Machine learning reveals historical seismic events in the Yellowstone caldera
Researchers detect, designate 10 times more earthquakes than previously recorded
image:
The Grand Prismatic hot spring in Yellowstone National Park is sourced from a magma chamber beneath it. The bright colours are produced by hydrophilic bacteria in the mineral-rich water.
view moreCredit: Bing Li / Western University
Yellowstone, a popular tourist destination and namesake of an equally popular TV show, was the first-ever national park in the United States. And bubbling beneath it – to this day – is one of Earth’s most seismically active networks of volcanic activity.
In a new study, published July 18 in the high impact journal Science Advances, Western engineering professor Bing Li and his collaborators at Universidad Industrial de Santander (Industrial University of Santander) in Colombia and the United States Geological Survey used machine learning to re-examine historical earthquake data from the Yellowstone caldera over a 15-year period. The team was able to retroactively detect and assign magnitudes to approximately 10 times more seismic events, or earthquakes, than previously recorded.
A caldera – like the one at Yellowstone Park spanning parts of Wyoming, Idaho and Montana – is a large depression or hollow formed when a volcano erupts and the magma chamber beneath it empties, leading to the collapse of the land above. This is different than a volcanic crater, which is formed by outward blasting.
The historical catalogue for the Yellowstone caldera now contains 86,276 earthquakes spanning the years 2008 to 2022, significantly improving previous understanding of volcanic and seismic systems through better data collection and systematic analyses.
A key finding in the study is that more than half of the earthquakes recorded in Yellowstone were part of earthquake swarms – groups of small, interconnected earthquakes that spread and shift within a relatively small area over a relatively short period of time. This is unlike an aftershock, which is a smaller earthquake that follows a larger mainshock in the same general area.
“While Yellowstone and other volcanoes each have unique features, the hope is that these insights can be applied elsewhere,” said Li, an expert in fluid-induced earthquakes and rock mechanics. “By understanding patterns of seismicity, like earthquake swarms, we can improve safety measures, better inform the public about potential risks, and even guide geothermal energy development away from danger in areas with promising heat flow.”
Molten-detecting machines
Prior to the application of machine learning, earthquakes were generally detected through manual inspection by trained experts. This process takes time, is cost-intensive and often detects fewer events than possible now with machine learning. Machine learning has sparked a data-mining gold rush in recent years as seismologists revisit the wealth of historical waveform data stored in datacenters across the world and learn more about current and previously unknown seismic regions around the world.
“If we had to do it old school with someone manually clicking through all this data looking for earthquakes, you couldn’t do it. It’s not scalable,” said Li.
The study also shows that earthquake swarms beneath the Yellowstone caldera have occurred along relatively immature, rougher fault structures, compared to more typical mature fault structures seen in regions such as southern California and even immediately outside the caldera.
The roughness was measured by characterizing earthquakes as fractals, which are geometric shapes that exhibit self-similarity, meaning they appear similar at different scales. First visualized by Benoit Mandelbrot in 1980, fractal patterns are seen in coastlines, snowflakes, broccoli, and even the branching of blood vessels. The fractal-based models, targeting roughness versus regularity, were able to characterize these earthquake swarms, which the researchers believe were caused by the mix of slowly moving underground water and sudden bursts of fluid.
“To a large extent, there is no systematic understanding of how one earthquake triggers another in a swarm. We can only indirectly measure space and time between events,” said Li. “But now, we have a far more robust catalogue of seismic activity under the Yellowstone caldera, and we can apply statistical methods that help us quantify and find new swarms that we haven’t seen before, study them, and see what we can learn from them.”
Journal
Science Advances
Method of Research
Computational simulation/modeling
Subject of Research
Not applicable
Article Title
Long-term dynamics of earthquake swarms in the Yellowstone caldera
Article Publication Date
18-Jul-2025
Curved fault slip captured on CCTV during Myanmar earthquake
Dramatic CCTV video of fault slip during a recent large earthquake in Myanmar thrilled both scientists and casual observers when it was posted to YouTube. But it was on his fifth or sixth viewing, said geophysicist Jesse Kearse, that he spotted something even more exciting.
When Kearse and his colleague Yoshihiro Kaneko at Kyoto University analyzed the video more carefully, they concluded that it had captured the first direct visual evidence of curved fault slip.
Earthquake geologists often observe curved slickenlines, the scrape marks created by blocks of rock moving past each other during faulting. But until now there has been no visual proof of the curved slip that might create these slickenlines.
The video confirmation of curved fault slip can help researchers create better dynamic models of how faults rupture, Kearse and Kaneko conclude in their paper published in The Seismic Record.
The video comes from a CCTV security camera recording along the trace of Myanmar’s Sagaing Fault, which ruptured 28 March in a magnitude 7.7 earthquake. The camera was placed about 20 meters to the east of the fault and was 120 kilometers away from the earthquake’s hypocenter.
The resulting video is astonishing. A fault in motion as never seen before — shaking followed by a visible northward slide of the land on the western side of the fault.
“I saw this on YouTube an hour or two after it was uploaded, and it sent chills down my spine straight away,” Kearse recalled. “It shows something that I think every earthquake scientist has been desperate to see, and it was just right there, so very exciting.”
Watching it over and over again, he noticed something else.
“Instead of things moving straight across the video screen, they moved along a curved path that has a convexity downwards, which instantly started bells ringing in my head,” Kearse said, “because some of my previous research has been specifically on curvature of fault slip, but from the geological record.”
Kearse had studied curved slickenlines associated with other earthquakes, such as the 2016 magnitude 7.8 Kaikoura earthquake in New Zealand, and their implications for understanding how faults rupture.
With the Myanmar video, “we set about to quantify the movement a bit more carefully, to extract objective quantitative information from the video rather than just pointing at it to say, look, it’s curved,” he said.
The researchers decided to track the movement of objects in the video by pixel cross correlation, frame by frame. The analysis helped them measure the rate and direction of fault motion during the earthquake.
They conclude that the fault slipped 2.5 meters for roughly 1.3 seconds, at a peak velocity of about 3.2 meters per second. This shows that the earthquake was pulse-like, which is a major discovery and confirms previous inferences made from seismic waveforms of other earthquakes. In addition, most of the fault motion is strike-slip, with a brief dip-slip component. [For a quick review of fault types, visit this USGS FAQ.]
The slip curves rapidly at first, as it accelerates to top velocity, then remains linear as the slip slows down, the researchers found.
The pattern fits with what earthquake scientists had previously proposed about slip curvature, that it might occur in part because stresses on the fault near the ground surface are relatively low. “The dynamic stresses of the earthquake as it’s approaching and begins to rupture the fault near the ground surface are able to induce an obliquity to the fault movement,” said Kearse.
“These transient stresses push the fault off its intended course initially, and then it catches itself and does what it’s supposed to do, after that.”
The researchers previously concluded that the type of slip curvature—whether it curves in one direction, or in the other—is dependent on the direction that the rupture travels, and is consistent with the north to south rupture of the Myanmar earthquake. This means that slickenlines can record the dynamics of past earthquakes, which can be useful for understanding future seismic risks.
Journal
The Seismic Record
Method of Research
Observational study
Subject of Research
Not applicable
Article Title
Curved Fault Slip Captured by CCTV Video During the 2025 7.7 Myanmar Earthquake
Article Publication Date
18-Jul-2025
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