About the research

In this study, probabilistic seismic hazard assessment (PSHA) for Iceland will be revised in terms of its sensitivity to its key inputs that generally contain large uncertainties. These uncertainties can significantly affect the hazard results and can be identified in all the inputs of PSHA, including characteristics of the seismic sources, seismicity parameters and selection of ground motion models (GMMs).

To this end, first the seismicity parameters which were poorly determined due to large uncertainties in the Icelandic earthquake catalogue, will be reassessed.

Then, the GMMs and its associated uncertainties as the most important element of any PSHA, will be studied using the Bayesian statistical method which allows the prior information of the model parameters to be combined with the likelihood of the observed data. This feature has made it beneficial for the estimation of GMMs in Iceland where due to both limited number of recordings and their narrow earthquake magnitude ranges, all regression coefficients for a given functional form cannot be properly constrained by data. The ground motion parameters will be then generated by an artificial neural network which is a powerful predictive tool that can be useful for complex problems where there is no well-defined relationship between input and output variables.

Finally, a sensitivity analysis will be applied to investigate the influence for uncertainties in the seismicity parameters and GMMs on the PSHA results in Iceland.

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