Mineral resource classification

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Mineral resource classification (easy version)

What it is and why it matters
- Mineral resource classification is a standard way to describe how much mineral is present and how sure we are about it. It helps miners, investors and planners judge potential value and whether it’s worth developing a deposit.
- International guidelines form the basis for reporting. The key idea is to separate what we know for sure from what is still uncertain, and to connect this information to whether mining is economically feasible.

Two main categories: Resources and Reserves
- Mineral Resources: deposits that have enough information to estimate their quantity and quality, but may not yet be economically mineable.
- Inferred Resources: crude estimates with low confidence. Based on limited or uneven data from rocks, outcrops, trenches or few drill holes.
- Indicated Resources: more data and higher confidence. Reasonable estimates of grade, tonnage and geometry.
- Measured Resources: the highest confidence level, with detailed sampling and a confident estimate of grade, amount and rock properties.
- Mineral Reserves: the economically mineable part of the Measured or Indicated Resources, shown to be economically viable with proper studies.
- Proved Reserves: highest confidence. The most certain estimate of recoverable ore, factoring in mining and processing costs and risks.
- Probable Reserves: lower confidence than Proved, but still sufficient to support decisions to develop a deposit.

How resources are estimated
- A block model is built by dividing the ore body into small, equally sized blocks. Each block gets data on grade, density and rock type from geological information and samples.
- Different estimation methods are used to assign a grade to each block. The choice depends on data quality, ore geometry and how precise the estimate needs to be.

Common estimation methods
- Nearest neighbor method: each block takes the grade from the closest sample. Simple and easy to understand, but can produce biased results and “stair-step” patterns.
- Inverse distance weighting: blocks get grades based on nearby samples, with closer samples weighted more strongly. A flexible and fast method, but can be sensitive to how data are collected and may not always be reliable.
- Kriging: a more advanced statistical method that uses spatial relationships to predict grades, providing best linear unbiased estimates and explicit confidence intervals. Powerful but computationally intensive and harder to interpret.

Block models and reporting
- The block model helps determine how much ore is available (resources) and how much could be mined profitably (reserves) after considering costs and technical factors.
- Reporting follows codes and standards to ensure information is consistent and credible, helping protect investors and the public from misleading claims.

Why standards exist
- In response to past fraud and misreporting, formal reporting standards were created to ensure mineral projects are evaluated scientifically and disclosed honestly.
- Not all deposits reported under these standards become economic mines, but the goal is to reduce fraud and improve decision making.

A note on history
- Notable scandals, like Bre-X, highlighted the need for strong reporting rules. Modern standards (such as NI 43-101 in Canada and other CRIRSCO-aligned codes worldwide) aim to prevent similar misrepresentations and protect investors.

In short
- Classification separates what we know from what we don’t, and ties that knowledge to economic viability. Resources become Reserves only after sufficient data and studies show mining is likely to be profitable. Estimation uses block models and methods like nearest neighbor, inverse distance weighting, or Kriging to predict ore grade and size, with varying levels of confidence.