Implicit modelling was first made well known by Leapfrog®, now more have picked up the process including MICROMINE, Mintec’s Minesight® and Maptek’s EurekaTM, others are sure to follow.

Implicit modelling is fast and easy to run multiple times to test hypotheses and rapidly assess new datasets. The ease with which the geologist can create and recreate allows refining to something approaching reality. To create meaningful and robust models, proper forethought, planning and understanding of the area’s geology is required.

Implicit modelling allows you to build the model you have in mind and apply various changes to test possibilities suggested by analysis of the data.

Both wireframed and implicit geological models require understanding of the geology and the processes around creating them. For implicit modelling, you need a basic understanding of the interpolation process.

The following help ensure models are robust and the process properly understood.

1. Has the modeller used historic knowledge?

Often those that came before were better trained in mapping, thinking and field work. In this day of 24/7 connectivity and constant demands, we rarely have the time to spend thinking. Geological understanding may have changed, but the fundamental basics and measurements remain. I regularly come across fully qualified geologists that mix strike and dip direction, a mistake that makes the data worthless. Sandstone mapped 100 years ago is still sandstone, granite 100 years ago is still granite today, even if the understanding of its origins has changed. Combining the sampling, mapping and past thought, even from the last year, with the current understanding always results in a more robust model.


figure 1

Historical understanding is that grade was contained in short scale veins. The implicit modelling grade shells indicate there may be a series of cross-cutting faults disrupting the grade. Pit mapping shows repetitive faults cut the ore body with throws of 8m vertical and up to 20m horizontally, disrupting the continuity of the grade more than expected.

2. Has the modeller utilised the information encapsulated inside available data?

Fractal patterns occur in geology over and again. The geological model must make sense on both the deposit and the larger regional scale. This also occurs across the datasets, where the grade distribution reflects underlying geology. A folded host may be replicated in folded grade, a faulted ore body will be visible in the grade, perhaps before it is seen in the geology (Figure 1). A simple flexure in the geology might be shown to be a fault with sharp offset in the grade. This works both ways and at different scales. The use of grade control data in a pit or underground environment can show significant discrepancies not seen in the geology. If there is a fault modelled but the grade shows no offset is a fault really there and is it material?

Implicit modelling used in targeting gold hosted in a ductile shear along an ultramafic contact. Simple implicit grade modelling combined with in-depth assessment showed the gold tended towards supergene enrichment in and around a folded volcanic horizon hosted within the sediments. A significant step in the basement co-incident with the grade shows the location of what was later modelled as a cross-cutting fault and which subsequently became the feeder for the mineralisation, completely turning the exploration model on its head. (Legend; Ultramafic = purple, volcanic = green, sediments = blue and cover = brown).


figure 2

3. Can the modeller describe and explain the model?

The implicit modelling algorithm may be indicating a dip on an intrusion normally thought to be vertical. We may write off the result because we “know” the intrusion is vertical. But the interpolant will always replicate the geology. We need to be able
to look at the results and read what the algorithm is telling us (Figure 2). Is the algorithm wrong or is our understanding of the geology flawed? A modeller who has thought about the model and crafted a result using all aspects of geology and having a full understanding of the mineralisation process should be able to intelligently explain the resulting model. Equally important is being able to tell which aspects of geology are most important for each deposit modelled and how they affect the result.


figure 3a

A series of porphyries were modelled explicitly on the “knowledge” that in these environments they always intrude vertically. Whilst the model was complex and never really hung together with respect to the geology in the drilling, it was never fully challenged. Close inspection of grade shows that it appears to start and stop abruptly within and outside of various porphyries. It was postulated that the grade was in fact thrusted apart by a series of east-dipping faults (green) and that the whole sequence may be tilted 20 degrees to the east as it ascends a thrust ramp.


figure 3b

Mapping and regional studies showed that there were indeed several smaller thrusts cutting the deposit and a large regional thrust just to the west (left) of the image, the geology was in fact tilted to the east. With this knowledge, the deposit was remodelled implicitly and the multitude of small low-continuity dykes filled out to become several large consistent but disaggregated porphyry bodies.

4. Does the output look realistic given what we know?

Sometimes what we know about a deposit is simply wrong (Figure 3). So question the model, test and dissect it and the knowledge of the geologist who built it. The model, implicit or explicit, might be poorly constructed, poorly thought out and poorly presented. Don’t dismiss the model outright. It may be that something important has been missed and the data is trying to spit out (Figure 4).

The same deposit as Figure 3 but showing a very simple isotropic undirected interpolant on the grade. The data clearly shows that “implicitly” the grade is dipping 
to the west and dissected by a series of faults (there are more thrusts in this system than shown). Despite seeing this, it took time to convince the audience as it clashed with current understanding.


figure 4

5. Has this model added to your knowledge?

Every time something new comes onto the market it is slow to be accepted, implicit modelling is no exception and has suffered considerable misunderstanding. Now that it has been picked up by the masses and a large number of software companies are building modules, I fear we are starting to move into misuse. Sometimes implicit modelling may not be the best tool for the job. The risk is that implicit modelling will be perceived as ‘geological modelling made easy,’ but this is when mistakes are made. It’s essential that people beware misuse.

When it comes to assessing geology models we rely heavily on those developing them. A bad model is easy 
to see; a dyke known to be continuous is poddy; a folded stratigraphy is not folded; a cylindrical intrusion has a weird blow out on the north side. The modeller will cop a bit of flack if they haven’t assessed the geology or had an idea of the outcomes prior to building the model. Implicit modelling is the easiest and fastest way to the wrong result, but used correctly, it will give you 90% of the result for 10% of the work.

Additional process questions:

1. What was the purpose of this model and was the outcome achieved?

2. What was your plan and approach to building this model?

3. What chronological order have you placed on the rocktypes and why?

4. Why have you selected to model that particular rocktype in that manner?

5. We know that Fault X has a significant effect on the deposit. How has it affected your model? Did you even use it?

6. What other significant structures (faults, fold planes, foliations, etc) have you used and why?

7. Are there any particular rock types you have ignored / merged and why?

8. What is the model trying to tell us not previously considered?

This web content is a summary of the article “Validating an implicit model”, originally published in the Unearthing 3D implicit modelling ebook. Download full ebook here.


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