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A partially non-ergodic ground-motion prediction equation for Europe and the Middle East

机译:欧洲和中东的部分非遍历地面运动预测方程

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摘要

A partially non-ergodic ground-motion prediction equation is estimated for Europe and the Middle East. Therefore, a hierarchical model is presented that accounts for regional differences. For this purpose, the scaling of ground-motion intensity measures is assumed to be similar, but not identical in different regions. This is achieved by assuming a hierarchical model, where some coefficients are treated as random variables which are sampled from an underlying global distribution. The coefficients are estimated by Bayesian inference. This allows one to estimate the epistemic uncertainty in the coefficients, and consequently in model predictions, in a rigorous way. The model is estimated based on peak ground acceleration data from nine different European/Middle Eastern regions. There are large differences in the amount of earthquakes and records in the different regions. However, due to the hierarchical nature of the model, regions with only few data points borrow strength from other regions with more data. This makes it possible to estimate a separate set of coefficients for all regions. Different regionalized models are compared, for which different coefficients are assumed to be regionally dependent. Results show that regionalizing the coefficients for magnitude and distance scaling leads to better performance of the models. The models for all regions are physically sound, even if only very few earthquakes comprise one region.
机译:估计了欧洲和中东的部分非遍历地面运动预测方程。因此,提出了一种解释区域差异的层次模型。为此,假定地面运动强度度量的缩放比例相似,但在不同区域不同。这是通过假设采用层次模型来实现的,其中将一些系数视为从基础全局分布中采样的随机变量。系数是通过贝叶斯推断来估计的。这使人们能够以一种严格的方式估计系数中的认知不确定性,从而估计模型预测中的不确定性。该模型是根据来自九个欧洲/中东地区的峰值地面加速度数据估算的。不同地区的地震和记录数量差异很大。但是,由于模型的分层性质,只有很少数据点的区域会从具有更多数据的其他区域中借用强度。这使得可以为所有区域估计单独的一组系数。比较了不同的区域化模型,假设不同的系数与区域相关。结果表明,对幅度和距离比例缩放系数进行区域划分可提高模型的性能。即使只有很少一部分地震构成一个区域,所有区域的模型也具有良好的物理性。

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