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.
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 Sam Herreid and Saurabh Kaushik, both postdoctoral scholars at the Byrd Polar and Climate Research Center at The Ohio State University, combined their model with global satellite imagery data to identify SCDs.
Read more at: Ohio State University
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