Study shows how California can predict earthquakes like weather

A new study has found an increase in magnetic field energy near some earthquakes in California one to three days before they occur, a finding its authors hope will be a step toward accurately predicting when earthquakes will strike — an elusive goal and mired in controversy.

“This study provides important evidence that physical change can be observed in the days leading up to an earthquake,” said Dan Schneider, director of research and development at QuakeFinder. Palo Alto, a company based in Palo Alto, which aims to find a way to predict major earthquakes, collaborated on the study with the Applied Science team at Google Research in Mountain View.

“While we still have a lot of work to do to improve signal isolation, this research supports our view that predicting earthquakes using the magnetic field may one day be possible,” he said.

Magnetic fields have been part of seismology research for decades, but the idea that they may be reliable and measurable precursors to earthquakes is controversial.

The USGS emphatically states on its website that there is no way to predict a major earthquake. “We don’t know how, and we don’t expect to know how anytime in the foreseeable future,” the agency says.

Jeffrey Love, a USGS geophysical researcher, expressed skepticism about the new study’s findings.

“As they themselves say, the results are not useful for predicting earthquakes,” he said.

The study, published September 1 in JGR Solid Earth, analyzed magnetic field data collected by magnetometers at 125 sensor stations along major faults in California before 19 earthquakes of magnitude 4.5 or higher from 2005 to 2019.

After applying the algorithms to 50% of the data set, the researchers found a signal indicating changes in the magnetic field 24 to 72 hours before the earthquakes occurred.

The study notes that the “statistical signal is of modest magnitude”, so it is not yet possible to predict an earthquake with the results.

And separating a meaningful signal before the earthquake from the rest of the “noise,” including solar weather and lightning, BART trains, passing cars, construction machinery and power lines, adds to the challenge. Schneider said sensors pick up on such “large-scale changes.”

“In this study we found that there is a tendency to increase the magnetic field energy (beyond normal background levels) in the window from 24 to 72 hours before earthquakes, but we do not yet know the reason for this increase,” he said. . “Several theories have been proposed, but evaluating them in light of the result of this study is another effort entirely.”

The study acknowledges the controversy over magnetic field changes, and says the process is further complicated by the rarity of large earthquakes and magnetometer coverage scattered along fault zones.

Love, a geophysicist with the US Geological Survey, said the findings did not make the case for magnetic fields as predictors of earthquakes. “I can’t see they do anything better than random prediction,” he said. “The results don’t seem to perform better than if they were just flipping a coin.”

A real meaningful test, he said, would be to predict an earthquake before it happens, rather than analyzing earthquakes that have already occurred. He said the forecast would have to say not only when the earthquake will occur, but also where and how large it will be — “otherwise it wouldn’t be a meaningful prediction.”

Schneider said that many of the previous studies were “anecdotal” revolving around data from a single earthquake “to try to identify something that could be considered a preliminary signal.” That raised the possibility that the signal was just a coincidence, he said.

He said the current research addressed this through “a careful study design”.

“Our study addressed the problem statistically and looked at several earthquakes from different pairs of stations to determine whether the signal could be consistently detected above the normal variations in the magnetic field at the stations,” he said. “Not only that, but the options to combine signals from station pairs and train the algorithms only on half of the data before evaluating the other half (splitting the test train) makes this one of, if not the most accurate studies in this area to date.”

Love said further study “might be beneficial” to continue, but it remains doubtful.

“Maybe the methods can be improved in the future,” he said, “but until that happens, we don’t have anything significant.” “The Earth is complex, and the Earth’s magnetic field is complex and changing all the time. To sort it all out and make it a useful and feasible way to predict earthquakes is really difficult.”

Schneider hopes that one day forecasting earthquakes will become a reality, just as scientists can now predict weather events including hurricanes and hurricanes.

“To get there, we first need to be able to isolate the signal from the noise better,” he said.

It will also be important to check whether the signals change according to size, or vary depending on the type of fault and local geography, he said.

Ultimately, he said, the scientific community will want a physical model that explains these signals “and thus improves our understanding of the geophysical effects of the earthquake process.”

Kelly Huang is a staff writer for the San Francisco Chronicle. Email: [email protected] Twitter: @kellieHwang

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