A USU geologist has been awarded an NSF grant to study earthquake precursors

You can see dark clouds forming in the distance and prepare for stormy weather. Hurricane forecasters can identify storms as they develop over the oceans, track their movement and make reasonable predictions about the direction of disturbances, how strong they will be, and when they might strike.

But that's not the case with earthquakes, says Utah State University geologist Sricharan Shridharan.

“If you could see the harbingers of earthquakes, you should be able to see a meter of rock, moving at less than a millimeter per second 10 to 20 kilometers beneath our feet,” he says. “This is not possible with the naked eye.”

Despite decades of research and careful documentation of very small observations — on the millimeter to centimeter scale — Shridharan says reliable, short-range earthquake prediction remains elusive.

To address this challenge, he proposes to scale up data collected on millimeter-scale faults to the metric scale, using an “earthquake machine” specifically designed for 1-meter biaxial deformation, combined with machine learning techniques. Shridharan, who led the effort with Gregory McClaskey of Cornell University, received nearly $300,000 in funding, nearly half of the total award, from a three-year National Science Foundation Earth Sciences Grant to pursue this approach.

“This device will enable us to adapt seismic interrogation techniques previously used in centimeter-scale experiments to metric-scale deformation experiments, allowing us to observe more realistic representations of natural fault systems,” says Shridharan. “This approach could provide significant improvements to earthquake prediction and thus improve earthquake risk mitigation efforts.”

Shridharan and his students, along with McClaskey, will use active source techniques to study pre- and post-earthquake deformation mechanisms on dry and fluid-saturated faults on a metric scale.

“Our goals include exploring whether it is possible to identify strong differences in the properties of seismic waves before and/or after earthquakes and to determine the physical mechanisms responsible for such differences,” he says.

Furthermore, the team will evaluate the feasibility of practically predicting cracks on metric-sized faults, using machine learning to analyze large data sets collected during earthquake experiments.

“Earthquakes affect people on every continent, claiming thousands of lives every year,” says Shridharan. “Yet they are among the most unpredictable natural hazards we face. Through our research, we want to change this.”

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