Mumbwa Copper–Gold Project (Zambia) – 3D AI Modelling

  • Work Conducted: Spectral inversion, gas anomaly modelling (CH₄, He), 3D block modelling

  • Commodities: Cu–Au–Co–REE

  • Results:

    • Mineralised zones defined to 500–600 m depth

    • IOCG style Cu-Au system identified

    • Multiple drill-ready targets generated

  • Outcome:
    Identification of district-scale mineral system with tier-one e

Technical Value:

  • Integration of surface and deep indicators into a unified predictive model

  • Ability to define vertical continuity and geometry of mineralised systems

  • Reduction of exploration uncertainty through multi-layer AI correlation

Outcome:
The project resulted in the definition of a district-scale mineral system with tier-one exploration potential, supported by a robust 3D geological and predictive framework.
The outputs provide a clear pathway for follow-up exploration, including targeted geophysics (IP, GPR/Loza) and optimised drilling programs focused on the highest-probability zones.

Geological Context:
The project area is located within the Mwembeshi Shear Zone, a region known for hosting structurally controlled Cu–Au systems and IOCG-style mineralisation. Mineralisation is interpreted to be associated with deep-seated hydrothermal systems linked to intrusive activity, with both oxide and sulphide expressions observed.

Key Results:

  • Mineralised zones delineated to depths of 500–600 m, supported by integrated spectral and gas anomaly modelling

  • Clear identification of IOCG-style Cu–Au mineralisation, with associated Co and REE signatures

  • Strong correlation between methane and helium anomalies and deep sulphide systems, indicating active fluid pathways

  • Mapping of structural corridors and fault intersections controlling mineralisation distribution

  • Identification of both:

    • Near-surface oxide zones (malachite, chrysocolla)

    • Deeper sulphide mineralisation (chalcopyrite, arsenopyrite, cobaltite)

  • Generation of multiple high-confidence, drill-ready targets, ranked by probability and geological consistency

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Asankrangwa Gold Project (Ghana) – MRE

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Rufunsa Gold Project (Zambia) – Integrated AI + GPR Targeting