Dangerous Ground: USU Geoscientist Awarded NSF Grant to Study Earthquake Precursors

LOGAN, UTAH, U.S.—You can see dark clouds forming in the distance and prepare for inclement weather. Hurricane forecasters can pinpoint storms as they develop over oceans, track their movement and offer reasonable predictions about where disturbance is headed, how strong it will grow and when it might strike.

Not so with earthquakes, says Utah State University geoscientist Srisharan Shreedharan.

"If you could see the precursors to an earthquake, you'd have to be able to see maybe a meter of rock, moving at less than a millimeter per second some 10-20 kilometers under our feet," he says. "That isn't possible with the naked eye."

Despite decades of research and meticulous documentation of very small—millimeter-to-centimeter-scale—observations, Shreedharan says reliable, short-term earthquake forecasting remains elusive.

To address this challenge, he proposes scaling the wealth of data collected on millimeter-scale faults to the meter scale, using a custom-built, one-meter biaxial deformation "earthquake machine," along with machine learning techniques. Shreedharan, leading this effort in collaboration with Gregory McLaskey of Cornell University, was awarded about $300,000 in funding, roughly half of the total award, from a three-year National Science Foundation Earth Sciences grant to pursue this approach.

"This apparatus will enable us to adapt seismic interrogation techniques previously used on centimeter-scale experiments to meter-scale deformation experiments, allowing us to observe more realistic representations of natural fault systems," Shreedharan says. "This approach could offer significant improvements to earthquake forecasting and thereby improve efforts to mitigate seismic hazards."

Shreedharan and his students, along with McLaskey, will use active source techniques to study the mechanics of pre- and post-seismic deformation on dry and fluid-saturated meter-scale faults.

"Our goals include exploring whether robust variations in seismic wave properties can be identified before and/or after earthquakes and identifying the physical mechanisms responsible for such variations," he says.

Further, the team will assess the potential for operational forecasting of ruptures on meter-scale faults, employing machine learning to analyze the large datasets collected during their earthquake experiments.

"Earthquakes affect people on every continent, costing thousands of lives each year," Shreedharan says. "Yet they're among the least predictable natural hazards we face. With our research, we want to change this."

More information:
Research supported by National Science Foundation Earth Sciences grant.
Mary-Ann Muffoletto, PIO, Utah State University College of Science, maryann.muffoletto@usu.edu

Expert Contacts:
Dr. Srisharan Shreedharan, assistant professor, Department of Geosciences, Utah State University, srisharan.shreedharan@usu.edu; Dr. Gregory McLaskey, associate professor, Department of Civil and Environmental Engineering, Cornell University, gcm8@cornell.edu

Provided by Utah State University