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Bayesian inference of earthquake rupture models using polynomial chaos expansion

机译:使用多项式混沌扩展的地震破裂模型的贝叶斯推动

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摘要

In this paper, we employed polynomial chaos (PC) expansions to understand earthquake rupture model responses to random fault plane properties. A sensitivity analysis based on our PC surrogate model suggests that the hypocenter location plays a dominant role in peak ground velocity (PGV) responses, while elliptical patch properties only show secondary impact. In addition, the PC surrogate model is utilized for Bayesian inference of the most likely underlying fault plane configuration in light of a set of PGV observations from a ground-motion prediction equation (GMPE). A restricted sampling approach is also developed to incorporate additional physical constraints on the fault plane configuration and to increase the sampling efficiency.
机译:在本文中,我们采用多项式混沌(PC)扩展来了解地震破裂模型对随机故障平面特性的反应。基于PC代理模型的敏感性分析表明,低静脉位置在峰值地速度(PGV)响应中起显着作用,而椭圆形贴片性仅显示二次冲击。此外,鉴于来自地面运动预测等式(GMPE)的一组PGV观察,PC代理模型用于最可能的底层故障平面配置的贝叶斯推动。还开发了一种限制的采样方法来纳入故障平面配置上的额外物理限制,并提高采样效率。

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