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

Chen, Jin; Tang, Hong; Chen, Wenkai

doi:https://doi.org/10.5194/nhess-20-3117-2020

Articles | Volume 20, issue 11

Nat. Hazards Earth Syst. Sci., 20, 3117–3134, 2020

https://doi.org/10.5194/nhess-20-3117-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Nat. Hazards Earth Syst. Sci., 20, 3117–3134, 2020

https://doi.org/10.5194/nhess-20-3117-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 11

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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Final revised paper (published on 24 Nov 2020)
Preprint (discussion started on 07 Jul 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Comments on the manuscript entitled: “Deep Learning of Aftershock Hysteresis Effect Based on Elastic Dislocation Theory” by Jin Chen, Hong Tang, and Wenkai Chen', Anonymous Referee #1, 07 Jul 2020
- AC1: 'Response to the Anonymous Referee1', H. Tang, 14 Sep 2020
RC2: 'Review of manuscript Deep Learning of Aftershock Hysteresis Effect Based on Elastic Dislocation Theory by Jin Chen et al.', Athanassios Ganas, 14 Aug 2020
- AC2: 'Response to the Referee2', H. Tang, 14 Sep 2020

Peer-review completion

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

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.