| dc.description.abstract | In this study, we use 19,887 records for 150 crustal earthquakes with moment magnitudes greater than 4.0 obtained from the Taiwan Strong-Motion Instrumentation Program network to build the Taiwan ground-motion prediction equations (GMPEs) for peak ground acceleration and spectral accelerations. The nonlinear regression analysis of ground-motion prediction model is the mixed-effect model with maximum likelihood method. Though this regression analysis to discuss the relationship of source, path, and site.
This paper describes the approaches for the presentation of the components of the error in ground-motion estimates for future earthquakes: (1) spatial-correlation mobile widow, (2)path diagram, (3) semi-variogram, (4) closeness index and (5) the distance of epicenter. Comparing the results with those obtained with the same data, but using the closeness index, semi-variogram and the distance of epicenter approaches, show that we get a lower path-to-path sigma with the combination of the spatial-correlation mobile window and the path diagram methods. For peak ground acceleration and spectral accelerations at periods of 0.3 s, 1.0 s, and 3.0 s, the path-to-path standard deviations obtained in the new approaches are 40%–55% smaller than the total standard deviation.
We also set up the ground-motion prediction equations for the single station, single source and single source to an array in this study. When we use these specific conditions GMPEs to analyze the variance, we can obtain the smaller single-station sigma, single-path sigma, and intra-event aleatory variability than general GMPEs. If we only use aleatory variability in PSHA, then the resultant hazard level would be 20% lower than the traditional one in 2475 year. | en_US |