Earthquakes take a dreadful human toll. Some 10,000 people die each year in earthquakes and their aftermath, but the toll can be much higher. Over 230,000 people died in the tsunami that followed the magnitude 9 quake off the coast of Sumatra in 2004; more than 200,000 died in Haiti in 2010 after the country was hit by a magnitude 7 quake; and more than 800,000 are thought to have died in a quake in China in 1556.
So a better way—any way—to forecast quakes would be hugely valuable.
Enter Bertrand Rouet-Leduc at Los Alamos National Laboratory in New Mexico and a few pals who have made a remarkable discovery. They've trained a machine-learning algorithm to spot the tell-tale signs that a laboratory earthquake is about to give way using only the sounds it emits under strain. The team is cautious about the new technique’s utility for real earthquakes, but the work opens up new avenues of research in this area.
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