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Both horizontal-to-vertical (H/V) spectral ratios and the spatial autocorrelation method (SPAC) have proven to be valuable tools to gain insight into local site effects by ambient noise measurements. Here, the two methods are employed to assess the subsurface velocity structure at the Piano delle Concazze area on Mt Etna. Volcanic tremor records from an array of 26 broadband seismometers is processed and a strong variability of H/V ratios during periods of increased volcanic activity is found. From the spatial distribution of H/V peak frequencies, a geologic structure in the north-east of Piano delle Concazze is imaged which is interpreted as the Ellittico caldera rim. The method is extended to include both velocity data from the broadband stations and distributed acoustic sensing data from a co-located 1.5 km long fibre optic cable. High maximum amplitude values of the resulting ratios along the trajectory of the cable coincide with known faults. The outcome also indicates previously unmapped parts of a fault. The geologic interpretation is in good agreement with inversion results from magnetic survey data. Using the neighborhood algorithm, spatial autocorrelation curves obtained from the modified SPAC are inverted alone and jointly with the H/V peak frequencies for 1D shear wave velocity profiles. The obtained models are largely consistent with published models and were able to validate the results from the fibre optic cable.
The prediction of the ground shaking that can occur at a site of interest due to an earthquake is crucial in any seismic hazard analysis. Usually, empirically derived ground-motion prediction equations (GMPEs) are employed within a logic-tree framework to account for this step. This is, however, challenging if the area under consideration has only low seismicity and lacks enough recordings to develop a region-specific GMPE. It is then usual practice to adapt GMPEs from data-rich regions (host area) to the area with insufficient ground-motion recordings (target area). Host GMPEs must be adjusted in such a way that they will capture the specific ground-motion characteristics of the target area. In order to do so, seismological parameters of the target region have to be provided as, for example, the site-specific attenuation factor kappa0. This is again an intricate task if data amount is too sparse to derive these parameters.
In this thesis, I explore methods that can facilitate the selection of non-endemic GMPEs in a logic-tree analysis or their adjustment to a data-poor region. I follow two different strategies towards this goal.
The first approach addresses the setup of a ground-motion logic tree if no indigenous GMPE is available. In particular, I propose a method to derive an optimized backbone model that captures the median ground-motion characteristics in the region of interest. This is done by aggregating several foreign GMPEs as weighted components of a mixture model in which the weights are inferred from observed data. The approach is applied to Northern Chile, a region for which no indigenous GMPE existed at the time of the study. Mixture models are derived for interface and intraslab type events using eight subduction zone GMPEs originating from different parts of the world. The derived mixtures provide satisfying results in terms of average residuals and average sample log-likelihoods. They outperform all individual non-endemic GMPEs and are comparable to a regression model that was specifically derived for that area.
The second approach is concerned with the derivation of the site-specific attenuation factor kappa0. kappa0 is one of the key parameters in host-to-target adjustments of GMPEs but is hard to derive if data amount is sparse. I explore methods to estimate kappa0 from ambient seismic noise. Seismic noise is, in contrast to earthquake recordings, continuously available. The rapidly emerging field of seismic interferometry gives the possibility to infer velocity and attenuation information from the cross-correlation or deconvolution of long noise recordings. The extraction of attenuation parameters from diffuse wavefields is, however, not straightforward especially not for frequencies above 1 Hz and at shallow depth. In this thesis, I show the results of two studies. In the first one, data of a small-scale array experiment in Greece are used to derive Love wave quality factors in
the frequency range 1-4 Hz. In a second study, frequency dependent quality factors of S-waves (5-15 Hz) are estimated by deconvolving noise recorded in a borehole and at a co-located surface station in West Bohemia/Vogtland. These two studies can be seen as preliminary steps towards the estimation of kappa0 from seismic noise.