Predicting laboratory earthquakes using physics-informed artificial intelligence

UNIVERSITY PARK, Pa. — By improving an artificial intelligence approach to predicting laboratory earthquakes, or earthquakes, engineers at Penn State are paving the way to one day help predict natural earthquakes.

“We are far from predicting natural earthquakes, but understanding the physics of earthquakes and how they evolve allows us to better understand the mechanisms of real earthquakes,” said Parisa Shokohi, a professor of engineering science and acoustics at Penn State and corresponding author. From 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.”

To monitor earthquakes in the field, instruments are placed on the Earth's surface, far from the depth at which earthquakes typically occur, which Shokohi said forces scientists to make simplifying assumptions. Earthquakes, on the other hand, are produced under tightly controlled conditions, allowing scientists to make detailed measurements of every aspect of the experiment. Researchers create laboratory earthquakes by moving blocks of rock together — known as friction experiments — to generate the laboratory equivalent of earthquakes, or baton slides, which they then monitor using ultrasonic transducers.

The team has developed a machine learning model for earthquake prediction, which can also automatically retrieve specific parameters – known as friction coefficients of rate and state – from ultrasonic monitoring of slip experiments. The rate and state friction parameters determine earthquake mechanisms; It determines the strength of the fault, indicating how close it is to failure. To estimate these parameters, the team developed a Physics-Based Neural Network (PINN) model—a modified machine-learning algorithm that incorporates the rate-and-state law of friction—to predict when an fault might fail and produce a laboratory earthquake.

The PINN model has the same or better accuracy of rateless and frictionless networks, as well as the ability to predict future earthquakes. This is because, according to the researchers, a broader understanding of physics provides a broader explanation than being limited to the details of a particular set.

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