How to select the best block size for your block model

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The parent block size is one of the most important parameters which impacts the quality of grade estimates in a resource model. A block size that is too small results in the grade estimates being oversmoothed, giving an incorrect grade-tonnage relationship. Ideally, we’d like to produce a locally accurate estimate at the smallest block size we can to give adequate resolution of the grades in the model – but how do we decide what block size to use? And once we’ve decided on a block size, what about other estimation parameters such as how many samples we use to estimate the grade of a block?

This is where kriging neighbourhood analysis (KNA) comes in. A KNA provides a quantitative method of testing different estimation parameters (e.g. block size) and, by assessing their impact on the quality of the resultant estimate, select the optimal value for each parameter. This will be dependent on several factors within the deposit; the inherent variability, the ranges of grade continuity, anisotropy and the data spacing. The variogram mathematically represents these factors and is a critical input for a KNA.

The statistics generated for KNA measure conditional bias. This refers to the ‘degree of oversmoothing’ (i.e. reduction in the variance of grades) in the block estimates compared to the theoretical true variance of grades at that block size. The KNA aims to determine the parameters (block size, number of samples, search radius and discretisation) that minimise the conditional bias in the estimate, along with ensuring that not too many negative weights are generated.

There are two conditional bias statistics used for optimisation:

  • Kriging efficiency (KE)
  • Slope of regression or conditional bias slope (SLOPE), which summarises the degree of oversmoothing of high and low grades.

Kriging efficiency measures the effectiveness of the kriged estimate to reproduce the local block grade accurately. It is calculated by comparing the kriging variance of the block with the theoretical variance of the block.

When the kriging variance is small relative to the block variance then the kriging efficiency approaches a value of one. When the kriging variance is high and dominates the block variance (as would be the case for poorly estimated blocks) then the kriging efficiency will be low (sometimes even negative).

Low kriging efficiency indicates a high degree of oversmoothing. Conversely, high kriging efficiency indicates a low degree of oversmoothing (Figure 1).

Kriging efficiency is often reported as a percentage with the optimal value being 100% (or 1).


Kriging efficiency

Figure 1    Kriging efficiency


 

The slope of regression summarises the degree of oversmoothing of high and low grades. This slope is equivalent to the regression slope of the estimated block grades against the corresponding true, but unknown, grades (Figure 2).

A slope close to one indicates that the regression between the estimated and true grades is likely to be very good, meaning there is limited oversmoothing. In this case it is likely that the grade tonnage relationship above cut-off is realistic.

Conversely, low slope values indicate that there is oversmoothing and hence a poor relationship between the estimated and actual block grades. In this instance it is unlikely that you will be able to accurately report selective estimates above a cut-off.


Slope of Regression

Figure 2   Slope of Regression

 

The conditional bias statistics can be generated for any combination of variogram and estimation parameters to test various parameters and determine the optimal estimation parameters. Once the statistics are determined for each value of a parameter, scenarios can be compared. Ideally the optimal result is a slope of one and a kriging efficiency of 100%; however, this is never achievable in practice. More typical results are slopes of greater than 0.9 and kriging efficiencies in the order of 80% to 90% for well-informed areas.

Snowden’s Supervisor Software allows a KNA to be run on multiple blocks within a domain, providing more confidence in the parameters being selected. Supervisor analyses the drillhole data and generates block centroids for a domain based on the minimum and maximum coordinates of the data. The user specifies tolerance distances from the data, in the X, Y and Z directions, to restrict which blocks are included in the KNA results to avoid excessive extrapolation away from the data (which always results in poor kriging statistics). The blocks generated for each block size can be viewed in the 3D viewer (Figure 3).


Blocks generated for a domain for use in the multi-block KNA

Figure 3    Blocks generated for a domain for use in the multi-block KNA


 

As multiple blocks are being tested by Supervisor, rather than providing a single result, the results are graphed as a box and whisker plot to show not only the average kriging efficiency and slope of regression, but also the range of results (Figure 4).


Graphical kriging efficiency and slope of regression output for multi-block KNA for the number of informing samples

Figure 4    Graphical kriging efficiency and slope of regression output for multi-block KNA for the number of informing samples


 

In areas of sparse drilling such as during the earlier exploration stages, or in domains with short ranges of grade continuity or elevated nugget effect (e.g. gold), the results will be lower. Additionally, in narrow domains the results may be poor due to the lack of data in the third dimension. This method can still be used in a relative sense however, to determine which scenario provides the better results.

While these statistics are useful as a guide the decision should always be tempered by reality. After determining which runs provide acceptable results, think about the practical aspects of your choice. Given several options which give similar results, always select the result which makes the most sense in terms of mining and geological considerations.

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