Calibration of ground motion models to Icelandic peak ground acceleration data using Bayesian Markov Chain Monte Carlo simulation

Abstract

Iceland is seismically the most active region in northern Europe. Large single earthquakes (~ \( M_{\text{w}} \) 7) and seismic sequences of moderate-to-strong earthquakes (~ \( M_{\text{w}} \) 6–6.5) have repeatedly occurred during past centuries in the populated South Iceland Seismic Zone (SISZ). The seismic hazard in Iceland has mainly been evaluated using ground motion models (GMMs) developed from strong-motion observations in other countries and only to a very limited extent from Icelandic data, despite a particularly rapid attenuation of ground motions with distance in Iceland. In this study, we evaluate the performance of these GMMs against the Icelandic strong-motion dataset, consisting of peak ground accelerations of moderate-to-strong (\( M_{\text{w}} \) 5–6.5) and local (0–80 km) earthquakes in the SISZ. We find that these GMMs exhibit both a strong bias against the dataset and a relatively large variability, which calls their applicability and earlier hazard analyses into question. To address this issue, we recalibrate each of the GMMs to the dataset using Bayesian regression and Markov Chain Monte Carlo simulations. This approach allows useful prior information of the GMM parameters to be combined with the likelihood of the observed data and provides posterior probability density functions of model residuals and regression parameters. The recalibrated GMMs are unbiased with respect to the data and have a low total standard deviation of around 0.17 (base-10 logarithmic units). The model-to-model variability in the median predictions vary primarily with distance, reaching 0.05 the lowest for \( M_{\text{w}} \) 6.3–6.5 at intermediate distances. While the lack of near-fault and far-field data, particularly at large magnitudes, and the different functional forms of the GMMs calibrated to the same dataset may affect the results, the recalibrated GMMs should represent well the ground motions of a typical sequence of moderate-to-strong SISZ earthquakes. We present the recalibrated GMMs of this study as promising candidates for future use in ground motion prediction in Iceland e.g., in the context of either a logic tree or the backbone approach in probabilistic seismic hazard assessment.

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Acknowledgements

This study was funded by the Icelandic Centre for Research Grant of Excellence (No. 141261051/52/53) and Project Grant (No. 196089-051), the Eimskip Doctoral Fund of the University of Iceland, the Icelandic Catastrophe Insurance, and the Research Fund of the University of Iceland. The authors gratefully acknowledge the support, and Dr. Símon Ólafsson at the Earthquake Engineering Research Center of the University of Iceland that provided the dataset of the Icelandic strong-motion network, also available at the Internet Site for European Strong-motion Data (www.isesd.hi.is). Finally, the authors would like to express their gratitude to the two reviewers for their constructive comments that led to significant improvements of the manuscript.

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Kowsari, M., Halldorsson, B., Hrafnkelsson, B. et al. Calibration of ground motion models to Icelandic peak ground acceleration data using Bayesian Markov Chain Monte Carlo simulation. Bull Earthquake Eng 17, 2841–2870 (2019). https://doi.org/10.1007/s10518-019-00569-5

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Keywords

  • Iceland
  • Ground motion models
  • Bayesian
  • Markov Chain Monte Carlo
  • Seismic hazard