How BHP (with SRK / DeepIQ) improved porphyry targeting through hybrid prospectivity modelling
ML plus expert geological features rank targets across the Andes. Teams narrowed search areas and prioritized drilling intelligently.
Context
Exploration teams aimed to identify new porphyry copper targets in a mature and heavily explored terrain. Traditional interpretation had limited ability to reveal subtle or deeply buried signatures. Better target discrimination was required.
Solution
A hybrid workflow combining ML and geoscience modeling was developed to evaluate geological, geophysical, and structural indicators. The models scored and ranked prospective zones for follow-up exploration.
Results
The approach produced improved target ranking and more confident area selection, allowing exploration teams to focus on the most promising zones.
Data Inputs
Regional geoscience datasets, structural geology, geochem, geophysics