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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/nhess-2020-161
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2020-161
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Jul 2020

07 Jul 2020

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A revised version of this preprint was accepted for the journal NHESS and is expected to appear here in due course.

Deep Learning of Aftershock Hysteresis Effect Based on Elastic Dislocation Theory

Jin Chen1,2, Hong Tang1,2, and Wenkai Chen3 Jin Chen et al.
  • 1Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • 2State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, P.R. China
  • 3Institute of Lanzhou Earthquake Research, China Earthquake Administration, Gansu Lanzhou 730000, China

Abstract. This paper selects fault source models of typical earthquakes across the globe and uses a volume extending 100 km horizontally from each mainshock rupture plane and 50 km vertically as the primary area of earthquake influence for calculation and analysis. A deep neural network is constructed to model the relationship between elastic stress tensor components and aftershock state at multiple time scales, and the model is evaluated. Finally, based on the aftershock hysteresis model, the aftershock hysteresis effect of the Wenchuan earthquake in 2008 and Tohoku earthquake in 2011 is analyzed, and the aftershock hysteresis effect at different depths is compared and analyzed. The correlation between the aftershock hysteresis effect and the Omori formula is also discussed and analyzed. The constructed aftershock hysteresis model has a good fit to the data and can predict the aftershock pattern at multiple time scales after a large earthquake. Compared with the traditional aftershock spatial analysis method, the model is more effective and fully considers the distribution of actual faults, instead of treating the earthquake as a point source. The expansion rate of the aftershock pattern is negatively correlated with time, and the aftershock patterns at all time scales are roughly similar and anisotropic.

Jin Chen et al.

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Jin Chen et al.

Jin Chen et al.

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Latest update: 23 Oct 2020
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Short summary
The spatial and temporal distribution characteristics of aftershocks around the fault are analyzed according to the stress changes after the main earthquake. The model can be used to predict the multi time scale anisotropy distribution of aftershocks fairly. The finite fault model of the main earthquake is used in the construction of the prediction model.The model is a depth neural network, the inputs are the stress components of each point, and the output is the probability of aftershock.
The spatial and temporal distribution characteristics of aftershocks around the fault are...
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