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Grade, tonnage & uncertainty

Kriging, conditional simulation, and grade–tonnage you can defend

Estimate a block model with an anisotropic variogram and a declared search neighbourhood, run conditional simulation to see the spread a single estimate hides, and report tonnage above cut-off with a real uncertainty band.

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Ordinary kriging on a 20 × 20 × 10 m lattice inside the vein shell, coloured by grade (g/t Au).

Target audience

Resource geologists, mining geostatisticians, mine planning engineers, Competent Persons, technical reviewers, and due-diligence teams.

Platform capabilities

Ordinary kriging producing an estimate and its kriging variance per block
Anisotropic variogram models with nugget and nested structures
Declared search neighbourhoods — a block with no sample in range is refused, not estimated
Sequential Gaussian simulation with stacks of equally probable realizations
Exceedance probability models against a stated cut-off
Grade–tonnage curves with P10 / P50 / P90 bands across realizations
Contained metal computed per realization rather than multiplied from two means
Assumption parameters required and never silently defaulted

Business benefits

Report a tonnage figure with the uncertainty attached to it
Show which samples informed a block, and which blocks were left uninformed
Give reviewers the parameters behind a number, not just the number
Separate the quantity from the classification judgement

Screenshot gallery

Mineral Resource Estimation in the product

The screens that support this workflow, shown in the working application.

P10, P50 and P90 tonnes and grade across the conditional simulations, at the recorded cut-offs.
Groundrush, a public open-file gold project: 113 drillholes in 3D around the vein shell solved from their logged contacts.
The review dossier: what ran, with what and by whom — and the decisions it was bound to.

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Mineral Resource Estimation: Kriging, Simulation & Grade–Tonnage | SpatialTechSolutions