We're closer than ever to predicting earthquakes

When a 4.4-magnitude earthquake struck Los Angeles on August 13, it didn’t come as a complete surprise to everyone. About a million Californians had received an early warning on their cellphones that a quake was imminent.

How did this happen? It was thanks to a newly developed app, MyShake, created by researchers at the University of California, Berkeley, in partnership with the California Governor’s Office of Emergency Services. The app essentially acts as a crowdsourced detector, collecting movement data from phones across the West Coast—from California to Washington state—and sending out alerts based on the phone’s location.

A screenshot from the MyShake app, one of the first digital tools to help predict earthquakes. MyShake/ YouTube Visitors in Nagasaki, Japan, sit cross-legged after an earthquake warning last August. AP

It doesn’t take much advance notice. “The warning ranges from a few seconds to tens of seconds depending on the location of the earthquake and the availability of phones,” says Richard Allen, director of the University of California, Berkeley’s seismological lab, who helped develop the technology.

It may seem like nothing—how much of a difference a few seconds really can make—but in earthquake forecasting, it may be like the invention of the telephone. For years, the idea of ​​being able to predict an earthquake was not even remotely far-fetched. As seismologist Ali Hutchison wrote in MIT Technology Review, until 2013, the very idea of ​​earthquake prediction was considered “as unserious a concept as the search for the Loch Ness Monster.”

Many prominent scientists still feel this way. “I’ve been studying earthquakes for more than 50 years, and I’ve seen many studies by scientists who have reported the antecedents of large earthquakes,” says Tom Heaton, a geologist at the California Institute of Technology. “In my experience, no one has developed a system for predicting earthquakes. I firmly believe that this is a problem of self-organized chaos. This is an important area of ​​physics that is largely unfamiliar to Earth scientists.”

Historically, earthquake forecasting has been a mix of quackery and charlatanism. From misguided theories about “earthquake weather” to a 1990 prediction that inspired “War of the Worlds”-style panic (including bunker-building) to Japanese seismologists who suggested in a 1933 study that catfish could predict earthquakes with 80 percent accuracy, there has been little to be optimistic about.

Even Japan, one of the world’s most seismically active regions, is wary of overpromising. When a 7.1-magnitude quake struck the country’s southern islands earlier this month, the Japan Meteorological Agency issued its first-ever “super-quake warning,” warning of a “higher-than-usual” chance of a second, stronger quake. But the agency makes clear on its website that a warning does not mean certainty. “Information that predicts earthquakes by specifying the date, time and location is a hoax,” the statement read.

“Most previous attempts have focused on the very complex physics of earthquakes,” says scientist Sergey Fomel, University of Texas at Austin.

But that view is slowly changing as science evolves. “There has been a debate in the seismology community for many years about whether earthquakes are chaotic or inevitable,” says Quentin Pelletry, an Earth scientist at the University of the Côte d’Azur in Nice, France. “If earthquakes are chaotic, we will never be able to predict them, no matter what technology we use.”

But if earthquakes are deterministic—meaning there are previous anomalies that can be observed in advance with the right instruments—“then earthquake prediction becomes possible,” says Bellitre.

Seismologists are trying to do this using machine learning, by searching for patterns and hidden data in ways that have never been attempted before.

“Most previous attempts have focused on the very complex physics of earthquakes,” says Sergey Fumel, a geologist at the University of Texas at Austin who led a seven-month earthquake prediction experiment in China last year. “In our approach, physics is combined with statistical data analysis using new machine learning and artificial intelligence tools. We extract physical features from the recorded data and combine them statistically to get the best prediction.”

Imaging of a Japanese earthquake off Kishu Island.

The results, published in the Bulletin of the American Seismological Society last September, exceeded their expectations. The AI ​​algorithm, trained to spot statistical bumps in real-time seismic data, correctly predicted about 70 percent of the earthquakes—14 in all—at least a week in advance and within 200 miles of their epicenters. (The algorithm missed one earthquake, and predicted eight that never happened.)

“The success rate of the Chinese experiment was astonishing,” Fomel told The Washington Post. “Given the complexity of the problem and the long history of previous failures, no one expected such an outcome.”

This data-driven approach to earthquake prediction could be the future of seismology, and scientists are exploring all the ways machine learning can get us closer to perfect predictions. At the University of California, Berkeley, and the University of California, Santa Cruz, researchers are developing a new model, called RECAST — short for “Recurrent Earthquake foreCAST” — that brings deep learning to earthquake prediction.

“We still have more questions than answers,” warns scientist Tom Heaton about the science of earthquake prediction.

The method works similarly to a language model, which “uses a huge amount of written text to generate the best guess of what the next word might be,” Killian Dasher-Cousineau, a researcher at the University of California, Berkeley, who led the study, told The Washington Post. “RECAST uses large catalogs of earthquakes to generate the best guess of when the next earthquake will occur.”

Pelletri has focused his research on subtle signals that occur before many major earthquakes, barely perceptible movements known as “seismic slips” that do not produce seismic waves but can be detected using GPS sensors.

Although this phenomenon has been studied in the past, Bellitri and his team tried to analyze all global data rather than just one specific event.

Researcher Killian Dasher Cousineau of the University of California, Berkeley.edu

“What we basically did was summarize all the GPS observations recorded before earthquakes of magnitude 7.0 or greater,” he told The Post.

They found that two hours before the earthquake, there was a huge acceleration in the horizontal motion of the sensors. That’s an exciting discovery, but it doesn’t mean we’ll be able to predict the next devastating earthquake. Identifying non-seismic slip as a real phenomenon is only half the battle. The other half is creating the tools to detect it.

“This will require developing sensors that can record ground motion at least 100 times more accurately than current geodetic surveys, which is a huge technological challenge,” says Beltre. But if it does happen, “we might be able to predict earthquakes.”

We’re closer than ever to earthquake alerts that don’t just send you a notification on your phone, but give you a few seconds’ warning to seek shelter. But the science—or at least the new data-driven, machine-learning approach to seismology—is still in its infancy. “We still have more questions than answers,” Heaton warns.

UC Santa Cruz logo With its seemingly endless tremors, UC Santa Cruz has been at the forefront of earthquake prediction.

Dr. Robert Geller, professor emeritus of seismology at the University of Tokyo, has long been an outspoken critic of earthquake predictions, even as a hypothetical idea.

“Carl Sagan once said, ‘Extraordinary claims require extraordinary evidence,’” he tells the Washington Post. “In other words, given the thousands of predictive claims that have ultimately been rejected over the past 140 years, unless these people have really strong data, they’re going to have a hard time getting support.”

But “really robust data” is exactly what many researchers have been gathering in recent years. Yangkang Chen, Ph.D., lead developer of the AI ​​study at the University of Texas, said last summer that they plan to test their methods in seismically active regions beyond western China, including the U.S. West Coast, Japan and the Mediterranean.

“We need to make sure that the same forecasting framework can be generalized,” Chen says. “Once we can replicate the same earthquake prediction success in multiple regions, we will focus on improving the forecasting performance by tweaking the AI ​​model and collaborating with outstanding data scientists.”

The MyShake app has had some success in providing concise earthquake warnings.

There’s reason to be optimistic, Chen says, because AI is entirely data-driven and not physics-driven. “It’s not biased by any human-made assumptions,” she says. “As long as it can come up with a statistically compelling conclusion, it’s showing something, regardless of whether you recognize the anomaly as a prior signal.”

At least for now, the data is just data. Data sets “have enormous potential to be very useful,” says Cousineau. “But they also come with their own set of peculiarities.” The challenge for seismologists today is to figure out what to do with all this data, so that future earthquake predictions aren’t just guesswork.

“Even if you think you have a better model, it may not be immediately obvious,” says Dacher-Coussino. “Imagine trying to settle a debate about whether a coin is biased or not after seeing only two coin flips.”

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