For more than 30 years, the models that researchers and government agencies use to forecast earthquake aftershocks have remained largely unchanged. While these older models work well with limited data, they struggle with the huge seismology datasets that are now available.
The new model outperformed the current model, known as the Epidemic Type Aftershock Sequence model, for earthquake catalogs of about 10,000 events and greater.
"The ETAS model is kind of brittle, and it has a lot of very subtle and finicky ways in which it can fail," said Dascher-Cousineau."So, we spent a lot of time making sure we weren't messing up our benchmark compared to actual model development." This is not the first time scientists have tried using machine learning to forecast earthquakes, but until recently, the technology was not quite ready, said Dascher-Cousineau. New advances in machine learning make the RECAST model more accurate and easily adaptable to different earthquake catalogs.
Using deep-learning models will also eventually allow researchers to expand the type of data they use to forecast seismicity.
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