The modeling software reveals patterns in continuous seismic waveforms during a series of intermittent magnitude 5 earthquakes

(a) Kı̄lauea volcano in Hawaii (inset) is shown in the center of the caldera. Dark gray lines are faults mapped before the 2018 collapse. Inverted white triangles are GNSS sites and orange triangles are analyzed seismic stations. (b) GNSS time series of UWEV station showing horizontal magnitude offsets to the north, east, and low. The skewed horizontal magnitude is used in the modeling and the gray shaded time periods are model training data. Credit: Geophysical Research Letters (2024). doi: 10.1029/2024GL108288

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 Kilauea volcano in Hawaii are part of a 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 strike-slip fault, the kind that can generate massive destruction.

The study was published in the journal Geophysical Research Letters.

“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 press against each other, they create weak tremors in the Earth, called sustained acoustic or seismic emissions. These signals appear as waveforms when recorded, but were previously thought to be noise, data that contains no information describing the fault condition. Instead, the Los Alamos researchers found that continuous acoustic emission waveforms are actually data-rich and can be used to infer the physical properties of a fault, such as displacement, friction, and thickness.

Most important, the Los Alamos scientists found largely predictable patterns in the signals, which constitute 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 was the first time they had successfully applied this approach to seismic faults, the layer where earthquakes originate. In this case, it was a series of very active Magnitude 5 events at Kilauea Volcano, which saw a months-long seismic event that caused the caldera to sink 1,600 feet.

During that period, a 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 successfully estimated the ground displacement and time until the next fault failure.

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.

More information: Christopher W. Johnson et al., Seismic Properties Predict Ground Motions During Recurring Caldera Collapse Sequences, Geophysical Research Letters (2024). doi: 10.1029/2024GL108288

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Citation: Modeling software reveals patterns in persistent seismic waveforms during a series of magnitude 5 earthquakes (2024, June 25) Retrieved June 26, 2024 from https://phys.org/news/2024-06-software- reveals-seismic-patterns-waveforms.html

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