In the wake of death and destruction left by the 2017 earthquake in Mexico City, it is natural to ask whether the event was unexpected and anomalous. Although such an intraslab earthquake (M w 7.1; depth 57 km; epicentral distance = 114 km from the city) was considered likely, the recordings in the city during the last 54 yrs reveal that the ground motion during the 2017 earthquake was anomalously large in the critical frequency range to the city (0.4-1 Hz). The intraslab earthquakes occur closer to Mexico City, at greater depth, and involve higher stress drop than their interplate counterparts. Consequently, the ground motion is relatively enriched at high frequencies as compared with that during interplate earthquakes, which is dominated by lower frequency waves (f < 0:5 Hz). This explains the observed difference in the damage pattern during the 2017 and the disastrous interplate earthquake of 1985 (M w 8.0). Electronic Supplement: Figures showing spectral ratios, peak ground acceleration (PGA) and peak ground velocity (PGV) as function of distance R, comparison of observed response spectra SA, and predicted median and 1 s SA from a site-specific ground-motion prediction equation (GMPE) model at CU, plot of accelerograms, Fourier acceleration spectra, and SA at SCT of interplate 1985 M w 8.0 and intraslab 2017 M w 7.1 earthquakes, accelerographic stations in Mexico City, which recorded the 2017 M w 7.1 earthquake, SA of the 2017 earthquake at sites in and near Condesa and Roma colonies, and basis for the estimation of exceedance rate of PGA at CU in Mexico City from intraslab earthquakes, and tables providing a list of 20 (interplate and intraslab) earthquakes with largest recorded PGA at CU in the 1964-2017 period, significant pre-1975 intraslab earthquakes, and a comparison of observed PGA and PGV at CU during the 2017 M w 7.1 earthquake with predictions from GMPE.

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An application of a linear multivariate Bayesian regression model, described in a companion article, to obtain a ground-motion prediction equation (GMPE) using a set of actual ground-motion records and a realistic functional form is presented. Based on seismological grounds and on an adopted functional form, we include a sound discussion about how the prior information required for the model can be defined. For the regression analyses we use two subsets of ground-motion records from the Next Generation of Ground-Motion Attenuation Models (NGA) database. We compare the results obtained with the Bayesian model with those obtained through the one-stage maximum-likelihood and the constrained maximum-likelihood methods. The advantages of the Bayesian approach over traditional regression techniques are discussed.

A reliable estimation of seismic hazard-facing Mexico City from local earthquakes has suffered from poor seismic instrumentation, complex crustal structure, large and variable site amplification, and lack of knowledge of recurrence period of earthquakes on the mapped faults. Owing to recent improvement in local seismic networks, an earthquake swarm activity, which occurred in June–August 2019, was well recorded. The largest event of the sequence, an Mw 3.2 earthquake, caused panic in the city and produced peak ground acceleration (PGA) exceeding 0.3g at the closest station (MHVM) about 1 km away. An analysis of the event shows that it had normal-faulting focal mechanism, consistent with northeast–southwest-oriented mapped faults in the region. It was located at a depth of ∼1  km and had a low stress drop (∼0.1  MPa). We find that the high PGA for this low stress-drop event resulted from high-frequency amplification at MHVM (about factor of ∼6 around 13 Hz), likely due to topographic site effects, superimposed on a pervasive broadband amplification of seismic waves at hill-zone sites in the Valley of Mexico (up to ∼10 in the frequency band of 0.2–10 Hz). Simulation of ground motion for a scenario Mw 5.0 earthquake, using an empirical Green’s function technique, reveals that such an event may give rise to significant seismic intensities in the lake-bed zone of Mexico City. The results emphasize the need to re-evaluate the seismic hazard to Mexico City from local crustal earthquakes in the Valley of Mexico.

The consideration of environmental losses in seismic design is discussed within the framework of cost optimization analysis. Within this context, the equivalent carbon emissions (CO2-e) are used as a proxy for the environmental cost related to seismic damage, and a model to assess the environmental cost in seismic loss estimation is introduced. The use of the model is illustrated through its application to the analysis of simple earthquake-resistant structural systems. It is concluded that under certain circumstances, the consideration of environmental issues in seismic design may be important, especially as the planning time horizon of the facility increases.

It is well understood that the range of application for an empirical ground-motion prediction model is constrained by the range of predictor variables covered in the data used in the analysis. However, in probabilistic seismic hazard analysis (PSHA), the limits in the application of ground-motion prediction models (GMPMs) are often ignored, and the empirical relationships are extrapolated. In this paper, we show that this extrapolation leads to a quantifiable increment in the uncertainty of a GMPM when it is used to forecast a future value of a given intensity parameter. This increment, which is clearly of epistemic nature, depends on the adopted functional form, on the covariance matrix of the regression coefficients, on the used regression technique, and on the quality of the data set. In addition, through some examples using the database of the Next Generation of Ground-Motion Attenuation Models project and some currently favored functional forms we study the increment in the seismic hazard produced by the extrapolation of GMPMs.

An application of a linear multivariate Bayesian regression model to compute pseudoacceleration (SA) ground-motion prediction equations (GMPEs) is presented. The model is able to include the correlation between observations for a given earthquake, the correlation between SA ordinates at different periods, and the correlation between regression coefficients of the ground-motion prediction model. We evaluate the advantages of the Bayesian approach over the traditional regression methods, and we discuss the differences between univariate and multivariate analyses. Because the application of the Bayesian method is in general complex and implies an increase in the numerical effort with respect to the traditional methods, our computer code to perform linear Bayesian analyses is freely available on request.