We present an analysis of the information collected by the ¿Sintió un sismo? (SUS) web‐based system. One of the most devastating events in central Mexico in the past 35 yr struck near the Mexican states of Puebla and Morelos on 19 September 2017. At the moment of the event, several programs and projects were in place to monitor and perform quick assessments of the magnitude of the earthquake and the severity of its effects on the population and infrastructure. The SUS platform gathers questionnaires designed in Spanish to estimate macroseismic intensities. The availability of such a system in the dominant language of the country permits a broad reach, only limited by the disparity of the services and internet access. By analyzing residuals of the median attenuation intensity of the event, we confirm previous observations on the site and regional effects in Central Mexico such as the strong influence of the Trans‐Mexican volcanic belt on the ground‐motion amplification. In addition, we obtained correlations between peak parameters and macroseismic intensities that reveal the character of the affected structures’ responses. We emphasize the potential usability of systems similar to the SUS at the regional level and their impact on the decision‐making process and support for further research using all available datasets.

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 (M w ) range from 6.0 to 8.1 and a source-to-site distance (R rup ) range from 240 to 490 km. Given the small number of available observations, a Bayesian regression scheme is used to obtain the coe cients of the ground-motion prediction model (GMPM). In addition, the epistemic uncertainty in the estimation of the regression coe cients 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.

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.