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One of the biggest gaps in mining-industry geoscience is between our understanding of ore deposit formation and application in resource estimation. Significant advances including imaging and analytical methods, has given better understanding of interactions between hydrothermal fluids and host rocks.

Advanced geostatistical methods and improved hardware and software capability allows rapid processing and interpolation of huge amounts of drillhole data. However, there is sometimes a significant lack of communication between geoscience and geostatistics resulting in reduced confidence in resource classification and, sometimes, poor business decisions. This is the opportunity and future of geological modelling.

The mineral system approach has been successfully used in the mining industry for decades and describes the process whereby concentrations of chemical elements resulting from natural processes are transported, focused and delivered from a source area to the deposit. The approach originally came from the oil and gas industry, where an understanding of the entire source-pathway-reservoir/trap system is critical for expensive exploration efforts.

Advances in software provide an opportunity to include mineral system components in the deposit modelling workflow. For example, a better understanding of fluid pathways (usually structures) and fluid-rock interactions, highlight the shape and continuity of high- grade domains as well as the boundaries between high- grade, low-grade and waste domains.

Consistent application of geological rules

Advanced implicit modelling software has the ability to include geological rules and timing relationships as constraints, This includes cross-cutting relationships between faults and other structural features, and the nature and position of contacts between geological units, e.g. depositional, conformable, erosional or intrusion-related. Critical to building a geologically valid and consistent model, particularly if complex relationships need to be expressed, such as with larger and/or high-grade ore deposits. Hopefully in the near future software that has intuitive and simple tools for applying rules and relations will become the industry standard ensuring that models honour our understanding of complex geology in 3D (Figure 1).

The ability to rapidly build and continuously test a range of geological interpretations is another significant advantage. This provides the capability to build multiple geologically valid scenarios from the same dataset,
 from pessimistic to optimistic interpretations. In mining, this will improve understanding of uncertainty and reduce reliance on a single resource and reserve model for planning and strategic decisions. This will also make the modelling process more scientific.

Figure 1: Example of complex geological model built with implicit modelling workflow that honours geological rules and timing relationships.

Implicit modelling used for resource estimation

The main advantage of implicit modelling is that mathematical interpolants build the surfaces between known points in drillholes, rather than ‘explicit’ or manual construction. In some modelling software, the interpolants are mathematically equivalent to kriging, one of the best known and consistently used linear geostatistical interpolation methods. There is now the opportunity to use the interpolants that generate the geological domains to also do the grade estimation, vastly simplifying and integrating geological and geostatistical modelling workflows. This potentially combines geoscience and geostatistics into a seamless workflow, providing good fit-for- purpose results and allowing for more geological controls in the estimation workflow and final block model.

Plan view of open pit with grade control contour lines following structural trends.

Bringing more geology into grade control models

The grade control stage of mining has the most geological information and usually the least amount of time to make decisions about boundaries. Poor decisions can be very costly and produce poor reconciliation results. At a recent mining geology conference, a world-renowned geostatistician stated:

Increasingly, reconciliation issues are now found to be related to a lack of orebody knowledge in terms of location of the geometric limits of ore, and inadequate understanding of the irregularities of contacts and the ability of mining to follow them.

This exactly relates to the knowledge gap between geology and geostatistics. Implicit modelling software can bridge the gap rapidly building 
and updating with geological rules intact.

Traditional grade control dig lines are generally straight lines with sharp corners (Figure 2), commonly constructed using data from a single bench. Structural trends in implicit modelling (which mimic structural pathways for ore fluids) means we can rapidly build more realistic grade control models (Figure 3) using all available data above and below that particular bench. The resulting dig lines look more like what you expect to see as the result of ore fluids flowing through host rocks. Also, with more GPS-guided and automated mining equipment, we can follow more irregular and curved contours.

Figure 2: Plan view of open pit with grade control blocks, which result in angular and unrealistic dig lines.
Figure 3: Plan view of open pit with grade control contour lines following structural trends.

Implicit modelling for geoscience training: from classroom to resource

The most critical component is that geoscientists have the appropriate knowledge and training to build complex and multi-discipline resource models that unlock value and benefits for their company, the industry and society.

New specialist skills required will include:

  • Downhole geophysical tools and sensors
  • Real-time sample analysis and systems
  • (e.g. laser-induced breakdown spectroscopy, portable x-ray fluorescence and x-ray diffraction)
  • Hyperspectral systems for scanning drill samples and mine faces
  • Monitor-while-drilling and ‘smart bit’ systems
  • Automated drill rigs and associated equipment
  • Data integration and analytics

Geological understanding will always be the most important skill in building geologically valid models. Implicit modelling will provide maximum benefit from the flood of real-time geoscience data.

It will be important that geoscientists develop multi-discipline skills to better understand the requirements of all technical disciplines and downstream customers of resource models. This is an opportunity for industry, service providers and universities to work together to develop training programs to attract and retain the best and showcase mining as a sophisticated, high-tech industry.

Models and scenarios that are easy to build and update are fundamental in helping communicate multi-discipline understanding. This is the future of geological modelling: implicit modelling workflows that honour geological rules will result in a better understanding of uncertainty, opportunities and risks and will enable ore bodies to be mined more efficiently, more environmentally responsible and more safely.

Paul Hodkiewicz
Senior Manager Technology Development