Scientists' AI-Powered Earthquake Detection Method Claims to Provide Months of Warning

The public could get days or months of warning of a major earthquake by identifying preceding low-level tectonic disturbances over large areas, according to research by a University of Alaska Fairbanks scientist who analyzed two major earthquakes in Alaska and California. The work was led by research assistant professor Tarsilo Girona of the University of Alaska Fairbanks’ Institute of Geophysics.

Girona, a geophysicist and data scientist, studies the precursor activity of volcanic eruptions and earthquakes. The study was co-authored by geologist Kyriaki Draimone of Ludwig Maximilian University of Munich, Germany. The detection method, based on machine learning, was published in Nature Communications.

Machine learning identifies earthquake triggers

“Our paper shows that advanced statistical techniques, especially machine learning, have the potential to identify early indicators of large-magnitude earthquakes by analyzing datasets derived from earthquake catalogs,” Girona said.

The authors wrote a computer algorithm to search the data for unusual seismic activity. They focused on two important earthquakes: the 2018 magnitude 7.1 Anchorage earthquake and the 2019 magnitude 6.4 to 7.1 Ridgecrest, California earthquake.

The researchers found that about three months of low-magnitude, anomalous regional earthquakes occurred in about 15% to 25% of south-central Alaska and southern California before each of the two earthquakes studied. Their research found that the disturbances that precede major earthquakes are primarily manifested in seismic activity with magnitudes less than 1.5.

The Anchorage earthquake struck on November 30, 2018, at 8:29 a.m., with its epicenter about 10.5 miles north of the city. It caused extensive damage to some roads and highways, and damaged several buildings.

Using their data-trained program, Girona and Drimony found that in the case of the Anchorage earthquake, the probability of a large earthquake occurring within 30 days or less suddenly rose to nearly 80 percent three months before the Nov. 30 earthquake. The probability rose to nearly 85 percent just a few days before it struck. They found similar probability results for the Ridgecrest earthquake sequence for a period beginning about 40 days before the start of the earthquake sequence.

The role of fluid pressure in shaping fault behavior

Girona and Drimoni propose a geological cause for the low-volume ancestral activity: a large increase in pore fluid pressure within the fault.

Pore ​​fluid pressure refers to the pressure of fluid within a rock. High pore fluid pressure can cause fault slip if the pressure is sufficient to overcome the frictional resistance between rock masses on either side of the fault.

“Increased pore fluid pressure in faults that lead to large earthquakes changes the mechanical properties of the faults, which in turn leads to uneven variations in the regional stress field,” Draimoni said. “We propose that these uneven variations control the abnormal low-magnitude seismic activity.”

Machine learning has a huge positive impact on earthquake research, Girona said.

“Modern seismic networks generate massive datasets that, when properly analyzed, can provide valuable insights into the precursors of seismic events,” he added. “This is where advances in machine learning and high-performance computing can play a transformative role, enabling researchers to identify meaningful patterns that could signal an impending earthquake.”

Dealing with the Challenges of Reliable Earthquake Warnings

The authors stress that their algorithm will be tested in near-real-time situations to identify and address potential challenges in earthquake prediction. They add that the method should not be used in new areas without training the algorithm on historical earthquakes in that area.

Girona said producing reliable earthquake forecasts has a “very important and often controversial dimension”.

“Accurate earthquake prediction can save lives and reduce economic losses by providing early warnings that allow for timely evacuation and preparedness. But the uncertainty inherent in earthquake prediction also raises important ethical and practical questions,” he said.

“False alarms can lead to unnecessary panic, economic disruption, and loss of public confidence, while wrong forecasts can have dire consequences,” he said.

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