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

Sricharan Shridharan, a geosciences faculty member at Utah State University, prepares to conduct an experiment with graduate students, from left, Kwabena Poku-Agyemang and Alejandro Aguilar, using a specially designed “earthquake machine” for 1-meter biaxial deformation. The NSF-funded research aims to allow scientists to observe more realistic representations of natural fault systems with the goal of improving earthquake prediction. Credit: Mary Ann Movolito

LOGAN, Utah, USA – You can see dark clouds forming in the distance in preparation 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 occur.

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

“If you could see the forerunner of an earthquake, 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 seismic risk mitigation efforts.”

In Shridharan's lab in Utah State University's Department of Earth Sciences, graduate students, from left, Sapana Regmi and Lindsay Broderick, perform a shear test on a load frame to measure the shear strength of a small rock sample. One goal of the NSF-funded research is to improve earthquake modeling. Credit: Mary Ann Movolito

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, costing thousands of lives every year,” says Shridharan. “However, they are among the most unpredictable natural hazards we face. Through our research, we want to change this.”

More information: Research supported by a National Science Foundation Earth Sciences grant. Mary Ann Muffoletto, PIO, Utah State University College of Science, [email protected]

Expert Contacts: Dr. Srisharan Shreedharan, Assistant Professor, Department of Earth Sciences, Utah State University, [email protected]; Dr. Gregory McClaskey, Associate Professor, Department of Civil and Environmental Engineering, Cornell University, [email protected]

Provided by Utah State University

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