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New research shows that artificial intelligence tools for earthquakes predict the risk of aftershocks in seconds
Current methods used to predict aftershocks — secondary earthquakes that can be more deadly than primary quakes — can take several hours or days, but now, new machine learning models have been developed that can predict where and how many aftershocks will occur after an earthquake in near real time.
AI-based forecasting tools have been created by researchers from BGS, the University of Edinburgh and the University of Padua. It was developed by training machine learning models on earthquake data from California, New Zealand, Italy, Japan and Greece – parts of the world that regularly experience earthquakes.
Rapid forecasts produced by AI-powered tools can help authorities decide on public safety measures and resource allocation in disaster-affected areas. The team analyzed the ability of artificial intelligence models to produce predictions about the number of aftershocks that will occur in the 24 hours following earthquakes of magnitude 4 or higher.
They compared the performance of their AI models with the most widely used forecasting system – known as the Epidemic-Type Aftershock Sequence (ETAS) model – which is used in practice in Italy, New Zealand and the United States.
While both types of models show similar performance in predicting aftershock hazards, the ETAS model took much longer to produce results — up to several hours or days on a single mid-range computer — because it involves running a large number of simulations, the team says.
By training AI tools on past earthquake records from regions with different tectonic landscapes, the researchers say their models can be used to predict aftershock risks in most parts of the world that are prone to earthquakes.
This research, published in the journal Earth, Planets and Space, was supported by the European Union's Horizon 2020 research and innovation program within the Marie Skłodowska-Curie SPIN Innovative Training Network.
This study demonstrates that machine learning models can produce aftershock forecasts within seconds, showing similar quality to that of ETAS forecasts. Their speed and low computational cost provide significant benefits for operational use: coupled with the immediate development of machine learning-based high-resolution seismic catalogues, these models will enhance our ability to monitor and understand seismic crises as they develop.
PhD student Foteini Dervisi, from BGS and the University of Edinburgh's School of Geosciences, who led the study.
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