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We develop an amplitude spectral ratio method for event couples from clustered earthquakes to estimate seismic wave attenuation (Q-1) in the source volume. The method allows to study attenuation within the source region of earthquake swarms or aftershocks at depth, independent of wave path and attenuation between source region and surface station. We exploit the high-frequency slope of phase spectra using multitaper spectral estimates. The method is tested using simulated full wave-field seismograms affected by recorded noise and finite source rupture. The synthetic tests verify the approach and show that solutions are independent of focal mechanisms but also show that seismic noise may broaden the scatter of results. We apply the event couple spectral ratio method to northwest Bohemia, Czech Republic, a region characterized by the persistent occurrence of earthquake swarms in a confined source region at mid-crustal depth. Our method indicates a strong anomaly of high attenuation in the source region of the swarm with an averaged attenuation factor of Qp < 100. The application to S phases fails due to scattered P-phase energy interfering with S phases. The Qp anomaly supports the common hypothesis of highly fractured and fluid saturated rocks in the source region of the swarms in northwest Bohemia. However, high temperatures in a small volume around the swarms cannot be excluded to explain our observations.

The H/V spectral ratio has emerged as a single station method within the seismic ambient noise analysis field by its capability to quickly estimate the frequency of resonance at a site and through inversion the average profile information. Although it is easy to compute from experimental data, its counter theoretical part is not obvious when building a forward model which can help in reconstructing the derived H/V spectrum. This has led to the simplified assumption that the noise wavefield is mainly composed of Rayleigh waves and the derived H/V often used without further correction. Furthermore, only the right (and left) flank around the H/V peak frequency is considered in the inversion for the subsurface 1-D shear wave velocity profile. A new theoretical approach for the interpretation of the H/V spectral ratio has been presented by Sanchez-Sesmaet al. In this paper, the fundamental idea behind their theory is presented as it applies to receivers at depth. A smooth H/V(z, f) spectral curve on a broad frequency range is obtained by considering a fine integration step which is in turn time consuming. We show that for practical purposes and in the context of inversion, this can be considerably optimized by using a coarse integration step combined with the smoothing of the corresponding directional energy density (DED) spectrum. Further analysis shows that the obtained H/V(z, f) spectrum computed by the mean of the imaginary part of Green's function method could also be recovered using the reflectivity method for a medium well illuminated by seismic sources. Inversion of synthetic H/V(z, f) spectral curve is performed for a single layer over a half space. The striking results allow to potentially use the new theory as a forward computation of the H/V(z, f) to fully invert the experimental H/V spectral ratio at the corresponding depth for the shear velocity profile (Vs) and additionally the compressional velocity profile (Vp) using receivers both at the surface and in depth. We use seismic ambient noise data in the frequency range of 0.2-50 Hz recorded at two selected sites in Germany where borehole information is also available. The obtained 1-D Vs and Vp profiles are correlated with geological log information. Results from shallow geophysical experiment are also used for comparison.

Permafrost inundated since the last glacial maximum is degrading, potentially releasing trapped or stabilized greenhouse gases, but few observations of the depth of ice-bonded permafrost (IBP) below the seafloor exist for most of the arctic continental shelf. We use spectral ratios of the ambient vibration seismic wavefield, together with estimated shear wave velocity from the dispersion curves of surface waves, for estimating the thickness of the sediment overlying the IBP. Peaks in spectral ratios modeled for three-layered 1-D systems correspond with varying thickness of the unfrozen sediment. Seismic receivers were deployed on the seabed around Muostakh Island in the central Laptev Sea, Siberia. We derive depths of the IBP between 3.7 and 20.7m15%, increasing with distance from the shoreline. Correspondence between expected permafrost distribution, modeled response, and observational data suggests that the method is promising for the determination of the thickness of unfrozen sediment.

We apply and evaluate a recent machine learning method for the automatic classification of seismic waveforms. The method relies on Dynamic Bayesian Networks (DBN) and supervised learning to improve the detection capabilities at 3C seismic stations. A time-frequency decomposition provides the basis for the required signal characteristics we need in order to derive the features defining typical "signal" and "noise" patterns. Each pattern class is modeled by a DBN, specifying the interrelationships of the derived features in the time-frequency plane. Subsequently, the models are trained using previously labeled segments of seismic data. The DBN models can now be compared against in order to determine the likelihood of new incoming seismic waveform segments to be either signal or noise. As the noise characteristics of seismic stations varies smoothly in time (seasonal variation as well as anthropogenic influence), we accommodate in our approach for a continuous adaptation of the DBN model that is associated with the noise class. Given the difficulty for obtaining a golden standard for real data (ground truth) the proof of concept and evaluation is shown by conducting experiments based on 3C seismic data from the International Monitoring Stations, BOSA and LPAZ.

We examine the use of ambient noise cross-correlation tomography for shallow site characterization using a modified two-step approach. Initially, we extract Rayleigh wave traveltimes from correlation traces of vertical component seismic recordings from a local network installed in Mygdonia basin, northern Greece. The obtained Rayleigh wave traveltimes show significant spatial variability, as well as distance and frequency dependence due to the 3-D structure of the area, dispersion, and anelastic attenuation effects. The traveltime data sets are inverted through a surface wave tomography approach to determine group velocity maps for each frequency. The proposed tomographic inversion involves the use of approximate Fresnel volumes and interfrequency smoothing constraints to stabilize the results. In the last step, we determine a final 3-D velocity model using a node-based Monte Carlo 1-D dispersion curve inversion. The reliability of the final 3-D velocity model is examined by spatial and depth resolution analysis, as well as by inversion for different model parameterizations. The obtained results are in very good agreement with previous findings from seismic and other geophysical methods. The new 3-D VS model provides additional structural constraints for the shallow sediments and bedrock structure of the northern Mygdonia basin up to the depth of similar to 200-250 m. Present work results suggest that the migration of ambient tomography techniques from large scales (tens or hundreds of km) to local scales (few hundred meters) is possible but cannot be used as a black box technique for 3-D modeling and detailed geotechnical site characterization.

Salt diapirs are common features of sedimentary basins. If close to the surface, they can bear a significant hazard due to possible dissolution sinkholes, karst formation and collapse dolines or their influence on ground water chemistry. We investigate the potential of ambient vibration techniques to map the 3-D roof morphology of shallow salt diapirs. Horizontal-to-vertical (H/V) spectral peaks are derived at more than 900 positions above a shallow diapir beneath the city area of Hamburg, Germany, and are used to infer the depth of the first strong impedance contrast. In addition, 15 small-scale array measurements are conducted at different positions in order to compute frequency-dependent phase velocities of Rayleigh waves between 0.5 and 25 Hz. The dispersion curves are inverted together with the H/V peak frequency to obtain shear-wave velocity profiles. Additionally, we compare the morphology derived from H/V and array measurements to borehole lithology and a gravity-based 3-D model of the salt diapir. Both methods give consistent results in agreement with major features indicated by the independent data. An important result is that H/V and array measurements are better suited to identify weathered gypsum caprocks or gypsum floaters, while gravity-derived models better sample the interface between sediments and homogeneous salt. We further investigate qualitatively the influence of the 3-D subsurface topography of the salt diapir on the validity of local 1-D inversion results from ambient vibration dispersion curve inversion.

Tsunami early warning (TEW) is a challenging task as a decision has to be made within few minutes on the basis of incomplete and error-prone data. Deterministic warning systems have difficulties in integrating and quantifying the intrinsic uncertainties. In contrast, probabilistic approaches provide a framework that handles uncertainties in a natural way. Recently, we have proposed a method using Bayesian networks (BNs) that takes into account the uncertainties of seismic source parameter estimates in TEW. In this follow-up study, the method is applied to 10 recent large earthquakes offshore Sumatra and tested for its performance. We have evaluated both the general model performance given the best knowledge we have today about the source parameters of the 10 events and the corresponding response on seismic source information evaluated in real-time. We find that the resulting site-specific warning level probabilities represent well the available tsunami wave measurements and observations. Difficulties occur in the real-time tsunami assessment if the moment magnitude estimate is severely over- or underestimated. In general, the probabilistic analysis reveals a considerably large range of uncertainties in the near-field TEW. By quantifying the uncertainties the BN analysis provides important additional information to a decision maker in a warning centre to deal with the complexity in TEW and to reason under uncertainty.

Constructing a hidden Markov Model based earthquake detector: application to induced seismicity
(2012)

The triggering or detection of seismic events out of a continuous seismic data stream is one of the key issues of an automatic or semi-automatic seismic monitoring system. In the case of dense networks, either local or global, most of the implemented trigger algorithms are based on a large number of active stations. However, in the case of only few available stations or small events, for example, like in monitoring volcanoes or hydrothermal power plants, common triggers often show high false alarms. In such cases detection algorithms are of interest, which show reasonable performance when operating even on a single station. In this context, we apply Hidden Markov Models (HMM) which are algorithms borrowed from speech recognition. However, many pitfalls need to be avoided to apply speech recognition technology directly to earthquake detection. We show the fit of the model parameters in an innovative way. State clustering is introduced to refine the intrinsically assumed time dependency of the HMMs and we explain the effect coda has on the recognition results. The methodology is then used for the detection of anthropogenicly induced earthquakes for which we demonstrate for a period of 3.9 months of continuous data that the single station HMM earthquake detector can achieve similar detection rates as a common trigger in combination with coincidence sums over two stations. To show the general applicability of state clustering we apply the proposed method also to earthquake classification at Mt. Merapi volcano, Indonesia.

The inversion of surface-wave dispersion curve to derive shear-wave velocity profile is a very delicate process dealing with a nonunique problem, which is strongly dependent on the model space parameterization. When independent and reliable information is not available, the selection of most representative models within the ensemble produced. by the inversion is often difficult. We implemented a strategy in the inversion of dispersion curves able to investigate the influence of the parameterization of the model space and to select a "best" class of models. We analyzed surface-wave dispersion curves measured at 14 European strong..-motion sites within the NERIES EC-Project. We focused on the inversion task exploring the model space by means of four distinct pararneterization classes composed of layers progressively added over a half-space. The classes differ in the definition of the shear-wave velocity profile; we considered models with uniform velocity as well as models with increasing velocity with depth. At each site and for each model parameterization, we performed an extensive surface-wave inversion (200,100 models for five seeds) using the conditional neighborhood algorithm. We addressed the model evaluation following the corrected Akaike's information criterion (AlCc) that combines the concept of misfit to the number of degrees of freedom of the system. The misfit was computed as least-squares estimation between theoretical and observed dispersion curve. The model complexity was accounted in a penalty term by AlCc. By applying such inversion strategy on 14 strong-motion sites, we found that the best parameterization of the model space is mostly three to four layers over a half-space: where the shear-wave velocity of the uppermost layers can follow uniform or power-law dependence with depth. The shear-wave velocity profiles derived by inversion agree with shear-wave velocity profiles provided by borehole surveys at approximately 80% of the sites.