California Earthquake Forecasts Based on Smoothed Seismicity: Model Choices

2011 ◽  
Vol 101 (3) ◽  
pp. 1422-1430 ◽  
Author(s):  
Q. Wang ◽  
D. D. Jackson ◽  
Y. Y. Kagan
Keyword(s):  
Smoothed Seismicity ◽  
Seismicity Model

Seismicity Parameters and Spatially Smoothed Seismicity Model for Iran

2016 ◽  
Vol 106 (3) ◽  
pp. 1133-1150 ◽  
Author(s):  
A. Khodaverdian ◽  
H. Zafarani ◽  
M. Rahimian ◽  
V. Dehnamaki
Keyword(s):  
Seismicity Parameters ◽  
Spatially Smoothed Seismicity ◽  
Smoothed Seismicity ◽  
Seismicity Model

scholarly journals The use of Monte Carlo simulations for seismic hazard assessment in the U.K.

2000 ◽  
Vol 43 (1) ◽  
Author(s):  
R. M. W. Musson
Keyword(s):  
Monte Carlo ◽  
Seismic Hazard ◽  
Hazard Assessment ◽  
Seismic Hazard Assessment ◽  
Probabilistic Seismic Hazard Assessment ◽  
Logic Tree ◽  
The United Kingdom ◽  
The Monte Carlo Method ◽  
Moderate Seismicity ◽  
Seismicity Model

The input required for a seismic hazard study using conventional Probabilistic Seismic Hazard assessment (PSHA) methods can also be used for probabilistic analysis of hazard using Monte Carlo simulation methods. This technique is very flexible, and seems to be under-represented in the literature. It is very easy to modify the form of the seismicity model used, for example, to introduce non-Poissonian behaviour, without extensive reprogramming. Uncertainty in input parameters can also be modelled very flexibly - for example, by the use of a standard deviation rather than by the discrete branches of a logic tree. In addition (and this advantage is perhaps not as trivial as it may sound) the simplicity of the method means that its principles can be grasped by the layman, which is useful when results have to be explained to people outside the seismological/engineering communities, such as planners and politicians. In this paper, some examples of the Monte Carlo method in action are shown in the context of a low to moderate seismicity area: the United Kingdom.

Maximum Earthquake Size and Seismicity Rate from an ETAS Model with Slip Budget

2020 ◽  
Vol 110 (2) ◽  
pp. 874-885
Author(s):  
David Marsan ◽  
Yen Joe Tan
Keyword(s):  
Seismic Moment ◽  
Aftershock Sequence ◽  
Physical Parameters ◽  
Model Parameters ◽  
Seismicity Rate ◽  
Omori Law ◽  
Epidemic Type Aftershock Sequence ◽  
Earthquake Size ◽  
Maximum Earthquake ◽  
Seismicity Model

ABSTRACT We define a seismicity model based on (1) the epidemic-type aftershock sequence model that accounts for earthquake clustering, and (2) a closed slip budget at long timescale. This is achieved by not permitting an earthquake to have a seismic moment greater than the current seismic moment deficit. This causes the Gutenberg–Richter law to be modulated by a smooth upper cutoff, the location of which can be predicted from the model parameters. We investigate the various regimes of this model that more particularly include a regime in which the activity does not die off even with a vanishingly small spontaneous (i.e., background) earthquake rate and one that bears strong statistical similarities with repeating earthquake time series. Finally, this model relates the earthquake rate and the geodetic moment rate and, therefore, allows to make sense of this relationship in terms of fundamental empirical law (the Gutenberg–Richter law, the productivity law, and the Omori law) and physical parameters (seismic coupling, tectonic loading rate).

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