We apply three data‐driven selection methods, log‐likelihood (LLH), Euclidean distance‐based ranking (EDR), and deviance information criterion (DIC), to objectively evaluate the predictive capability of 10 ground‐motion models (GMMs) developed from Iranian and worldwide data sets against a new and independent Iranian strong‐motion data set. The data set includes, for example, the 12 November 2017 Mw 7.3 Ezgaleh earthquake and the 25 November 2018 Mw 6.3 Sarpol‐e Zahab earthquake and includes a total of 201 records from 29 recent events with moment magnitudes 4.5≤Mw≤7.3 with distances up to 275 km. The results of this study show that the prior sigma of the GMMs acts as the key measure used by the LLH and EDR methods in the ranking against the data set. In some cases, this leads to the resulting model bias being ignored. In contrast, the DIC method is free from such ambiguity as it uses the posterior sigma as the basis for the ranking. Thus, the DIC method offers a clear advantage of partially removing the ergodic assumption from the GMM selection process and allows a more objective representation of the expected ground motion at a specific site when the ground‐motion recordings are homogeneously distributed in terms of magnitudes and distances. The ranking results thus show that the local models that were exclusively developed from Iranian strong motions perform better than GMMs from other regions for use in probabilistic seismic hazard analysis in Iran. Among the Next Generation Attenuation‐West2 models, the GMMs by Boore et al. (2014) and Abrahamson et al. (2014) perform better. The GMMs proposed by Darzi et al. (2019) and Farajpour et al. (2019) fit the recorded data well at short periods (peak ground acceleration and pseudoacceleration spectra at T=0.2 s⁠). However, at long periods, the models developed by Zafarani et al. (2018), Sedaghati and Pezeshk (2017), and Kale et al. (2015) are preferable.

中文翻译：

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基于独立数据集的伊朗概率地震危险性分析中地面运动模型（GMM）的排名

我们应用三种数据驱动的选择方法，对数似然（LLH），基于欧氏距离的排名（EDR）和偏差信息标准（DIC），以客观地评估10种地面运动模型（GMM）的预测能力。伊朗和全球数据集与新的独立伊朗强烈运动数据集相对比。数据集包括，例如2017年11月12日的7.3 Ezgaleh地震和2018年11月25日的6.3 Sarpol-e Zahab地震，并包含距离最近的29个最近事件的201条记录，矩震级为4.5≤Mw≤7.3到275公里。这项研究的结果表明，GMM的先验sigma是LLH和EDR方法在对数据集进行排名时使用的关键度量。在某些情况下，这会导致所产生的模型偏差被忽略。相反，DIC方法没有任何歧义，因为它使用后西格玛作为排名的基础。因此，DIC方法具有从GMM选择过程中部分消除遍历假设的明显优势，并且当地面运动记录在幅度和距离上均匀分布时，可以更客观地表示特定地点的预期地面运动。 。因此，排名结果表明，在伊朗概率地震灾害分析中使用的，完全由伊朗强烈运动开发的局部模型的性能优于其他地区的GMM。在下一代Attenuation-West2模型中，Boore等人的GMM。（2014）和Abrahamson等人。（2014）表现更好。Darzi等人提出的GMM。（2019）和Farajpour等。（2019）在短时间内很好地拟合了记录的数据（T = 0.2s⁠的峰值地面加速度和伪加速度谱）。然而，长期以来，Zafarani等人开发的模型。（2018），Sedahghati和Pezeshk（2017）和Kale等人。（2015）为佳。