Turn Fragmented Geological Data Into an Exploration System

The problem

Exploration information can be spread across decades of geological reports, maps, drilling campaigns, geochemistry, geophysics, satellite imagery and different databases, often in incompatible formats built at different times by different teams.

The challenge isn't having more data. It's making the datasets that already exist usable together.

What we deploy

A unified exploration environment that combines these geological datasets and applies machine learning where it's useful: to identify patterns, analyze anomalies, and help prioritize exploration targets.

How it works

Mirage engineers work with geoscience teams to inventory and normalize existing datasets, historical reports, drilling logs, geochemical and geophysical surveys, satellite and GIS layers, into a single environment. ML models are applied where they add real value: flagging geochemical or geophysical anomalies, correlating patterns across datasets that were previously reviewed in isolation, and helping rank areas for further exploration.

Operational interface

This is decision-support infrastructure for exploration teams, not a claim that the system finds minerals on its own.

Systems connected

Operational outcomes

Exploration teams get one environment to work from instead of reconciling reports, drilling logs and imagery manually across separate tools, with anomaly analysis and target ranking to focus further fieldwork.

How it adapts

The datasets available, and the ML applied to them, vary by commodity and by exploration stage. The system is scoped to the data your geoscience team actually has and the targets you're actually trying to prioritize.

FAQ