White hydrogen spotting becomes artificial intelligence’s latest mission
Researchers have developed a model to help identify underground sources of naturally occurring H2. Ohio State University researchers have developed a new artificial intelligence model capable of scanning the surface of the Earth for signs that white hydrogen reserves are hidden below. The deep learning algorithm was used to spot possible indicators that these natural H2 deposits were present. The researchers applied the AI to narrow down the possible locations where semicircular depressions (SCDs) in the ground could suggest the presence of white hydrogen deposits below. SCDs and ovoids are c…
Researchers have developed a model to help identify underground sources of naturally occurring H2.
Ohio State University researchers have developed a new artificial intelligence model capable of scanning the surface of the Earth for signs that white hydrogen reserves are hidden below.The deep learning algorithm was used to spot possible indicators that these natural H2 deposits were present.
The researchers applied the AI to narrow down the possible locations where semicircular depressions (SCDs) in the ground could suggest the presence of white hydrogen deposits below. SCDs and ovoids are common ground formations around deposits of natural H2. That said, they don’t always indicate that H2 is present, so the AI was used to help narrow down the results to those that were the most promising. The AI was also used to help find the locations in the first place, as the SCDs frequently appear in areas of low elevation, which means that it is easy for vegetation or even agriculture to disguise them.White hydrogen below SCDs have been found in the US, Namibia, Brazil, Russia, France and the US.
These discoveries suggest that naturally occurring H2 is more commonplace than had previously been thought. Not long ago, the common belief was that H2 reserves underground were exceptionally rare and would be too difficult to find and access to make them a worthwhile source of what is now being viewed as a clean carbon-free fuel.
The researchers who developed the AI were led by Byrd Polar and Climate Research Center postdoctoral scholars Sam Herreid and Saurabh Kaushik from Ohio State University. They combined data from their artificial intelligence model with global satellite imagery to spot SCDs that were potential indicators of white hydrogen deposits.
The researchers accomplished this by compiling a list of the known locations of SCDs, so they could train the artificial intelligence search algorithm. Remote sensing data was used to conduct an analysis of how those sites appeared from above. Once they had that information, they used geomorphic and spectral patterns to identify the locations worldwide that were most likely to have SCDs that would also be indicators of the presence of white hydrogen below.