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Sensitivity study of forecasted aftershock seismicity based on Coulomb stress calculation and rate- and state-dependent frictional response

机译:基于库仑应力计算和速率与状态依赖摩擦响应的预测余震地震活动敏感性研究

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摘要

We use the Dieterich (1994) physics-based approach to simulate the spatio- temporal evolution of seismicity caused by stress changes applied to an infinite population of nucleating patches modeled through a rate- and state- dependent friction law. According to this model, seismicity rate changes depend on the amplitude of stress perturbation, the physical constitutive properties of faults (represented by the parameter Aσ), the stressing rate and the background seismicity rate of the study area. In order to apply this model in a predictive manner, we need to understand the impact of physical model parameters and the correlations between them. Firstly we discuss different definitions of the reference seismicity rate and show their impact on the computed rate of earthquake production for the 1992 Landers earthquake sequence as a case study. Furthermore, we demonstrate that all model parameters are strongly correlated for physical and statistical reasons. We discuss this correlation emphasizing that the estimations of the background seismicity rate, stressing rate and Aσ are strongly correlated to reproduce the observed aftershock productivity. Our analytically derived relation demonstrates the impact of these model parameters on the Omori-like aftershock decay: the c- value and the productivity of the Omori law, implying a p-value smaller or equal to 1. Finally, we discuss an optimal strategy to constrain model parameters for near-real time forecasts.
机译:我们使用Dieterich(1994)基于物理学的方法来模拟由应力变化引起的地震活动的时空演化,应力变化适用于通过速率和状态相关的摩擦定律建模的无限数量的成核斑块。根据该模型,地震活动率的变化取决于应力扰动的幅度,断层的物理本构特性(由参数Aσ表示),应力强度和研究区域的背景地震活动率。为了以预测方式应用此模型,我们需要了解物理模型参数的影响以及它们之间的相关性。首先,我们讨论了参考地震活动率的不同定义,并以案例研究的形式展示了它们对1992年Landers地震序列的地震产生率的影响。此外,我们证明,由于物理和统计原因,所有模型参数都高度相关。我们讨论这种相关性,强调背景地震活动率,应力率和Aσ的估计值之间具有很强的相关性,以重现观察到的余震生产率。我们的解析推导关系证明了这些模型参数对类似Omori的余震衰减的影响:C值和Omori定律的生产率,意味着p值小于或等于1。最后,我们讨论了一种优化策略来约束模型参数以进行近实时预测。

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