Clayton's blog post, Use Drill Hole Spacing for Mineral Resource Classification 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 measures for classification assignment where final decisions are guided by supported, predetermined criteria. The chosen spacing would ideally be supported by uncertainty studies (drill hole spacing studies using simulation), but could consider estimation quality measures (kriging efficiency, kriging variance, slope of regression) or heuristic approaches (data thinning, reconciliation, benchmarking).
Other classification approaches include: (a) search pass used during interpolation of grades, (b) extrapolation (i.e. distance to closest data), (c) mapping, bulk sampling, or proximity to existing production fronts, (d) vintage and quality of sample information, (e) exclusions as a result of other factors (pillars, water bodies, social and environmental), or (f) Reasonable Prospects of Eventual Economic Extraction (RPEEE).
Regardless of the chosen approach, there are often challenges for stakeholders to understand the classification criteria, especially when not transparently communicated. Investors, exploration geologists, mine geologists, and managers need clear comprehension of classification criteria during drill hole planning, technical due diligence, and auditing. Public disclosure of classification criteria often lacks transparency caused by direct application of non-transparent measures such as search pass classification and estimation quality measures.
Classification criteria should be differentiated from flagging resource categories to blocks. For example, direct determination of final resource categories from estimation quality measures or search pass criteria oftenleads to strange resource classification patterns that may cause issues during mine planning. It is common to see some inferred mineral resources disclosed in reserve statements due to spatial arrangement of the resource categories.
The author proposes the use of simplified geometries for classification categories, generally capturing the predetermined criteria. Each assigned category could contain a small portion of blocks that either exceed or do not meet the nominal criteria for that category as a result of the generalization. Even the smoother drill hole spacing approach can lead to isolated patches of measured and indicated, which may not meet the economic criteria for reserves during conversion despite meeting the drill spacing criteria. More drilling in these general areas may be required to meet the critical mass required for justifying the costs of extraction.
Drill hole spacing calculations offer more transparency, albeit not entirely. Given geometric complexity involved in the drill hole spacing calculation, the method used to assign local drill hole spacing will influence the result. It remains crucial to demonstrate how drill hole spacing relates to actual drilling grid configurations. Drill hole spacing studies are often completed based on idealised grid configurations. The drill hole spacing calculation should be tested on the idealised grid configurations to facilitate a translation between the idealised grid and the drilling information driving the final classification.
There are various factors beyond drill hole spacing that influence classification decisions. Practitioners may choose to use data as-is, but understanding the deposit and data quality may lead to modifications in decisions. Local modifications to the classification become more difficult as models become larger and more complex. The author has observed and used two novel approaches to whittling down this overwhelming problem; 1) assign drill hole spacing directly to sample information, and 2) in underground scenarios, assign classification directly to underground reporting shapes.
For the first approach, a drill hole spacing can be calculated at each informing composite. As a first pass, the composites can be flagged by their nominal resource category based on the drill hole spacing criteria. Further upgrading or downgrading of the composite flags can occur incorporating additional criteria such as data quality, complexity of classification geometries and presence of other features such as mapping, proximity to production fronts and bulk sampling. The categories can then be directly estimated using categorical estimation or implicit modeling techniques for final flagging of the classification.
The second approach, in the authors opinion, is one of the most defendable and comprehensive classification approaches in the industry. Instead of assigning classification to individual blocks, an underground stope optimisation is run using both resource and reserve cut-off and operational criteria to produce two sets of underground reporting shapes. The classification criteria is then directly applied to individual stopes. The resource stopes are then clipped by the reserve shapes and only remaining resource shapes meeting minimum recoverable geometric criteria are retained. As a component of classification, the Qualified Person inspects the stopes and downgrades them where the underlying support data and geological features are known to cause reconciliation issues. In one example at a mine visited by the author, resource reporting shapes intersecting a cross-cutting dyke were removed, as it was understood that ground stability deteriorated in proximity to the dyke. A simulation-based workflow and modern mine planning considering uncertainty would lead to a slightly different approach; however, the concept of classifying larger volumes would remain.
Drill hole spacing techniques provide a solid basis for establishing robust classification schemes. Many other factors and properties of the estimates help aid decision making and customize resource classification to be fit for purpose. The ultimate goal is a reproducible, defendable, transparent, and useful classification that addresses the end uses of the model, whether that be exploration, disclosure or mine planning.
