Geographically Varying Ground Motion Predictions: Case Study for Two Historical Events

Abstract eng:
Ground motions induced by earthquake vary geographically. The use of geographically weighted regression technique to estimate geographically varying ground motion measures has been considered in the literature to predict the peak ground acceleration, spectral accelerations and Arias intensity (AI). It has been shown that its use provides equivalent or better performance as compared to the geostatistical interpolation techniques. An advantage of geographically varying ground motion prediction model (GVGMPM) is that it could take into account the underlying physics by adopting a functional form that is similar to that of a conventional ground motion prediction equation (GMPE). In this study, we develop GVGMPM to predict the cumulative absolute velocity (CAV) for the 1999 Chi-Chi earthquake and the 2008 Wenchuan earthquake. The CAV can be used as an indicator of structural damage that is more predictable than other ground motion intensity measures and has recently been considered as an alternative to AI in earthquake engineering and geotechnical applications. We show that the developed GVGMPM out-performs the preferred geostatistical interpolation technique and GMPE.

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