Johnson and collaborator Chris Marone, a geophysicist at The Pennsylvania State University, have already run lab experiments using the school’s earthquake simulator. The simulator produces quakes randomly and generates data for an open-source machine-learning algorithm—and the system has achieved some surprising results. The researchers found the computer algorithm picked up on a reliable signal in acoustical data—“creaking and grinding” noises that continuously occur as the lab-simulated tectonic plates move over time. The algorithm revealed these noises change in a very specific way as the artificial tectonic system gets closer to a simulated earthquake—which means Johnson can look at this acoustical signal at any point in time, and put tight bounds on when a quake might strike.
For example, if an artificial quake was going to hit in 20 seconds, the researchers could analyze the signal to accurately predict the event to within a second. “Not only could the algorithm tell us when an event might take place within very fine time bounds—it actually told us about physics of the system that we were not paying attention to,” Johnson explains. “In retrospect it was obvious, but we had managed to overlook it for years because we were focused on the processed data.” In their lab experiments the team looked at the acoustic signals and predicted quake events retroactively. But Johnson says the forecasting should work in real time as well.
If this method succeeds, he thinks it is possible experts could predict quakes months or even years ahead of time. “This is just the beginning,” he says. “I predict, within the next five to 10 years machine learning will transform the way we do science.”