New Information Technology Findings from University of Iceland Discussed (Calibration of Ground Motion Models To Icelandic Peak Ground Acceleration Data Using Bayesian Markov Chain Monte Carlo Simulation)

2019 JUN 12 (NewsRx) -- By a News Reporter-Staff News Editor at Information Technology Daily -- Data detailed on Information Technology have been presented. According to news originating from Reykjavik, Iceland, by NewsRx correspondents, research stated, “Iceland is seismically the most active region in northern Europe. Large single earthquakes (Mw 7) and seismic sequences of moderate-to-strong earthquakes (Mw 6-6.5) have repeatedly occurred during past centuries in the populated South Iceland Seismic Zone (SISZ).”

Financial supporters for this research include Icelandic Centre for Research Grant of Excellence, Eimskip Doctoral Fund of the University of Iceland, Icelandic Catastrophe Insurance, Research Fund of the University of Iceland.

Our news journalists obtained a quote from the research from the University of Iceland, “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 (Mw 5-6.5) and local (0-80km) 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 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.”

According to the news editors, the research concluded: “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.”

For more information on this research see: Calibration of Ground Motion Models To Icelandic Peak Ground Acceleration Data Using Bayesian Markov Chain Monte Carlo Simulation. Bulletin of Earthquake Engineering, 2019;17(6):2841-2870. Bulletin of Earthquake Engineering can be contacted at: Springer, Van Godewijckstraat 30, 3311 Gz Dordrecht, Netherlands. (Springer - www.springer.com; Bulletin of Earthquake Engineering - http://www.springerlink.com/content/1570-761x/)

The news correspondents report that additional information may be obtained from B. Halldorsson, University of Iceland, Faculty of Civil and Environmental Engineering, School of Engineering and Natural Sciences, Earthquake Engineering Research Center, Reykjavik, Iceland. Additional authors for this research include M. Kowsari, B. Hrafnkelsson, J.T. Snaebjornsson and S. Jonsson.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1007/s10518-019-00569-5. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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