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

• Published in: Bulletin of Earthquake Engineering
• Main Authors: Kowsari, Milad, Halldorsson, Benedikt, Hrafnkelsson, Birgir, Snæbjörnsson, Jónas Þór, Jonsson, Sigurjon
• Other Authors: Crustal Deformation and InSAR Group, Earth Science and Engineering Program, Physical Science and Engineering (PSE) Division, Faculty of Civil and Environmental Engineering, School of Engineering and Natural Sciences, Earthquake Engineering Research Centre, University of Iceland, Reykjavík, Iceland, Division of Processing and Research, Icelandic Meteorological Office, Reykjavík, Iceland, Faculty of Physical Sciences, School of Engineering and Natural Sciences, University of Iceland, Reykjavík, Iceland, School of Science and Engineering, Reykjavík University, Reykjavík, Iceland
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Springer Nature 2019
• Subjects: Iceland; Ground motion models; Bayesian; Markov Chain Monte Carlo; Seismic hazard
• Online Access: http://hdl.handle.net/10754/631608; https://doi.org/10.1007/s10518-019-00569-5

Iceland is seismically the most active region in northern Europe. Large single earthquakes (~ $$ M_{\text{w}} $$Mw 7) and seismic sequences of moderate-to-strong earthquakes (~ $$ M_{\text{w}} $$Mw 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 ($$ M_{\text{w}} $$Mw 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 $$ M_{\text{w}} $$Mw 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 ...