Horst Langer

Attenuation in Southeastern Sicily (Italy) by applying different coda methods

The attenuation in Southeastern Sicily has been investigated using 40aftershocks of the December 13 1990, earthquake. The quality factor ofcoda waves (Qc) was estimated in the frequency range 1.5–24 Hz,applying three different methods in time and frequency domains. On thewhole, a clear dependence of Qc on frequency was observed,according to the general law Q = Q0(f/f0)n . Thefrequency dependence relationships obtained from the analysis of codawaves at three lapse time windows (10, 20 and 30 seconds) show that, forall methods, Q0 (Qc at 1 Hz) significantly increases with lapsetime. In particular, Q0 is approximately 20 at short lapse time (10s) and increases to about 70 at longer lapse time (30 s). This is attributedto the fact that larger lapse times involve deeper parts of the crust andupper lithosphere which may be characterized by larger quality factors.Moreover, the value of the exponent n decreases with increasing codalengths from about 1.3 to 0.9, suggesting a decrease in heterogeneity ofthe medium with depth.Finally, Qc-values here found are of the same order as thosereported from other tectonic regions like the Anatolian Highlands orSouthern Spain, while significantly higher than in the neighboring volcanicarea of Mt. Etna.

Estimation of seismic waveform governing parameters with neural networks

We investigate the application of multilayer perceptron neural networks on the inversion of waveform governing parameters related to the seismic source and the propagation medium. These parameters are given by the size of the source, thicknesses and velocities of the layers, and a parameter κ describing the whole path attenuation of the wave due to absorption. Synthetic SH waves radiated from a circular source model are used for this study. The neural network returns a mapping function which can be used for an entire class of signals, provided that the parameters are within the limits of the model space explored during the training. The application of the mapping function to a set of signals is mathematically simple and fast. This can be a considerable advantage over systematic search techniques, such as simulated annealing or genetic algorithms, since the stability of the results that are found with the neural network can be tested easily with examples not used for the estimation of the mapping function. The use of an appropriate transform of the signal (i.e., spectra or autocorrelation function) gives slightly better results than the crude waveforms. The seismic waveform-governing parameters can be identified with a reasonable accuracy if an appropriate network topology is chosen and if the number of examples used for the training phase is sufficiently large. Even in the case where 16 parameters of the models are searched and the global error remained somewhat unsatisfying, important parameters, such as the source radius or the velocity of the uppermost layers, are still recognized with a fair accuracy. The error is, at least to some degree, an effect of the nonuniqueness of the inversion problem. Performing a search with simulated annealing 31 times for an example seismogram, we obtain 31 solutions with a scatter for the different parameters which is of the same order as the errors obtained with the network.

High‐Frequency Spectral Decay in P‐Wave Acceleration Spectra and Source Parameters of Microearthquakes in Southeastern Sicily, Italy

In widely used ω −2 source models the characteristics of high-frequency radiation are described as being flat for frequencies between the source corner fre- quency and an upper limiting frequency fmax. Deviations from this behavior are de- scribed in a parameter κ, which is understood as a general measure of the changes the signal undergoes on its way from the source to the receiver. In this study, we calculated κ in southeastern Sicily by using microearthquakes belonging to three different seis- mic sequences occurring in the area in 1990, from 1999 to 2000, and in 2002. The selected events form four different clusters whose seismic sources are located within a 2-km radius. Although the source-to-station paths are approximately the same inside a given cluster, the values of κ change considerably at the same recording site from one event to another, also in the case of events having the same magnitude. We parame- terized κ in terms of event (κ E ) and path (κ P and κ Diff ) contributions. The term κ P represents the contribution on total κ of both the whole source-to-station path and the near-surface geology, while κ Diff models the possible spatial variation in the parameter measured with respect to a reference source-station direction. Results show that the source contribution is not negligible and that a positive correlation with source size exists. Moreover, the hypothesis of a laterally homogeneous crustal structure within the area in question is not appropriate, and significant variation in attenuating proper- ties of the medium may occur in a very small distance range (also in the order of a few tens of meters). Our analysis suggests that the origin of the previously mentioned variability is located near the recording site. Synthetic spectra are also computed in order to verify the actual significance of the parameterization employed and its capacity to separate the source and the path contribution to κ. We describe our spectra as a product of a Brune-type source spectrum and an exponential shaping term accounting for propagation effects. The seismic moments range between 3:8 × 10 11 and 5:2 × 10 13 N · m, the source radii range between 176 and 669 m, while the stress drop varies from 0.01 to 0.67 MPa. Online Material: Tables of stations in the deployed portable seismic networks, coordinates, and average values of κ.

From Seismic Input to Damage Scenario: An Example for the Pilot Area of Mt. Etna Volcano (Italy) in the KnowRISK Project

In this paper we present a multidisciplinary approach aimed at assessing seismic risk regarding non-structural damage. The study has been carried out in the framework of the European KnowRISK Project and focuses on the pilot area of Mt. Etna volcano (Italy). Both instrumental data and as well as macroseismic observations provide unique opportunities for testing innovative and classical approaches for assessing seismic risk. Starting from the seismic hazard analysis, we first identify a test site (Zafferana) affected by non-structural damage. We produce seismic scenarios based on macroseismic and ground-motion data and finally obtain the relevant risk map using the Italian census data to classify buildings into vulnerability classes and a model to predict damage distribution.

Identification of Model Parameters Governing Seismic Waveforms with Multi-Layer Perceptrons

Abstract We examine the application of neural networks on the identification of parameters governing seismic waveforms. Important parameters are recognized fairly well even for complex models. It is shown that the stated identification problem can be solved with an adequate accuracy by multilayer perceptron neural networks, provided an appropriate topology and a sufficient number of training patterns are used. Different representation both in time and frequency domain are considered.