Geologic risk is the subjective measure of the willingness of the modeller to put all their eggs in one basket. Committing to a single model is all too often the case in resource consulting the result of established polygon-based modelling and estimation practices paired with time limitations.
Integrating implicit modelling into the workflow has not only expedited the first iteration of a model, but has facilitated construction of multiple versions and refinements to test and compare with known data. Done in the same time a single model would once have been.
In my formative years, my primary tool for constructing geologic models was the extruded polygon on section, well-established as providing complete control. It was simply drawing polygons to define a body on section and connecting those polygons with triangulated extrusions to form a solid mass to represent geology. A simple model would take weeks, if not months, of painstaking digitisation.
I would seldom introduce faulting or any additional shape-morphing factors unless substantial time was available. In most cases, complexities were ignored or simplified, the model would flex and undulate like a battered accordion, only generally making geologic sense. It was my attempt to simulate a complex system while working within the limitations of “section-view blinders.”
The risk of the single model
Typically, new project data needs to be vetted, corrected, modelled, estimated and reported within a 2-3 month window. When the raw data has been cleaned up remaining time is a premium asset. Spending multiple weeks or months on a polygonal model would leave insufficient time to review, refine or even reasonably understand the deposit before a resource needs to be calculated and reports written.
Additional hypotheses would be overlooked in favor of the first-pass model. The polygon approach of rendering a single model to represent absolute reality leads to a high degree of risk, as no alternative hypotheses were ever investigated.
Taking a global view
Implicit modelling significantly reduces the degree of “geologic risk” in model construction by allowing many models to be built in a relatively short time while still viewing the data globally. Complex geometry becomes easily understood if viewed globally, factors such as fault movement make much more sense viewing the entire relationship. Complex geometry becomes much more practical when the computer is doing most of the heavy lifting. Questionable interpretations are quickly identified when not trapped in a limited section-view (i.e. “section-view blinders”).
New modifications can be seen at once, quickly resolving inconsistencies and errors. Additionally, geostatistical analysis can be performed to identify which model is statistically the most valid and choose that with the least amount of waste material in an ore boundary, the best variography results, the most normal distribution of grade values, and so on. By reducing the geologic risk, statistical risk also diminishes.
Process of refinement
I build revisions on a theme rather than drastically different and mutually-exclusive models. An early iteration might be “usable” (comparable to a polygonal approach) but with questions remaining. The existing model can be copied to retain the original, then the copy refined. I may come up with half a dozen or more iterations that are copies of copies, each one a modification of the last.
For instance, I might have a completely un-faulted model, with a second introducing the major faults, a third including minor faults, and fourth incorporating dike intrusions. I can test how different hypotheses will affect my model differently. Implicit modelling software will typically update models automatically when new data is available, quickly proving some iterations false once a new season of drilling or mapping data is imported, while other modelling iterations will likely have a degree of validation. Remaining models can be refined and expanded upon with additional models to test when new data is available.
Channel for discussion
Creating multiple models is extremely pragmatic when communicating with a client. With a polygonal model I would provide rough versions to which they would advise on refining. Today, when I present a half-dozen model iterations, it opens a channel for discussion about which is considered the best, given their knowledge of the field aspects.
Often presenting a hypothesis not considered that may become a new target for investigation. This ability to contribute to the conversation regarding geologic interpretation, potentially aiding future endeavors beyond the model estimation, is a value-added benefit I couldn’t provide without implicit modelling.
The geology model frames all decisions in the resource extraction industry; calculation of resources; mine planning; recovery and ultimately, profitability. Every downstream decision is at least as wrong as the geology model, starting with the best model is critical.
A geology model can be thought of as a single realisation of geologic interpretation within the sample data set, permissive of many possible interpretations. Furthermore, most geologic sample data are themselves based on interpretation during logging. This demonstrates that more than one model outcome is possible and more than one should be constructed and evaluated. In fact, a final interpretation may be presented to downstream users with error bars when more than one model is evaluated acknowledging the risk involved.
This is ideal, where the evaluation of several models allows understanding of project risk and likelihood of success. However, up to the present day the connection between the economic evaluation and the underlying assumptions are usually poorly understood. The model is usually taken as ground truth, regardless of how well it was constructed or its risks understood. If it is insufficiently characterised, the risk of the resulting business decision is an unknown.
The standard construction of a geology model typically relies on the sectional construction of lines and polygons, either hand-drawn on paper sections or on a computer screen. These 2D representations are then converted to 3D by tying successive 2D slices together, like stacking slices of bread to make a loaf.
Important geologic features may change from deposit or commodity type, but the basic method of drawing lines, then erasing them to redraw them with the addition of more data, has been the same. Usually, only one model will be produced because it is simply too time- intensive. The shapes of geological objects are frequently much more complicated than stacking slices of bread.
Enter implicit modelling
The heavy lifting is now completed in a fraction of the time and in 3D by fast processors and efficient mathematical algorithms. Implicit modelling allows a geologist to focus on the geology and examine possible outcomes of the data using repeatable, objective algorithms rather than drawing only to complete one possible subjective version.
The precipice of change
The industry is at the precipice of change, implicit techniques are gaining traction. The attraction of fostering multiple hypotheses and having time to fully examine them in 3D is quickly approaching critical mass.
Obstacle to acceptance
Yet geologists and managers sometimes worry that the speed of implicit modelling provides less intimacy with the data and that it can feel a bit like a black box where the geologist has lost control. This fundamental obstacle to wider acceptance will fade with familiarity and training.
Speed is the great advantage of implicit modelling; one can arrive at the wrong answer very quickly and the first model or even the second and third can be quickly dismissed as important details and the geology it represents are realised.
Implicit modelling software is just another tool in the geologist’s toolbox to be properly applied to the appropriate situation. Itis, however, fundamentally different: poor logging techniques and sloppy standards are made obvious to the simplicity and unbiased eye of a computer algorithm. Not only will implicit modelling help geologists examine more geologic possibilities faster, but it will also drive improvement in data management and logging practices. Putting appropriate focus on the data that drives business decisions.
Modelling speed and a focus on the data will drive an important understanding of geologic risk and uncertainty. Fundamentally we will be able to quantify risk and, with the easy days of mining behind us, understand the geologic uncertainty of resource extraction projects so critical to continued success.
Patrick ‘PJ’ Hollenbeck & Marc
Independent Consulting Geologists