Fleet Space Technologies

Machine learning meets real-world exploration! From 24,400 copper assays to nine new targets and a decision to put 5,000 metres of drilling behind them. At Buen Retiro in Chile, we combined Fleet Space Technologies ’s 3D seismic imaging with Fitzroy Minerals Inc. (TSX-FTZ, OTC-FTZFF) geological and geophysical data to help decide where to drill next. Drilling has now started. For me, this is the most exciting part: seeing how our predictions hold up against what comes out of the ground. Each new hole will bring fresh evidence to refine the model and guide the next drilling decision. Proud of our team and our partners at Fitzroy. Looking forward to the results. Full announcement below 👇 https://lnkd.in/gpNZXhrW

The discovery gap is not just a geology problem. It is a decision problem. As exploration moves deeper and under cover, questions about geometry, depth and continuity are increasingly unresolved when drilling capital has to be committed. The Seismic Shift looks at how seismic-led 3D structural constraint changes what teams can know before the most expensive test. Drilling.

Most programs already have enough data to generate targets. The harder question is which interpretation deserves capital. As targets move deeper and beneath cover, gravity, magnetics, MT and geochemistry can still show a response, but multiple geological interpretations may fit the same signal. Seismic adds the structural constraint, helping narrow those possibilities before drilling capital is committed. The Seismic Shift explores why structural confidence is becoming critical to better exploration and resource decisions.

Drilling proves the rock at each intercept. It does not resolve the geometry between them. Once mineralisation is confirmed, contacts, fault offsets, continuity and open margins between holes still have to be interpreted. High-resolution active seismic can constrain that geometry in 3D, helping teams direct additional metres to the uncertainty that can still change the model. The Seismic Shift follows seismic from project generation through resource delineation.

Most teams are not short of data. They have years of gravity, magnetics, electrical surveys, geochemistry and drilling. The harder question is whether those datasets provide enough structural context to support the next decision. The Seismic Shift white paper examines what changes when existing data is constrained against a depth-resolved 3D structural framework, and why that matters as targets move deeper and beneath cover.

Machine learning can find patterns in almost any dataset. The harder question is whether those patterns are geologically meaningful. Fleet’s ML operates inside an integrated 3D structural model, grounded against drilling, with quantified uncertainty and traceability back to the evidence. Without seismic and structural constraint, pattern recognition finds correlations. With it, you can produce ranked targets your team can interrogate, defend and drill.