Comparisons among the five ground-motion models developed using RESORCE for the prediction of response spectral accelerations due to earthquakes in Europe and the Middle East

This article presents comparisons among the five ground-motion models described in other articles within this special issue, in terms of data selection criteria, characteristics of the models and predicted peak ground and response spectral accelerations. Comparisons are also made with predictions from the Next Generation Attenuation (NGA) models to which the models presented here have similarities (e.g. a common master database has been used) but also differences (e.g. some models in this issue are nonparametric). As a result of the differing data selection criteria and derivation techniques the predicted median ground motions show considerable differences (up to a factor of two for certain scenarios), particularly for magnitudes and distances close to or beyond the range of the available observations. The predicted influence of style-of-faulting shows much variation among models whereas site amplification factors are more similar, with peak amplification at around 1s. These differences are greater than those among predictions from the NGA models. The models for aleatory variability (sigma), however, are similar and suggest that ground-motion variability from this region is slightly higher than that predicted by the NGA models, based primarily on data from California and Taiwan

MAGNITUDE DEPENDENCE OF STRESS DROP: WHAT DOES THE OBSERVED MAGNITUDE SCALING OF GROUND-MOTIONS TELL US?

International audienceThe behavior of earthquake stress-drop magnitude scaling has been the topic of significant debate in the earthquake source community over the past two decades. Methodologies which have been adopted by a large number of source studies require corrections for source radiation pattern, path attenuation and site amplification that ultimately introduce large uncertainties for stress-drop estimates. In this study, we adopt a different strategy: we analyze directly the ground-motions (Y) and their dependencies with magnitude (M). We first use simple stochastic models (e.g. [1]) comprised of a [2, 3] source spectrum and various models of magnitude-dependent stress drop. We show that magnitude-dependent stress-drop and constant stress-drop models lead to different scaling of ground-motions (dlogY/dM) with frequency. Using the results of [4], we then analyze the magnitude dependency of NGA-West 2 ground-motions for source-site configurations where stress-drop is the key controlling factor of ground-motions (moderate distances and rock-sites). In addition, the use of a neural network method allows us to obtain fully record-driven evaluations of (dlogY/dM) with frequency both for simulated and observed records. The comparison between these observed and simulated (dlogY/dM) allows us to discuss the scaling of the stress-drop with magnitude. We do not observe strong differences of the magnitude scaling of ground-motions between mainshocks and aftershocks

A non linear adaptive filter for digital data communication

This paper presents a recursive time-varying adaptation step algorithm for updating the linear and quadratic coefficients vectors of a second-order Volterra filter. Simulations are carried in an equalization setup to compare the performance of this algorithm with other variable step least mean square (LMS) algorithms. The obtained results show that this algorithm brings substantial increase in the adaptation speed while keeping simplicity of the conventional LMS algorithm

NEW INSIGHT IN THE DERIVATION OF AMPLIFICATION FACTOR BY TAKING INTO ACCOUNT SOIL PARAMETERS

International audienceIt is currently admitted that the amplification factor (AF) is one of the best tools to describe site effects. AF depends on soil parameters that are derived from the geometrical and mechanical soil properties of the soil profile. Thus, it is important to identify which soil parameters shape the form of the AF. The aim of this paper is to measure the effects of various site parameters on the variation of AF. As the problem is highly complex, a tool using the GRNN (Generalized Regression Neural Network) to understand which soil parameters have been developed. For a particular soil profile it has been found that values of AF derived from GRNN approach are closer to that of 1D linear viscoelastic seismic analysis particularly if the number of parameters increases. Based on this result a sensitivity analysis has been conducted to identify which parameters give good AF. For the practical case where we have to introduce only two parameters, it has been observed that the couple [resonance frequency (f 0) , time-averaged shear-wave velocity in the top 30 m (Vs30)] is the most interesting