Fitzroy Minerals Inc. has announced the results of an integrated seismic and machine-learning targeting study completed with Fleet Space Technologies at its Buen Retiro Copper Project in Copiapo, Chile. The study generated nine ranked, previously untested target areas within 400 metres of surface, marking a significant step toward testing the sulphide potential of a property that the company believes shares geological characteristics with major iron oxide copper-gold deposits such as Candelaria and Mantoverde.
The importance of the announcement lies in the application of advanced technology to overcome a longstanding exploration challenge: extensive alluvial gravel cover that makes traditional surface mapping and target generation difficult. Fleet’s Ambient Noise Tomography surveys mapped the project’s deeper geology well beyond the depths of current drilling, providing a 3D framework to trace known mineralized trends and identify new opportunities. The machine-learning model combined this 3D subsurface data with prior geophysical surveys, geological models, and approximately 24,400 copper assays from 140 drill holes to predict zones where copper mineralization above a 0.5% Cu threshold might be located.
According to the company, the targets focus on gravel-covered geology and exclude previously mined ground or areas within 200 metres of an existing drill hole, ensuring that exploration efforts are directed at genuinely untested ground. A summary of the four datasets and copper assay results used in the new ML targeting is available Click Here to see Table 1 - Summary of the 4 datasets and copper assay results used in the new ML targeting..
Merlin Marr-Johnson, President and CEO of Fitzroy, commented that the company now has a tool to test the exploration optionality of a large IOCG system in one of Chile’s most prolific copper-producing regions. He noted that if the ML targeting is successful, it will be a cost-effective discovery tool creating value for shareholders. Fitzroy has already started a 5,000-metre drilling program using diamond and reverse circulation rigs to test the new targets. As drill logs and assay results come in, Fitzroy and Fleet will update the ML model, so each new hole serves three purposes: testing a specific target, improving or validating the model, and informing the next drilling decision.
The technical method followed four steps: prediction, refinement, domain grouping, and drill planning. The ML model learned the relationship between geophysical data and copper assays from existing drill holes to estimate a quantified uncertainty for any point in space. Blocks were then removed if they fell within the mined-out open pit, mapped gravel cover, or within 200 metres of an existing drill hole. The remaining blocks were grouped into nine geographic domains with similar predicted responses. The targets are conceptual and have not yet been tested by drilling; they do not confirm the presence, grade, size or continuity of mineralization. The reliability of the model depends on the quality and coverage of the underlying data and tends to decrease with distance from existing drill holes and with depth.
The collaboration with Fleet Space Technologies, an Australian frontier technology company that uses seismic-led 3D subsurface imaging, represents a notable integration of machine learning into mineral exploration. The news was disseminated via NEWMEDIAWIRE. For Fitzroy, the announcement signals a shift beyond the oxide copper mineralization that has been the focus to date, opening the door to testing the broader sulphide potential of the property. The company’s shares are listed on the TSX Venture Exchange under the symbol FTZ and on the OTCQX under FTZFF.
