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The 2014 Earthquake Model of the Middle East: ground motion model and uncertainties

Danciu, L. et al.

Physical risk



We summarize the main elements of a ground-motion model, as built in three-year effort within the Earthquake Model of the Middle East (EMME) project. Together with the earthquake source, the ground-motion models are used for a probabilistic seismic hazard assessment (PSHA) of a region covering eleven countries: Afghanistan, Armenia, Azerbaijan, Cyprus, Georgia, Iran, Jordan, Lebanon, Pakistan, Syria and Turkey. Given the wide variety of ground-motion predictive models, selecting the appropriate ones for modeling the intrinsic epistemic uncertainty can be challenging. In this respect, we provide a strategy for ground-motion model selection based on data-driven testing and sensitivity analysis. Our testing procedure highlights the models of good performance in terms of both data-driven and non-data-driven testing criteria. The former aims at measuring the match between the ground-motion data and the prediction of each model, whereas the latter aims at identification of discrepancies between the models. The selected set of ground models were directly used in the sensitivity analyses that eventually led to decisions on the final logic tree structure. The strategy described in great details hereafter was successfully applied to shallow active crustal regions, and the final logic tree consists of four models (Akkar and Çağnan in Bull Seismol Soc Am 100:2978–2995, 2010; Akkar et al. in Bull Earthquake Eng 12(1):359–387, 2014; Chiou and Youngs in Earthq Spectra 24:173–215, 2008; Zhao et al. in Bull Seismol Soc Am 96:898–913, 2006). For other tectonic provinces in the considered region (i.e., subduction), we adopted the predictive models selected within the 2013 Euro-Mediterranean Seismic Hazard Model (Woessner et al. in Bull Earthq Eng 13(12):3553–3596, 2015). Finally, we believe that the framework of selecting and building a regional ground-motion model represents a step forward in ground-motion modeling, particularly for large-scale PSHA models.

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