Combining the data from a multitude of geophysics sensors across the globe could enable automated monitoring for low-yield nuclear detonations.
When an oil refinery in Philadelphia exploded in June 2019, physicist Joshua Carmichael was at home in Los Alamos, New Mexico, taking care of his wife after a surgery. He’d been spending his days at Los Alamos National Laboratory developing a tool that could determine the likelihood that a given set of “signals” represented an explosion—or at least he hoped it could. He flipped open his laptop and started sucking in data sources: information from air and seismic sensors, observations from meteorological satellites, even social media reports of shaking. That afternoon, his system reported back: There was a 99% chance that the Philadelphia event had been an explosion.
Carmichael knew that, of course; he’d begun with that information. But the mishap was a test of his algorithms, to see if they would work on real-world data.
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Source: Physics Today
