Variography is the step most engineers rush — and the one that most often explains why two QPs produce different resource estimates from the same dataset. A well-constructed variogram is not a formality; it is the spatial fingerprint of your deposit.
Why Variography Gets Skipped
In a typical resource estimation workflow, variography sits between data compositing and grade interpolation. It is computationally straightforward but interpretively demanding — and under time pressure, teams frequently accept default parameters or copy variogram models from previous estimates on nearby projects. Neither practice is defensible under NI 43-101 or SK-1300.
The variogram model controls the search ellipse orientation, the interpolation weights assigned to each sample, and ultimately the smoothing behaviour of the kriged estimate. Getting it wrong does not produce an error message; it produces a plausible-looking block model with systematically biased grades.
The Three Parameters That Matter Most
Range
The range defines the distance beyond which samples are spatially uncorrelated. Overestimating the range inflates the search neighbourhood and incorporates samples that carry no meaningful spatial information. Underestimating it produces an estimate that is essentially a nearest-neighbour interpolation with extra steps.
Nugget
The nugget-to-sill ratio is a direct measure of short-scale variability relative to the total variance. High nugget ratios — above 0.4 for gold deposits — are a warning sign that should trigger a review of sample support, preparation protocols, and QAQC data before the variogram model is finalised.
"A variogram model fitted to noisy experimental data without geological validation is not a model — it is a mathematical rationalisation of insufficient data."
Anisotropy
Most deposits are anisotropic: continuity along strike differs from continuity down dip, which differs from continuity across the mineralised horizon. Fitting an isotropic variogram to an anisotropic deposit is one of the most common sources of directional grade bias in block models submitted for public disclosure.
A Practical Fitting Workflow
- Calculate experimental variograms in at least four directions — along strike, down dip, across strike, and vertically
- Plot each direction separately before fitting any model
- Identify the direction of maximum continuity visually before applying automated fitting tools
- Validate the fitted model against a second dataset (e.g., RC vs. diamond core) where available
- Document all fitting decisions and the geological rationale for the anisotropy axes
Under SK-1300 Item 13(b), the QP is required to describe the interpolation method and the parameters used. A variogram model submitted without documented fitting rationale is a disclosure gap — and a liability in technical due diligence.
When the Data Are Too Sparse to Variogram
Early-stage deposits often lack the drill density needed to compute a reliable experimental variogram. In these cases, the appropriate response is not to invent a variogram — it is to use a simpler interpolation method (inverse distance weighting or nearest neighbour), classify the result as Inferred, and disclose the limitation explicitly. A well-reasoned Inferred estimate is more defensible than a Indicated estimate built on a fictional spatial model.
JNA Resource Advisory provides independent variography review and block model audits as part of technical due diligence and QP sign-off engagements. Contact us to discuss your project.