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Edinburgh Uni study shows AI earthquake analysis can predict aftershock risk in seconds

KANIKA SINGH RATHORE, 25/11/202525/11/2025

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Earthquake A new study from the University of Edinburgh shows that AI-powered forecasting tools can predict the risk of aftershocks just seconds after the initial shock.

Researchers from the University of Edinburgh, the British Geological Survey and the University of Padua created AI-powered forecasting tools that were developed by training machine learning models on earthquake data.

The data came from parts of the world where earthquakes occur regularly California, new zealand, Italy, Japan And Greece.

The team analyzed the AI ​​model’s ability to predict how many aftershocks would occur within 24 hours after an earthquake of magnitude 4 or greater.

They compared the performance of their AI model to the most widely used forecasting system, known as the epidemic-type aftershock sequence (ETAS) model, which is used in Italy, New Zealand, and the US.

While both model types show similar performance in predicting aftershock risk, the ETAS model takes several hours or days on a single mid-range computer to produce results.

AI models can produce very fast results to warn of any further shocks.

Foteni Dervisi, a PhD student at the University of Edinburgh’s School of Geosciences and the British Geological Survey, who led the study, said: “This study shows that machine learning models can produce aftershock forecasts within seconds, showing comparable quality to ETAS forecasts.

“Their speed and low computational cost offer major advantages for operational use: combined with the real-time development of machine learning-based high-resolution earthquake catalogues, these models will enhance our ability to monitor and understand seismic hazards as they develop.”

Faster forecasts produced by AI-powered tools can help officials make decisions about public safety measures and resource allocation in disaster-hit areas.

The research was published in the journal Earth, Planets & Space and was supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie SPIN Innovative Training Network.

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