Geothermal Resource Classification in Catalonia (Spain) Using AI-Derived Predictions

Effective categorization of geothermal resources is essential for strategic and sustainable energy development. Despite the considerable geothermal potential in Spain, utilization remains limited, underscoring the need for advanced and practical assessment methods. This study proposes a structured framework for geothermal resource classification in Catalonia, based on a constructed matrix of features that integrates subsurface temperature, geothermal gradient, and thermal conductivity. The matrix enables resource clustering into potential classes, improving interpretability and regional assessment. The analysis employed predictions from a pretrained hybrid artificial intelligence model optimized using a modified Bat algorithm. At depths between 50 and 150 m, a 20–30% increase in temperature leads to a 30–50% rise in geothermal potential. Approximately 11.5% of sites exhibit high potential and 28.2% moderate potential, indicating the reliability of the adopted framework for geothermal energy source site prioritization. The proposed matrix offers a scalable tool for geothermal evaluation, minimizing exploration risk and supporting sustainable energy planning across diverse geological settings.