Identification of Natural Hydrogen Seeps: Leveraging AI for Automated Classification of Sub‐Circular Depressions
Н.И. Гинзбург, Jimmy Daynac, Sofyane Hesni, Ugo Geymond, Vincent Roche · Earth and Space Science · 2025
Abstract Hydrogen has long been used as an energy vector, but the recent discovery of natural hydrogen (H 2 ) opens the door for its use as a direct energy source. Identifying H 2 seepages is therefore crucial to advance exploration. Although the scientific community does not yet fully understand the parameters controlling H 2 leaks from underground, sub‐circular depressions (SCDs) appear to be key indicators associated with these emissions. However, distinguishing SCDs from similar landforms remains a challenge. This study leverages open‐source multispectral and high‐resolution imagery to train a deep learning model (YOLOv8) for classifying rounded landforms and detecting H 2 ‐related structures (i.e., SCDs). The model achieved 90% accuracy with Google Maps© imagery, outperforming Sentinel‐2 multispectral data. Applied to a pre‐existing data set from Brazil, the model allowed a large‐scale screening, discarding 52% of the structures as non‐H 2 emitting ones and pinpointing high‐potential areas for field validation. Future enhancements, including, for example, higher‐resolution input data and morphometric analysis, would aim to reduce false positives and boost predictive accuracy. This approach significantly improves H 2 exploration efficiency, with global applicability including some region‐specific adjustments during post‐processing analyses.