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 resources because they focus on the geology. I have many words to describe my reaction to this, but most are unprofessional. More commonly, there are experienced modelers who have not kept up with powerful developments in probabilistic resource modeling, categorical variable simulation, trend modeling and modeling with a trend, geometallurgical modeling and so on. These experienced professionals would not (and should not) advocate for the use of advanced techniques they do not understand. It is essential that experienced modelers take some time from their busy schedules to “sharpen the axe” before they lose their edge.
December 7, 2020
resources training blog
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 most part, short scale high-frequency variations cancel out. This explains why simulated realizations from a fixed modeling workflow with fixed parameters often understate uncertainty. Careful consideration of large scale geological uncertainty is required to provide realistic asessements of production uncertainty. There are alternatives to incorporate such larger scale uncertainty.
October 9, 2020
resources simulation estimation trends gmm blog
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.
September 8, 2020
simulation estimation geometallurgy nonlinear blog
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 best estimate calculated with relatively widely spaced exploration drill holes will not represent what can be achieved at the time of mining. This is referred to as the Information Effect and is managed in different ways...
August 5, 2020
resources simulation estimation classification blog
A trend in geostatistics could be a general tendency in the direction industry is taking, such as towards automation. A trend in geostatistics is also a non-stationary or locally-varying mean in a regionalized variable, particularly for a continuous variable. This kind of trend is common and must be considered in probabilistic resource modeling; otherwise, high values are smeared into volumes they do not belong and low values dilute high valued volumes. Two questions arise: (1) how to model the trend? and (2) how to model with a trend?
June 30, 2020
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...
June 10, 2020
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), projection pursuit multivariate transformation (PPMT) for multivariate modeling, and simulating regionalized variables based on turning bands, sequential or spectral algorithms...
April 13, 2020
simulation estimation gaussian ppmt gmm htpg blog
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 uncertainty in local and global resources...
March 29, 2020
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 realizations with fixed input parameters within fixed geological boundaries...
March 27, 2020
resources simulation estimation blog