How GoldSpot Discoveries (now EarthLabs) focused drilling through AI-targeted target generation
Data-driven prospectivity models highlighted structures and zones. Drilling intersected predicted mineralization more often.
Context
Drilling campaigns risked wasting resources on low-prospectivity areas due to limited geological visibility. Traditional mapping and early data could not sufficiently narrow down targets. This increased cost and reduced discovery efficiency.
Solution
ML prospectivity models were trained to highlight promising zones based on structural and geochemical patterns. Outputs guided geologists toward areas with higher predicted mineralization.
Results
The models led to successful drilling intercepts within predicted zones, showing direct correlation between ML predictions and mineralization results.
Data Inputs
Geological maps, geochem, geophysics, remote sensing