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This article presents updated seismic hazard curves, spectra, and maps of ground motion intensity measures for the northern region of the Dominican Republic (DR) obtained using a probabilistic seismic hazard analysis (PSHA). The analysis performed uses as input data an earthquake recurrence model based on fault slip rates derived from GPS measurements published in the aftermath of the 2010 Haiti earthquake. The seismicity rate data are used to calibrate a composite characteristic earthquake model, which is combined with a Poisson process to provide a temporal characterization of earthquake occurrence. The seismic hazard curves and maps presented include parameters such as (horizontal) peak ground acceleration and pseudo-spectral response accelerations at 0.2s and 1.0s periods for 5% damping at firm rock sites. The results show that the ground motion parameters with a 2% probability of exceedance (PE) in 50 years determined in this study are up to 46% larger than the corresponding parameters specified in the current DR building code seismic hazard maps for the northern DR. Moreover, the design response spectra for a site in the city of Santiago specified in the code is significantly lower than the 2% PE in 50 years uniform hazard spectra determined in this study for vibration periods smaller than 0.5s, a range that includes the majority of the structures that define the built environment of the DR. [ABSTRACT FROM AUTHOR]

Seismic hazard analysis is often conducted by removing dependent events from earthquake catalogs, and the occurrence of events is described via a homogeneous Poisson process. Accordingly, the temporal clustering behavior of events is ignored in hazard estimations, resulting in underestimating the level of damaging ground motion. Statistical models which characterize aftershock sequences and their branching structures can be implemented in order to account for the effect of these dependent events and subsequently determine the probability of exceedance of clusters in a given period of time. This article presents a new framework to include aftershock statistical models into probabilistic seismic hazard analysis (PSHA) based on a smoothing approach. For this purpose, the hierarchical structure of aftershock sequences is modeled using the epidemic-type aftershock sequence (ETAS) model, and the obtained results are compared with those of the Reasenberg-Jones (RJ) model as well. The results show that the maximum relative differences in peak ground acceleration (PGA) estimation between the conventional Poissonian-PSHA and Clustering-PSHA based on the RJ and ETAS models are about 69% and 81% (at 2% exceedance probability level), respectively. Furthermore, modeling the behavioral pattern of aftershock sequences with branching structure using the ETAS model leads to higher PGA values compared to the RJ model, which only considers the primary aftershocks. [ABSTRACT FROM AUTHOR]

A site-specific probabilistic seismic hazard analysis (PSHA) has been performed for the only nuclear power plant site in Brazil, located 130 km southwest of Rio de Janeiro at Angra dos Reis. Logic trees were developed for both the seismic source characterisation and ground-motion characterisation models, in both cases seeking to capture the appreciable ranges of epistemic uncertainty with relatively few branches. This logic-tree structure allowed the hazard calculations to be performed efficiently while obtaining results that reflect the inevitable uncertainty in long-term seismic hazard assessment in this tectonically stable region. An innovative feature of the study is an additional seismic source zone added to capture the potential contributions of characteristics earthquake associated with geological faults in the region surrounding the coastal site. [ABSTRACT FROM AUTHOR]

Estimation of ground-motion amplitudes of different hazard levels is of paramount importance in planning of urban development of any metropolis. Such estimation can be computed through a probabilistic seismic hazard analysis (PSHA). This paper concentrates on the PSHA of an area located in Shiraz city, southern Iran. The area includes whole of Shiraz city (i.e., one of the largest and most populous cities of Iran) and its outskirts. Conventional and Monte Carlo simulation-based approaches are utilized to perform the PSHA of the studied area. Two areal seismic source models are delineated, and thence seismicity parameters of all zones associated with their corresponding uncertainties are computed. Uncertainties in ground-motion prediction are accounted for via three ground-motion prediction equations (GMPEs) within the logic tree framework. These GMPEs are applied to estimate bedrock ground shaking (Vs30 = 760 m/s) for several return periods (i.e., 75, 475, 975, and 2475 years). In general, the results of the two abovementioned PSHA approaches show relatively similar results. However, the Monte Carlo simulation-based approach overpredicts bedrock spectral accelerations at periods of 0.4-2.5 s compared to the conventional PSHA approach for return periods of 475, 975, and 2475 years. [ABSTRACT FROM AUTHOR]

In this paper, I introduce a novel approach to modelling the individual random component (also called the intra-event uncertainty) of a ground-motion relation (GMR), as well as a novel approach to estimating the corresponding parameters. In essence, I contend that the individual random component is reproduced adequately by a simple stochastic mechanism of random impulses acting in the horizontal plane, with random directions. The random number of impulses was Poisson distributed. The parameters of the model were estimated according to a proposal by Raschke J Seismol 17(4):1157-1182, (2013a), with the sample of random difference ξ = ln( Y )- ln( Y ), in which Y and Y are the horizontal components of local ground-motion intensity. Any GMR element was eliminated by subtraction, except the individual random components. In the estimation procedure, the distribution of difference ξ was approximated by combining a large Monte Carlo simulated sample and Kernel smoothing. The estimated model satisfactorily fitted the difference ξ of the sample of peak ground accelerations, and the variance of the individual random components was considerably smaller than that of conventional GMRs. In addition, the dependence of variance on the epicentre distance was considered; however, a dependence of variance on the magnitude was not detected. Finally, the influence of the novel model and the corresponding approximations on PSHA was researched. The applied approximations of distribution of the individual random component were satisfactory for the researched example of PSHA. [ABSTRACT FROM AUTHOR]

We introduce a new approach for ground motion relations (GMR) in the probabilistic seismic hazard analysis (PSHA), being influenced by the extreme value theory of mathematical statistics. Therein, we understand a GMR as a random function. We derive mathematically the principle of area equivalence, wherein two alternative GMRs have an equivalent influence on the hazard if these GMRs have equivalent area functions. This includes local biases. An interpretation of the difference between these GMRs (an actual and a modeled one) as a random component leads to a general overestimation of residual variance and hazard. Beside this, we discuss important aspects of classical approaches and discover discrepancies with the state of the art of stochastics and statistics (model selection and significance, test of distribution assumptions, extreme value statistics). We criticize especially the assumption of logarithmic normally distributed residuals of maxima like the peak ground acceleration (PGA). The natural distribution of its individual random component (equivalent to exp( ε) of Joyner and Boore, Bull Seism Soc Am 83(2):469-487, ) is the generalized extreme value. We show by numerical researches that the actual distribution can be hidden and a wrong distribution assumption can influence the PSHA negatively as the negligence of area equivalence does. Finally, we suggest an estimation concept for GMRs of PSHA with a regression-free variance estimation of the individual random component. We demonstrate the advantages of event-specific GMRs by analyzing data sets from the PEER strong motion database and estimate event-specific GMRs. Therein, the majority of the best models base on an anisotropic point source approach. The residual variance of logarithmized PGA is significantly smaller than in previous models. We validate the estimations for the event with the largest sample by empirical area functions, which indicate the appropriate modeling of the GMR by an anisotropic point source model. The constructed distances like the Joyner-Boore distance do not work well for event-specific GMRs. We discover also a strong relation between magnitude and the squared expectation of the PGAs being integrated in the geo-space for the event-specific GMRs. One of our secondary contributions is the simple modeling of anisotropy for a point source model. [ABSTRACT FROM AUTHOR]