Competitions can be useful to determine best practices, stress test legacy techniques, investigate new alternatives and determine the circumstances where certain techniques work well. In numerical prediction, perhaps the best-known competitions are hosted by Kaggle where nearly half a million…

Probabilistic modeling of open pit resources is well established; simulate at high resolution, average to a selective mining unit (SMU) size that represents future mining operations and grade control information, then report expected resources. This blog addresses probabilistic modeling of underground resources

The mining industry is reliant on disclosing mineral resources and reserves to investors to demonstrate value and secure capital to bring new mines into production. Reporting codes have evolved to protect investors. Security exchange commissions require (1) mineral resources to be classified into…

Clayton's blog post, provides an opportune moment to discuss some of the practical implications of Mineral Resource classification practices. It should be noted that the author fully endorses the use of drill hole (ddh) spacing over other…

Mineral resource estimation is based on block models of mineral grades within geological domains, with the block size chosen to reflect site-specific geological factors and mining selectivity. Estimated grades are typically calculated using ordinary kriging with composited drill hole data from the geological domain. Sample lengths are often relatively small for high resolution geological information, but different vintages may exist with varying sample lengths. Compositing is recommended to ensure consistency and improve block estimates. The optimal composite length is a critical decision, as too short or long lengths can increase errors due to high variability or loss of resolution, respectively.

The paper “Modeling Geospatial Uncertainty of Geometallurgical Variables with Bayesian Models and Hilbert–Kriging” by Hoffimann and others draws attention to the important topic of multivariate geostatistical modeling of geological and metallurgical variables. As with all methodologies, there are…

To impute is to put upon something or someone without their acceptance and, perhaps, without just cause. I always think about the trailer on many emails I receive: If you have received this message in error,… The sender imputes responsibility on the recipient without asking for or gaining…

Some of the greatest harm to the advancement of geostatistics has been caused by experienced geostatisticians who have stagnated. As an example, and at the extreme, a senior level executive in a mining company told me not long ago that inverse distance to a high power was sufficient for their…

An important goal of modern resource modeling is accurate and precise assessments of uncertainty for production volumes relevant for technical and economic decision making. Volumes of relevance often relate to weekly, monthly, quarterly or annual production. These volumes are large and, for the…

Many rock properties like mass fractions, volume fractions, thickness, and position average linearly. For rock properties that average nonlinearly, one rock type would have more influence on the effective property. This is common with variables such as recovery and throughput. In special circumstances, the effective property can go outside the range of the two pure components. This would depend on specific chemical reactions or increased efficiency of a mixture relative to a pure component, for example, performance of a semi-autogenous grinding (SAG) mill.

In most cases, mining will benefit from significantly more data in the future than available at the time of resource calculation. There may be one hundred times more data at the time of mining from blast hole samples or dedicated grade control drilling. The uncertainty will be greatly reduced. The…

A trend in geostatistics could be a general tendency in the direction industry is taking, like toward automation. A trend in geostatistics is also a non-stationary or locally-varying mean in a regionalized variable, particularly for a continuous variable. We would denote such a trend as . This kind…

Mining provides critical resources and products that society needs, but what of resource modeling within the mining sector? A critical analysis of what we do in resource modeling suggests that the cost of a precise and accurate quantification of resource uncertainty is modest with appropriate software and trained professionals. The benefits are far greater than the cost, raw data are turned into support information for important short- and long-term decisions...

Gaussian algorithms have infected virtually all corners of geostatistics: radial basis functions (RBF) for interpolation and implicit modeling, hierarchical truncated pluriGaussian (HTPG) for categorical variable modeling, detrending non-stationary variables with a Gaussian Mixture Model (GMM…

The state of nature is uncertain. Geological variability at all scales and sparse sampling make resource uncertainty an inevitable reality for mining companies and the concerned public. The workflow for probabilistic resource estimation is established, providing accurate and precise estimates of…

Geostatistical simulation of mineral deposits for resource estimates and long-range planning is established. Professionals have access to the methodology, software and training to reliably calculate probabilistic resources. We have come a long way. In the early days, simulation amounted to multiple…