UNIVERSITY PARK, Pa. — For the first time, the magnitude, time and duration of earthquakes in a laboratory setting were predicted by a team of researchers from Penn State and Los Alamos National Laboratory.

This research improves our understanding of earthquakes and could eventually lead to prediction measures in real-life scenarios, according to the researchers who published their results in a recent issue of Nature Geosciences.

For decades, researchers have been able to create earthquakes in a lab setting. However, finding a pattern of when they will occur has remained elusive. For this work, researchers used machine learning — a computer-based approach to data analysis — to probe for the missing cues.

The machine learning approach predicted when the earthquakes would strike by looking at acoustic signals generated each time the earthlike medium moved. The acoustics showed a pattern of increasing intensity as the lab earthquakes ramped up in strength. These acoustical signals have long been considered noise, especially early in the earthquake cycle, because a pattern proved difficult to find.

"You can make earthquakes in the lab in such a regular fashion that you can kind of predict them but that's not what was happening," said Chris Marone, Penn State. "Machine learning keyed us into listening to the sounds that are coming out of the fault. The method tells us when the next earthquake will occur and how long until the following one comes."

A series of earthquakes was created in the lab by sandwiching a layer of sheared granular material between two pieces of rock. The researchers created both slow- and fast-slip earthquakes to understand if the mechanics of each were similar.

Fast-slip earthquakes, which are thought of as traditional earthquakes, operate at high speeds and can cause massive amounts of damage to structures. Slow-slip earthquakes operate in a similar fashion but at speeds hundreds to thousands of times slower. For example, a fast-slip earthquake might last for seconds as fault surfaces rip past one another. However, a slow-slip event could take months to cover the same distance along fault lines. Traditional earthquakes have been studied for many decades yet slow earthquakes were just identified about two decades ago.

This research shows that the mechanics of each, and the methods for prediction, are the same.

Seismometers capture acoustic signals, so most areas susceptible to earthquakes create the same data used in these lab predictions. However, researchers do not yet know if the same methods will work in a real-life setting because the current approach requires a series of similar earthquakes to train the machine learning algorithm. This method could work in places such as the Cascadia subduction zone in the U.S. Northwest, where a string of earthquakes has occurred within the past decade, however most earthquake-prone regions are less active.

"It's not clear if our technique will work in the field because there are very few places on Earth where we have had multiple, similar earthquakes in the same place, but we're going to find out" Marone said. "Most times, the earthquakes on a tectonic plate boundary are all different. A famous example is the San Francisco earthquake of 1906. That hasn't occurred since."

Image Researchers do not yet know if the same methods will work in a real-life setting because the current approach requires a series of similar earthquakes to train the machine learning algorithm. IMAGE: Penn State

Looking at any prediction measures could point researchers in the right direction, Marone said. His group is now applying the same machine-learning techniques to probe other ways of predicting earthquakes. This new research could yield methods that require less data.

"Right now, we're taking our lab data and applying different machine learning approaches to see if we can spot some transitions or characteristics that yield the same results," Marone said.

Claudia Hulbert, Bertrand Rouet-Leduc, Paul Johnson and Christopher Ren, of Los Alamos National Laboratory, and Jacques Rivière and David Bolton, of Penn State, contributed to this research.

The U.S. Department of Energy and the National Science Foundation supported this research.