AI helps pinpoint hidden sources of underground 'gold hydrogen'

Researchers at Ohio State University have developed a deep learning model to scan the Earth for surface expressions of subsurface reservoirs of naturally occurring free hydrogen.

Green hydrogen production concept
Green hydrogen production concept - AdobeStock

Researchers used the algorithm to help narrow down the potential locations of ovoids or semicircular depressions (SCDs) in the ground that form near areas associated with natural or ‘gold hydrogen’ deposits.

Though these circular patterns often appear in areas of low elevation and can be hidden by agriculture or other vegetation. Recent discoveries of these circles in the US, Mali, Namibia, Brazil, France and Russia have revealed that they exist in greater numbers than previously thought.

Lead researchers Sam Herreid and Saurabh Kaushik, both postdoctoral scholars at the Byrd Polar and Climate Research Centre, Ohio State, combined their model with global satellite imagery data to identify SCDs.

Research teams compiled a list of known SCD locations to train the search algorithm. After using remote sensing data to analyse what these sites look like from above, they drew on geomorphic and spectral patterns to determine which sites around the world are most likely to be associated with SCDs related to geologic hydrogen.

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