Articles | Volume 20, issue 11
https://doi.org/10.5194/nhess-20-3117-2020
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, Hong Tang, and Wenkai Chen

Viewed

Total article views: 1,968 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,366 552 50 1,968 54 52
  • HTML: 1,366
  • PDF: 552
  • XML: 50
  • Total: 1,968
  • BibTeX: 54
  • EndNote: 52
Views and downloads (calculated since 07 Jul 2020)
Cumulative views and downloads (calculated since 07 Jul 2020)

Viewed (geographical distribution)

Total article views: 1,968 (including HTML, PDF, and XML) Thereof 1,964 with geography defined and 4 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 Jun 2024
Download
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.
Altmetrics
Final-revised paper
Preprint