Prediction of strong ground motion using the hybrid empirical method and its use in the development of ground-motion (attenuation) relations in eastern North America

Ground-motion (attenuation) relations are used to estimate strong ground motion for many engineering and seismological applications. Where strong-motion recordings are abundant, these relations are developed empirically from strong-motion recordings. Where recordings are limited, they are often developed from seismological models using stochastic and theoretical methods. However, there is a large degree of uncertainty in calculating absolute values of ground motion from seismological models in regions where data are sparse. As an alternative, I propose a hybrid empirical method that uses the ratio of stochastic or theoretical ground-motion estimates to adjust empirical ground-motion relations developed for one region to use in another region. By using empirical models as its basis, the method taps into the vast amount of observational data and expertise that has been used to develop empirical ground-motion relations in high-seismic regions such as western North America (WNA). I present a formal mathematical framework for the hybrid empirical method and apply it to the development of ground-motion relations for peak ground acceleration and acceleration response spectra in eastern North America (ENA) using empirical relations from WNA. The application accounts for differences in stress drop, source properties, crustal attenuation, regional crustal structure, and generic-rock site profiles between the two regions. The resulting hybrid empirical ground-motion relations are considered to be most appropriate for estimating ground motion on ENA hard rock with a shear-wave velocity of 2800 m/sec for earthquakes of M (sub w) > or =5.0 and r (sub rup) < or =70 km. However, it has been extended to larger distances using stochastic ground-motion estimates so that it can be used in more general engineering applications such as probabilistic seismic hazard analysis.