AI-Driven Drilling Targeting and Resource Modelling

GeoSpectra applies an integrated, mineral systems–based framework, combined with advanced machine learning, to generate, refine, and optimize drilling targets and to model deposits and estimate reserves across the full exploration lifecycle.

In early-stage exploration, we integrate 2D geospatial data from multisource datasets—including structural interpretations, ground geophysics, geochemistry, and geological mapping—to delineate alteration systems, structural controls, and prospective zones. These inputs are integrated using knowledge- and data-driven modelling approaches to produce ranked scout-drilling targets.

As exploration progresses and drilling data becomes available, by incorporating earlier borehole assay data, alteration logs, and structural measurements, and using semi-supervised machine learning algorithms in a 3D GIS environment, the system leverages both labelled (drilling) and unlabeled (geospatial) data to model subsurface continuity, refine ore body geometry, and identify optimal directional and infill drilling targets.

This integrated approach enhances targeting precision, reduces exploration risk, and improves discovery efficiency by maximising both resource tonnage and ore grade. Following drilling programs, GeoSpectra conducts resource modelling and reserve estimation, delivering robust, data-driven models to support technical evaluations and strategic decision-making.