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Sensitivity analysis study of the source parameter uncertainty factors for predicting near-field strong ground motion

机译:预测近场强大地面运动源参数不确定性因素的敏感性分析研究

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Uncertainty factors have substantial influences on the numerical simulations of earthquakes. However, most simulation methods are deterministic and do not sufficiently consider those uncertainty factors. A good approach for predicting future destructive earthquakes that is also applied to probabilistic hazard analysis is studying those uncertainty factors, which is very significant for improving the reliability and accuracy of ground-motion predictions. In this paper, we investigated several uncertainty factors, namely the initial rupture point, stress drop, and number of sub-faults, all of which display substantial influences on ground-motion predictions, via sensitivity analysis. The associated uncertainties are derived by considering the uncertainties in the parameter values, as those uncertainties are associated with the ground motion itself. A sensitivity analysis confirms which uncertainty factors have large influences on ground motion predictions, based upon which we can allocate appropriate weights to those uncertainty factors during the prediction process. We employ the empirical Green function method as a numerical simulation tool. The effectiveness of this method has been previously validated, especially in areas with sufficient earthquake record data such as Japan, Southwest China, and Taiwan, China. Accordingly, we analyse the sensitivities of the uncertainty factors during a prediction of strong ground motion using the empirical Green function method. We consequently draw the following conclusions. (1) The stress drop has the largest influence on ground-motion predictions. The discrepancy between the maximum and minimum PGA among three different stations is very large. In addition, the PGV and PGD also change drastically. The Arias intensity increases exponentially with an increase in the stress drop ratio of two earthquakes. (2) The number of sub-faults also has a large influence on various ground-motion parameters but a small influence on the Fourier spectrum and response spectrum. (3) The initial rupture point largely influences the PGA and Arias intensity. We will accordingly pay additional attention to these uncertainty factors when we conduct ground-motion predictions in the future.
机译:不确定性因素对地震数值模拟具有重大影响。然而,大多数仿真方法是确定性的,并且不充分考虑那些不确定性因素。一种良好的方法,可以预测未来的破坏性地震,该地震也适用于概率危害分析,研究了那些不确定性因素,这对于提高地面运动预测的可靠性和准确性非常重要。在本文中,我们调查了几个不确定性因素,即初始破裂点,应力下降和子故障的数量,所有这些都通过灵敏度分析显示对地运动预测的实质性影响。通过考虑参数值中的不确定性来导出相关的不确定性,因为这些不确定性与地面运动本身相关联。敏感性分析证实了哪些不确定性因素对地面运动预测的影响很大,基于我们可以在预测过程中为这些不确定性因素分配适当的权重。我们使用经验绿色功能方法作为数值模拟工具。此方法的有效性先前已被验证,特别是在具有足够的地震记录数据的地区,如日本,中国和台湾,中国。因此,我们使用经验绿色功能方法分析了在强大的地面运动预测期间的不确定性因素的敏感性。因此,我们得出以下结论。 (1)压力下降对地面运动预测的影响最大。三个不同站中最大和最小PGA之间的差异非常大。此外,PGV和PGD也会大大变化。 arias强度随着两个地震的应力下降比例呈指数增长。 (2)子故障的数量也对各种地运动参数产生了很大的影响,但对傅里叶频谱和响应光谱的影响很小。 (3)初始破裂点在很大程度上影响PGA和Arias强度。因此,当我们在未来进行地面运动预测时,我们将额外关注这些不确定性因素。

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