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

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