Abstract
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 timescales, 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 timescales 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 timescales are roughly similar and anisotropic.
Highlights
After the occurrence of strong earthquakes, there is often a large number of aftershocks, which constitute the aftershock sequence
In 2018, DeVries et al (2018) proposed a deep neural network to study the spatial distribution of aftershocks following the main earthquake
The aftershock hysteresis model under multiple timescales is obtained by using the neural network to train the constructed training dataset
Summary
After the occurrence of strong earthquakes, there is often a large number of aftershocks, which constitute the aftershock sequence. Stein and Lisowski systematically discussed the influence of the static stress of the main earthquake on the spatial distribution of aftershocks (Stein and Lisowski, 1983). In 2018, DeVries et al (2018) proposed a deep neural network to study the spatial distribution of aftershocks following the main earthquake. A neural network classifier based on stress variation was designed by the authors to determine the possibility of a spatial distribution of aftershocks (DeVries et al, 2018). This idea combines traditional physical analysis mechanisms with data-driven ma-
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