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首页> 外文期刊>Seismological research letters >Analysis of Mean Seismic Ground Motion and Its Uncertainty Based on the UCERF3 Geologic Slip-Rate Uncertainty for California
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Analysis of Mean Seismic Ground Motion and Its Uncertainty Based on the UCERF3 Geologic Slip-Rate Uncertainty for California

机译：基于Ucerf3地质滑动率不确定性的均值抗震地面运动分析及其不确定性

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摘要

The Uniform California Earthquake Rupture Forecast v.3 (UCERF3) model (Field et al., 2014) considers epistemic uncertainty in fault-slip rate via the inclusion of multiple rate models based on geologic and/or geodetic data. However, these slip rates are commonly clustered about their mean value and do not reflect the broader distribution of possible rates and associated probabilities. Here, we consider both a double-truncated 2 sigma Gaussian and a boxcar distribution of slip rates and use a Monte Carlo simulation to sample the entire range of the distribution for California fault-slip rates. We compute the seismic hazard following the methodology and logic-tree branch weights applied to the 2014 national seismic hazard model (NSHM) for the western U.S. region (Petersen et al., 2014, 2015). By applying a new approach developed in this study to the probabilistic seismic hazard analysis (PSHA) using precomputed rates of exceedance from each fault as a Green's function, we reduce the computer time by about 10(5)-fold and apply it to the mean PSHA estimates with 1000 Monte Carlo samples of fault-slip rates to compare with results calculated using only the mean or preferred slip rates. The difference in the mean probabilistic peak ground motion corresponding to a 2% in 50-yr probability of exceedance is less than 1% on average over all of California for both the Gaussian and boxcar probability distributions for slip-rate uncertainty but reaches about 18% in areas near faults compared with that calculated using the mean or preferred slip rates. The average uncertainties in 1s peak ground-motion level are 5.5% and 7.3% of the mean with the relative maximum uncertainties of 53% and 63% for the Gaussian and boxcar probability density function (PDF), respectively.
机译：均匀的加州地震破裂预测v.3（Ucerf3）模型（Field等，2014）通过包括基于地质和/或大地测量数据的多个速率模型来考虑故障滑移率的认知不确定性。然而，这些滑动速率通常围绕其平均值聚集，并且不反映可能的速率和相关概率的更广泛的分布。在这里，我们考虑双截断的2个Sigma高斯和滑动速率的BoxCar分配，并使用Monte Carlo仿真来对加利福尼亚防滑率的分布进行样本。我们根据西部美国地区的2014年国家地震危险模型（NSHM）的方法和逻辑树分支权重计算地震危害（Petersen等，2014,2015）。通过在本研究中开发的新方法，使用从每个故障的预先计算的概率地震危害分析（PSHA）作为绿色的功能，我们将计算机时间减少约10（5） - 折叠并将其应用于平均值PSHA估计有1000个蒙特卡罗样本的故障率，以比较仅使用平均值或首选滑动速率计算的结果。对应于50倍的概率峰值接地运动的平均概率峰值接地运动的差异平均小于10岁的加利福尼亚州的滑移率不确定性，但达到约18％与使用均值或优选的滑移率计算的故障附近的区域。 1S峰地运动水平的平均不确定性分别为高斯和博格尔概率密度密度函数（PDF）的相对最大不确定性为5.5％和7.3％。

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