Stanford’s detection system AI can predict earthquakes

A group of researchers has revealed a new way to use artificial intelligence (AI) to enhance our ability to read seismic waves and, by doing so, improve our understanding of how they start, and even how they stop.

Published in Nature Communications, the paper details a method that automatically detects earthquakes while controlling much of the noise inherent in seismic data.

RELATED: 11 of the deadliest earthquakes of all time

Artificial intelligence earthquake detection

Mostafa Mousavi and a team of researchers are using artificial intelligence to focus on millions of subtle shifts in the Earth’s crust. They hope these small movements will serve as a rationalization of sorts for deciphering the warning signs of large earthquakes.

“By improving our ability to detect and locate these very small earthquakes, we can get a clearer view of how earthquakes interact or propagate along the fault, how they start, and even how they stop,” says Stanford University geophysicist Gregory Burosa, one of the authors The paper, illustrated in a Stanford press release.

The team developed several machine learning systems to detect earthquakes. Among them is CRED, which was developed in 2019 and which draws inspiration from audio player algorithms in virtual assistant systems.

The new paper details the team’s latest iteration, a model that detects very small earthquakes with weak signals that are often ignored by current methods. They call their new system an earthquake transducer. The system uses an “attention mechanism” to pour in large amounts of data and focus on the most important elements.

Predict future earthquakes

To test the seismic transformer, the team trained their algorithms on data that included one million hand-labeled seismographs recorded over the past two decades globally, excluding Japan. For the test, they then picked five weeks of continuous data recorded in Japan around the time of the 6.6-magnitude Tottori earthquake and its aftershocks 20 years ago.

During testing, the model detected and identified 21,092 events – more than two and a half times the number of earthquakes captured by hand. Moreover, Earthquake Transformer used data from only 18 stations from 57 stations that Japanese scientists originally used to study sequences.

According to Pirosa, the system is ready-to-use in real-time earthquake detection.

“Monitoring earthquakes using almost real-time machine learning will come very soon,” Peruza said. “The more information we get about the three-dimensional deep fault structure through improved monitoring of small earthquakes, the better we can predict which earthquakes will lie in the future.”

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