Silvia Núñez García

Site Effects and Peak Ground Accelerations Observed in Guadalajara, Mexico, for the 9 October 1995 Mw 8 Colima–Jalisco, Earthquake

On 3 June 1932, an M sxa08.2 shallow thrust subduction earthquake struck the Colima–Jalisco (CJ) region of Mexico at an epicentral distance of ∼250u2009u2009km from Guadalajara, the second largest town in Mexico. The return period of these CJ earthquakes has been estimated from 77 to 126 years, which suggests the next is likely to occur between 2009 and 2058. As a step toward estimating the seismic hazard posed by potential M su2009u20098.2+ events on Guadalajara, we present a study consisting of the following: (1)xa0the analysis of the strong ground motions recorded at Guadalajara on 11 free‐field accelerographs for the CJ 9 October 1995 M wxa08 earthquake; (2)xa0the analysis of the site effects in Guadalajara observed for this earthquake; and (3)xa0the estimation of the spatial distribution of peak ground acceleration (PGA) in Guadalajara for this event. We propose, validate, and apply a recurrent neural network (RNN) technique to the recorded PGA. Important site effects were identified (using the horizontal‐to‐vertical and H/Hrocksite techniques) on Guadalajara’s sandy soil layers with thicknesses h >20u2009u2009m to the basaltic rock basement. The estimated PGAs in Guadalajara for the CJ 1995 earthquake varied from ∼2 to ∼27u2009u2009cm/s2 for soil layers with h ≤5u2009u2009m and h >50u2009u2009m, respectively. We expect the hybrid technique to obtain broadband synthetics (Chavez etxa0al. , 2011) and the proposed RNN methodology can be used to estimate Guadalajara’s seismic hazard for M wu2009u20098+ CJ scenario earthquakes.nnOnline Material: Figures of Fourier amplitude spectra, H/V and H/H spectral ratios, and signal‐to‐noise spectral ratios.

Neural estimation of strong ground motion duration

This paper presents and discusses the use of neural networks to determine strong ground motion duration. Accelerometric data recorded in the Mexican cities of Puebla and Oaxaca are used to develop a neural model that predicts this duration in terms of the magnitude, epicenter distance, focal depth, soil characterization and azimuth. According to the above the neural model considers the effect of the seismogenic zone and the contribution of soil type to the duration of strong ground motion. The final scheme permits a direct estimation of the duration since it requires easy-to-obtain variables and does not have restrictive hypothesis. The results presented in this paper indicate that the soft computing alternative, via the neural model, is a reliable recording-based approach to explore and to quantify the effect of seismic and site conditions on duration estimation. An essential and significant aspect of this new model is that, while being extremely simple, it also provides estimates of strong ground motions duration with remarkable accuracy. Additional but important side benefits arising from the model’s simplicity are the natural separation of source, path, and site effects and the accompanying computational efficiency.