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Geological intelligence

Turn a validated drillhole database into an exploration picture: composites, domains, variography, block estimates, prospect ranking and drill targets — computed locally, on the data you already own.

GeoMate Geological Intelligence Engine: kriging and variogram parameters, grade distribution, confidence classes and a grade-tonnage sensitivity curve. Project name and estimate figures are blurred.
GeoMate Geological Intelligence Engine: kriging and variogram parameters, grade distribution, confidence classes and a grade-tonnage sensitivity curve. Project name and estimate figures are blurred.

The engine reads the same validated database the rest of the platform writes to — no export, no re-import, no separate modelling package to reconcile. You choose the element, composite length, cut-off and density, set the variogram and search parameters, and the run produces domains, shoots, block estimates, a grade-tonnage curve and a ranked list of prospects with generated drill targets.

Estimation is a block model by kriging, driven by parameters you set rather than hidden defaults: composite length, cut-off grade, bulk density, search radius, variogram azimuth and dip, lag, number of lags and angular tolerance. Confidence is reported per composite, and every output carries an exploration-target label — the engine informs a competent person’s judgement, it does not replace it.

In practice

A campaign closes with several hundred holes across a dozen prospects. Rather than exporting to a modelling package and waiting a week, the team composites at 2 m, kriges a block estimate above a 0.5 g/t cut-off, and gets a ranked prospect list the same afternoon — enough to decide where the next rig goes.

What’s inside

  • Compositing at a chosen length, with a grade cut-off and bulk density
  • Experimental variograms — azimuth, dip, lag, number of lags and tolerance
  • Block model estimation by kriging, with configurable search radius
  • Domain and shoot detection, with high / moderate / low confidence classes
  • Grade-tonnage sensitivity across cut-off grades
  • Prospect ranking and automatic drill-target generation
  • Results labelled as exploration targets — never presented as a compliant resource
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