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Exploration module

Mineral Prospectivity

AI-Assisted Prospectivity Mapping, from Evidence Layers to Ranked Targets. Mineral Prospectivity turns geoscience data into ranked exploration targets. A data catalogue covers open, licence-gated and commercial sources — Copernicus elevation, GA/CSIRO ASTER mineral maps and global fault data stream in live — and drillholes and geochemistry are read straight from the GeoTech DataHub. The Evidence Builder turns them into layers; Model Studio runs knowledge-driven methods (weighted overlay, fuzzy gamma, AHP) and data-driven ones (weights of evidence, logistic regression, random forest, XGBoost, PU-bagging). Validation uses spatial cross-validation, and every high-scoring cell explains itself through SHAP contributions or evidence weights.

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Prospectivity on open data around Kalgoorlie: Copernicus elevation, GA/CSIRO ASTER AlOH content, and a knowledge-driven orogenic-gold model with its targets.

Product positioning

AI-Assisted Prospectivity Mapping, from Evidence Layers to Ranked Targets

Build evidence layers from open and licensed geoscience data, run knowledge- and data-driven models, validate them, and rank exploration targets.

Key capabilities

A data catalogue of open, licence-gated and commercial geoscience sources
Live pulls of Copernicus DEM, GA/CSIRO ASTER mineral maps and fault data
Drillholes and geochemistry read from the GeoTech DataHub
Evidence Builder for prospectivity layers
Knowledge-driven models: weighted overlay, fuzzy gamma, AHP
Data-driven models: weights of evidence, logistic regression, random forest, XGBoost, PU-bagging
Spatial cross-validation
Explainable scores: SHAP contributions and evidence weights
Ranked targets, reports and exports
An AI assistant that sets up and runs models from plain-language requests

Target users

Exploration geologists
Exploration managers
Geochemists
GIS analysts
Geoscience data scientists

Product screenshots

Mineral Prospectivity in action

A closer look at the working application — the screens your team will use day to day.

Ranked exploration targets from a data-driven model, scored and listed beside the prospectivity map (demonstration data).

Enterprise evaluation

How teams should evaluate Mineral Prospectivity

A production evaluation should prove technical fit, governance, deployment model, data readiness, and workflow value before procurement.

Deployment fit

Review cloud, self-hosted, ArcGIS-connected, and open-source GIS integration options before implementation.

Governance model

Validate authentication, data access, audit logging, and review controls with technical stakeholders.

Workflow proof

Walk through the Mineral Prospectivity workflow end to end — from governed data in to the deliverable out — with validation, governance, and provenance checked at each step.

Adoption plan

Define users, training needs, rollout sequence, success metrics, and implementation responsibilities.

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Turn open data into ranked targets

Talk to us about product fit, implementation scope, integrations, and deployment support.

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Mineral Prospectivity Mapping | AI Exploration Targeting | SpatialTechSolutions