Continental-Scale Prospectivity Modelling of Volcanogenic Massive Sulphide Deposits in Europe Using a Mineral-System and Explainable Machine-Learning Framework

Growing demand for critical raw materials over the coming decades underscores the need for robust, continental-scale frameworks to identify new mineral resources. Volcanogenic massive sulphide (VMS) deposits supply Cu, Zn, Pb, Au, and Ag, and they play a crucial role in meeting Europe’s growing demand for strategic raw materials. Despite Europe’s long mining history and extensive geological datasets, mineral prospectivity assessments remain largely restricted to national boundaries, limiting the ability to evaluate mineral systems that operate across regional tectonic domains. This study develops the first integrated European-scale prospectivity model for VMS by merging harmonised public geoscience datasets within a mineral-system and machine-learning (ML) framework. This work is carried out as part of the EU-funded TERRAVISION project, which aims to enhance the entire critical raw materials value chain towards implementing sustainable mining practices.

The modelling approach builds on and extends existing regional-scale frameworks by addressing several persistent challenges in regional-scale exploration. A positive–unlabelled training strategy was used to mitigate the lack of reliable negative labels, and ML models capable of estimating uncertainty, along with multiple explainability techniques, were applied. To ensure that predictors capture meaningful geological processes, both data-driven and knowledge-based feature selection were implemented. Model explainability was evaluated through three complementary approaches: (i) built-in feature importance from the ML classifier, (ii) permutation feature importance to assess the robustness of predictor influence, and (iii) SHapley Additive exPlanations (SHAP) values to quantify local and global predictor contributions. Together, these methods provide transparent, interpretable insights into the geological and geophysical variables that indicate prospectivity patterns. The model successfully identified over 97% of known VMS deposits and occurrences with spatial patterns showing strong correlation between high-probability areas and established mineralisation. Importantly, they also highlight prospective trends in regions with limited documented exploration.

The analysis highlights several metallogenic zones that exhibit geological and geophysical signatures consistent with favourable mineral-system conditions, but where known deposits are sparse. These areas represent potential greenfield opportunities at a continental scale. The study also illustrates the value of applying the mineral system concept to regional datasets. Harmonised lithological data and spaceborne geophysical data contribute significantly to mapping crustal-scale structures and tectonic domains with a history of submarine seafloor volcanic activity, a key requirement for VMS formation. More broadly, the proposed framework is transferable to other deposit types and illustrates the strategic potential of continental-scale, process-informed and explainable ML approaches to strengthening Europe’s strategic raw-material knowledge base through consistent, process-informed regional assessments.