Global-scale AI-powered prediction of hydrogen seeps

International audience Natural hydrogen (H2) holds promising potential as a clean energy source, but its exploration remains challenging due to limited knowledge and a lack of quantitative tools. In this context, identifying active H2 seepage areas is crucial for advancing exploration efforts. Here, we focus on sub-circular depressions (SCDs) that often mark high H2 concentration in soils, thought to correspond to deeper fluxes seeping at the surface, making them promising targets for exploration. Coupling open-access Google Earth© images and in-field H2 measurement data, an artificial intelligence model was trained to detect seepage zones. The model achieves an average precision of 95 %, detects and maps seepage zones in new regions like Kazakhstan and South Africa, highlighting its potential for global application. Moreover, preliminary spatial analyses show that geological features control the distribution of H2-SCDs that can emit billions of tons of H2 at the scale of a sedimentary basin. This study paves the way for a faster and more efficient methodology for selecting H2 exploration targets.