AI USE CASE
AI Mineral Deposit Location Prediction
Predict likely mineral deposit locations by fusing geological data with satellite imagery using ML.
What it is
This use case applies machine learning and computer vision to multi-source geological datasets and satellite imagery to identify high-probability mineral deposit zones. Exploration teams can reduce survey costs by 20–40% by focusing fieldwork on AI-ranked target areas rather than broad campaigns. Early adopters report 2–3x improvement in discovery hit rates compared to conventional desk-based methods. The system continuously improves as new drilling and survey data are fed back into the model.
Data you need
Historical geological surveys, borehole/drill logs, geochemical assay data, and multispectral or hyperspectral satellite imagery covering the target area.
Required systems
- data warehouse
Why it works
- Close collaboration between ML engineers and senior exploration geologists throughout model design and validation.
- Integration of multiple data modalities (geophysics, geochemistry, remote sensing) rather than relying on a single source.
- Establishing a closed feedback loop where new drill results are used to retrain and improve the model continuously.
- Phased rollout starting with a well-characterised brownfield area where ground truth exists to validate predictions.
How this goes wrong
- Insufficient or poorly labelled historical drilling data leads to biased predictions and missed deposits.
- Satellite imagery resolution or spectral bands are inadequate for the geology of the target region.
- Geologists do not trust or adopt model outputs, reverting to manual interpretation and nullifying ROI.
- Model overfits to one geological province and performs poorly when applied to new regions.
When NOT to do this
Do not deploy this system if your organisation lacks digitised historical drill logs and georeferenced survey data — the model will have nothing meaningful to learn from and predictions will be unreliable.
Vendors to consider
Sources
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