US scientist’s new method could forecast earthquakes months before they strike

Scientists have developed a new method that could accurately forecast an earthquake months before it occurs.

Developed by a University of Alaska Fairbanks scientist, the method looks into the identification of prior low-level tectonic unrest across large areas.

The study focuses on the precursory activity of volcanic eruptions and earthquakes and uses machine learning to predict any such event.

Led by research assistant professor Társilo Girona of the UAF Geophysical Institute, the study analyzed two major earth quakes in Alaska and California (the 2018 magnitude 7.1 Anchorage earthquake and the 2019 Ridgecrest, California, earthquake sequence of magnitudes 6.4 to 7.1.).

Scientists detected abnormal low-magnitude regional seismicity

Prior to each of the two studied earthquakes, they found that approximately three months of abnormal low-magnitude regional seismicity had occurred across about 15% to 25% of Southcentral Alaska and Southern California.

Their research finds that unrest preceding major earthquakes is mostly captured by seismic activity with a magnitude below 1.5.

The Anchorage earthquake occurred Nov. 30, 2018, at 8:29 a.m., with an epicenter located approximately 10.5 miles north of the city. It caused extensive damage to some roads and highways, and several buildings sustained damage, according to the study .

Machine learning could identify precursors to large-magnitude earthquakes

“Our paper demonstrates that advanced statistical techniques, particularly machine learning, have the potential to identify precursors to large-magnitude earthquakes by analyzing datasets derived from earthquake catalogs,” Girona said.

The authors wrote a computer algorithm, a set of computer instructions that teach a program to interpret data, learn from it, and make informed predictions or decisions, to search the data to look for abnormal seismic activity.

Utilizing their data-trained program, Girona and Kyriaki Drymoni, co-author of the study, found with the Anchorage earthquake that the probability of a major earthquake happening in 30 days or fewer increased abruptly to approximately 80% around three months before the November 30 earthquake.

The probability increased to approximately 85% just a few days before it occurred. They had similar probability findings for the Ridgecrest earthquake sequence for a period beginning about 40 days prior to the onset of the quake sequence, according to a press release by UAF.

Researchers propose a geologic cause for the low-magnitude precursor activity: A significant increase in pore fluid pressure within a fault.

Pore fluid pressure refers to the pressure of fluid within a rock. High pore fluid pressures can potentially lead to fault slip if the pressure is sufficient to overcome the frictional resistance between the blocks of rock on either side of the fault, according to the study published in Nature Communications .

“Increased pore fluid pressure in faults that lead to major earthquakes changes the faults’ mechanical properties, which in turn leads to uneven variations in the regional stress field,” Drymoni said.

“We propose that these uneven variations … control the abnormal, precursory low-magnitude seismicity.”

Scientists claim that machine learning is having a major positive impact on earthquake research.

Modern seismic networks produce enormous datasets

Girona stated that modern seismic networks produce enormous datasets that, when properly analyzed, can offer valuable insights into the precursors of seismic events.

“This is where advancements in machine learning and high-performance computing can play a transformative role, enabling researchers to identify meaningful patterns that could signal an impending earthquake,” remarked Girona.

The algorithm developed by the researchers would soon be tested in near-real-time situations in an attempt to address potential challenges for earthquake forecasting.

Researchers maintained that the method should not be employed in new regions without training the algorithm with that area’s historical seismicity. They also claimed that producing reliable earthquake forecasts has a “deeply important and often controversial dimension.”

“Accurate forecasting has the potential to save lives and reduce economic losses by providing early warnings that allow for timely evacuations and preparation. However, the uncertainty inherent in earthquake forecasting also raises significant ethical and practical questions,” concluded Girona.

“False alarms can lead to unnecessary panic, economic disruption, and a loss of public trust, while missed predictions can have catastrophic consequences.”