Using AI to pinpoint hidden sources of clean energy underground

As efforts to transition away from fossil fuels strengthen the hunt for new sources of low-carbon energy, scientists have developed a deep learning model to scan the Earth for surface expressions of subsurface reservoirs of naturally occurring free hydrogen. 

Researchers used the algorithm to help narrow down the potential whereabouts 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, they can be hidden by agriculture or other vegetation. Recent discoveries of these circles in the U.S., Mali, Namibia, Brazil, France and Russia have unveiled that they exist in greater numbers than previously thought.  

To help uncover these nearly invisible semicircular depressions, two recent papers describe how lead authors , and at home in the U.S., laws like the Inflation Reduction Act are including provisions to expand the clean energy production industry.  

Despite how fast-moving the field currently seems to be, it’ll take at least a few more years before natural hydrogen reservoirs are successfully integrated as a reliable source of clean energy. To that end, what researchers should focus on now is how they should go about deepening our understanding of these hydrogen systems, said Moortgat.  

“The biggest challenge is that we need to find more SCDs and then really investigate how these things form,” he said. “Once we discover a lot more, we will be in a better position to again use AI tools to find similar ones worldwide.” 

Ian M. Howat of Ohio State was also a co-author. 

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