Empirical-Stochastic Ground Motion Prediction for Eastern North America


An alternative approach based on a hybrid-empirical model is utilized to predict the ground motion relationship for eastern North America (ENA). In this approach, a stochastic model is first used to derive modification factors from the ground motions in western North America (WNA) to the ground motions in ENA. The ground motion parameters are then estimated to develop an empirical attenuation relationship for ENA using empirical ground motion relationships from WNA. We develop an empiricalstochastic source model for both regions to obtain ground motions at different magnitudedistance range of interest. At short distances ( 30 R ≤ km) and large magnitudes ( 4 . 6 ≥ w M ), an equivalent point-source model is carried out to consider the effect of finite-fault modeling on the ground-motion parameters. Source focal depth and Brune stress drop are assumed to be magnitude dependent. We choose three well-defined empirical attenuation relationships for WNA in order to compare the empirical ground motion processes between the two regions. A composite functional attenuation form is defined and in turn a nonlinear regression analysis is performed using a genetic algorithm (GA) for a wide range of magnitudes and distances to develop an empirical attenuation relationship from the stochastic ground-motion estimates in ENA. The empiricalstochastic attenuation relationship for horizontal PGA and spectral acceleration (SA) are applicable to earthquakes of w M 5.0 to 8.2 at distances of up to 1000 km. The resulting attenuation model developed in this study is compared with those used in the 2002 national seismic hazard maps, derived in the 2003 EPRI studies and recorded in ENA. The comparison of the results to the other attenuation functions and the available ENA data show a reasonable agreement for the ENA ground motions.

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@inproceedings{Tavakoli2005EmpiricalStochasticGM, title={Empirical-Stochastic Ground Motion Prediction for Eastern North America}, author={Behrooz Tavakoli and Shahram Pezeshk}, year={2005} }