Imagine trying to find a needle in a haystack, but the haystack is the size of Europe. Instead of digging random holes and hoping for the best, this system uses smart computer patterns to predict exactly where valuable minerals are hidden. It's like using a high-tech metal detector that analyzes old maps and soil data to point to the most promising spots.
Mineral exploration is currently plagued by high early-phase costs and low targeting accuracy, leading to wasted resources and unnecessary environmental damage.
An EIS Toolkit and a QGIS Plugin for mineral prospectivity mapping, utilizing AI and deep learning.
If you are a mineral exploration company dealing with high early-phase exploration costs — this project developed an AI toolkit and QGIS plugin that improves targeting accuracy. This reduces the number of unnecessary drill sites and minimizes the environmental footprint.
If you are an environmental consultancy dealing with the ecological impact of mining surveys — this project developed data analysis methods that make exploration more responsible. By maximizing existing data, it reduces the physical footprint on nature.
If you are a secondary raw materials processor dealing with inefficient recovery of critical metals — this project developed tools tested for secondary raw materials prospectivity. This helps identify high-value waste streams for recycling.
Based on available project data, the system applies AI and machine learning to improve the accuracy of early-phase targeting, which reduces the high costs associated with unsuccessful exploration attempts.
The project has developed an EIS Toolkit and a QGIS Plugin, which are implemented in public software repositories, suggesting a move toward scalable digital tools.
Based on available project data, the tools have been implemented in public software repositories, but specific commercial licensing terms are not detailed.
The project applies the UNFC code to harmonize diverse populations of mineral deposits and occurrences, ensuring data consistency for training AI models.
The project period runs from 2022-05-01 to 2025-10-31.
The consortium is heavily weighted toward industrial application with a 44% industry ratio, comprising 8 industry partners (including 3 SMEs) and 10 research/academic entities. This balance ensures that the AI tools are developed with commercial utility in mind, covering key metallogenic belts across 8 countries, including strategic reference sites in Brazil and South Africa.
Contact GEOLOGIAN TUTKIMUSKESKUS in Finland
Contact us to explore the EIS Toolkit for your mineral exploration pipeline.
