Project description
New mapping to prioritise mineral prospective areas
Mineral prospectivity mapping is a data-driven or knowledge-driven approach used to make better use of mineral exploration data. It helps outline and prioritise prospective areas for exploring undiscovered mineral deposits. The EU-funded EIS project will develop new data analysis methods. Specifically, it will merge artificial intelligence, machine learning and deep learning, with new geomodels and mineral systems modelling. This will reduce exploration costs and improve the accuracy of the targeting of the early phase exploration. EIS aims also to emphasise the importance of critical raw materials to the EU's economy and welfare. Bringing together 17 partners from across the EU and South Africa and Brazil, the consortium represents the metal-producing regions.
Objective
Exploration Information System (EIS) proposal has been compiled by a consortium, which consists of 17 partners from leading research
institutes (4), academia (5), service providers (4) and industry (4). The consortium members come from six European Union member states (FI, FR, DE, ES, CZ, SE) and South Africa. One associate member of the consortium comes from Brazil. This consortium represents the main metal producing regions of Europe: Fennoscandian Shield, Iberian Pyrite Belt and Central European Belt. These economically most important metallogenic belts of the EU have diverse geology with evident potential for different types of new mineral resource. The mineral deposits in these belts are the most feasible sources of critical, high-tech and other economically important metals in the EU. Furthermore, the project has reference sites in South Africa and Brazil. The project consortium has also a vast international collaboration network, e.g. 50% of the Advisory Board members have been invited from outside EU.
EIS will develop new data analysis methods by applying artificial intelligence, machine learning, deep learning into mineral prospectivity mapping together with new geomodels and mineral systems modelling. Methods developed reduce the current high exploration costs and improve the accuracy of the targeting of the early phase exploration. This makes mineral exploration responsible in terms of energy efficiency or minimizing footprint of mineral exploration on nature as the aim is to make most out of the already existing exploration data. Project will apply UNFC code to harmonize the diverse population of mineral deposits and occurrences which will be used as training sites and validation data sets in prospectivity mapping for critical raw materials within EU. In addition, tools will be tested for secondary raw materials prospectivity. Project will also raise awareness of general public on the importance of critical raw materials to the EU's economy and welfare.
Fields of science
- natural sciencescomputer and information sciencesdata science
- engineering and technologyenvironmental engineeringmining and mineral processing
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencesearth and related environmental sciencesgeology
Keywords
Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
02151 Espoo
Finland