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A far-field ground motion prediction model for interface earthquakes at the hill zone of Mexico City

Abstract:

This study presents an updated attenuation model to predict the peak ground acceleration (PGA), peak ground velocity (PGV), 5% damped pseudo-spectral acceleration (SA), and the average spectral acceleration (AvgSA) at the hill zone of Mexico City for interface earthquakes. The strong-motion dataset comprises 33 earthquakes recorded at CU station, covering a moment magnitude (Mw) range from 6.0 to 8.1 and a source-to-site distance (Rrup) range from 240 to 490 km. Given the small number of available observations, a Bayesian regression scheme is used to obtain the coefficients of the ground-motion prediction model (GMPM). In addition, the epistemic uncertainty in the estimation of the regression coefficients is evaluated, showing its impact on the framework of a probabilistic seismic hazard analysis (PSHA). The results are compared with models previously developed for the CU station, discussing the differences observed between the median predictions and their standard deviations. Likewise, seismic hazard curves are computed and compared with empirical curves obtained by counting the number of times per year that a given value of ground-motion intensity is exceeded. The results show that the dispersion of the GMPM proposed is lower than the previous models for PGA and SA, which means better predictability and more reliable estimates of the seismic hazard at the site.