Articles | Volume 20, issue 11
Nat. Hazards Earth Syst. Sci., 20, 3117–3134, 2020
Nat. Hazards Earth Syst. Sci., 20, 3117–3134, 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.

Related authors

The Effect of the Wenchuan and Lushan Earthquakes on the Size Distribution of Earthquakes along the Longmenshan Fault
Chun Hui, Changxiu Cheng, Shi Shen, Peichao Gao, Jin Chen, Jing Yang, and Min Zhao
Nat. Hazards Earth Syst. Sci. Discuss.,,, 2021
Preprint withdrawn
Short summary

Related subject area

Earthquake Hazards
Geologic and geodetic constraints on the magnitude and frequency of earthquakes along Malawi's active faults: the Malawi Seismogenic Source Model (MSSM)
Jack N. Williams, Luke N. J. Wedmore, Åke Fagereng, Maximilian J. Werner, Hassan Mdala, Donna J. Shillington, Christopher A. Scholz, Folarin Kolawole, Lachlan J. M. Wright, Juliet Biggs, Zuze Dulanya, Felix Mphepo, and Patrick Chindandali
Nat. Hazards Earth Syst. Sci., 22, 3607–3639,,, 2022
Short summary
Probabilistic fault displacement hazard analysis for the north Tabriz fault
Mohamadreza Hosseini and Habib Rahimi
Nat. Hazards Earth Syst. Sci., 22, 3571–3583,,, 2022
Short summary
Landslides triggered by the 2015 Mw 6.0 Sabah (Malaysia) earthquake: inventory and ESI-07 intensity assignment
Maria Francesca Ferrario
Nat. Hazards Earth Syst. Sci., 22, 3527–3542,,, 2022
Short summary
Pseudo-prospective testing of 5-year earthquake forecasts for California using inlabru
Kirsty Bayliss, Mark Naylor, Farnaz Kamranzad, and Ian Main
Nat. Hazards Earth Syst. Sci., 22, 3231–3246,,, 2022
Short summary
An updated area-source seismogenic model (MA4) for seismic hazard of Italy
Francesco Visini, Carlo Meletti, Andrea Rovida, Vera D'Amico, Bruno Pace, and Silvia Pondrelli
Nat. Hazards Earth Syst. Sci., 22, 2807–2827,,, 2022
Short summary

Cited articles

Bird, P.: An updated digital model of plate boundaries, Geochem., Geophys., Geosys., 4, 1027,, 2003. 
Bodri, B.: A neural-network model for earthquake occurrence, J. Geodyn., 32, 289–310,, 2001. 
Bondár, I. and Storchak, D. A.: Improved location procedures at the International Seismological Centre, Geophys. J. Int., 186, 1220–1244,, 2011. 
Cheng, D., Zhang, Y., and Wang, X.: Coseismic deformation and fault slip inversion of the 2017 Mw7.3 Halabjah, Iraq, earthquake based on Sentinel-1A data, Acta Seismologica Sinica, 41, 484–493,, 2019. 
DeVries, P. M. R., Viégas, F., Wattenberg, M., and Meade, B. J.: Deep learning of aftershock patterns following large earthquakes, Nature, 560, 632–634,, 2018. 
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.
Final-revised paper