By Annie Sneed
Predicting earthquakes is the holy grail of seismology. After all, quakes are deadly precisely because they’re erratic—striking without warning, triggering fires and tsunamis, and sometimes killing hundreds of thousands of people. If scientists could warn the public weeks or months in advance that a large temblor is coming, evacuation and other preparations could save countless lives.
So far, no one has found a reliable way to forecast earthquakes, even though many scientists have tried. Some experts consider it a hopeless endeavor. “You’re viewed as a nutcase if you say you think you’re going to make progress on predicting earthquakes,” says Paul Johnson, a geophysicist at Los Alamos National Laboratory. But he is trying anyway, using a powerful tool he thinks could potentially solve this impossible puzzle: artificial intelligence.
Researchers around the world have spent decades studying various phenomena they thought might reliably predict earthquakes: foreshocks, electromagnetic disturbances, changes in groundwater chemistry—even unusual animal behavior. But none of these has consistently worked. Mathematicians and physicists even tried applying machine learning to quake prediction in the 1980s and ’90s, to no avail. “The whole topic is kind of in limbo,” says Chris Scholz, a seismologist at Columbia University’s Lamont–Doherty Earth Observatory.
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