The new intelligence approaches provide robust time and cost-efficient way to analyze the mineral exploration related spatial data to produce mineral prospectivity maps with uncertainty estimates, create better satellite data to be used in mineral prospectivity mapping or to estimate grain size of drill core in exploration and production drilling in mines.

Essential exploration techniques include geological, geochemical and geophysical surveys. These techniques allow us to locate and thoroughly investigate geological processes responsible for mineral deposits and the indications of ore forming processes.

The same techniques can also be applied for direct identification of mineral deposits if the sampling density is high enough. Drilling is the ultimate identification method. Mineral exploration is stepwise procedure, where the sampling density gets higher in each step.

There is a need for quantitative computer techniques that can aid in decision-making process and target selection in mineral exploration. Neural networks, fuzzy systems and machine learning algorithms can be used as a tool in this.

The main aim of this project and consortium is to make mineral exploration more efficient and thus save time and money spent in the exploration phase.

Our aim is to enhance the various steps in mineral exploration so that the time used will be reduced dramatically, even by 50%.

The use of machine learning algorithms in exploration data integration, pattern recognition from geophysical data, drill core image enhancing, or hyperspectral data processing will be examples of the usage of this modern technology that will bring mineral exploration to a new level.

The project aims to develop the use of artificial intelligence in aiding mineral exploration techniques and –concepts to locate new areas and targets within under-explored areas, greenfield areas, or alternatively within data-rich, well-explored brownfield exploration terrains with lots of data available.

The main objective of the AIMEX consortium is to integrate knowledge from scientific innovations amongst machine learning, mineral prospectivity analysis, sustainability research, drill core imaging, drill core scanning and into smart software applications for mineral potential and mining feasibility mapping for exploration.

The novel approach provides a robust time and cost-efficient way to analyze the mineral exploration related spatial data to produce mineral prospectivity and mining feasibility maps with uncertainty estimates.