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Earthquake site responses or site effects are the modifications of surface geology to seismic waves. How well can we predict the site effects (average over many earthquakes) at individual sites so far? To address this question, we tested and compared the effectiveness of different estimation techniques in predicting the outcrop Fourier site responses separated using the general inversion technique (GIT) from recordings. Techniques being evaluated are (a) the empirical correction to the horizontal-to-vertical spectral ratio of earthquakes (c-HVSR), (b) one-dimensional ground response analysis (GRA), and (c) the square-root-impedance (SRI) method (also called the quarter-wavelength approach). Our results show that c-HVSR can capture significantly more site-specific features in site responses than both GRA and SRI in the aggregate, especially at relatively high frequencies. c-HVSR achieves a "good match" in spectral shape at similar to 80%-90% of 145 testing sites, whereas GRA and SRI fail at most sites. GRA and SRI results have a high level of parametric and/or modeling errors which can be constrained, to some extent, by collecting on-site recordings.
Knowledge of the quality factor of near-surface materials is of fundamental interest in various applications. Attenuation can be very strong close to the surface and thus needs to be properly assessed. In recent years, several researchers have studied the retrieval of attenuation coefficients from the cross correlation of ambient seismic noise. Yet, the determination of exact amplitude information from noise-correlation functions is, in contrast to the extraction of traveltimes, not trivial. Most of the studies estimated attenuation coefficients on the regional scale and within the microseism band. In this paper, we investigate the possibility to derive attenuation coefficients from seismic noise at much shallower depths and higher frequencies (> 1 Hz). The Euroseistest area in northern Greece offers ideal conditions to study quality factor retrieval from ambient noise for different rock types. Correlations are computed between the stations of a small scale array experiment (station spacings < 2 km) that was carried out in the Euroseistest area in 2011. We employ the correlation of the coda of the correlation (C-3) method instead of simple cross correlations to mitigate the effect of uneven noise source distributions on the correlation amplitude. Transient removal and temporal flattening are applied instead of 1-bit normalization in order to retain relative amplitudes. The C-3 method leads to improved correlation results (higher signal-to-noise ratio and improved time symmetry) compared to simple cross correlations. The C-3 functions are rotated from the ZNE to the ZRT system and we focus on Love wave arrivals on the transverse component and on Love wave quality factors Q(L). The analysis is performed for selected stations being either situated on soft soil or on weathered rock. Phase slowness is extracted using a slant-stack method. Attenuation parameters are inferred by inspecting the relative amplitude decay of Love waves with increasing interstation distance. We observe that the attenuation coefficient gamma and Q(L) can be reliably extracted for stations situated on soft soil whereas the derivation of attenuation parameters is more problematic for stations that are located on weathered rock. The results are in acceptable conformance with theoretical Love wave attenuation curves that were computed using 1-D shear wave velocity and quality factor profiles from the Euroseistest area.
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.
In probabilistic seismic hazard analysis, different ground-motion prediction equations (GMPEs) are commonly combined within a logic tree framework. The selection of appropriate GMPEs, however, is a non-trivial task, especially for regions where strong motion data are sparse and where no indigenous GMPE exists because the set of models needs to capture the whole range of ground-motion uncertainty. In this study we investigate the aggregation of GMPEs into a mixture model with the aim to infer a backbone model that is able to represent the center of the ground-motion distribution in a logic tree analysis. This central model can be scaled up and down to obtain the full range of ground-motion uncertainty. The combination of models into a mixture is inferred from observed ground-motion data. We tested the new approach for Northern Chile, a region for which no indigenous GMPE exists. Mixture models were calculated for interface and intraslab type events individually. For each source type we aggregated eight subduction zone GMPEs using mainly new strong-motion data that were recorded within the Plate Boundary Observatory Chile project and that were processed within this study. We can show that the mixture performs better than any of its component GMPEs, and that it performs comparable to a regression model that was derived for the same dataset. The mixture model seems to represent the median ground motions in that region fairly well. It is thus able to serve as a backbone model for the logic tree.