Resource estimates are the corner stone of technical and investment decision-making. Prior to
mining, resource estimation uncertainty has the greatest potential to lead to poor investment
decisions, despite a significant component of resource estimation uncertainty being unknowable at
this critical stage in the mining cycle. This presents a conundrum to the resource geologist in terms
of risk evaluation and resource classification.
Ahead of mining, there is normally considerable focus on drill hole spacing analysis to determine the
‘optimal’ drill hole spacing, taking grade and geological continuity into account; cost-benefit analyses
balance improved resource definition against cost; the law of diminishing returns and the exponential
cost increases acting together as spacing is reduced. Typically, a lower limit to the drill hole spacing
is identified. After completing the resource drilling to an agreed spacing, resource estimation is then
undertaken. At this point it is common to attempt to bracket the resource estimation uncertainty. In
many cases conditional simulation is used, based on a modelled, but largely assumed, variogram
model. It is also assumed that the histogram of the available drill hole sample data is representative
of the in-ground mineralisation.
The purpose of this paper is not to diminish the importance of drill hole spacing and simulation
studies, but rather to illuminate an important aspect of estimation uncertainty that, although
previously recognised, is typically overlooked – the question of how representative the available data
set is in characterising the true (but unknown) distribution of mineralisation. To do this, we lift the lid
on one of OceanaGold’s former operations, the mined-out Globe Progress Mine, by resurrecting a
high quality, close-spaced, reverse circulation (RC) grade control data set. The Globe Progress Mine
is within the West Coast Region of New Zealand, it was closed and transitioned to rehabilitation in
2016 and is now known as the Reefton Restoration Project.
The exhaustive Globe Progress grade control data was used to repeatedly ‘redrill’ the deposit by
extracting 35 m × 35 m spaced subsets from the original 5 m × 5 m spaced grade control data.
Utilising closely spaced grade control data removed the need for assumptions regarding short range
continuity, which are necessary with forward-looking analyses that are based upon broader spaced
resource drilling. The extraction process used a nearest neighbourhood algorithm, repeatedly
moving the origin in 5 mE, or 5 mN increments. Individual resource estimates were then completed
for each of the extracted drill hole data sets (49 in total) as well as a grade control estimate based
upon the exhaustive data set. Whilst the data unpinning each of the 49 estimates changed, the
geological assumptions, variography, and modelling parameters remained constant. This approach
was taken to isolate the impact of changing the input data. The 49 estimates were then compared
against each other and the grade control estimate.
The mean of all 49 sensitivity estimates was close to that of the grade control estimate in terms of
contained gold, tonnes and grade. Whilst it is acknowledged that the grade control estimate itself is
subject to some degree of estimation uncertainty, the close match between the average of the 49
estimates and the grade control estimate suggests that the resource estimation methodology is
reasonable and appropriate. Whilst this comparison is important, the focus of this study is on the
component of estimation uncertainty related to the underlying data, and this is reflected in the spread
across the estimates. The spread across the 49 global estimates (highest to lowest) for this particular
Mineral Resource Estimation Conference 2023 | Perth, Australia | 24–25 May 2023 142
case study was found to be significant (approximately 20 per cent in grade and metal) and that is attributable solely to the underlying data. This exercise quantifies a component of the estimation uncertainty that is inherent to all drill hole data and is distinct from the uncertainties associated with modelling methodology choices, sample and subsample quality and drill hole spacing-related interpolation uncertainty. Importantly, this uncertainty is unknowable prior to mining; we can only directly compare the resource drill hole data against grade control data after mining has taken place.
For the case study, approximately 65 per cent of the estimates fell within 5 per cent of the grade control estimate for contained-gold, suggesting that for many projects the histogram of drill hole data is unlikely to differ noticeably from that of the in-ground resource. However, about 15 per cent of the estimates in this study differed by more than 7.5 per cent, suggesting that a not-insignificant proportion of estimates will be materially compromised. Whether or not the available drill hole data is representative comes down to the ‘luck of the draw’ and cannot be known at the time of resource estimation.
Given the challenge of attempting to evaluate forward-looking estimation uncertainty, a component of which that can only be quantified retrospectively, what should we do as resource geologists? What are the implications for risk evaluation, resource classification and reconciliation? Furthermore, without rigorous post-mining data checks, resource geologists may conflate suboptimal modelling with the shortcomings of the underlying data. An example of this problem is discussed in the following section.