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

Earthquake forecasting tools powered by AI can forecast the risk of aftershocks seconds after the initial tremor, a new study from Edinburgh University suggests.

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

The data came from parts of the world that regularly experience earthquakes such as California, New Zealand, Italy, Japan and Greece.

The team analysed the AI models’ ability to produce forecasts of how many aftershocks will take place within 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 Italy, New Zealand and the US.

While both model types show similar performance at forecasting aftershock risk, the ETAS model took up to several hours or days on a single mid-range computer to produce results.

The AI models could produce a much quicker result to warn of any further tremors.

PhD student Foteini Dervisi, of 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 that of ETAS forecasts.

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

The fast forecasts produced by AI-powered tools could assist authorities with decision-making about public safety measures and resource allocation in disaster-hit areas.

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