Use case
Case Study: Gold Prospectivity - Val-d'Or, Québec
August 21, 2026
MinersAI applied its gold prospectivity model to Val-d'Or, Québec, integrating 20 datasets across structural, geophysical, and geochemical layers. Trained on a third of the district and tested across the full 6,600 km² area, the model recovered known gold systems along structurally prepared ground — validating its ability to generalize into unseen terrain.
The Region
The Val-d'Or district sits in the southern Abitibi Greenstone Belt — one of the most productive Archean gold provinces in the world, with more than 250 million ounces of gold across the broader belt. Val-d'Or is positioned along a major deflection in the Cadillac-Larder Lake deformation zone, a regional structure that has controlled gold mineralization across the southern Abitibi for billions of years.
The district is a strong demonstration ground for prospectivity modeling: the geology is structurally complex, public SIGEOM data coverage is deep, and the gold systems are well-characterized enough to test whether the model is learning meaningful exploration relationships or fitting artefacts.
The Challenge
Orogenic gold in Val-d'Or is not a simple geochemical bullseye. Mineralization follows structural grain — shear zones, high-strain domains, lithological boundaries, and second- or third-order faults plays off the Cadillac-Larder Lake deformation zone. A prospectivity model needs to learn that structural architecture, not just proximity to assay points.
The additional challenge: can a model trained on one part of the district carry that structural signature into ground it has never seen?
The test design: The model was trained on a smaller eastern AOI (~2,200 km²) and evaluated across a broader Val-d'Or AOI (~6,600 km²) — three times the training area. The result is not a fit metric; it is a transfer test.
What We Built
The project integrates 20 datasets across five evidence layers, built from public SIGEOM geology, geophysics, geochemistry, and satellite imagery:
A key platform contribution here is feature engineering. Rather than rasterizing fault traces as single-pixel lines, the platform converts faults into distance and density features, communicating broader structural influence to the model. The difference between 'a fault exists here' and 'this area sits inside a structurally influenced corridor' is the difference between a weak and a strong model input for an orogenic goldsystem.
What the Model Found
The prospectivity surface does not produce isolated bullseyes around scattered assay points. It lights up a corridor-shaped pattern consistent with the fault- and shear-controlled gold architecture of the district — exactly the structural expression a Val-d'Or geologist would expect.
In the broader 6,600 km² test area, the thresholded high-prospectivity surface captures:
• 246 of 433 Au-positive mineralized body targets
• 150 fault/shear-coded targets
• 47 mine and deposit records
The captured set spans a meaningfulcross-section of the district:
• Mine Bras D'Or (Zone Dumont) — fault/shear-controlled, ~1.1 Mt at6.24 g/t Au. One of the strongest sampled model responses in the test area.
• Mine Lucien C. Beliveau (New Pascalis) — ~1.8 Mt at 3.17 g/t Au. Captured in both training and testing surfaces.
• Mine Goldex and Mine Lamaque / Triangle Zone — major active and historical Val-d'Or systems flagged inside high-prospectivity zones.
• Mine Lac Herbin, Mine Kiena, Mine Akasaba, Mine Simkar — closed or resource-bearing deposits reinforcing the structural corridor theme.
The model recovers known gold systems along structurally prepared ground — not one isolated anomaly, but a district-scale corridor pattern. That is what earns confidence in the ground it flags that is not yet drilled.
Model Performance
ROC-AUC: Micro/macro0.815 / 0.818. Average precision 0.395 / 0.406. This is the right kind of result for a regional prioritization screen: a ranking surface that enriches known mineralization into the higher-prospectivity portion of the landscape.
Feature importance confirms the model is learning the right exploration signals for a Val-d'Or orogenic gold system:
The top four features — electromagnetics, stratigraphy, positive-Au geochemistry, and magnetics — account for ~85% ofmodel weighting. That ranking is geologically sensible: the EM and magnetic layers carry the regional framework, direct Au geochemistry supplies the mineralization signal, and fault buffering and spectral indices refine the structural corridors.
The Takeaway
In a single project, MinersAI organized aVal-d'Or gold prospectivity workflow from source data to model-ready evidence to validated prospectivity map — trained on 2,200 km², tested on 6,600 km², and recovering the district-scale structural control on gold that geologists have mapped for decades.
The commercial value is straightforward: MinersAI gives the user a way to structure a complex exploration problem without losing traceability. The inputs, the feature engineering decisions, the model outputs, and the validation evidence stay connected in one project — so results can be reviewed, communicated, and built upon rather than re-derived from scratch.
A note on interpretation: This is a regional prioritization model, not a drill-ready resource statement. Its value is in ranking ground, surfacing structural trends, and helping a geologist decide where to spend interpretation and field time next.