Imagine trying to find a needle in a haystack, but the needle is a rare metal needed for electric cars. Instead of digging random holes, this project uses smart computer models and soil chemistry to pinpoint exactly where to look. It also figures out how to do this without upsetting local communities or damaging nature.
The EU lacks raw material autonomy, with domestic supply for many critical metals below 3%. Meanwhile, the green transition is driving a demand surge that will outstrip supply by over 100% in ten years.
Machine learning prospectivity and resource models, surficial geochemistry exploration methods, and social media analysis tools for community sentiment.
If you are a mineral exploration firm dealing with high failure rates in finding new deposits — this project developed machine learning prospectivity modelling that identifies high-potential areas for Ni, Cu, Co, V, Ti, Cr and PGE. This reduces the risk and cost of drilling in the wrong locations.
If you are an EV battery producer dealing with a supply chain where demand will exceed supply by more than 100% within a decade — this project developed a map of EU exploration potential for Cobalt and Nickel. This helps secure a domestic, stable source of raw materials.
If you are an ESG consultancy dealing with local opposition to mining projects — this project developed machine learning based language analyses of social media to understand public sentiment. This allows for more socially friendly exploration methods and better community engagement.
Based on available project data, no specific pricing or cost for the developed tools is provided.
The project focuses on technology development validated at small scales, with the goal of increasing domestic supply across the EU and third countries.
Based on available project data, there is no specific information regarding IP licensing or patent status.
The project runs from 2022-06-01 to 2025-10-31, with current activities focused on data modelling and interpretation.
It integrates via the Mineral Systems Approach, providing refined ore deposit models and surficial geochemistry methods to guide regional and local exploration.
The consortium is well-balanced for technology transfer, consisting of 19 partners across 12 countries. With a 32% industry ratio (6 companies, including 4 SMEs), there is a strong bridge between the 8 universities and 5 research centers and the commercial market, ensuring that the geological models are grounded in industrial reality.
Contact OULUN YLIOPISTO in Finland
Contact us to connect with the SEMACRET consortium for early access to prospectivity models.
