By refining an artificial intelligence approach to predicting earthquakes in the laboratory, or labquakes, engineers at Penn State are paving the way to one day help forecast natural earthquakes.
"We are a long way off from predicting natural earthquakes, but understanding the physics of labquakes and how they evolve allows us to better understand the mechanics of real earthquakes," said Parisa Shokouhi, Penn State professor of engineering science and of acoustics and corresponding author of the work published in Scientific Reports. "We can study the precise conditions under which a gently creeping fault suddenly becomes unstable and triggers an earthquake, such as the amount of stress, the roughness of the fault or the role of small loose rock particles at the interface, to name a few possibilities."
To monitor earthquakes in the field, instruments are placed at the Earth's surface, far away from the depth where earthquakes typically occur, which Shokouhi said forces scientists to make simplified assumptions. Labquakes, on the other hand, are produced under tightly controlled conditions, allowing scientists to make detailed measurements on every aspect of the experiment. Researchers create labquakes by sliding together blocks of rock - known as friction experiments - to generate the laboratory equivalent of earthquakes, or stick-slips, that they then monitor with ultrasonic transducers.
The team developed a machine learning model for labquake prediction that can also automatically retrieve specific parameters - known as rate and state friction parameters - from the ultrasonic monitoring of stick-slip experiments. The rate and state friction parameters define the mechanics of the labquakes; they determine the strength of the fault, signaling how close it is to failure. To estimate these parameters, the team developed a physics-informed neural network (PINN) model - a modified machine learning algorithm that incorporates the rate and state friction law - to predict when the experimental fault might fail and produce a labquake.
The PINN model has the same or better accuracy as networks that do not incorporate the rate and state friction law, as well as the ability to predict labquakes further into the future. This is because, according to the researchers, the broader understanding of physics informs a wider interpretation than limiting it to the specifics of a particular experimental set up.
