Artificial intelligence at Los Alamos advances earthquake prediction capabilities

A team from Los Alamos National Laboratory used machine learning – an application of artificial intelligence – to detect hidden signals that precede an earthquake. The findings at Hawaii's Kīlauea volcano are part of a groundbreaking, years-long research effort at Los Alamos, and this latest study represents the first time scientists have been able to detect these warning signs in a slip fault, the kind that can generate massive destruction.

“We wanted to see if we could pull the signals out of the noise and determine where the system is in the loading cycle in terms of approaching the major slip that causes the earthquakes,” said Christopher Johnson, a Los Alamos seismologist and team leader. researcher. “This is the first time we have been able to apply this method to an earthquake of this type and this strength.”

The team used data recorded between June 1, 2018, and August 2, 2018, by the USGS Hawaiian Volcanoes Observatory. At this time, the volcano experienced more than 50 earthquakes of varying strength. The researchers focused on 30-second windows of seismic data, and their model identified something like a fingerprint, a hidden signal, tracking the loading cycle of each event. On average, this hidden signal appeared to persist before significant detectable ground motion occurred.

The results, combined with previous tests, suggest that some seismic faults share similar physics, meaning this method could be used to assess earthquake hazards around the world.

Patterns in noise

The research builds on previous work by Los Alamos on faults in California and the Pacific Northwest, where machine learning was able to detect these initial signals.

When tectonic plates push against each other, they create weak vibrations in the ground called continuous acoustic or seismic emissions. These signals appear as waveforms when recorded but were previously thought to be noise—data with no information describing the state of the fault. Instead, the Los Alamos researchers found that continuous acoustic emission waveforms are actually rich in data and can be used to infer physical properties of the fault, such as displacement, friction, and thickness.

More importantly, the Los Alamos scientists found highly predictable patterns in the signals, a kind of timeline for failure.

“When we look at these ongoing signals, we can pull up information that tells us where in the load cycle we are going wrong,” Johnson said. “We look at how the noise evolves and this gives us details about its current state and where it is in the slip cycle.”

From slow sliding to sticky sliding

The team's research, published in the journal Geophysical Research Letters, was the first time they had successfully applied this approach to seismic faults, the layer from which earthquakes originate. In this case, it was a series of extremely active 5-magnitude events at Kilauea volcano, which witnessed a months-long seismic event that plunged the caldera 1,600 feet deep.

During that time, the Global Navigation Satellite System measured Earth's displacement on the millimeter scale. The machine learning model then analyzed this data, processed the seismic signals, and estimated ground displacement and the time until the next failure occurred successfully.

Previously, Los Alamos researchers had applied similar machine learning models to slow-slip events, which cause the ground to subtly shake days, months, or even years before a seismic event occurs. These large datasets have been useful in training machine learning models. But the most destructive earthquakes are caused by strike-slip faults, such as those at Kilauea volcano, which can generate much stronger ground motions more quickly, and have so far eluded prediction.

Research paper: “Seismic properties predict ground motions during a recurring caldera collapse sequence.” Geophysical Research Letters. doi: 10.1029/2024GL108288

Funding: US Department of Energy, Office of Science, Office of Basic Energy Sciences, and Earth Sciences Program.

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