An example of application we saw early on for neural pattern prediction, now coming to fruition. Here in Nature. Other apparently subtle, but important links to patterns?
Artificial intelligence nails predictions of earthquake aftershocks
A neural-network analysis outperforms the method scientists typically use to work out where these tremors will strike.
A machine-learning study that analysed hundreds of thousands of earthquakes beat the standard method at predicting the location of aftershocks.
Scientists say that the work provides a fresh way of exploring how changes in ground stress, such as those that occur during a big earthquake, trigger the quakes that follow. It could also help researchers to develop new methods for assessing seismic risk.
“We’ve really just scratched the surface of what machine learning may be able to do for aftershock forecasting,” says Phoebe DeVries, a seismologist at Harvard University in Cambridge, Massachusetts. She and her colleagues report their findings1 on 29 August in Nature.
Aftershocks occur after the main earthquake, and they can be just as damaging — or more so — than the initial shock. A magnitude-7.1 earthquake near Christchurch, New Zealand, in September 2010 didn’t kill anyone: but a magnitude-6.3 aftershock, which followed more than 5 months later and hit closer to the city centre, resulted in 185 deaths. ... "
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