In the current practice of probabilistic seismic hazard analysis (PSHA), the different estimates of ground motions predicted by ground motion models (GMMs) are attributed to epistemic uncertainty. The epistemic uncertainties arise from the lack of knowledge which is reflected in imperfect models and can be handled by either logic tree or backbone approaches. The use of backbone approach for PSHA provides a more robust estimation of the GMM contribution to the epistemic uncertainty. In this study, we quantify the epistemic uncertainty in the Iranian strong-motion data by a scale factor that can be calibrated to the recorded strong-motions. The scale factor is then added and subtracted from the backbone GMM to fairly cover the spread in the predictions from other GMMs. For this purpose, we used the Iranian strong-motion database that includes 865 records from 167 events up to 2013, with the moment magnitude range of 5.0 ≤ M ≤ 7.4, and distances up to 120 km including a variety of fault mechanisms. On the other hand, several candidate GMMs were selected from local, regional, and worldwide data. Then, we applied a data-driven method based on the deviance information criterion to rank the candidate GMMs and select the best GMM as the backbone model. The results of this study show that the epistemic uncertainty varies approximately from 0.1 to 0.3 in base-10 logarithmic units. It generally has minima in the magnitude range of prevalent data (M 5.5–6.5) and increases for small (M 4.5–5.5) and large earthquake magnitudes (M 6.5–7.5). The results also show that the scale factors generally grow with distance. Moreover, notable site effects are seen in the Iranian strong-motions. We conclude therefore that the proposed backbone GMMs along with the estimated scales factors of this study are promising for use in future earthquake hazard estimation in Iran, as they capture the recorded data and provide information on the upper and lower bounds of ground motion estimates.

中文翻译：

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基于主干地面运动模型捕获伊朗强运动数据中的认识不确定性

在概率地震危险性分析（PSHA）的当前实践中，由地震动模型（GMM）预测的地震动的不同估计是由于认识不确定性引起的。认识论上的不确定性源于缺乏知识，这反映在不完善的模型中，可以通过逻辑树或主干方法来处理。在PSHA中使用骨干方法可以更可靠地估计GMM对认知不确定性的影响。在这项研究中，我们通过比例因子对伊朗的强运动数据中的认知不确定性进行量化，该比例因子可以根据记录的强运动进行校准。然后将比例因子添加到主GMM并从中减去，以公平覆盖其他GMM的预测范围。以此目的，我们使用了伊朗的强运动数据库，该数据库包括截至2013年的167个事件的865条记录，矩震级范围为5.0≤M≤7.4，距离范围长达120 km，其中包括各种断层机制。另一方面，从本地，区域和全球数据中选择了几个候选GMM。然后，我们基于偏差信息准则应用数据驱动方法对候选GMM进行排序，并选择最佳GMM作为主干模型。这项研究的结果表明，以10为底的对数单位，认知不确定性大约在0.1到0.3之间变化。通常，它在流行数据（M 5.5-6.5）的震级范围内具有最小值，而在小地震（M 4.5-5.5）和大地震震级（M 6.5-7.5）范围内增大。结果还表明，比例因子通常随距离而增长。此外，在伊朗的强烈运动中可以看到明显的场地影响。因此，我们得出结论，提议的骨干GMM与本研究的估计比例因子一起有望在伊朗未来的地震灾害估计中使用，因为它们可以捕获记录的数据并提供有关地面运动估计的上下限的信息。