Data‐Driven Seismic‐Hazard Models Prepared for a Seismic Risk Assessment in the Dead Sea Region

Abstract

We derive a probabilistic seismic-hazard model for the Dead Sea region to allow for seismic risk estimation, which will be part of a subsequent study. This hazard model relies as much as possible on data-driven approaches, utilizing a seismic catalog compiled for the region by integrating data from different sources. We derive seismicity models using two different smoothing approaches and estimate a hypocentral depth distribution from historical observations. We do not include paleoseismological evidence apart from the observed seismicity. Because ground-motion records are sparse in the region, we formulate the model in the European Macroseismic intensity scale from 1998. However, the collected macroseismic intensity data are still too few to derive a local intensity prediction equation (IPE). Thus, we choose among existing equations derived for different regions and combine them in a logic tree. Here, motivated by Scherbaum et al. (2010), we propose a two-step approach to select the IPEs for the logic tree. First, we cluster a set of 10 candidate IPEs to identify groups of models that can be considered similar with respect to the distribution of the predicted values for a selection of the explanatory variables (i.e., magnitude and distance), using a k-mean approach (Steinhaus, 1956). Then, we apply different ranking techniques (Scherbaum et al., 2009;Kale and Akkar, 2013) to identify within each cluster the most suitable model to be included in the logic tree. The resulting hazard models are consistent with existing probabilistic seismic-hazard models for the region. We estimate a moderate-to-high hazard of intensity grade VII-VIII with 10% probability of exceedance within 50 years in close vicinity to the Dead Sea transform fault, the dominant seismogenic structure in this region.

Multicriteria seismic risk assessment in Puerto Vallarta metropolitan area, Mexico

“…These results lead to the conclusion that despite the complex site effects characteristics the earthquake has a major impact on the seismic hazard at a large territorial scale. In this way, it is important to notice that even that we do not include the seismic source parameters as criteria in the MCE analysis (e.g., Erden and Karaman 2012; Giannaraki et al 2019;Haas et al 2016;Mohsen et al 2012), we used the source parameter to estimate the PGA criterion. Our results are consistent with previous work that found that parameters with a large weight have a more critical impact in the seismic hazard at large scale while parameters with a lower weight become more important on smaller scales (e.g., Mohanty et al 2007; Nat and Thingbaijam 2009).…”

Principles of Probabilistic Seismic Hazard Assessment (PSHA) and Site Effect Evaluation and Its Application for the Volcanic Environment in El Salvador

“…A robust seismic hazard map must be the result of a rigorous computation employing a logic-tree formulation based on all methods explained in this chapter and the combination of several GMPEs [23,[38][39][40], possible maximum magnitudes and slip rates. Besides, it is advisable to check the covariance or the associated error or dispersion of the final results [17,24,25].…”

The Effect of Alternative Seismotectonic Models on PSHA Results – a Sensitivity Study for the Case of Israel

“…Two recent papers (Al-Tarazi and Sandvol, 2007;Haas et al, 2016) use the gridded-seismicity approach (Frankel, 1995) to produce hazard maps for the entire DST region, based on recorded and historical seismic catalogues, without defining any linear or areal source zones. This approach is becoming more common in areas in which the seismic sources are undefined, or for 10 representation of background seismicity, but is inappropriate for representing large known mapped faults, such as the DST (e.g.…”