Scientists have used artificial intelligence to find hidden earthquake signals along the San Andreas Fault

Predicting earthquakes has been an impossible dream for seismologists practically since the discipline first emerged more than 150 years ago. To this day, in fact, the USGS’ official FAQs state unequivocally: “No. Neither the USGS nor any other scientists have ever predicted a major earthquake.” […] “We don’t expect to know how at any time in the foreseeable future.”

But don’t lose hope yet. Researchers trained machine learning tools on tectonic microstrain data collected near the San Andreas Fault in California, and discovered previously unknown “slow-slip events,” which they report “may influence the timing and occurrence” of low-frequency earthquakes (LFEs). Some geologists have already described the field’s growing understanding of these slow, virtually imperceptible tectonic shifts on the Earth’s surface as nothing less than a “revolution” in seismology due to “the fact that they can lead to large, catastrophic earthquakes.” In other words, by understanding how slow slips turn into LFEs, the new study has brought seismologists closer to understanding true early warning signs that could one day help predict major earthquakes.

“We wanted to know whether important slow displacement processes might be hidden in years of continuous deformation measurements,” lead author of the new study, geophysicist and seismologist Zahra Zali, said in a statement translated via Google. “AI has enabled us to recognize their patterns, which otherwise would have gone unnoticed.”

“Lasimi” transformation.

Faults can release stress built up due to pressure on tectonic plates either quickly, via a seismic event, or slowly, via ‘seismic’ slip. These recent slow slips, Zali and her colleagues write in Nature Communications, “can last from minutes to months.” But because these events did not generate any of the roaring waves recorded by seismometers, the phenomenon was not well understood until recently.

“These events are difficult to identify using traditional methods because they are small and often hidden within complex background signals,” Zali said. (Indeed, as Penn State geologist Chris Marrone pointed out in 2019, both LFEs and slow slides were considered “non-existent and theoretically impossible not so long ago.”)

Zali and her colleagues in Germany and the United States collected a continuous daily feed of measurements via wells along California’s San Andreas Fault near Parkfield. They used sensitive strain gauges capable of picking up minute deformations in these deep rocks—from just seconds to several weeks—to bridge the data gap between what seismometers and high-precision global positioning (GPS) sensors can currently collect.

The team had nearly eight years to work with this data, taken from four pressure gauges along the Parkfield section of the fault between 2009 and 2016: exactly the kind of torrent of information one would want a dedicated deep-learning AI to comb through in search of patterns. Zali and her colleagues found that these slow slip events often coincided in time with LFEs locally, which they defined as falling within 6.2 miles (10 kilometers) proximal and less than 12.4 miles (20 kilometers) deep.

“Our results show that these ‘slow earthquakes’ are not isolated phenomena […] “Which suggests that slow slip plays an important role in the evolution of stress conditions along active faults,” Patricia Martinez Garzon, Zali’s co-author and professor of applied seismology at GFZ, explained in a statement translated via Google.

Robot rock

Zali and her colleagues hope to conduct further AI analyzes on fault lines outside San Andreas to more precisely confirm this clear relationship between slow slips and more significant seismic activity. This work would help clarify the somewhat difficult fact, which is that they have much more data on LFEs in California, nearly 500,000 small earthquakes during their time period, compared to the only 92 slow-slip events they were able to identify near Parkfield.

Discovering these quiet precursors to major seismic activity, according to Zali, will be essential to understanding how fault lines build up and develop stresses that can rupture and turn into natural disasters later.

“Many important fracturing processes occur without causing devastating earthquakes,” Zali noted. “By revealing these hidden signals, we can get a more complete picture of how faults behave between earthquakes and how stress is transmitted through the Earth’s crust.”

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