Deep learning model boosts earthquake analysis

Known as RECAST (Recurrent Earthquake forecast), the programme was created by researchers at the University of California, Santa Cruz and Technical University of Munich. It’s claimed that RECAST outperformed the current model, known as the Epidemic Type Aftershock Sequence (ETAS) model, for earthquake catalogues of about 10,000 events and greater. The work is published in Geographical Physical Letters.

“The ETAS model approach was designed for the observations that we had in the 80s and 90s when we were trying to build reliable forecasts based on very few observations,” said lead author Kelian Dascher-Cousineau, who recently completed his PhD at UC Santa Cruz. “It’s a very different landscape today.” 

Due to the proliferation of more sensitive seismological equipment and an increase in data storage capabilities, earthquake catalogues have become larger and more detailed. According to study co-author Emily Brodsky, the traditional ETAS model was not built to handle these larger data sets.