Licia Faenza

Variations of crustal elastic properties during the 2009 L'Aquila earthquake inferred from cross‐correlations of ambient seismic noise

[1] We retrieve seismic velocity variations within the Earth’s crust in the region of L’Aquila (central Italy) by analyzing cross‐correlations of more than two years of continuous seismic records. The studied period includes the April 6, 2009, Mw 6.1 L’Aquila earthquake. We observe a decrease of seismic velocities as a result of the earthquake’s main shock. After performing the analysis in different frequency bands between 0.1 and 1 Hz, we conclude that the velocity variations are strongest at relatively high frequencies (0.5– 1H z) suggesting that they are mostly related to the damage in the shallow soft layers resulting from the co‐seismic shaking. Citation: Zaccarelli, L., N. M. Shapiro, L. Faenza, G. Soldati, and A. Michelini (2011), Variations of crustal elastic properties during the 2009 L’Aquila earthquakeinferred from cross‐correlations of ambient seismic noise, Geophys. Res. Lett., 38, L24304, doi:10.1029/2011GL049750.

Toward a New Probabilistic Framework to Score and Merge Ground‐Motion Prediction Equations: The Case of the Italian Region

The ground‐motion prediction equation (GMPE) is a basic component for probabilistic seismic‐hazard analysis. There is a wide variety of GMPEs that are usually obtained by means of inversion techniques of datasets containing ground motions recorded at different stations. However, to date there is not yet a commonly accepted procedure to select the best GMPE for a specific case; usually, a set of GMPEs is implemented (more or less arbitrarily) in a logic‐tree structure, in which each GMPE is weighted by experts, mostly according to gut feeling. Here, we discuss a general probabilistic framework to numerically score and weight GMPEs, highlighting features, limitations, and approximations; finally, we put forward a numerical procedure to score GMPEs, taking into account their forecasting performances, and to merge them through an ensemble modeling. Then, we apply the procedure to the Italian territory; in addition to illustrating how the procedure works, we investigate other relevant aspects (such as the importance of the focal mechanism) of the GMPEs to different site conditions. Online Material: Figures showing regression analysis for peak ground acceleration (PGA) values and location map, and earthquake catalog and summary table of parameters corresponding to each ground‐motion prediction equation (GMPE) implemented.

Bayesian Inference on Earthquake Size Distribution: A Case Study in Italy

Abstract This article is focused on the study of earthquake size statistical distribution by using Bayesian inference. The strategy consists in the definition of an a priori distribution based on instrumental seismicity and modeled as a power-law distribution. By using the observed historical data, the power law is then modified in order to obtain the posterior distribution. The aim of this article is to define the earthquake size distribution using all the seismic database available (i.e., instrumental and historical catalogs) and a robust statistical technique. We apply this methodology to the Italian seismicity, dividing the territory in source zones as done for the seismic hazard assessment, taken here as a reference model. The results suggest that each area has its own peculiar trend: while the power law is able to capture the mean aspect of the earthquake size distribution, the posterior emphasizes different slopes in different areas. Our results are in general agreement with the ones used in the seismic hazard assessment in Italy. However, there are areas in which a flattening in the curve is shown, meaning a significant departure from the power-law behavior and implying that there are some local aspects that a power-law distribution is not able to capture.

A Bayesian Seismic Hazard Analysis for the city of Naples

In this paper we explore the feasibility of formulating the hazard assessment procedure to include the information of past earthquakes into the probabilistic seismic hazard analysis, together with the use of an ensemble modeling technique. This strategy allows on one hand to enlarge the information used in the evaluation of the hazard, from alternative models for the earthquake generation process to past shaking, and on the other hand to explicitly account for the uncertainties. The Bayesian scheme we propose is applied to evaluate the seismic hazard of Naples. The framework in which we have embedded the tools is flexible to include all types of uncertainties. Here we focus on a sensitive study of the earthquake occurrence by implementing models that span from random to cluster–type temporal behavior and models that include quasi-periodic occurrence of earthquakes on faults. We implement 5 different spatio–temporal models to parameterize the occurrence of earthquakes potentially dangerous for Naples. Subsequently we combine these hazard curves with ShakeMaps of past earthquakes that have been felt in Naples since 1200 A.D.. The results are posterior ensemble hazard curves for three exposure times, e.g., 5, 10, and 50 years, in a dense grid that cover the municipality of Naples, considering rocky soil and including the site amplification. Our results show the importance to include the data from past shaking since the difference between the prior and the posterior is about 8 − 15% for the different exposure times.