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Capturing epistemic uncertainty in the Iranian strong-motion data on the basis of backbone ground motion models

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Abstract

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.

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Acknowledgments

We wish to express our sincere gratitude to the anonymous reviewers for their useful comments and suggestions that improved the manuscript. We also thank the Building and Housing Research Centre (BHRC) of Iran for providing the strong-motion database. The third author would like to thank Dr. Mohammad Reza Ebrahimi for his contribution in presenting seismicity.

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Authors and Affiliations

  1. Faculty of Civil and Environmental Engineering, School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland

    Milad Kowsari

  2. Earthquake Engineering Research Centre, University of Iceland, Selfoss, Iceland

    Milad Kowsari

  3. Department of Civil Engineering, University of Tehran, Tehran, Iran

    Saeid Ghasemi

  4. Department of Civil Engineering, University of Memphis|, Memphis, TN, USA

    Zoya Farajpour

  5. International Institute of Earthquake Engineering and Seismology, Tehran, Iran

    Mehdi Zare

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  1. Milad Kowsari
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  2. Saeid Ghasemi
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  4. Mehdi Zare
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Correspondence to Milad Kowsari.

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Kowsari, M., Ghasemi, S., Farajpour, Z. et al. Capturing epistemic uncertainty in the Iranian strong-motion data on the basis of backbone ground motion models. J Seismol 24, 75–87 (2020). https://doi.org/10.1007/s10950-019-09886-3

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