The study found that changes in magnetic field fault lines may give a faint signal before California earthquakes

There is no technology yet to predict when an earthquake will occur. For decades, researchers have tried to determine if there are reliable precursors that could serve as early warning before shaking begins.

A recent case study delves into decade-long California earthquakes to analyze a faint signal they hope will provide a glimpse into early detection for the public.

The results were recently published in the American Geophysical Union (AGU) Journal of Geophysical Research: Solid Earth.

The researchers analyzed data from 125 magnetometer sensing stations along fault lines across California. They looked at stations days before 19 earthquakes with a magnitude of 4.5 or greater spanning from 2010 to 2019. The results showed changes in the magnetic field near the sites 24 to 72 hours before the earthquake, according to the study authors.

The data was analyzed with the help of artificial intelligence and the researchers used an algorithm to correlate data from two stations to guard against noise the sensors might pick up – vehicular traffic, construction or other human activity. Data from two stations was also compared to other time periods to help eliminate false positives, according to the study authors, because the magnetic field is always active.

Think of the sensors as a microphone that is sensitive to changes in the magnetic field, according to the study’s authors. The “magnitude” captured by the sensors tends to rise slightly in the days before the earthquake.

The study was led by an applied science team with Google Research and QuakeFinder, a company that has been collecting earthquake data since 2005. The study also helped mining technology company Imdex Technology.

“This study provides significant evidence that physical change can be observed in the days leading up to an earthquake,” Dan Schneider, director of research and development at QuakeFinder, said in a written statement. “While we still have a lot of work to do to identify the strongest signals, this research supports our view that predicting earthquakes using the magnetic field may one day be possible.”

Jason Miller, director of product management at Google Applied Science, said in a statement that he was excited by the data that hinted at “physical phenomena previously unknown in earthquake prediction.”

But the researchers said there are a few large-scale earthquakes to study in California, which makes it difficult to determine whether the initial signals in one fault will apply to others. The precursor signal may be present at one location, but may be substantially different in another part of the state where geography is different.

They say more study is needed.

“To move in the direction of a meaningful prediction system, it is necessary to know how to isolate the signals that contribute to the magnetic energy increase,” Schneider said in an email. “Going forward, engaging more organizations and applying more resources to the challenge will give it the best opportunity to benefit Californians and those who live in earthquake-prone areas around the world.”

The report warns that the faint statistical signals found in the earthquake study “are of modest magnitude, which means we cannot provide a direct prediction that can be used to alert the public.” “This study provides evidence of an observable physical change in the days before the earthquake, but more scientific study is needed to understand this process.”

The USGS currently operates an early warning system that feeds applications such as MyShake and ShakeAlertLA. It is not an indicator of earthquakes but indicates that an earthquake has started and provides a few seconds of early warning before its effects can be felt locally.

It began issuing public alerts in Los Angeles County in 2018, then expanded to the rest of California a year later and later to Oregon and Washington.

But experts said that to be most effective, the earthquake early warning system needs to be integrated with more urban infrastructure, such as public transportation systems.

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