Hoboken, N.J., December 8, 2025 — On Saturday December 6, 2025 Alaska was rocked by a 7.0 magnitude quake. Though not always so forceful, earthquakes happen every day. On average, about 55 of them strike daily, according to the United States Geological Survey (USGS), totaling some 20,000 annually worldwide. About once a year, one reaches 8.0 points or greater and 15 others hit within the magnitude 7 range on the Richter scale, which measures earthquakes by the energy they release. For example, in just 2025 an 8.8 earthquake offshore from the Kamchatka Peninsula in Russia, made the list of the 10 largest earthquakes ever recorded, according to USGS.
Earthquakes result in human fatalities, damaged infrastructure, economic disruptions, and can create lasting psychological trauma for individuals affected by them. They are also becoming more costly, according to the 2023 USGS and the Federal Emergency Management Agency (FEMA) report — in part because more people now live in the earthquake-prone areas. The report estimated that earthquakes cost the U.S. around $14.7 billion per year.
Knowing when major earthquakes may hit and what their outcomes may be for a particular location would help prepare better and reduce their ill-effects, but so far predicting them is not possible.
However, knowing what the earth subsurface looks like can help better assess the risks, says Kathrin Smetana, Assistant Professor in the Department of Mathematical Sciences at Stevens. “You may have layers of solid rock, or you may have sand or clay,” she says, and waves behave differently in different materials, which in turn affects the movement at the surface.
To better understand the composition of this geological layer, scientists run special simulations called Full Waveform Inversion, a seismic imaging technique that reconstructs how the subsurface looks. Then, they model synthetic earthquakes, simulating the propagation of seismic waves caused by synthetic earthquakes on a computer, and evaluate seismic waves at the locations of the seismographs on the surface. These evaluations are then compared with seismograms — graph outputs of seismographs and visual records of ground motion for real earthquakes. When, after numerous iterations, the synthetic earthquake data is close to the real seismic data, scientists have a better idea of what the subsurface looks like.
Essentially, scientists start with their best guess of how the subsurface would look in a particular region, and then keep running simulations, improving the model with every iteration until it matches the data from a real event.
“You compare the data from your computer simulation with actual data that you got from earthquakes,” says Smetana. “This allows you to find out what the subsurface looks like and what effect an earthquake has on the composition of the subsurface — and that ultimately, helps determine the risk for an earthquake at a certain location.”
That technique is key for developing monitoring and prediction tools, but it requires running thousands of simulations that use millions of input parameters, which takes a long time and is resource intensive. “With existing techniques, just one single simulation may last several hours on a powerful computer cluster,” says Smetana. “Running so many simulations that are needed for monitoring can be prohibitively expensive.”
To address this problem, Smetana is collaborating with computational seismologists Rhys Hawkins and Jeannot Trampert from the Department of Earth Sciences at Utrecht University and Matthias Schlottbom and Muhammad Hamza Khalid from the Department of Applied Mathematics at the University of Twente in the Netherlands. The team was able to build a reduced model that can be used to simulate seismic waves caused by an earthquake for various parameter configurations much faster than existing methods.
“Essentially we reduced the size of the system that you need to solve by about 1000 times,” Smetana says. “It was a very interdisciplinary project, and we found a clever way to construct the reduced model while still maintaining the accuracy of the prediction. “I really enjoy interdisciplinary collaborations and this one in particular because you learn to see things with a new perspective, which, in my opinion, ultimately helps finding creative and novel approaches to solve a problem in an interdisciplinary team.” The team’s findings are described in the paper titled Model Order Reduction for Seismic Applications, published in the SIAM Journal on Scientific Computing on September 17, 2025.
Although the teams’ model can’t predict when earthquakes may happen, it might ultimately be used to assess the risk of an earthquake occurrence. “If you get a good picture of the subsurface, you have a better idea of assessing the risk of future earthquakes,” Smetana explains.
Another area where the team’s work might potentially be helpful in the future is simulating tsunamis, which may form when earthquakes happen below the sea. However, depending on the location of the earthquake, it may take at least an hour for them to reach the land, providing time to perform simulations.
Having realistic images of the subsurface will allow for a better assessment of earthquake risks. “There's no way to predict earthquakes at this time,” Smetana says. “But our work can help generate a realistic view of the subsurface with less computational power, which would make our models more practical and help us be more earthquake resilient.”
About Stevens Institute of Technology
Stevens is a premier, private research university situated in Hoboken, New Jersey. Since our founding in 1870, technological innovation has been the hallmark of Stevens’ education and research. Within the university’s three schools and one college, more than 8,000 undergraduate and graduate students collaborate closely with faculty in an interdisciplinary, student-centric, entrepreneurial environment. Academic and research programs spanning business, computing, engineering, the arts and other disciplines actively advance the frontiers of science and leverage technology to confront our most pressing global challenges. The university continues to be consistently ranked among the nation’s leaders in career services, post-graduation salaries of alumni and return on tuition investment.
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