Unlocking Morocco's AI-Native Mineral Exploration
Blending AI and geoscience, we fast-track the search for materials driving the ecological transition across Morocco’s mineral corridors.
Mining in Morocco
A key contributor to the global supply of critical minerals — Morocco couples geological diversity with world-class infrastructure and policy frameworks that attract sustainable exploration investment.
#1 Mining Destination in Africa
Ranked first in Africa for mining investment attractiveness (Fraser Institute, 2024). Stable governance, advanced geology, and renewable power integration position Morocco as the continent's safest exploration environment.
Critical Metals Hub
Morocco hosts over 70 metallic deposits — including cobalt, copper, silver, manganese, and rare earths — concentrated along the Anti-Atlas, Saghro, and Rif belts.
Gateway to Europe
Morocco's deep-water ports (Tanger Med, Safi) and renewable power targets (52% by 2030) support low-carbon mineral supply chains into European gigafactories.
Most Attractive Mining Jurisdictions in Africa (2024)
Source: Fraser Institute Mining Investment Attractiveness Index, 2024
Morocco's mining districts and mineral deposits across the Anti-Atlas, High Atlas, and Rif geological belts.
Critical metal market insights powering Mirage's strategy
Mirage Exploration aligns its focus with the world's fastest-growing materials for the energy transition. From cobalt batteries to copper grids, each mineral we target supports decarbonised infrastructure.
Demand for cobalt is expected to double from 190,000 tonnes in 2023 to nearly 390,000 tonnes by 2030 (IEA). Used primarily in EV and grid-scale batteries, cobalt ensures stability and energy density in lithium-ion cathodes.
- 65% of cobalt demand from EV batteries by 2030
- Key applications: EV batteries, renewable storage, aerospace alloys
- Main producers: DRC (70%), Indonesia (10%), Morocco (<2%)
Cobalt - Powering Electric Mobility
Projected demand growth • Unit: kt
Source: International Energy Agency (IEA), McKinsey Energy Insights, Benchmark Mineral Intelligence
How Mirage transforms Morocco's geodata into predictive intelligence
Mirage's exploration engine fuses multi-source inputs into a continuously updating prospectivity model. Scroll the workflow to see the field-to-cloud loop that powers our discovery pace.
Data Fusion
ONHYM databases, government archives, and ASTER, Sentinel, and Landsat feeds collapse into a governed lake.
Feature Engineering
Multi-source aeromagnetism, conductivity, gravity, seismic, and geochemistry data combined with structural and geological studies computed for every kilometre cell. ONHYM aeromagnetic surveys, airborne electromagnetics, and satellite-derived lineaments create comprehensive feature vectors.
Grid Scoring
Prospectivity grids weight evidence layers to prioritise drill-ready km² tiles.
Learning Modes
Supervised models recognise proven signatures while unsupervised clustering reveals unknown anomalies.
Field Loops
UAV, geochemistry, and mapping campaigns create on-field feedback loops that validate or confirm model predictions, continuously improving its performance over time.
Learning Pathways
Known mineral systems reinforce the models while anomaly-centric discovery flags emerging targets for field validation.
Mirage feature vectors
Mirage's models transform multi-source geological, geochemical, and remote sensing data into predictive feature vectors used to prioritise drill-ready targets.
From archives to live prospectivity maps
Mirage orchestrates a vertical data stack: historical intelligence, satellite refresh, generative modelling, and actionable prospectivity outputs tightly coupled with geologist review.
Archives
Digitized ONHYM and BRGM reports, drill logs, and artisanal mining records, combined with geophysical, geochemical, and lithological data.
Satellite
ASTER, Sentinel-1/2, Landsat feeds refreshed for each modelling pass.
ML Models
The model is trained on POCs to recognize known mineral signatures through supervised learning, while also identifying previously unknown anomalies using unsupervised methods.
Prospectivity Maps
Dynamic heatmaps that guide drilling, ESG, and capital allocation decisions.
Cobalt, copper, zinc, and silver intelligence in motion
Mirage's models learn Morocco's metallogenic DNA, surfacing the next cobalt, copper, zinc, and silver targets with quantified confidence and ESG context.
Cobalt Corridors
Bou Azzer style cobalt-copper veins anchor supervised labels and transfer signatures into under-sampled belts.
Copper Belts
Stratabound and structural copper systems are modelled with elevation, alteration, and fluid pathway features.
Zinc Horizons
Prospective zinc lenses emerge where structure and geochemistry intersect across Anti-Atlas sediments.
Silver Districts
Silver-bearing quartz veins such as Imiter and Zgounder drive anomaly detection for precious metal extensions.
Proof of-concept (POCs) districts powering the Mirage atlas
Four pilot districts feed supervised learning and rapid field loops, letting Mirage benchmark predictions before scaling across Morocco's belts.
Bou Azzer
Bou Azzer is a world-class cobalt deposit hosted in Neoproterozoic ophiolites, making it one of the few primary cobalt mines globally.
Imiter
Imiter is one of the largest silver deposits in Africa, formed as an epithermal vein system within Precambrian volcanic and sedimentary rocks.
Zgounder
Zgounder is a high-grade silver deposit of epithermal origin, hosted in Proterozoic metasedimentary rocks of the Anti-Atlas region.
Jebilet
The Jebilet massif is a volcanogenic massive sulfide (VMS) district in Morocco, characterized by copper-, zinc-, and pyrite-rich deposits formed through ancient submarine hydrothermal activity.
Machine learning inference of mineral potential zones across Morocco
Blue nodes highlight modelled potential, while glowing orange markers represent validated proof of concept zones. Layered visuals reveal depth across Morocco's belts.
Legend
Ecological exploration embedded from data to drill site
Mirage pairs AI-driven efficiency with environmental stewardship, targeting low-carbon field programs and transparent governance across every Moroccan district we engage. By using predictive models to pinpoint high-probability targets, our approach reduces unnecessary drilling, minimizes land disturbance, optimizes water and energy use, and promotes data-driven, responsible exploration that preserves ecosystems while accelerating discovery.
Environmental
- AI-driven targeting reduces drilling by 60-80% compared to traditional exploration
- Hydro and biodiversity baselines logged before each activation
Social
- Community procurement and training loops in mining provinces
- Transparent engagement with cooperatives on data usage and access
Governance
- Traceable model updates with audit trails for regulators
- Ethical sourcing standards aligned with EU battery directives