grade and Mn:Co ratios for six cobalt-grade intervals.

Since there is a good general correlation between manganese and cobalt grade, with R2 = 0.9, it is reasonable to estimate the missing manganese values from the cobalt grade. In addition, manganese has little economic value and most of the unassayed manganese values have a relatively low cobalt grade (averaging 0.1% Co). A regression study was done to determine the best method for estimating the missing manganese assays, as follows:

1. A data set was assembled that contained all the samples with assays for both cobalt and manganese.

2. Preliminary statistics were done which indicated that manganese and cobalt were best correlated on log-log plots, which implies a power curve in the form:

Y = A x B

3. Based on the log-log correlation and assumption that relative sampling errors are the same for cobalt and manganese, an intermediate variable, MnCo, was created that was equal to the square root of manganese times cobalt (i.e., the geometric mean of cobalt and manganese).

4. Average manganese and cobalt grades were computed for each of the cobalt-bearing lithologic units, using small grade ranges of the MnCo variable with about the same number of samples in each cell. A total of 64 outlier pairs were identified at this stage. The outliers removed from the data before computation of the regression equations and 14,147 assay pairs were used for regression analysis.

5. The average manganese and cobalt grades were plotted on log-log graphs. Since the averages were computed using the geometric mean of the two assays, the resulting curves are equivalent to doing a lognormal major-axis regression.

6. The resulting graphs were very linear, but appeared to have different slopes for low and high grade values. Lithology was confirmed to be a significant variable, and power curves were derived for low and high cobalt values as shown in Table 17-6.

7. The regression equations were tested against the original data, as shown in Table 17-7, and were found to be globally unbiased. Estimation error varied between 15 and 36 percent RSD (relative standard deviation) in the cobalt-bearing zones. Outside the cobalt-bearing zones, the correlation between Co and Mn is less reliable, and the relative standard error is only 44 percent RSD.


TABLE 17-6 Geovic Mining Corp. Nkamouna Project, Cameroon Regression Coefficients for Estimating Manganese from Cobalt

Lithology Code Low

Power Low

Constant Low-High

Crossover High Power

High Constant

Upper Laterite 1 0.8830 5.3799 0.6579 1.0000 5.6500

Upper FB 2 0.8873 4.8428 0.1408 0.9898 5.9212

Hardpan FB 3 0.9536 5.6337 0.3753 1.1002 6.5042

Lower FB 4 0.8733 4.6533 0.3736 1.0606 5.5956

Ferralite 5 0.7040 3.2144 0.1873 1.0401 5.6438

Other >5 0.8491 4.4092 0.1965 1.0020 5.6552 TABLE 17-7 Geovic Mining Corp. Nkamouna Project, Cameroon Results From Applying Regression Equations to the Test Data Set

Lith Code Count

Average Co

Average Mn

Average Mn

Regress

Average Regress

Error

Average log(e)

(RegErr)

StdDev log(e)

(RegErr) R

squared 1 750 0.0332 0.2591 0.2584 -0.0007 0.0027 0.1496 0.9319 2 630 0.0698 0.4460 0.4460 0.0001 -0.0026 0.2758 0.9090 3 2,151 0.1238 0.7801 0.7886 0.0085 -0.0077 0.3528 0.9266 4 3,273 0.1459 0.8419 0.8457 0.0039 -0.0024 0.3084 0.9295 5 6,664 0.1593 0.9197 0.9207 0.0010 -0.0060 0.2756 0.8128

>5 679 0.0934 0.5985 0.5954 -0.0031 -0.0865 0.4171 0.8444

17.5 Topographic Model

Topographic data were provided by Geovic as two AutoCAD drawing files. The first of these files, Plan_5000.dwg, contains topographic contours at 3-meter contour intervals in the area of pit/drill hole sampling, is dated July 2006, uses the UTM Zone 33/WGS84 coordinate system, and has a scale of 1:5000. The second drawing, Mada_Bible_2006, covers a much larger area with a scale of 1:10000 and contains 10-meter contours. The date of the second drawing is April 2006.

Data from the two source files were combined so that the 3-meter contours were the primary source of data and the 10-meter contours were used to extend elevation data to the model area plus 500 meters outside the model area. (Note-The Mada Bible contour data are internally 2-dimensional contours and elevations were assigned interactively in Datamine.)

A triangulated digital terrain model (DTM) was created from the combined elevation model for use in resource estimation and mine planning.


17.6 Compositing

Compositing was done using simple, length-weighted compositing to combine the original sample intervals into even 1-meter lengths starting from the top of the pit/drill hole. Missing assays were treated as unknown values and they were not included in the weighted average. At least 0.5 meters of assayed length was required before a composite value was saved. Lithologic codes were assigned to composites according based on the assay lithology that covered the majority of the composite interval.

Since over 85 percent of the samples were collected using regular 1-meter lengths starting from the top of the pit/drill hole, the effect of compositing on the data is minimal.

17.7 Lithologic Surface Models

Digital terrain (DTM) models for depth to the formation contacts (Topo, Bottom of Upper Laterite, Top of Lower Limonite, and Bottom of Lower Limonite) were created in the flat model system using the formation depth data and the Datamine SURTRI process. In the flat coordinate system, the elevation of the topographic surface is set to zero elevation. The elevations of the formation contacts are negative values equal to minus-formation-depth.

These models were constrained by an outer limit string that limited extrapolation to 150 meters beyond the area sampled by pits/drill holes. The Z-coordinates for the outer limit points were assigned manually to provide extrapolated depths that were consistent with depths on the outer edge of pits and drill holes. The formation depth DTM models were checked in Datamine using north-looking and west-looking cross-section views to ensure that the estimated depths for shallow holes were consistent with holes that penetrated the bottom of the Lower Limonite zone.

17.8 Top of Mineralization Model and Indexed Formation Depth Models

The indexed formation models were created such that the sample data could be viewed as though the top of mineralization was a flat surface. This was accomplished by creating a DTM model in which the elevation of the topographic surface was set equal to the depth of the top of cobalt mineralization. The depth to each of the formation contacts was then subtracted from the depth to the top of mineralization and used to create the indexed formation depth DTM models. A 150-meter extrapolation limit was also used to limit extrapolation for these models.

The depth to the top of mineralization was checked in Datamine using north-looking and west-looking cross-section views to check the DTM models and to adjust the depth to top of mineralization for inconsistencies.

The cross-section plot in Figure 17-1 shows pits and drill holes plotted in untransformed coordinates, flat model coordinates and coordinates indexed parallel to the top of mineralization. Cobalt grades are shown as color-coded histogram and the interpreted lithology contacts are shown as lines where the lithology contacts intersect with the cross section.


The section with untransformed coordinates, at the top of Figure 17-1, demonstrates the difficulty of visualizing the deposit without a vertical exaggeration. Even though the maximum topographic relief is only about 200 meters, any significant vertical exaggeration rapidly becomes unwieldy, and correlations between pits/drill holes are distorted. (Vertical exaggerations between 10x and 30x were used for interpretation and review of the model in the Datamine design window.)

Because the deposit is only a thin skin just below the surface and the surface topography is relatively flat, the deposit is more easily viewed as though the surface topography is flat, as shown in the middle section of Figure 17-1. This plot also demonstrates the variability in the thickness of the various lithologic units and that the deposit is composed of a variable-thickness barren zone consisting of the Upper Laterite (Granular Zone) and most of the breccia.

The difficulty of modeling the formation contact boundaries is shown in the flat-coordinates cross section, since many pits were terminated before reaching the bottom of the Lower Limonite. (For example, pits NKM-572.0 to NKM-575.0 at approximately 370600E.) In those cases the contact at the bottom of the Lower Limonite is estimated based on nearby pits that do intersect the contact.

It may also be observed that the contact between the barren overburden and the mineralized horizon is generally very sharp and is sub-parallel to the top of the Lower Limonite zone. In addition, higher grade mineralization tends to be associated with the upper parts of the mineralized zone, so resource modeling was done in an indexed coordinate system in which the top of mineralization was defined as Z equal to zero (0) elevation. The bottom cross-section shows the pits and drill holes plotted in the indexed coordinate system.

17.9 Basic Statistics by Lithologic Unit

Note The tables in this section have been updated to include those data used for the current estimate. The figures have not been updated, however, because the quantity of additional data is small relative to the resolution of the Figures.

Basic statistics were performed on each lithologic unit to evaluate the correlation between cobalt, nickel, and manganese as a function of lithology. These statistics show that cobalt grade is strongly correlated with lithology with the lowest cobalt grade in the uppermost unit, the Upper Laterite (Unit 1), and highest grade in the bottom unit, the ferralite zone (Unit 5). Tables 17-8 to 17-10 show statistics for cobalt, nickel and manganese, respectively. Figure 17-2 shows that there is a very strong linear correlation between manganese grade and cobalt grade. This is consistent with the strong association between cobalt and the manganese mineral asbolane. Nickel shows a weak correlation with cobalt in the Upper Laterite and breccia units, but nickel grade doubles in the ferralite zone and is much higher-grade than would be expected if nickel were correlated with cobalt.


TABLE 17-8 Geovic Mining Corp. Nkamouna Project, Cameroon Basic Statistics for 1-m Composited Cobalt Grade Unit Code Missing

AssaysNumber Assays

Minimum Grade

Maximum Grade

Average Grade

Standard Deviation

Coefficient of Variation

1 401 3,623 0.0025 0.831 0.0262 0.0178 0.6792 74 1,110 0.0025 2.093 0.0424 0.1077 2.5403 71 2,493 0.0025 2.73 0.0774 0.1921 2.4824 91 3,328 0.0025 2.45 0.1149 0.2042 1.7775 113 6,779 0.001 2.26 0.1565 0.1558 0.9968 1 34 0.002 1.56 0.1273 0.3042 2.39010 1 48 0.0251 0.331 0.0778 0.0511 0.65711 2 72 0.0251 0.774 0.1025 0.0994 0.97012 0 2 0.0251 0.0251 0.0251

ALL 754 17,489 0.001 2.73 0.1026 0.1622 1.581 TABLE 17-9 Geovic Mining Corp. Nkamouna Project, Cameroon Basic Statistics for 1-m Composited Nickel Grade

TABLE 17-10 Geovic Mining Corp. Nkamouna Project, Cameroon Basic Statistics for 1-m Composited Manganese1 Grade

1 Includes manganese assays estimated from cobalt grade

Unit Code Missing Assays

Number Assays

Minimum Grade

Maximum Grade

Average Grade

Standard Deviation


1 401 3621 0.025 0.68 0.1578 0.0359 0.2282 74 1110 0.02 1.161 0.1651 0.0885 0.5363 71 2493 0.01 2.84 0.2062 0.1773 0.8604 91 3324 0.01 1.65 0.2829 0.195 0.6895 113 6774 0.01 2.44 0.6395 0.244 0.3828 1 34 0.02 0.7 0.1741 0.1548 0.889

10 1 48 0.1511 1.48 0.4506 0.2396 0.53211 2 72 0.1511 1.64 0.5917 0.2816 0.47612 0 2 0.1511 0.1511 0.1511

ALL 754 17478 0.01 2.84 0.3783 0.2859 0.756

Unit Code Missing Assays

Number Assays

Minimum Grade

Maximum Grade

Average Grade

Standard Deviation


1 403 3623 0.0271 4.572 0.215 0.1107 0.5152 74 1110 0.03 12.761 0.2872 0.6396 2.2273 71 2493 0.0186 25.66 0.4871 1.2364 2.5384 95 3328 0.02 17.6 0.6791 1.1727 1.7275 118 6779 0.01 16.6999 0.9103 0.8724 0.9588 1 34 0.05 5.964 0.622 1.2444 2.00110 1 48 0.18 1.83 0.446 0.2788 0.62511 2 72 0.17 4.34 0.6258 0.57 0.91112 0 2 0.193 0.193 0.193 -

ALL 765 17489 0.01 25.66 0.6193 0.9405 1.519


FIGURE 17-2 Geovic Mining Corp. Nkamouna Project, Cameroon Nickel and Manganese Correlation to Cobalt by Lithologic Unit.

1 Includes manganese assays estimated from cobalt grade

Lognormal cumulative frequency plots were prepared to further evaluate the metal distributions. The lognormal cumulative frequency plot is a specialized plot in which the cumulative frequency above a cutoff grade plots as a straight line if the distribution is lognormal. Cobalt, nickel and manganese grade distributions are discussed in detail in Section 17.9.1.

17.9.1 Cobalt Grade Distributions

The lognormal probability plot for cobalt in the Upper Laterite Zone, shown in Figure 17-3, plots as a straight line up to about 0.1 percent cobalt, which suggests that it is nearly a pure lognormal distribution. The upward curve at the higher-grade end of this plot indicates 1 to 5 percent higher-grade outliers compared to a simple lognormal curve that are caused by erratic higher-grade intersections in otherwise weakly mineralized material.

The remaining curves have very complex shapes suggesting mixtures of several grade distributions. In particular, the distribution for the Lower Limonite (ferralite) appears to contain a mixture of three populations. The component grade distributions were explored for each zone by fitting a mixture of three lognormal distributions to the distribution for each zone.


FIGURE 17-3 Geovic Mining Corp. Nkamouna Project, Cameroon Cumulative Frequency Plots of Cobalt Grade (Raw Samples Excluding Trace Assays)

This study indicated that each zone contained the same three distributions but with different combinations of the component distributions. The three distributions include a low-grade population with a median grade of 0.027 percent Co, a mid-grade distribution with a median grade of 0.125 percent Co, and a high-grade distribution with a median grade of 0.41 percent Co. The overall distribution fit for the breccia zones and ferralite zone is shown in Figure 17-4.

The cumulative frequency plots for nickel grade, shown in Figure 17-5 are still multimodal, but are very different from cobalt. The Upper Laterite has the lowest grade of these distributions and is composed of a low variability population with a few low-grade outliers. The nickel grade distributions in the breccia zones are similar, but grade increases with depth. The lower breccia zone is slightly bimodal, possibly because of the inclusion of a few samples from the higher-grade Lower Limonite (ferralite). The ferralite has significantly higher-grade nickel content than the overlying zones and is composed of a relatively high-grade, low variability population with a few low-grade outliers.

The manganese grade distributions, shown in Figure 17-6, are very similar to the cobalt distributions, but are about 8 times higher grade.

0.00

01

0.00

1

0.01

0.050.

1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.95

0.99

0.99

9

0.99

99

Fraction Samples above Cutoff

0.001

0.01

0.1

1

10

Cob

alt G

rade

(%C

o)

LegendUpper LateriteUpper BrecciaFerricrete BrecciaLower BrecciaLowerLimonite


FIGURE 17-4 Geovic Mining Corp. Nkamouna Project, Cameroon Distributions Fitted for Cobalt Grade - Breccia Zones and Ferralite Zone

0.001 0.01 0.1 1 10

Cobalt Grade (%Co)

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Freq

uenc

y

LegendActualModelLow GradeMineralizedHigh Grade

0.001

0.01

0.1

1

10

0 .0001

0.00 1

0.01

0.05

0.1

0. 20.30.40.50.60.70.8

0.9

0.9 5

0.99

0.999

0.9999

All Breccia Plus Lower Limonite(Units 2-5) Cobalt

Cob

alt C

utof

f Gra

de (%

Co)



FIGURE 17-5 Geovic Mining Corp. Nkamouna Project, Cameroon Cumulative Frequency Plots of Nickel Grade (Raw Samples Excluding Trace Assays)

0.00

01

0.00

1

0.01

0.050.

1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.95

0.99

0.99

9

0.99

99


0.01

0.1

1

10

Nic

kel G

rade

(%N

i)

LegendUpper LateriteUpper BrecciaFerricrete BrecciaLower BrecciaLower Limonite


FIGURE 17-6 Geovic Mining Corp. Nkamouna Project, Cameroon Cumulative Frequency Plots of Manganese Grade (Raw Samples Excluding Trace Assays)

17.10 Grade Zone Models

Based on the identification of three distinct populations of cobalt grade in the basic statistical analysis, grade zoning was used to define the spatial distribution of each population. Grades zones were defined in the TOMI model coordinate system as follows:

1. Nearest neighbor models were created for the metal that was being grade-zoned. The resulting block grades were plotted on plan maps and color coded so that the grade zones were visually distinctive.

2. Polygonal outlines were drawn on plans to define the boundaries of each zone. Since the grade zones do not have distinct grade cutoffs, but instead have large grade ranges where a value could be in either of two grade zones, grade zone boundaries were defined only where either lateral or vertical continuity was present. These boundaries were used to assign grade-zone codes to blocks and composites.

0.00

01

0.00

1

0.01

0.050.

1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.95

0.99

0.99

9

0.99

99


0.01

0.1

1

10

100

Man

gane

se G

rade

(%M

n)

LegendUpper LateriteUpper BrecciaFerricrete BrecciaLower BrecciaLower Limonite


FIGURE 17-7 Geovic Mining Corp. Nkamouna Project, Cameroon Grade Distributions by Grade Zone for Uncapped, Nearest-Neighbor Cobalt Grade

Grade distributions are shown in Figure 17-7 for the grade zones, using a nearest-neighbor model to decluster the samples and to more accurately represent the area of the grade zones. The resulting

0.001 0.01 0.1 1 10

Cobalt Grade (%Co)

0

0.01

0.02

0.03

0.04

0.05

0.06

Freq

uenc

y

0.28

0.29

0.3

Freq

uenc

y LegendLow GradeMineralizedHigh GradeTotal

0.001

0.01

0.1

1

10

0 .0001

0 .001

0.01

0.0 5

0.1

0.20.30.40.50.60.70 .8

0.9

0. 95

0 .99

0.999

0.999 9

Cobalt Distributions by Grade ZoneNearest-Neighbor Model - No Caps

Cob

alt C

utof

f Gra

de (%

Co)



distributions are generally similar to the theoretical distributions in Figure 17-4, although there are some differences, as follows.

1. The low-grade distribution contains a large spike at 0.025 percent Co that is not present in Figure 17-4. This spike is related to the constant assay used for visually low-grade samples that were not assayed (i.e., because the visual appearance of the samples indicated very low grade).

2. There are few samples above 0.10 percent Co in the low grade zone compared to the theoretical population. These samples are erratic samples that were not included in the mineralized zone because there was no horizontal or vertical continuity to other samples.

3. There are many samples below 0.10 percent Co and many high grade samples above 0.4 percent Co in the mid grade (mineralized) zone compared to the theoretical population. Again these samples are erratic samples with poor continuity.

4. The high-grade zone contains too many samples below 0.3 percent Co. This difference is caused by the difficulty of defining the high-grade zone, which overlaps with the mid-grade zone over more than 50 percent of its distribution.

Overall, the grade zones are believed to be sufficiently reliable for resource estimation, although the overlapping nature and the difficulty of defining the grade zones strongly indicates that the grade zone boundaries should not be treated as hard boundaries for grade estimation.

Grade zones were created for nickel and manganese using a similar method to that used for cobalt. The only significant difference is that only two grade zones were used for nickel grade.

17.11 Variograms

Variograms were run on 1-meter composite assays using the log-transformed grades in the index model to evaluate the continuity of cobalt, nickel, and manganese mineralization. The log-transformed variograms were then converted to relative variograms using the standard covariance transformation method. Directional variograms were computed parallel to the top of mineralization at azimuths of 0, 30, 60, 90, 120, and 150 to evaluate directional anisotropies. In addition, the average variogram parallel to the top of mineralization and the vertical variogram were run to assess the average continuity parallel and perpendicular to mineralization.

Note The variogram study and the plots can be found in Appendix A of the March 12, 2007 Technical Report. These have not been updated since the prefeasibility study because the number of additional samples does not affect the results for the feasibility update.

The variograms, which are shown graphically in Appendix A of the March 12, 2007 Technical Report, have the following characteristics:


17.11.1 Cobalt Low-Grade Zone Variograms

The directional variograms for low-grade cobalt indicate a slight directional trend at an azimuth of approximately 45 degrees. The sill of the vertical variogram is 0.25, with a nugget effect of 0.035 and a range of about 8 meters. The average horizontal variogram rises sharply from the nugget effect for about 35 meters, then increases gradually for the next 275 meters, where it flattens out at about 80 percent of the population variance.

These variograms indicate a continuous long-range process combined with erratic short-range continuity and a very strong vertical anisotropy.

17.11.2 Cobalt Mid-Grade Zone Variograms

The variograms for mid-grade cobalt are similar to those for the low-grade zone with a slight directional trend at an azimuth of approximately 45 degrees. The sill of the vertical variogram is 0.20, with a nugget effect of 0.05 and a range of 7.5 meters. The average horizontal variogram rises sharply from the nugget effect within the first 35 meters, then increases gradually over the next 800 to 1,000 meters.

Again these variograms indicate a very continuous long-range process combined with erratic short-range continuity and a very strong vertical anisotropy. In this case, however, the short-range component is about 70 percent of the total variability and the long-range component is only about 14 percent of the total variability.

A reasonable explanation for this variogram is that cobalt grade was initially very continuous over a range of 800 to 1,000 meters. As the deposit weathered, the cobalt distribution became more erratic as cobalt concentrated preferentially in the mineral absolane, and repeated collapse and brecciation in small pockets created short-range discontinuities.

Although definition of the short-range variogram structure is extremely important for grade control and assessment of dilution, it is impossible to define because there is not enough closely-spaced data (i.e., spacing between 2 and 15 meters).

17.11.3 Cobalt High-Grade Zone Variograms

Variograms in the cobalt high-grade zone are very similar to those in the mid-grade zone, although the long range continuity is shorter with only about 250 meters of range. Short range continuity is once again difficult to define. A possible hole effect component may be present, which is suggestive of small pods of mineralization with sizes smaller than 25 meters.

17.11.4 Nickel Low-Grade Zone Variograms

Variograms in the nickel low-grade zone indicate very continuous mineralization with a nugget effect that is only 3 percent of the total variability. A short-range component contributing 60 percent of the total


variability is present with a range of 120 meters, followed by a long-range component with 37 percent of the total variability and a range of 1,000 meters.

17.11.5 Nickel High-Grade Zone Variograms

Variograms in the nickel high-grade zone are similar to those in the cobalt high-grade variograms with a predominate spike of very high variability for short distances, which disappears after about 75-meters and turns into a long-range structure with a range of 1,000 meters. Further investigation is required to define the source of the poor short-range continuity.

17.11.6 Manganese Variograms

Manganese variograms are very similar to cobalt variograms except for the manganese high-grade zone variograms, which are completely chaotic and do not show any continuity.

17.12 Grade Estimation

Block grades were estimated for cobalt, nickel, and manganese using inverse-distance-power (IDP) estimation with grade-zoning controls. The primary function of the grade zones was to control selection of composites so that the selected composites were representative of the block that was estimated. Since the grade zones were created using composite grade as the primary basis for drawing the zone boundaries and there was considerable overlap among the grade-zone populations, the grade estimation composite selection procedure must treat the grade-zone boundaries like fuzzy boundaries rather than exact lines that divide the populations. This was accomplished as follows:

1. All composites from the low-grade zone plus the lower-grade composites from mid-grade zones were used to estimate the low-grade zone. Only a few low-grade outliers from the high-grade zone were used to estimate the low-grade zone except for nickel, which did not have a mid-grade zone.

2. The mid-grade zone was estimated using all mid-grade zone composites plus the higher-grade composites from the low-grade zone and the lower-grade samples from the high-grade zone.

3. The high-grade zone was estimated using all high-grade zone composites plus the higher-grade composites from the mid-grade zone. None of the composites from the low-grade zone were used to estimate the high-grade zone except for nickel, which did not have a mid-grade zone.

In addition to the grade-range selection parameters for each grade zone, capping grades were established for each grade zone based on the composite grade distributions. These parameters are summarized in Tables 17-11 through 17-13.


TABLE 17-11 Geovic Mining Corp. Nkamouna Project, Cameroon Grade Range Parameters and Capping Grades for Cobalt

Composite Zone Grade Ranges (%Co)

Low-Grade

Mid-Grade

High-Grade

Estimated Zone

Min.

Max.

Min.

Max.

Min.

Max.

Capping Grade (%Co)

Low-Grade

0.00

100

0.00

0.07

0.00

0.07

0.10

Mid-Grade

0.08

100

0.00

100

0.00

0.40

0.60

High-Grade

0.30

100

0.30

100

0.00

100

2.00

TABLE 17-12 Geovic Mining Corp. Nkamouna Project, Cameroon Grade Range Parameters and Capping Grades for Nickel

Composite Zone Grade Ranges (%Ni)

Low-Grade

Mid-Grade

High-Grade

Estimated Zone

Min.

Max.

Min.

Max.

Min.

Max.

Capping Grade (%Ni)

Low-Grade

0.00

100

NA

NA

0.00

0.40

1.00

High-Grade

0.30

100

NA

NA

0.00

100

2.00

TABLE 17-13 Geovic Mining Corp. Nkamouna Project, Cameroon Grade Range Parameters and Capping Grades for Manganese

Composite Zone Grade Ranges (%Mn)

Low-Grade

Mid-Grade

High-Grade

Estimated Zone

Min.

Max.

Min.

Max.

Min.

Max.

Capping Grade (%Mn)

Low-Grade

0.00

100

0.00

0.50

None

None

0.70

Mid-Grade

0.50

100

0.00

100

0.00

1.25

2.50

High-Grade

None

None

1.00

100

0.00

100

12.0


An elliptical search was used to select data for estimation with maximum ranges adjusted to provide continuous selection of data. All sample selection was done using no more than one composite from any individual pit/drill hole. At least one sample was required for estimation.

The IDP power and number of total points used for estimation were set for each zone to provide the appropriate level of variance reduction, or smoothing, as estimated from the variogram. IDP weighting anisotropies and direction were used depending on the variograms for the metal and grade zone that was estimated. The final search and IDP parameters are listed in Table 17-14 and Table 17-15. The IDP model summary statistics are compared to the nearest-neighbor model statistics as summarized in Table 17-16.

TABLE 17-14 Geovic Mining Corp. Nkamouna Project, Cameroon Composite Selection Parameters

Search Ellipse

Zone

Azimuth Primary

PrimaryRadius

Secondary

Radius

Vertical Radius

Max.

Compos Cobalt - Low Grade

45

500

400

2.5

9

Cobalt - Mid Grade

45

500

300

5.0

8

Cobalt - High Grade

45

500

300

2.5

8

Nickel - Low Grade

0

400

400

1.5

9

Nickel - High Grade

0

400

400

1.5

9

Manganese - Low Grade

0

400

400

1.5

9

Manganese - Mid Grade

0

400

400

1.5

9

Manganese - High Grade

0

400

400

1.5

9


TABLE 17-15 Geovic Mining Corp. Nkamouna Project, Cameroon Inverse Distance Weighting Parameters

IDP Weighting Anisotropies

Zone

Azimuth Primary

Primary

Radius, m

Secondary Radius, m

Vertical Radius

Exponent Power

Cobalt - Low Grade 45 500 300 3.0 2.40 Cobalt - Mid Grade 45 250 125 10.0 3.50 Cobalt - High Grade 45 200 150 5.0 3.00 Nickel - Low Grade 0 200 200 15.0 2.50 Nickel - High Grade 0 200 200 10.0 3.00 nganese - Low Grade 0 200 200 15.0 1.70 Manganese - Mid Grade 0 200 200 10.0 2.45 Manganese - High Grade 0 200 200 10.0 2.15

TABLE 17-16 Geovic Mining Corp. Nkamouna Project, Cameroon Inverse Distance Modeling Statistics and Smoothing Factors

Typical plan maps showing the various grade models are included in Appendix A of the March 12, 2007 Technical Report.

17.13 Sample Grid-Spacing Model

Sample grid spacing was measured using the estimation variance from point kriging with a zero-nugget, linear variogram that had a slope of 0.5. This particular linear variogram and point kriging is used because it provides a simple, direct index to the drill hole spacing. With these parameters, the kriging variance for a block that is estimated from a single, isolated drill hole is equal to the distance from the drill hole to the block center. The kriging variance for a block in the center of a square grid of drill holes is equal to approximately 28 percent of the size of the grid compared to 71 percent actual distance from any hole to the center of the grid. The kriging variance for blocks outside of the drill grade is just slightly less than the distance to the side of the square formed by the drill holes. Thus, a grid spacing of 100

Zone Number Average Relative Average Relative Bias SmoothingBlocks IDP Variance NN Variance Ratio Factor

IDP NNCobalt - Low Grade 1,354,236 0.0238 0.190946967 0.0237 0.314924603 1.004 0.606Cobalt - Mid Grade 790,114 0.1389 0.13218381 0.1371 0.20516737 1.013 0.644Cobalt - High Grade 139,903 0.4137 0.341336129 0.4205 0.522655069 0.984 0.653Nickel - Low Grade 1,381,473 0.1599 0.190552178 0.1591 0.316450445 1.005 0.602Nickel - High Grade 892,871 0.6356 0.086744892 0.637 0.121130224 0.998 0.716Manganese - Low Grade 1,437,750 0.2109 0.189053844 0.2102 0.335209048 1.003 0.564Manganese - Mid Grade 687,926 0.852 0.074916573 0.849 0.131780117 1.004 0.568Manganese - High Grade 147,469 2.343 0.363748504 2.405 0.636877 0.974 0.571


meters may be implemented by selecting blocks with a kriging variance of 28. In addition, extrapolation will be limited to a conservative 28 meters outside the 100-meter grid.

An elliptical search ellipse with lateral radii of 300 meters and vertical radius of 2.0 meters was used to define the sample grid spacing model. A maximum of 15 composites were used for this model with no more than one composite from any pit/drill hole. All samples were used with no limits on grade zone. The value kriged was a dummy value that was set to one (1.0) for all composites with a cobalt grade greater than or equal to zero (0.00). Composites with undefined cobalt values were not used.

17.14 Resource Classification Model

Resource classification was done for each block based on the sample grid spacing model. Determination of the appropriate grid size for each resource class was done based on the continuity of cobalt grade above a cutoff grade of 0.10 percent cobalt. The sample grid spacing and extrapolation limits for each resource category are as follows:

Measured resources - maximum 100 meter grid spacing or 28 meters extrapolation and at least 4 samples in the search ellipse.

Indicated resources - maximum 200 meter grid spacing or 56 meters extrapolation.

Inferred resource - grid spacing more than 200 meters within the laterate envelope or geologic boundary, which generally was 400 meters or less, or extrapolation more than 56 meters.

The criteria for measured resources did not previously include the minimum four samples condition, thus the current classification is slightly more conservative than the prefeasibility study. 17.14.1 Comparison of Inverse Distance and Nearest Neighbor Models

The effect of the grade caps, grade zones and composite selection parameters is evaluated by comparing an NN model with the same caps and grade zones as the IDP model to a raw NN model that does not have any caps or grade zones. These comparisons are done using only measured and indicated resources to ensure that the conclusions are applicable to reserves as well as resources. The results of the comparison are summarized in Table 17-17 for a range of cutoff grades.

At all cutoff grades, the grade-zoned, capped NN model has about 1 to 2 percent lower cobalt grade than the raw NN model. This reduction in grade is primarily caused by the grade caps in the high-grade cobalt zones. Tonnage in the capped NN model is 1 to 3 percent lower than tonnage in the raw NN model for cutoffs up to 0.25 percent Co. Above 0.25 percent Co capped NN tonnage is about 4 percent lower than raw NN. This indicates that the cobalt grade zones are slightly conservative compared to the raw NN model, which project high grades one-half the distance between pits/drill holes.


TABLE 17-17 Geovic Mining Corp. Nkamouna Project, Cameroon Comparison of IDP Model and Nearest Neighbor Models (Measured and Indicated Resources)

Cutoff%Co Tonnes Grade Tonnes Grade Tonnes Grade Cobalt Cobalt Cobalt Cobalt

(1000's) (%Co) (1000's) (%Co) (1000's) (%Co) Tonnes Grade Metal Tonnes Grade Metal0.5 4,298 0.764 4,723 0.839 4,907 0.848 91.00% 91.00% 82.81% 96.26% 98.88% 95.18%

0.45 5,509 0.700 5,730 0.775 5,942 0.783 96.14% 90.35% 86.86% 96.43% 98.93% 95.39%0.4 7,172 0.636 7,110 0.706 7,401 0.712 100.87% 89.98% 90.77% 96.07% 99.20% 95.30%

0.35 9,557 0.570 9,190 0.631 9,575 0.636 104.00% 90.31% 93.93% 95.97% 99.35% 95.35%0.3 12,825 0.508 12,012 0.559 12,558 0.562 106.77% 90.75% 96.89% 95.65% 99.58% 95.25%

0.25 17,333 0.447 16,579 0.480 17,289 0.482 104.55% 93.04% 97.27% 95.89% 99.54% 95.45%0.2 25,407 0.375 26,613 0.383 27,382 0.386 95.47% 98.04% 93.59% 97.19% 99.07% 96.28%

0.19 28,024 0.358 29,304 0.365 30,127 0.369 95.63% 98.06% 93.78% 97.27% 99.07% 96.37%0.18 31,435 0.339 32,848 0.346 33,607 0.350 95.70% 98.14% 93.92% 97.74% 98.90% 96.67%0.17 35,632 0.320 36,978 0.327 37,850 0.330 96.36% 97.94% 94.38% 97.70% 98.99% 96.71%0.16 40,783 0.300 41,259 0.310 42,274 0.313 98.85% 96.92% 95.80% 97.60% 99.09% 96.71%0.15 46,551 0.282 46,216 0.293 47,073 0.297 100.72% 96.27% 96.96% 98.18% 98.86% 97.06%0.14 53,254 0.265 51,931 0.277 52,681 0.281 102.55% 95.69% 98.13% 98.58% 98.73% 97.32%0.13 60,259 0.250 57,550 0.263 58,124 0.267 104.71% 94.99% 99.46% 99.01% 98.58% 97.60%0.12 67,614 0.236 63,057 0.251 63,573 0.255 107.23% 94.14% 100.95% 99.19% 98.55% 97.75%0.11 74,872 0.225 68,920 0.239 69,394 0.243 108.64% 93.79% 101.89% 99.32% 98.54% 97.87%0.1 82,393 0.214 75,485 0.228 75,745 0.231 109.15% 93.82% 102.41% 99.66% 98.41% 98.07%

0.06 100,983 0.190 101,270 0.190 100,869 0.194 99.72% 99.83% 99.54% 100.40% 98.15% 98.54%TOTAL 226,715 0.098 226,744 0.099 226,751 0.100 99.99% 99.87% 99.86% 100.00% 98.68% 98.68%

Ratio NN to Raw NNRaw NN Ratio IDP to NNBase Case IDP Nearest Neighbor


The effect of IDP smoothing on the resource model is evaluated by comparing the IDP model to the grade-zoned NN model, which has the same grade caps, grade zones, and composite selection parameters as the IDP model. This comparison shows the typical smoothing effects that are expected for an IDP model compared to an NN model. In general, tonnage is higher and grade is lower in the IDP model than in the NN model, which reflects dilution and ore losses that are introduced by the smoothing from the IDP estimation. Using a cutoff grade of 0.12 percent Co, the apparent dilution is equivalent to 7 percent dilution of tonnage at a grade of 0.033 percent Co.

17.15 Resource Summary

The mineral resource is summarized by resource category and lithologic unit in Table 17-18. The cutoff grades in this table are different for each lithology and are approximate economic cutoffs based on the different processing characteristics of each lithology. Resources are also shown graphically for other cobalt cutoff grades in Figures 17-8 to 17-10.

TABLE 17-18 Geovic Mining Corp. Nkamouna Project, Cameroon Summary of Measured, Indicated, and Inferred Resource

Resource Cutoff Tonnes Average Average AverageLithology Category (%Co) (1000's) %Co %Ni %Mn

Upper Laterite Measured 0.12 42 0.301 0.318 1.569 Upper Breccia Measured 0.23 229 0.468 0.490 2.190 Ferricrete Breccia Measured 0.23 1,447 0.527 0.550 2.689 Lower Breccia Measured 0.23 2,905 0.448 0.545 2.228 Ferralite Measured 0.12 26,839 0.226 0.689 1.178 Total Measured 31,462 0.263 0.667 1.352 Upper Laterite Indicated 0.12 44 0.272 0.291 1.371 Upper Breccia Indicated 0.23 157 0.326 0.401 1.812 Ferricrete Breccia Indicated 0.23 604 0.461 0.474 2.242 Lower Breccia Indicated 0.23 1,588 0.426 0.480 2.059 Ferralite Indicated 0.12 27,475 0.207 0.673 1.087 Total Indicated 29,869 0.224 0.657 1.166 Total M+I 61,331 0.244 0.662 1.262 Upper Laterite Inferred 0.12 67 0.158 0.207 1.091 Upper Breccia Inferred 0.23 4 0.286 0.426 1.817 Ferricrete Breccia Inferred 0.23 10 0.459 0.497 2.486 Lower Breccia Inferred 0.23 215 0.393 0.445 1.423 Ferralite Inferred 0.12 17,117 0.177 0.556 1.057 Total Inferred 17,412 0.180 0.553 1.063


FIGURE 17-8 Geovic Mining Corp. Nkamouna Project, Cameroon Resource Tonnage by Cobalt Cutoff Grade, Resource Class, and Lithology


FIGURE 17-9 Geovic Mining Corp. Nkamouna Project, Cameroon Resource Cobalt Grade by Cobalt Cutoff Grade, Resource Class, and Lithology


FIGURE 17-10 Geovic Mining Corp. Nkamouna Project, Cameroon Resource Nickel Grade by Cobalt Cutoff Grade, Resource Class, and Lithology


17.16 Estimation of Mining Dilution and Cutoff Grade

Because the resource model is based on 1-meter thick blocks and the deposit will be mined as seams, the individual blocks must be combined into mineable seam thicknesses. The following assumptions were used to combine the blocks into seams:

1. The top of ore will be identified during mining by a combination of preliminary grade-control drilling, in-pit sampling, and visual definition by the grade-control geologist and will be well defined.

2. Since the 1-meter pit/drill hole samples are randomly positioned relative to the top of mineral, the original samples contain an average of 0.5-meters dilution. Thus, the top of mineralization already contains sufficient mining dilution and no dilution was taken at the top of ore.

3. A minimum mining thickness of 2 meters was used for ore. Ore seams less than 2-meters thick were expanded to meet the minimum thickness.

4. Included waste seams less than 2-meters thick were added to the ore seams as internal dilution.

5. Definition of ore and waste blocks is based on ferrilite-equivalent cobalt grade. The equivalent grade is based one two equivalencies: First, nickel grade is converted to an equivalent cobalt grade based on the prices and recoveries for cobalt and nickels. Different factors are used for breccia and ferrilite because of the different PUG recoveries the two material types. Second, the breccia equivalent cobalt grade is converted to the ferrilite-equivalent cobalt grade that has the same value per ton processed as the original breccia grade. The equivalent grade formulae are:

Equivalent cobalt grade from nickel:

cobalt contained 1%at tonnemined 1 from RevenueNet nickel contained 1%at tonnemined 1 from RevenueNet

Grade Nickel %%)(

==

==

RevenueCoRevenueNi

NiRevenueCoRevenueNiNiNiEqCO


Equivalent ferrilite cobalt grade from breccia cobalt grade:

cobalt 1%at feedplant leach ferrilite tonne1for RevenueNet leached ferrilite of per tonne leaching) through (minecost Totalleached breccia of per tonne leaching) through (minecost Total

Re

cobalt contained 1%at ferrilite tonnemined 1for RevenueNet cobalt contained 1%at breccia tonnemined 1for RevenueNet

convert togradecobalt Breccia*)(

===

=

==

=

=+=

ntCoRevenuePlaCostFlCostBx

CovenuePlantCostFeCostBxmentCostAdjust

eRevenueCoFxRevenueCoB

eRevenueCoFxRevenueCoBtorRevenueFac

BxComentCostAdjusttorRevenueFacBxCoBxCoFeEqCo

The economic parameters for computing NetCobalt are summarized in Table 17-19.

6. Although the breakeven cutoff is calculated as 0.066 percent Co for ferrilite and 0.10 percent Co for breccia, a cutoff of 0.175 percent ferrilite-equivalent cobalt was used for minable seam definition. The higher cutoff was used to improve project cash flow.

The mineable seam boundaries were determined using the Datamine COMPSE process, which optimizes the mining seams by maximizing ore, while at the same time minimizing dilution. The COMPSE process was implemented by creating a pseudo-drill hole for each vertical stack of blocks in the model.

17.17 Scheduling Block Model

The seam block model was combined into 50x50-meter wide blocks to facilitate mine planning and scheduling by accumulating all of the blocks for each seam in the seam model. The contents of each scheduling block include:

a) Seam Number Seams are numbered from top to bottom in increments of 10. One is added to the seam number for ore seams, thus 11, 21, 31, etc are ore seams and 10, 20, and 30 are waste seams.

b) Block Coordinates X and Y are the centroid coordinates of the 50x50-meter block. Z is based on the average elevation and thickness of the component seam blocks. Elevations are adjusted based to prevent gaps and overlaps in the vertical stack of seams.

c) Block Size Always 50x50-meters horizontal dimension, average seam thickness vertically.


TABLE 17-19 Geovic Mining Corp. Nkamouna Project, Cameroon Economic Parameters for Calculation of the Net Value per Pound Cobalt

Value

Parameter Upper

Laterite

Upper Ferricrete Breccia

Hardpan FerricreteBreccia

Upper FerricreteBreccia

Lower Limonite

Density t/m3 1.31 1.80 1.69 1.34 1.40 PUG tonnes recovery 17 45 45 45 17 PUG cobalt recovery 59 73 73 73 59 PUG nickel recovery 26 54 54 54 26 PUG manganese recovery 54 73 73 73 54 Cobalt Leach Recovery 67% Nickel Leach Recovery 29% Manganese Leach Recovery 65% Cobalt Price $15.55 per lb sold Nickel Price $3.75 per lb sold Reclamation $0.15 per tonne mined Ore Mining $1.30 per tonne ore mined Waste Mining $1.00 per tonne waste mined Ore Control $0.30 per tonne ore Mine Technical $0.35 per tonne ore PUG plant $1.50 per tonne of ore Leach Plant $42.50 per tonne leached General and Administrative $7.60 per tonne leached Freight Cobalt $0.087 per pound cobalt sold Freight Nickel $0.087 per pound nickel sold Marketing 1.5% of sales price

d) Average density, average thickness, total tonnes, and total volume for the entire block

e) Average Grade including equivalent-ferrilite-cobalt, cobalt, nickel, and manganese.

f) Block Number identifying a vertical stack of seam blocks (equivalent to the Datamine IJK value).

g) Tonnes, Cobalt, Nickel, and Manganese for each combination of ore type (Breccia, Ferrilite) with resource class (Measured, Indicated, Inferred).

h) Flag - An accumulator indicating the total number of 10x10-meter blocks were accumulated into the scheduling block.

i) Pit A dummy value for XPAC software. Always set to one (1.0).


17.18 Reserve Estimation

Mine design started with the completion of the resource model. The seam model was then diluted to represent the thickness expected to be mined using reasonably selective equipment and methods. The dilution is based on a minimum of one meter of ore so that less than one meter is considered waste and if the interburden between ore layers is less than 2 meters, it is taken with the ore. There were many areas where the interburden was 1 to 2 meters in thickness with some low grade values and it was determined that it would be easier to mine this with the ore than try to segregate the waste, thereby simplifying the mining method. The ore zones become much more uniform by allowing 2 meters of low grade interburden to be mined as ore.

Economic evaluation criteria are based on supplying a fixed 2,000 tonnes ore per day of product from the physical upgrade (PUG) plant to the process plant at an average of 1.87:1 Waste:Ore ratio. This yields an average mine production rate of 23,000 tonnes per day with approximately 8,000 tonnes per day of ore, and a maximum of 28,000 tonnes per day for equipment sizing and operating cost estimation.

After the estimated economic costs and recoveries were applied to the resource model, floating cones were run in Whittle 4X to develop the final pit outline. The Whittle output was color coded to develop a series of mining blocks. The blocks were then scheduled using Runges XPAC software to provide the desired plant feed of 2,000 tonnes per day. Conditions were placed on the blocks to where the downhill side had a flat slope to provide drainage, and all the blocks below the initial blocks were mined sequentially.

17.18.1 Cobalt and Nickel Recoveries

Estimates of cobalt and nickel recovery were projected from the results of the physical upgrade test work performed by Mountain States Research & Development and process test work performed by Hazen Research. The final criteria for statement of ore reserves are:

Physical Upgrade (PUG) factors

Breccia ore upgrade factors

Cobalt = 1.72 Nickel = 1.17 Manganese = 1.71 PUG Concentrate = 41.7% of ore weight

Ferralite ore upgrade factors

Cobalt = 3.21 Nickel = 1.53 Manganese = 3.03


PUG Concentrate = 19.2% of ore weight

Leach/SXEW Recoveries

Cobalt = 92% Nickel = 52% Manganese = 82%

Recoveries for the PUG plant can be calculated by multiplying the upgrade factor times the weight percent of ore in the concentrate. For ferralite ore, the cobalt recovery is 19.2% * 3.21 = 61.6%. For an average ore composition of 10 percent breccia and 90 percent ferralite, the concentrate would contain 21.5 percent of the run-of mine ore fed to the PUG plant. This concentrate is the feed to the process plant from which 92 percent of the cobalt is recovered. Total recoverable metal value was assigned to each block in the Whittle 4X evaluation.

17.18.2 Density and Specific Gravity

Specific gravity was assigned to the block model based on lithology. The block model and the mine production schedule are in dry tonnes. Moisture and swell need to be added to calculate the correct tonnages and volumes of material to be handled by the mining equipment. The following factors were used for density:

Specific Gravity and Density

Waste overburden material, wet, in place = 1.86 tonnes/m3 Waste overburden material, dry, in place = 1.55 tonnes/ m3 Waste interburden material, wet, in place = 1.92 tonnes/ m3 Waste interburden material, dry, in place = 1.42 tonnes/ m3 Breccia ore material, wet, in place = 1.91 tonnes/ m3 Breccia ore material, dry, in place = 1.66 tonnes/ m3 Ferralite ore material, wet, in place = 1.89 tonnes/ m3 Ferralite ore material, dry, in place = 1.40 tonnes/ m3 Waste overburden material, wet, loose = 1.48 tonnes/ m3 Waste interburden material, wet, loose = 1.52 tonnes/ m3 Breccia ore material, wet, loose = 1.52 tonnes/ m3 Ferralite ore material, wet, loose = 1.50 tonnes/ m3

17.18.3 Pit Design

Mining will occur in five stages. The first stage will be excavating a nominal 4-meter bench of overburden with a front-shovel. The second stage will be a similar depth cut of approximately 4 meters to approach the top of the mineralized breccias, as defined by exploration and development drill holes and test shafts. It too will be mined by the front-shovel. The third stage begins with rotary drilling of


the remaining area, a nominal 8 meter depth. The drilling will be sampled on one meter intervals to determine the location of ore and interburden. Waste remaining on top of the ore zone will be ripped and dozed to minimize dilution. The remaining material will be mined in one or two passes (stages 4 and 5) with a backhoe excavator, segregating the ore and interburden. Ore thicknesses less than one meter will be removed as interburden. Interburden thickness of less than 2 meters will be removed as ore. Figure 17-11 shows the mine block outlines and the as-mined surface which averages 15 meters below the original surface. 17.18.4 Mineral Reserve Statement

The Nkamouna mineral reserves presented in Table 17-20 are classified as a Proven plus Probable. Individual reserves by block include ore tonnes, cobalt grade, nickel grade, manganese grade, interburden and overburden tonnes. The mineral reserve is 54 million tonnes at a cobalt grade of 0.248 percent and a nickel grade of 0.688 percent.


TABLE 17-20Geovic Mining Corp.Nkamouna Cobalt Project Feasibility StudyProven & Probable Reserves by Block

Block ORE ORE ORE ORE INTRBRDN OVRBRDN TOTAL STRIP Proven Proven Proven Proven Probable Probable Probable ProbableMine Tonnes Co Ni Mn Tonnes Tonnes Tonnes Waste:Ore Tonnes Co Ni Mn Tonnes Co Ni Mn

1 1,954,903 0.272 0.725 1.435 203,372 5,267,865 7,426,585 2.80 570,784 0.296 0.738 1.548 1,384,119 0.261 0.719 1.3882 2,654,602 0.301 0.650 1.568 116,833 6,037,114 8,815,802 2.32 1,753,291 0.298 0.616 1.555 901,311 0.308 0.716 1.5933 4,155,706 0.307 0.702 1.604 855,685 6,778,785 11,760,520 1.84 2,816,047 0.346 0.698 1.781 1,339,659 0.226 0.709 1.2304 2,876,778 0.319 0.623 1.652 435,016 5,231,875 8,532,579 1.97 1,717,974 0.329 0.624 1.698 1,158,804 0.305 0.621 1.5855 2,132,687 0.236 0.718 1.278 243,123 3,027,880 5,398,264 1.53 981,403 0.243 0.696 1.312 1,151,284 0.230 0.738 1.2506 2,404,229 0.251 0.718 1.344 118,396 3,007,945 5,532,147 1.30 848,753 0.262 0.710 1.394 1,555,476 0.245 0.722 1.3177 2,257,202 0.252 0.765 1.351 190,162 5,022,732 7,471,165 2.31 2,001,891 0.257 0.754 1.375 255,312 0.211 0.853 1.1678 1,886,336 0.294 0.850 1.542 138,379 3,338,585 5,363,797 1.84 982,018 0.328 0.842 1.698 904,319 0.257 0.859 1.3739 3,775,799 0.219 0.835 1.203 104,728 5,003,425 8,891,306 1.35 2,287,317 0.238 0.827 1.288 1,488,482 0.191 0.848 1.07410 1,461,120 0.210 0.682 1.162 120,319 2,808,813 4,390,319 2.00 394,201 0.217 0.680 1.192 1,066,918 0.208 0.683 1.15111 2,347,707 0.245 0.707 1.325 85,719 2,973,233 5,409,878 1.30 798,108 0.263 0.692 1.411 1,549,599 0.235 0.714 1.28112 1,793,625 0.252 0.568 1.348 74,570 2,839,513 4,710,672 1.62 913,494 0.268 0.567 1.422 880,131 0.235 0.570 1.27213 373,363 0.249 0.530 1.339 - 818,619 1,193,836 2.19 176,073 0.260 0.544 1.392 197,291 0.239 0.518 1.29214 1,502,348 0.231 0.651 1.254 102,264 2,369,230 3,974,159 1.65 381,336 0.252 0.662 1.351 1,121,012 0.224 0.648 1.22215 2,373,663 0.227 0.690 1.241 83,167 2,444,659 4,903,947 1.06 1,293,978 0.244 0.666 1.318 1,079,684 0.207 0.719 1.15016 2,062,819 0.226 0.564 1.233 60,659 2,435,199 4,561,985 1.21 845,147 0.224 0.583 1.223 1,217,672 0.227 0.551 1.23917 1,483,448 0.255 0.708 1.370 111,433 1,827,730 3,421,464 1.31 527,419 0.273 0.710 1.455 956,029 0.246 0.707 1.32318 1,358,205 0.245 0.596 1.320 86,897 2,193,854 3,639,634 1.68 423,561 0.251 0.582 1.349 934,644 0.242 0.602 1.30819 906,293 0.257 0.603 1.369 159,741 1,693,459 2,754,853 2.04 132,126 0.253 0.631 1.353 774,167 0.257 0.598 1.37220 1,208,560 0.231 0.726 1.257 142,499 2,484,534 3,833,610 2.17 1,104,794 0.238 0.721 1.289 103,766 0.157 0.783 0.92421 983,303 0.216 0.700 1.190 119,074 2,729,471 3,831,307 2.90 815,399 0.219 0.711 1.201 167,904 0.203 0.648 1.13222 1,758,920 0.248 0.667 1.328 10,772 3,953,706 5,731,719 2.25 1,271,721 0.260 0.667 1.383 487,199 0.215 0.666 1.18323 3,165,735 0.212 0.755 1.170 220,776 5,762,936 9,151,179 1.89 2,552,008 0.221 0.739 1.210 613,727 0.175 0.824 1.00624 1,925,068 0.193 0.715 1.085 333,283 4,850,794 7,101,812 2.69 468,070 0.206 0.703 1.141 1,456,997 0.189 0.719 1.06725 3,099,797 0.216 0.602 1.189 41,151 3,995,646 7,144,855 1.30 1,169,253 0.227 0.603 1.241 1,930,544 0.209 0.601 1.15826 657,373 0.188 0.549 1.061 1,332 2,413,456 3,077,056 3.67 199,892 0.191 0.548 1.075 457,481 0.187 0.549 1.05527 1,484,816 0.232 0.615 1.262 167,189 4,564,551 6,217,195 3.19 1,325,275 0.238 0.614 1.290 159,541 0.182 0.617 1.02928 697,219 0.224 0.438 1.218 - 2,355,524 3,057,654 3.38 116,276 0.222 0.477 1.214 580,943 0.224 0.430 1.219

TOTAL 54,741,624 0.248 0.688 1.331 4,326,540 98,231,134 157,299,298 1.87 28,867,610 0.264 0.690 1.406 25,874,014 0.230 0.683 1.250

17.0 mineral resource 43-101.pdf

Documents

resource model

flattened model

prefeasibility model

current model

prefeasibility estimate

mineral resource estimate

threedimensional block

additional pits