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.
Until now, most scientists used a technique that calculates how an earthquake changes the stress in nearby rocks and then predicts how likely that change would result in an aftershock in a particular location. This stress-failure method can explain aftershock patterns successfully for many large earthquakes, but it doesn’t always work.
The network treated each cell as its own little isolated problem to solve, rather than calculating how stress rippled sequentially through the rocks.
When the researchers tested their system on 30,000 mainshock-aftershock events, the neural-network forecast predicted aftershock locations more accurately than did the usual stress-failure method.
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