Integration of Multiple Data Sources into an Iron Ore Resource Estimate – An

Analysis of the Options

Alastair Cornah1

In brownfield mining projects it is common that multiple overlapping sources of ‘hard’ data are

available to the resource estimator. For example this may include sample information from various

types of drilling campaigns (such as diamond, sonic, reverse circulation or percussion drilling),

channel samples and blasthole samples. Each data source is likely to be associated with a different

level of precision and some may be associated with bias.

In making a choice to exclude one or more data sources the resource estimator judges that, in terms

of helping to minimise estimation error, the benefit of that data is outweighed by its lack of

precision or bias. However, various geostatistical techniques are available to account for differences

in data precision (and also bias) during estimation; thus maximising the value of each source of

information. In this paper some tools that are available to handle differences in precision between

data sources are evaluated.

Conditional simulation of iron grade within a test area was used to help analyse the most effective

estimation pathway to integrate diamond drillhole data (which is assumed to be error free) with

lower precision channel sample data. A single realisation which was conditional to the diamond

drillhole data was ‘virtually sampled’ at a geometry approximately equivalent to a channel sampling

grid. A series of channel sample datasets were built by applying several sampling error distributions

to the virtual channel sample data; errors were assumed to be unbiased and follow a normal

distribution.

Various estimations of iron grade in the test area were run using the resulting datasets including:

• estimation of drillholes only (ignoring the channel sample data) using ordinary kriging;

• integration of drillhole and channel datasets, estimation using ordinary kriging;

• integration of drillholes and channels into estimation using cokriging;

• integration of drillholes and channels into estimation using collocated cokriging;

• integration of drillholes and channels into estimation using ordinary kriging with variance of

measurement error.

The estimations were compared against the simulated ground truth, allowing estimation error to be

analysed in each case. The analysis indicates that if channel samples are lower precision than the

diamond drillhole samples, and under the outlined assumptions, integrating these data sources

using ordinary kriging with variance of measurement error provides the best estimate.

1 Principal Consultant, Quantitative Group. PO Box 1304, Fremantle, WA, Australia 6959. ac@qgroup.net.au.