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On the applicability of available regression models for estimating Newmark displacements for low to moderate magnitude earthquakes. The case of the Betic Cordillera (S Spain)

机译:论可用回归模型的适用性估算低至适量地震的纽马克位移。 Betic Cordillera的情况(SCAIL)

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摘要

Newmark displacement estimation is generally computed using empirical models. These models are estimated from large datasets that mainly comprise moderate-to-high magnitude events (Mw > 6.0). In this work, we study the performance of several of these models to study moderate-to-low magnitude scenarios. For this purpose, data from the Betic Cordillera, S Spain, with magnitudes ranging from Mw 3.5 to 6.3, were used to compare with model predictions. The results show that errors in the estimates depend on the magnitude of events or on the yielding acceleration considered to estimate the displacement. The availability of an appropriate range of data (magnitude and yielding acceleration), when defining the regression model, may overcome the differences due to specific characteristics of the seismotectonic context of the area where data derives from. The results also show that performance of models including several ground motion predictors is better than those based on a single parameter, regardless of the combination these predictors. Furthermore, regression models with polynomial forms present a better performance than other functions based on the logarithm of these predictors. Finally, new specific models for the Betic Cordillera are proposed, especially suitable for low magnitude events (< 5.0) and low critical accelerations (< 0.1 g), based on simplified polynomial forms of models.
机译:纽约标记位移估计通常使用经验模型计算。这些模型从大型数据集估计,主要包括中等至高幅度事件(MW> 6.0)。在这项工作中,我们研究了几个这些模型的性能,以研究中等到低幅度方案。为此目的,来自Betic Cordillera,S的数据的数据,幅度范围为MW 3.5至6.3,用于与模型预测进行比较。结果表明,估计中的错误取决于所考虑的事件的大小或屈服加速度,以估计位移。在定义回归模型时,适当的数据范围(幅度和产生加速)的可用性可以克服由于数据衍生的区域的地震型背景的特定特征导致的差异。结果还表明,无论这些预测因子组合如何,包括多个地面运动预测器的模型的性能优于基于单个参数的模型。此外,具有多项式形式的回归模型具有比基于这些预测器的对数的其他功能的性能更好。最后,提出了新的新型泡菜细胞的模型,特别适用于基于简化多项式的模型形式的低幅度事件(<5.0)和低临界加速(<0.1g)。

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