The distance function tool in Leapfrog Geo allows you to calculate the distance from a given object or objects within a project, and derive distance buffers – solids whose walls are a set distance from the objects we are interested in. Although conceptually simple, this tool can be applied to drillhole planning and resource classification to quickly and easily inform and justify our planning, and quantify current and potential reserves.
In the example below, we have an ore body which is partially drilled out; to the south it is poorly constrained and we need to plan an additional drillhole program to gather more information in this area.
While we can visually identify areas that have limited numbers of pierce points, ideally, we want to quantify in some way what areas are poorly controlled, and ultimately what impact our new drilling will have on our resource. In this example we will assume that anywhere within 50m of a drillhole will be classed as measured, 50-75m as indicated, 75-100m inferred and >100m as unclassified. Note that you could also use these same classifications to categorise confidence.
To start this process, right click on the Numeric Models folder in the project tree and select ‘new distance function’. In the next dialogue box, click the ‘select objects’ button, and then move ‘drillhole traces from ‘available objects’ on the left side, to ‘selected objects’ on the right. Click OK, to go back to the main distance function dialogue box, and then go to the ‘buffers’ tab. Press ‘add’ to add a calculated distance isosurface, and type in the distance from the objects you want it to be. In this case, we will make 3, at 50, 75 and 100 metres respectively. We will be coming back to these later.
Having made the distance buffer, we now need to evaluate it onto our body, by right clicking on the ore body and selecting the evaluations option, and then moving our distance function from ‘available’ on the left to ‘selected’ on the right. If we use the select display option in the shape list for the ore body we can now view colour it by the distance to the drillholes, and set up a discrete colour scheme to reflect our categories.
At this point, if you want to understand the distribution of the drilling in the interior of the ore body you can create a block model and code it using the same distance function.
Now we can easily see both the distribution of our pierce points, and how well constrained our ore body is internally; we can now use the drillhole planning tool to target these areas.
Once the drillhole planning is done (and while we are doing it!) it would be good to understand how this additional data will affect the confidence in and classification of our model. We now repeat the initial steps in this workflow, but this time when selecting our objects in the distance function, choose our planned drillholes in addition to the drillhole traces. When we colour our solid again we will be able to see how the next round of drilling will improve our understanding of the ore body.
Although we have now visually justified the placement of our drillholes it would also be good to quantify the impact of additional drilling, and compare the benefits of potential resource increases vs. the cost of the additional drillholes. To do this we will revisit the distance buffers/isoshells that we constructed earlier, and use the combined models feature.
Right click on the ‘combined models’ folder and choose ‘new combined model’, then tick the boxes against the geological model containing your ore body, and your initial distance function. In the following dialogue box tick the boxes next to the isoshells in the distance function and your orebody, and press ‘ok’ to make the combined model. You can now go to the output volumes folder and view the resultant solids. You can get the total volume of each solid by clicking on it in the scene view and looking at the information pane that appears in the bottom left of the screen, or by right clicking on it in the project tree and selecting ‘properties’.
We can now directly compare the potential increase in volume of our resource with the addition of the new drilling. In the example presented here the measured portion of the resource will be increased from 52.6 million cubic meters to 60.7 million cubic metres, an increase of 15.4%.
Utilising the distance function to analyse the distribution of our data relative to the model is a fast and efficient methodology to understand the confidence in and classifications of our geology and resource models. This allows us to optimise our drillhole planning, and maximise the benefit to the project of our limited drilling budgets.