Effect of Spatial Correlation on Estimated Ground-Motion Prediction Equations

Abstract

The consideration of spatially correlated seismic hazard could be of importance for seismic risk assessment. The estimation of this correlation for the peak ground acceleration and the pseudospectral acceleration has been reported in the literature, although it is not presented as a necessary ingredient for developing ground-motion prediction equations (GMPEs). In the present study, we show that spatial correlation can be incorporated in the existing regression algorithms given by Joyner and Boore for assessing a GMPE. The modified algorithms can be used to estimate both GMPE coefficients and spatial correlation model parameters simultaneously. In particular, they are used to investigate the influence of spatial correlation on GMPEs and to assess parameters of an empirical spatial correlation model by considering a set of 592 California records. Analysis results indicate that the effects of incorporating spatial correlation on the estimated GMPEs are insignificant, and that spatial correlation parameters obtained using the modified algorithm are similar to those estimated based on statistical analysis of regression residuals.