Fitzroy Minerals Inc. (TSXV: FTZ, OTCQX: FTZFF, FSE: C3Y) has announced the results of an integrated seismic and machine-learning (ML) targeting study completed with Fleet Space Technologies at its Buen Retiro Copper Project in Copiapo, Chile. The study produced a ranked portfolio of nine new, untested exploration targets within 400 metres of surface, all located beneath extensive alluvial gravel cover that has historically complicated surface mapping and target generation.
The ML model combined 3D Ambient Noise Tomography (ANT) data with prior geophysical surveys, geological models, and approximately 24,400 copper assays from 140 drill holes. Fleet’s regional and infill ANT surveys used natural seismic noise to build a 3D picture of the rock beneath the surface, mapping the project’s deeper geology well beyond the depths of current drilling. The model identified nine areas where subsurface characteristics closely resemble the signature of rocks hosting Buen Retiro’s known copper mineralization. The targets exclude previously mined ground and areas within 200 metres of an existing drill hole, focusing on ground with genuine exploration value.
Fitzroy has 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 full dataset summary and copper assay results used in the targeting are available via 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, said the company believes Buen Retiro shares many geological characteristics of the major iron oxide copper-gold (IOCG) deposits at Candelaria and Mantoverde, yet the sulphide potential of the wider property has barely been tested. To date, the oxide copper mineralization and its potential to support a low-capital-intensity, near-term production operation have been the focus. “Now, however, we have a tool with which to test the exploration optionality of this large IOCG system in one of Chile’s most prolific copper-producing regions,” Marr-Johnson said, adding that if the ML targeting is successful it will be a cost-effective discovery tool creating value for shareholders.
The news matters for the raw materials mining sector because it demonstrates how machine learning and seismic imaging can be applied to mature copper districts where gravel cover has obscured traditional exploration. Copper is a critical input for electrification and renewable energy infrastructure, and cost-effective discovery methods could help unlock new supply in a prolific producing region. The announcement was distributed via NEWMEDIAWIRE.
The targets are conceptual exploration targets that have not yet been tested by drilling and 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. Fitzroy notes the targets are intended to be used alongside conventional geological interpretation, not in place of it. Dr. Scott Jobin-Bevans, a Qualified Person as defined by NI 43-101, has reviewed and approved the technical information in the release.
