Articles | Volume 20, issue 11
Nat. Hazards Earth Syst. Sci., 20, 3117–3134, 2020
https://doi.org/10.5194/nhess-20-3117-2020
Nat. Hazards Earth Syst. Sci., 20, 3117–3134, 2020
https://doi.org/10.5194/nhess-20-3117-2020

Research article 24 Nov 2020

Research article | 24 Nov 2020

Deep learning of the aftershock hysteresis effect based on the elastic dislocation theory

Jin Chen et al.

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AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (13 Oct 2020) by Filippos Vallianatos
AR by H. Tang on behalf of the Authors (16 Oct 2020)  Author's response    Manuscript
ED: Publish as is (18 Oct 2020) by Filippos Vallianatos
AR by H. Tang on behalf of the Authors (19 Oct 2020)  Author's response    Manuscript
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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-timescale 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 deep neural network; the inputs are the stress components of each point; and the output is the probability of an aftershock.
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