First, let’s take a look at a contour map created using the EZ-Map tool in the RockWorks Utilities.

The EZ-Map tool uses simple triangulation to create contours.  For some data sets, this may be all you need to create a reasonable looking map.  However, it will often be necessary to create grid-based maps with smoother contour lines that extend to the edge of the project.  Note the increase in the groundwater elevation on the eastern edge of the map.  This is something that can be resolved by switching to a grid-based map.  We’ll explore grid-based mapping tools next.

Here is a map created using the default Inverse Distance Weighting settings.  I should note that I had both the “Smoothing” and “High-Fidelity” options turned on during grid creation.

The “bull’s eyes” that you see around the high and low points in the map are typical of the Inverse Distance interpolation method.  One way to resolve this is to decrease the number of points used during interpolation.

To do this, change the Number of Points used for the Inverse Distance Algorithm from 8 (the default setting) to 4 (which is more appropriate for a data set of this size).

Here is a map created with the modified “Number of Points” value:

The bull’s eye effect has been muted somewhat, but notice that the contours don’t honor the data extremely well.  Let’s move on to Kriging.

In the map below, I let the RockWorks program choose the appropriate variogram settings.  With Kriging especially, which can create fairly blocky models, I highly recommend that you turn on both the grid “Smoothing” and “High-Fidelity” options when
creating a contour map. 

This may be a little bit more to your liking, but the general groundwater flow direction could still be better represented along the borders of the map.  Just to cover all of our
bases, here is another map created using Triangulation gridding.  Unfortunately, there are some problems with edge effects in the resulting map as well.

None of these are really doing it for me.  At this point, I think that a lot of people would probably resort to hand drawing their contour maps, or adding additional control points to the data set to force the contours into the shape they have in mind.  Before you resort to these tedious and time-consuming options, I would recommend you look at the Densify and Polyenhancement options available in RockWorks15.

Here is a diagram showing the contour maps created with the Inverse Distance interpolation algorithm, with and without Densify turned on.  As you can see, the densification process (which adds additional control points to the data set before
interpolation using triangulation) straightens out the contour lines quite a bit.

I did the same test using the Kriging algorithm and got the following results.

Last but not least, here is a contour map created with the Polyenhancement option turned on.  When this option is activated, the program fits the data to a polynomial surface and then warps that surface to honor the data points (in this case, I choose a 2nd order polynomial surface).  I think I have my map!

In real life, I’ve found first, second and third order polynomials useful when creating groundwater contour maps.  If the groundwater flow direction is fairly constant through the area, go with a 1^st order polynomial (which is a planar surface).  If it is variable because of topography or a feature such as a river or stream, then a 2^nd or 3^rd order polynomial is the way to go.

Link to original paper: http://www.rockware.com/assets/products/165/casestudies/6/9/computing_aggregate_reserves.pdf

 Introduction

 The purpose of this study is to compute the total economic reserves for a site that includes two carbonate units; an upper limestone and a lower dolomite separated by a shale unit. Quality analyses have been obtained at one-foot intervals within the carbonates. The following diagram depicts a typical log showing the lithology, stratigraphy, and aggregate quality.

Figure 1: Typical log depicting aggregate quality (bargraph on left), stratigraphy (patterns in center), and lithology (subdivisions within stratigraphy)

Step 1. The Problem

Modeling the rock quality en-masse is problematic because the node values would include the quality values for both the limestone and the dolomite. The following diagrams depict a solid model based on the rock quality and a stratigraphic block model. Notice how the rock quality (I-Data) model interpolates quality values where there is no corresponding carbonate.

 Compare this stratigraphic model with bulk rock quality model above and note how quality values were interpreted within overburden (light yellow) and interburden.

Step 2. The Solution

The solution to this problem is to use the “Stratabound” option within the I-Data / Model menu. Two rock-quality models were created; one for the upper limestone and another for the lower dolomite.

In the example below, the I-data model is confined to points and nodes within the Hanford Limestone unit.

Figure4: Hanford Limestone Rock-quality Model

 In this example, the I-Data model is confined to points and nodes within Shuller Dolomite.

Figure5: Shuller Dolomite Rock Quality Model

Step 3. Combining the Models

The next step involved adding the two models together and removing all voxels with a quality value less than 50 (the minimum acceptable quality).

Figure6: Fence diagram depicting combined rock-quality models for upper limestone and lower dolomite.

Figure 7: Block Model depicting voxels with a quality value greater than 50.

Figure 8: Block model depicting zones from previous model in which the thickness for any single contiguous ore zone is more than 6 feet thick for any given column.

Figure 10: Block model depicting zones from previous model in which the stripping ratio is less than 1.2. This area represents a good place to start mining in order to gain the highest return on investment.

 Step 4. Checking the Model

The final, and most important step, is to create a 3D log diagram, combine it with the final ore model, and examine the data to see if it make sense.

Figure 11: 3-Dimensional Lithology/Quality Logs Combined With Final Ore Model.

Figure 12: Enlargement of area around highest-ROI ore depicting lithology and quality logs.

Step 5. Conclusion

By combining the preceding approach with increasingly more tolerant filter cutoffs, it is possible to create a mining strategy that will yield the highest return on investment from the onset.

We hope you will find this new resource useful. Thank you for your interest in the Rockware, Inc. Blog.

Kind Regards,
The RockWare Team