Deep learning of aftershock patterns following large earthquakes

Nature
Phoebe M R DeVriesBrendan J Meade

Abstract

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 aftershocks4-8, but its applicability has been disputed9-11. Here we use a deep-learning approach to identify a static-stress-based criterion that forecasts aftershock locations without prior assumptions about fault orientation. We show that a neural network trained on more than 131,000 mainshock-aftershock pairs can predict the locations of aftershocks in an independent test dataset of more than 30,000 mainshock-aftershock pairs more accurately (area under curve of 0.849) than can classic Coulomb failure stress change (area under curve of 0.583). We find that the learned aftershock pattern is physically interpretable: the maximum change in shear stress, the von Mises yield criterion (a scaled version of the second invariant of the deviato...Continue Reading

References

May 21, 2005·Science·Charles J AmmonDavid Wald

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Citations

Nov 7, 2019·Plants·Muhammad Hammad SaleemKhalid Mahmood Arif
Oct 4, 2019·Nature·Arnaud Mignan, Marco Broccardo
Mar 13, 2020·Optics Letters·Ronghui LinXiaohang Li
Oct 13, 2020·Physics Reports·Jingfang FanHans Joachim Schellnhuber
Mar 6, 2021·Nature Communications·Wenhuan KuangJie Zhang
Jun 2, 2021·Nature Chemistry·Nongnuch ArtrithAron Walsh
Oct 7, 2021·Proceedings of the National Academy of Sciences of the United States of America·Carlos FloydGaregin A Papoian
Oct 11, 2019··Zhan ShiXiangru Huang

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Software Mentioned

Theano
Keras
TensorFlow

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