Predictions independently corroborated by field exploration
MVP enabled formation of a standalone commercial entity
5
heterogeneous geospatial data types fused into a single analytical surface
Hundreds of thousands of square kilometres scored in a single pass
An MVP mineral intelligence platform was built that fused geological, geochemical, remote sensing, and historic exploration data across hundreds of thousands of square kilometres of under-explored African terrain. Machine learning ranked every zone by mineral potential with explainability and uncertainty scoring, and predictions were independently validated by subsequent field exploration, directly enabling the formation of a standalone venture.
The Problem
A client had spotted a genuine commercial opportunity: large parts of Africa remain geologically under-explored relative to their mineral potential. Mining firms operating in these regions make targeting decisions with incomplete information, relying heavily on traditional fieldwork, intuition, and whatever fragmented public data they can assemble. The client had access to proprietary datasets (geological surveys, geochemical assays, historic exploration records) that, if properly combined with publicly available remote sensing and geological data, could form the basis of a compelling intelligence product for the mining sector.
The Solution
We led teams on the technical build of an MVP mineral intelligence platform: a web-based, interactive mapping application that fused multiple geospatial data layers into a unified analytical surface. An ETL pipeline catalogued every dataset with provenance metadata and applied source-specific transformations to bring everything into a common projection and tiling scheme. The target region was tessellated into grid cells, and for each cell the pipeline computed a feature vector drawing from geology (lithology, fault proximity, contact zone density), geochemistry (interpolated element concentrations, anomaly scores), remote sensing (mineral alteration signatures, vegetation stress indices), structural data (slope, drainage patterns, lineament density), and historic exploration records.
Machine learning models trained on known deposit locations ranked every zone by mineral potential. Rather than a binary classification, the model produced a continuous ranking score paired with an uncertainty estimate: cells with sparse input data or conflicting signals received wider uncertainty bounds, signalling that the prediction should be treated with caution. Each high-potential zone came with two forms of explanation: a breakdown of which input features drove the score, and a "comparable known sites" module identifying which known deposits the zone most closely resembled. That kind of analogy is how exploration geologists naturally think, and embedding it in the platform made the outputs far more actionable than a raw score.
The front end was a browser-based interactive map where users could toggle data layers, overlay the ML ranking surface, click any grid cell to see its feature breakdown and comparable sites, and draw spatial selections to filter and export subsets. Export supported standard GIS formats (GeoPackage, Shapefile, KML) so outputs could be loaded directly into existing planning tools. Cloud infrastructure handled the geospatial compute requirements, with a CI/CD pipeline automating testing and deployment for rapid iteration during the MVP phase.
Results and Impact
| Metric | Outcome |
|---|---|
| Prediction validation | Model-identified high-potential areas independently corroborated by subsequent field exploration |
| Commercial outcome | MVP delivery directly enabled formation of a standalone entity |
| Data layers fused | Five distinct source types integrated into a single analytical surface |
| Coverage | Hundreds of thousands of square kilometres scored and ranked in a single pass |
| User workflow | Complete exploration workflow: map, interrogate, explain, compare, export |
The strongest validation came from the field. When independent exploration activity in the target region returned results that aligned with the model's top-ranked zones, it provided concrete evidence that the data fusion and ML approach was identifying real geological signals, not just fitting noise.
Key Takeaways
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The product needed a workflow, not just predictions. Exploration geologists needed map layers they could toggle, drill-down to understand, provenance they could verify, and exports they could load into their own tools. The platform's value came from supporting the full decision process, not just the scoring step.
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Credibility came from explainability and validation against known ground. Before anyone would trust the model on unknown territory, it had to demonstrate sensible behaviour on territory that was already understood. Showing which signals drove each prediction turned sceptical domain experts into engaged users.
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Data fusion was the hard problem, not the ML. The modelling itself was relatively standard. The real technical weight sat in harmonising heterogeneous geospatial data at scale: reprojecting, tiling, interpolating sparse samples, extracting features from multi-band rasters, and maintaining provenance through every transformation.