The mining industry has resisted the acceptance of resource domain wireframes derived using implicit methods. There’s also been bad press because it provides a conduit for some rapid and poorly thought through ‘blobby’ models. However, this is changing as more become familiar with the techniques and see the advantages, which, we argue, outweigh the limitations of traditional sectional wireframing. This brief review touches on key questions and is a starting point for discussion.
The development of a robust resource domain model requires familiarity with the commodity and geology of the deposit, with the geological and statistical justification of the domain and with the strengths and weaknesses of the methods and software used.
In the past, some models built using implicit tools were sub- optimal, reflecting the form of a mathematical function rather than a geological shape. This can be put down to the practitioner’s poor knowledge of the software’s algorithms and an unfortunate acceptance of such output. Geologists are now more adept in implicit techniques and the software itself has similarly evolved. This was equally true of sectional methods when the GMPs replaced older 2D techniques.
Careful and relevant geological domaining in resource estimation is crucial to developing a robust and reliable resource estimate. Mathematical models alone are not sufficient to accurately define a resource.
Reluctance to accept the implicit approach for resource domaining
Resource practitioners are probably the most conservative people in the mining industry (as Competent Persons they carry the responsibility for public signing off on Resource statements). They feel most comfortable with methods used early in their careers.
Distrust of the unknown
Especially when implicit models produce unexpected results (no matter how fundamentally correct and unbiased)
Lack of training makes it difficult to overcome early problems in controlling implicit models, so practitioners revert back to known techniques.
Resistance to Change
Feelings that new technology may take away or diminish the role of the professional modeller.
Unease at the organic look of implicit models compared to traditional structured boxy shapes of polygonal models (an irrational but common observation).
The mathematics behind the implicit models is unknown and tends not to be trusted.
No software or method is JORC compliant as this requires the input of a Competent Person and appropriate methodology at all levels in the estimate.
Bad initial experience
Commonly due to lack of necessary skills at an early stage to control the model outcome effectively.
Perceived lack of control
A perception of lack of control on development and building compared to manual techniques.
Which is the correct model?
Does the implicit model snap to points on drillholes?
Becoming competent in new software methods requires considerable investment in time to train and gain experience.
Reasons for using implicit modelling for resource domaining
Comparing volume, grade and geometry of well thought-through implicit models with traditional models is normally favourable, with the implicit model often exceeding reliability.
Implicit models are easily repeatable, making the incorporation of new data relatively straightforward. They are also more auditable than manual wireframes.
Implicit methods make it much easier to deal with larger grade control data sets, assisting with 3D interpretation of trends and improving reliability of local grade estimates.
Implicit models provide a better 3D extrapolation when modelling from irregularly spaced data and are not sectionally biased as can sometimes occur with manual models.
With complex, high nugget deposits, implicit methods can better link mineralised intervals between holes.
Grade and volume dilution can be incorporated with much greater control, often eliminating unnecessary dilution that occurs in manual models.
Implicit modelling makes it feasible to produce multiple realisations of a deposit according to a range of assumptions, highlighting the level of risk in a single model approach.
Surfaces can be generated more rapidly from the data, saving modellers time and companies money.
The new geological workflow built into several implicit modelling methods enables geologists to bring more geology into the resource model and produce more geologically refined models.
The speed of the implicit approach allows more time for gathering fundamental information to inform.
The implicit approach allows management to overcome the One Model Syndrome, where a projects technical and financial development hinges on a single model. Multiple realisations of a resource model can be used alongside the manual model to determine how conservative, liberal or otherwise, i.e. the level of risk inherent in the model. The implicit approach allows management to determine the weaknesses of a geological or resource domain model.
It is something of a myth that implicit technologies only work well in data rich environments (e.g. grade control). Implicit modelling can work as well, if not better, in data-poor environments. If data is genuinely sparse, both manual and implicit methods often fail due to the realisation that constructing a model that has no demonstrated geological control or geological continuity is going to be very poorly constrained.
These views are commonplace in the metalliferous mining industry and are partially justified by early experiences with implicit modelling software. However, the software has developed significantly, addressing early concerns and incorporating more geologically relevant workflows. The ability of the geologists has also improved. Many mining companies, operations and consultancies are realising the technical and financial advantages. As labour costs rise and mines seek efficiencies, teams dedicated to time-consuming wireframing is not cost effective. No model can yet be generated entirely automatically, a hybrid approach with geologist input to steer implicit models is expected to be required for some time. The pendulum has swung in favour of implicit methods for many good reasons.
Focus on snapping
Vein modelling tools mean that exact intersection points can be chosen if required, providing the means for snapping, but reasons should be examined carefully.
Is snapping to points essential for a good model? One question frequently asked is do and should implicit techniques honour contacts? Often, this refers to the process of snapping in 3D to either grade or geological contacts points on drillholes. The answer is not simply yes or no. If the contact is a hard geological boundary such as a vein footwall or hanging wall snapping can be necessary. However, if it is a soft boundary such as a nuggety gradational grade change, then the answer is not so obvious. In this situation, it is likely there is no definite contact discernable in the core or field and the “contact” becomes a grade contour that serves the purpose of defining ore types for mining.
Figure 1 shows this problem of grade contouring with a typical gold deposit intercept in a drillhole. The intercept was given a broad geological description, minimum mining width and cut off grade, and several geologists were asked to select the correct point to snap to to achieve a required cut off grade domain boundary. All selected slightly different intervals. The implicitly modelled answer was somewhere between them all. Each contact selected was precise but not necessarily any more correct than the implicit solution. Snapping to an absolute point is very subjective and the application of ‘rules’ means that snapping is not as important as other considerations, such as complexity and minability of resultant shapes.
The aim of snapping or selecting a grade boundary is to ensure that below cut off grade samples are excluded from the estimate. From a review of several manually constructed wireframes, the number of samples below cut off significantly exceeded that selected by implicit methods, despite the geologist snapping to exact points.
Focus on geological modelling
Another common criticism of implicit techniques is the perceived lack of geological and structural control on the grade or ore body outline. In early versions of the software this was indeed the case, subsequent refinements and improvements mean it is no longer. Complex structural trends can be incorporated, analogous to key features of traditional wireframing of a skeletal framework of the controlling structures underlying the grade distribution. However, implicit modelling is far simpler and less time consuming.
It is now becoming a common approach amongst all implicit software that the structural trends themselves can be controlled directly from primary geological inputs of dip and strike (from structural measurements down hole, surface and underground mapping. The ability to rapidly build hard geological contacts such as footwall and hanging wall structures encourages the use of rigorous geological domaining where once it may have been given less importance due to time constraints. Ore bodies with multiple, complex strike directions that were once difficult to build can now be tackled with greater geological rigor. Recent improvements in vein modelling workflows and grade/contact selections mean straight forward vein systems can also be tackled.
The principles of stratigraphy are now also being incorporated ensuring easier, logical geological building of models rather than using abstract Boolean operations.
Modern techniques now also incorporate local manual control to overcome this once frequently commented on issue.
Focus on the mathematical algorithms
Probably the most prevalent criticism of implicit approaches is that they are “Black Box”. The algorithms used in GOCAD’s SKUA are based on the well known and documented Discrete Surface Interpolator (DSI) algorithm. The Leapfrog® Radial Basis Function (RBF) is based on Dual Kriging, a rapid optimised derivation of Ordinary Kriging.
All are openly available for review. As in any modelling, no outputs should be trusted without rigorous comparison with the informing data and local geological knowledge. Similarly, prudent validation by section and plan with the informing data sets should always be performed.
The use of implicit technologies for resource domaining requires a paradigm shift in thought and approach away from the sectional CAD-based techniques that have dominated for over 30 years. Thomas Kuhn (Professor MIT, Harvard, Berkeley 1922-1996) thought of science as a kind of mob psychology and not a purely rational search for truth. “Only once the groundswell of opinion is sufficient then the new science or technology become accepted despite how fundamentally correct the theory or technology was in the first place.”
Failure to recognise the importance of new technology is summed up in this quote by Sir William Preece, Chief Engineer of the British Post Office, circa 1876: “The Americans have need of the telephone, but we do not. We have plenty of messenger boys.”
Evidence from numerous comparisons with pre- existing resource domain models over the last few years has demonstrated that, when used correctly by experienced geologists, implicit models can equal or exceed the quality and reliability of manually- derived sectional wireframe methods. However, it is critical that fundamental geological skills be employed when developing any model, regardless of software or method employed.
Corporate Consultant Mining Geology
SRK Consulting, Cardiff, UK
Malet JL. Discrete smooth interpolation in geometric modelling. Computer aided design 1992. Vol 24, p 178
Chiles J-P and Delfiner P, “Geostatistics Modelling Spatial Uncertainty”, Wiley Interscience, 1999, p 186
Dowd P A, “A review of geostatistical techniques for contouring”, NATA ASI Series, Vol F17, Fundamental Algorithms for Computer Graphics. Edited by R.A. Earnshaw Springer Verlag..
Galli A. and Murillo E. “Dual Kriging – Its properties and its uses in direct contouring” Geostatistics for Natural Resources Characterization, NATA ASI Series C: Mathematical and Physical vol 122 part 2, Dohrecht, Holland, pp 621-634
Kugn T. 1962. The Structure of Scientific Revolutions