Sand & Gravel Case Study

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Anthony Stafford
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1 Sand & Gravel Case Study Table of Contents Jim Reed RockWare Incorporated 1/12/06 Table of Contents...1 Table of Figures...1 Creating a Borehole Database...2 Displaying the Boreholes...2 Generating the Initial Sand, Gravel & Clay Models...3 Computing Sand & Gravel Reserves...4 Appendix I...5 Table of Figures Figure 1: 2-dimensional striplog...3 Figure 2: 3-dimensional composite striplog...3 Figure 3: 3-dimensional percentage log...3 Figure 4: 3-Dimensional depiction of all boreholes...3 Figure 5: Sand Percentage Logs...3 Figure 6: Sand Model...3 Figure 7: Sand > 40%...3 Figure 8: Gravel Percentage Logs...4 Figure 9: Gravel Model...4 Figure 10: Gravel > 40%...4 Figure 11: Clay Percentage Logs...4 Figure 12: Clay Model...4 Figure 13: Clay < 20%...4 Figure 14: Sand + Gravel Model...4 Figure 15: Sand+Gravel > 80%...4 Figure 16: Optimum Pit Design. Max Slope = 45 deg. No Benches...5 Figure 17: Optimum Pit Design Max Slope = 45 deg. Bench Height = 10 Feet...5 Figure 18: Optimum Pit Design Max Slope = 45 deg. Bench Height = 20 Feet...5 1

2 Creating a Borehole Database A series of exploration boreholes were drilled. Samples were taken every five feet and sieved in order to determine the relative percentages of sand, gravel and clay (or other non-sand/gravel material). These samples were restricted to the interval below the base of the soil profile and the top of the bedrock. The borehole locations, stratigraphy (see Table 1), and sieve analyses (see Table 2) were then entered into a relational database. Table 1: Information that was recorded for each borehole. Name Easting Northing Elevation Soil Depth Unique borehole identifier (e.g. BH-01, BH-02, etc.) UTM easting from GPS (in feet) UTM northing from GPS (in feet) Elevation from GPS (in feet) Depth to base of soil (in feet). Bedrock Depth Depth to top of bedrock (in feet). Total Depth Total depth of borehole (in feet). Table 2: Information that was recorded for each sample interval. Depth-1 Depth-2 Depth to top of sampled interval (feet). Depth to base of sampled interval (feet). Sand % Sand (0 to 100) Gravel % Gravel (0 to 100) Clay % Clay or other non-sand/gravel material(0-100) Displaying the Boreholes Two and three-dimensional striplogs were constructed for each borehole. The two-dimensional logs (Figure 1) include textual descriptions of the lithotypes as well as bargraphs depicting the relative percentages of sand, gravel, and clay. These logs are best suited for printed reports in which all of the borehole information is depicted within a single, albeit complex diagram. The composite three dimensional logs (Figure 2) show the lithology as color-coded cylinders with bargraphs depicting the sand and gravel content. Although interesting, these logs quickly become confusing when plotting multiple logs in a three-dimensional diagram (Figure 4). 2

The three-dimensional percentage logs (Figure 3) show the relative concentration of a

These "spindle logs" are ideally suited when plotting a single constituent (Figures

striplog Figure 3: 3-dimensional percentage log.

Gravel & Clay Models Solid block models for the sand, gravel, and clay data were

3 The three-dimensional percentage logs (Figure 3) show the relative concentration of a single constituent (e.g. % sand). These "spindle logs" are ideally suited when plotting a single constituent (Figures 5, 8 & 11) Figure 1: 2-dimensional striplog Figure 2: 3-dimensional composite striplog Figure 3: 3-dimensional percentage log. Figure 4: 3-Dimensional depiction of all boreholes Generating the Initial Sand, Gravel & Clay Models Solid block models for the sand, gravel, and clay data were created by using a modeling algorithm that estimates grade levels for a three dimensional matrix of imaginary blocks. Figure 5: Sand Percentage Logs Figure 6: Sand Model Figure 7: Sand > 40% 3

Figure 8: Gravel Percentage Logs Figure 9: Gravel Model Figure 10: Gravel > 40% Figure 11: Clay Percentage Logs Figure 12: Clay Model Figure 13: Clay < 20% Computing Sand

This combined model was then filtered to show only those regions where the sand and/or gravel are greater than 80 percent (Figure 15).

4 Figure 8: Gravel Percentage Logs Figure 9: Gravel Model Figure 10: Gravel > 40% Figure 11: Clay Percentage Logs Figure 12: Clay Model Figure 13: Clay < 20% Computing Sand & Gravel Reserves The sand and gravel models were combined by adding each of the block values (Figure 14). This combined model was then filtered to show only those regions where the sand and/or gravel are greater than 80 percent (Figure 15). Finally, a pit was generated (Figures 16, 17, & 18) by using a "floating cone" algorithm that removes material above the ore based on user-defined criteria (e.g. maximum slope, bench height, ore grade, etc.) Figure 14: Sand + Gravel Model Figure 15: Sand+Gravel > 80% 4

Figure 16: Optimum Pit Design. Max Slope = 45 deg.

Bench Height = 10 Feet Figure 18: Optimum Pit Design Max

Bench Height = 20 Feet Appendix I Classification Scheme

5 Figure 16: Optimum Pit Design. Max Slope = 45 deg. No Benches Figure 17: Optimum Pit Design Max Slope = 45 deg. Bench Height = 10 Feet Figure 18: Optimum Pit Design Max Slope = 45 deg. Bench Height = 20 Feet Appendix I Classification Scheme Minimum Size (mm) Maximum Size (mm) Classification Pebbles 2 20 Gravel Sand Silt Clay 5

Dry Muliwai Sample Location Map

DMM2 DMM1 P:\AthruR\K838-Makua\GIS\Layouts\Dry Muliwai.mxd.mxd - 3/26/3 - JC DMM3 5 Dry Muliwai Sample Location Map Figure 2-4 P:\AthruR\K838-Makua\GIS\Layouts\orth Background Muliwai.mxd.mxd - 3/26/3