Lightning might not strike twice, but earthquakes can. And forecasting where aftershocks will hit might now be a little easier thanks to an assist from artificial intelligence. Aftershocks can be more destructive than the quakes they follow, making it all the more important for experts to be able to predict them.
But while seismologists have methods to forecast when aftershocks will hit and how strong they will be, there is more uncertainty about how to predict where they will strike. Hoping to address that, a group of researchers trained a "deep learning" program with data about tens of thousands of earthquakes and aftershocks to see if they improve predictions.
"The previous baseline for aftershock forecasting has a precision of around three percent across the testing data set. Our neural network approach has a precision of around six percent," said Phoebe DeVries, co-author of the study published in the journal Nature on Thursday.
"This approach is more accurate because it was developed without a strongly held prior belief about where aftershocks ought to occur," DeVries, a post-doctoral fellow at Harvard, told AFP. The researchers used a type of artificial intelligence known as deep learning, which is loosely modeled on the way the human brain makes connections.
The program allowed the researchers to map relationships "between the characteristics of a large earthquake - the shape of the fault, how much did it slip, and how did it stress the earth - and where aftershocks occurred," said Brendan Meade, professor of earth and planetary science at Harvard, and a study co-author.
The researchers tested the network by holding back a quarter of their data set, and feeding the remaining information into the program.
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