Machine learning can predict simulated earthquakes by listening to fault lines

In lab tests involving simulated tabletop earthquakes, researchers at the Los Alamos National Laboratory in New Mexico demonstrated that machine-learning technology can play a role in predicting major tremors by analyzing acoustic signals to find failing fault lines. For the experiment, earthquakes were modeled by the researchers using two large blocks of steel, which were put under stress. This resulted in them rubbing against one another like tectonic plates on the Earth's surface. The movement released energy in the form of seismic waves -- which was then analyzed by the team's artificial intelligence. "We discovered that an artificial intelligence can learn to discern a very specific pattern in the sound emitted by the fault before it ruptures," Bertrand Rouet-LeDuc, one of the researchers on the project, told Digital Trends.