Deep learning of aftershock patterns following large earthquakes

@article{DeVries2018DeepLO,
  title={Deep learning of aftershock patterns following large earthquakes},
  author={P. R. DeVries and F. Vi{\'e}gas and M. Wattenberg and B. Meade},
  journal={Nature},
  year={2018},
  volume={560},
  pages={632-634}
}
Aftershocks are a response to changes in stress generated by large earthquakes and represent the most common observations of the triggering of earthquakes. The maximum magnitude of aftershocks and their temporal decay are well described by empirical laws (such as Bath’s law1 and Omori’s law2), but explaining and forecasting the spatial distribution of aftershocks is more difficult. Coulomb failure stress change3 is perhaps the most widely used criterion to explain the spatial distributions of… Expand
Reply to: One neuron versus deep learning in aftershock prediction
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One neuron is more informative than a deep neural network for aftershock pattern forecasting
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