Artificial intelligence predicts earthquakes with unprecedented accuracy

An artificial intelligence algorithm developed by the University of Texas has successfully predicted 70% of earthquakes in a single experiment, demonstrating potential improvements in earthquake preparedness and risk management. The algorithm’s performance in an international competition highlights its accuracy and adaptability. Copyright: SciTechDaily.com

Researchers at the University of Texas have developed an artificial intelligence that predicted 70% of earthquakes during an experiment in China, suggesting the potential for mitigating future earthquake risks.

The AI, trained on seismic data, also took first place in an international competition, confirming its effectiveness and opening the door for further improvements in areas like California and Texas.

Breakthrough in Earthquake Prediction Using Artificial Intelligence

A new attempt to predict earthquakes with the help of artificial intelligence has raised hopes that the technology could one day be used to reduce the impact of earthquakes on lives and economies. An AI algorithm developed by researchers at the University of Texas at Austin was able to correctly predict 70 percent of earthquakes a week in advance during a seven-month experiment in China.

Damage in New Zealand after a powerful earthquake in 2011. Researchers at the University of Texas at Austin are working to predict earthquakes using artificial intelligence. Copyright: Flickr/Martin Fluff Experiment results and global implications

The AI ​​was trained to spot statistical bumps in real-time seismic data that the researchers correlated with past earthquakes. The result was a weekly forecast in which the AI ​​successfully predicted 14 earthquakes within about 200 miles of where it estimated they would occur, with roughly calculated magnitudes. It missed one earthquake and gave eight false alarms.

It is not yet known whether the same approach will work in other locations, but the effort represents a milestone in research into earthquake prediction using artificial intelligence.

Challenges and future trends

“Earthquake prediction is the ultimate goal,” says Sergey Fumel, a professor at the University of Texas Bureau of Economic Geology and a member of the research team. “We’re not even close to predicting anything anywhere in the world, but what we’ve achieved tells us that what we thought was an impossible problem can in principle be solved.”

The experiment was part of an international competition held in China where the AI ​​developed by the University of Texas came in first among 600 other designs. The University of Texas team was led by the bureau’s seismologist and lead developer of the AI, Yangkang Chen. The results of the experiment were published in the Bulletin of the Seismological Society of America.

This map shows the location of earthquakes predicted by AI in China (blue dots) linked by a red line to where each actual earthquake occurred (red dots). The numbers indicate the week in which the earthquake occurred. During the 30-week experiment, the AI ​​developed by the University of Texas at Austin failed to detect just one earthquake. Copyright: Yangkang Chen/Jackson School of Geosciences Impact on preparedness and further research

“You can’t predict when an earthquake will happen. It’s a matter of milliseconds, and the only thing you can control is how prepared you are,” said Alexandros Savvaidis, a senior research scientist who heads the bureau’s TexNet program, the state’s earthquake network. “Even at 70 percent, that’s a huge result and could help reduce economic and human losses and has the potential to dramatically improve earthquake preparedness around the world.”

The researchers said their method worked by following a relatively simple machine-learning approach. The AI ​​was given a set of statistical features based on the team’s knowledge of earthquake physics, and then told to train itself on a five-year database of seismic recordings.

Once trained, the AI ​​made its predictions by listening for signs of incoming earthquakes amid the background vibrations in the ground.

“We are very proud of this team for taking first place in this prestigious competition,” said Scott Tinker, director of the office. “Of course, it’s not just location and size that matters, but timing as well. Earthquake prediction is a tough problem, and we can’t overstate how difficult it is.”

The researchers are confident that in places with strong earthquake-tracking networks, such as California, Italy, Japan, Greece, Turkey and Texas, AI can improve its success rate and narrow its prediction range to a few tens of miles.

One of the next steps is to test the AI ​​in Texas, where the state experiences a high rate of minor earthquakes and some moderate earthquakes. The bureau’s TexNet network hosts 300 seismic stations and more than six years of continuous records, making it an ideal location to validate the method.

Ultimately, the researchers want to integrate the system with physics-based models, which could be important where data is thin, or in places like Cascadia, where the last major earthquake occurred hundreds of years before seismometers were available.

“Our future goal is to combine physics and data-driven methods to have something generalizable, like chatGPT, that we can apply anywhere in the world,” Chen said.

The new research represents an important step towards achieving this goal.

“It may be far-fetched, but it is many advances like this one, taken together, that drive science forward,” Tinker said.

Reference: “Earthquake Prediction Using Big Data and Artificial Intelligence: A 30-Week Real-Time Case Study in China” by Omar M. Saad, Yunfeng Chen, Alexandros Savvaidis, Sergey Fomel, Xiuxuan Jiang, Dino Huang, Yabo Abouli, Serge Innocent Aubui, Shanshan Yong, Xin An Wang, Xing Zhang, Yangkang Chen, September 5, 2023, Bulletin of the Seismological Society of America. DOI: 10.1785/0120230031

The research was supported by TexNet, the Texas Consortium for Computational Seismology, and Zhejiang University. The office is a research unit of the Jackson School of Geosciences.

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