From drill holes to resource models you can defend

A variogram measures how quickly the similarity between two samples decreases as the distance between them increases, and how that varies with direction. It is the model of your deposit’s spatial structure, and both kriging and simulation derive their behaviour from it, meaning that an error in the variogram propagates into every estimate, every confidence interval, and every classification decision downstream. It is the step that most rewards experience, and the first thing an auditor or independent reviewer will examine.

Kriging returns the most probable estimate at each unsampled location and minimises estimation variance. In doing so it smooths out the natural variability of the deposit, which makes it conservative and safe, and makes it a poor guide to anything that depends on grade variability. Conditional simulation generates many equally probable realisations of the grade distribution, each honouring the sample values and reproducing the statistical properties of the data, so the variability is preserved rather than averaged away. The trade-off runs both ways: kriging gives up realism for safety, and any single simulated realisation is a riskier estimate than the kriged one. Simulation’s value lies in the spread across all realisations, which is what risk quantification, planning under uncertainty and sensitivity analysis need. In practice the two are complementary: kriging for the reported estimate, simulation for the confidence around it.

Geostatistics quantifies uncertainty through two complementary approaches. Kriging variance provides a local confidence indicator at every estimated block, showing how reliable the estimate is given the available data and the spatial continuity model. Conditional simulations generate a set of equally probable grade models, each honoring the sample data and reproducing the variogram, allowing you to compute grade-tonnage distributions, confidence intervals on resources, and NPV risk profiles. Together, they replace a single best estimate with a full probability distribution of outcomes.

Kriging, in most cases. Its conservatism is a feature when a single defensible number has to be reported and defended, and its block-by-block variance supports classification directly. Simulation then sits alongside it, quantifying the uncertainty around the reported figure and supporting mine planning, cut-off decisions and NPV risk analysis. If you would like to discuss the right approach for a specific deposit, our geostatisticians can advise.

Yes. Resource geologists and competent persons worldwide use Isatis.neo to produce mineral resource estimates reported under JORC, NI 43-101 and CIM. It provides fully justified and traceable estimation parameters, complete audit trails and reproducible workflows, all of which compliant resource reporting requires. For questions about a specific jurisdiction or reporting standard, our geostatisticians can advise you directly.