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Characterization of polarization attributes of seismic waves using continuous wavelet transforms
(2006)
Complex-trace analysis is the method of choice for analyzing polarized data. Because particle motion can be represented by instantaneous attributes that show distinct features for waves of different polarization characteristics, it can be used to separate and characterize these waves. Traditional methods of complex-trace analysis only give the instantaneous attributes as a function of time or frequency. However. for transient wave types or seismic events that overlap in time, an estimate of the polarization parameters requires analysis of the time-frequency dependence of these attributes. We propose a method to map instantaneous polarization attributes of seismic signals in the wavelet domain and explicitly relate these attributes with the wavelet-transform coefficients of the analyzed signal. We compare our method with traditional complex-trace analysis using numerical examples. An advantage of our method is its possibility of performing the complete wave-mode separation/ filtering process in the wavelet domain and its ability to provide the frequency dependence of ellipticity, which contains important information on the subsurface structure. Furthermore, using 2-C synthetic and real seismic shot gathers, we show how to use the method to separate different wave types and identify zones of interfering wave modes
Magnitude estimation for microseismicity induced during the KTB 2004/2005 injection experiment
(2011)
We determined the magnitudes of 2540 microseismic events measured at one single 3C borehole geophone at the German Deep Drilling Site (known by the German acronym, KTB) during the injection phase 2004/2005. For this task we developed a three-step approach. First, we estimated local magnitudes of 104 larger events with a standard method based on amplitude measurements at near-surface stations. Second, we investigated a series of parameters to characterize the size of these events using the seismograms of the borehole sensor, and we compared them statistically with the local magnitudes. Third, we extrapolated the regression curve to obtain the magnitudes of 2436 events that were only measured at the borehole geophone. This method improved the magnitude of completeness for the KTB data set by more than one order down to M = -2.75. The resulting b-value for all events was 0.78, which is similar to the b-value obtained from taking only the greater events with standard local magnitude estimation from near-surface stations, b = 0.86. The more complete magnitude catalog was required to study the magnitude distribution with time and to characterize the seismotectonic state of the KTB injection site. The event distribution with time was consistent with prediction from theory assuming pore pressure diffusion as the underlying mechanism to trigger the events. The value we obtained for the seismogenic index of -4 suggested that the seismic hazard potential at the KTB site is comparatively low.
Earthquake rupture length and width estimates are in demand in many seismological applications. Earthquake magnitude estimates are often available, whereas the geometrical extensions of the rupture fault mostly are lacking. Therefore, scaling relations are needed to derive length and width from magnitude. Most frequently used are the relationships of Wells and Coppersmith (1994) derived on the basis of a large dataset including all slip types with the exception of thrust faulting events in subduction environments. However, there are many applications dealing with earthquakes in subduction zones because of their high seismic and tsunamigenic potential. There are no well-established scaling relations for moment magnitude and length/width for subduction events. Within this study, we compiled a large database of source parameter estimates of 283 earthquakes. All focal mechanisms are represented, but special focus is set on (large) subduction zone events, in particular. Scaling relations were fitted with linear least-square as well as orthogonal regression and analyzed regarding the difference between continental and subduction zone/oceanic relationships. Additionally, the effect of technical progress in earthquake parameter estimation on scaling relations was tested as well as the influence of different fault mechanisms. For a given moment magnitude we found shorter but wider rupture areas of thrust events compared to Wells and Coppersmith (1994). The thrust event relationships for pure continental and pure subduction zone rupture areas were found to be almost identical. The scaling relations differ significantly for slip types. The exclusion of events prior to 1964 when the worldwide standard seismic network was established resulted in a remarkable effect on strike-slip scaling relations: the data do not show any saturation of rupture width of strike- slip earthquakes. Generally, rupture area seems to scale with mean slip independent of magnitude. The aspect ratio L/W, however, depends on moment and differs for each slip type.
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.
Records from ocean bottom seismometers (OBSs) are highly contaminated by noise, which is much stronger compared to data from most land stations, especially on the horizontal components. As a consequence, the high energy of the oceanic noise at frequencies below 1 Hz considerably complicates the analysis of the teleseismic earthquake signals recorded by OBSs.
Previous studies suggested different approaches to remove low-frequency noises from OBS recordings but mainly focused on the vertical component. The records of horizontal components, which are crucial for the application of many methods in passive seismological analysis of body and surface waves, could not be much improved in the teleseismic frequency band. Here we introduce a noise reduction method, which is derived from the harmonic–percussive separation algorithms used in Zali et al. (2021), in order to separate long-lasting narrowband signals from broadband transients in the OBS signal. This leads to significant noise reduction of OBS records on both the vertical and horizontal components and increases the earthquake signal-to-noise ratio (SNR) without distortion of the broadband earthquake waveforms. This is demonstrated through tests with synthetic data. Both SNR and cross-correlation coefficients showed significant improvements for different realistic noise realizations. The application of denoised signals in surface wave analysis and receiver functions is discussed through tests with synthetic and real data.
The Ceres earthquake of 29 September 1969 is the largest known earthquake in southern Africa. Digitized analog recordings from Worldwide Standardized Seismographic Network stations (Powell and Fries, 1964) are used to retrieve the point source moment tensor and the most likely centroid depth of the event using full waveform modeling. A scalar seismic moment of 2.2-2.4 x 10(18) N center dot m corresponding to a moment magnitude of 6.2-6.3 is found. The analysis confirms the pure strike-slip mechanism previously determined from onset polarities by Green and Bloch (1971). Overall good agreement with the fault orientation previously estimated from local aftershock recordings is found. The centroid depth can be constrained to be less than 15 km. In a second analysis step, we use a higher order moment tensor based inversion scheme for simple extended rupture models to constrain the lateral fault dimensions. We find rupture propagated unilaterally for 4.7 s from east-southwest to west-northwest for about 17 km ( average rupture velocity of about 3: 1 km/s).
We have analyzed the recently developed pan-European strong motion database, RESORCE-2012: spectral parameters, such as stress drop (stress parameter, Delta sigma), anelastic attenuation (Q), near surface attenuation (kappa(0)) and site amplification have been estimated from observed strong motion recordings. The selected dataset exhibits a bilinear distance-dependent Q model with average kappa(0) value 0.0308 s. Strong regional variations in inelastic attenuation were also observed: frequency-independent Q(0) of 1462 and 601 were estimated for Turkish and Italian data respectively. Due to the strong coupling between Q and kappa(0), the regional variations in Q have strong impact on the estimation of near surface attenuation kappa(0). kappa(0) was estimated as 0.0457 and 0.0261 s for Turkey and Italy respectively. Furthermore, a detailed analysis of the variability in estimated kappa(0) revealed significant within-station variability. The linear site amplification factors were constrained from residual analysis at each station and site-class type. Using the regional Q(0) model and a site-class specific kappa(0), seismic moments (M-0) and source corner frequencies f (c) were estimated from the site corrected empirical Fourier spectra. Delta sigma did not exhibit magnitude dependence. The median Delta sigma value was obtained as 5.75 and 5.65 MPa from inverted and database magnitudes respectively. A comparison of response spectra from the stochastic model (derived herein) with that from (regional) ground motion prediction equations (GMPEs) suggests that the presented seismological parameters can be used to represent the corresponding seismological attributes of the regional GMPEs in a host-to-target adjustment framework. The analysis presented herein can be considered as an update of that undertaken for the previous Euro-Mediterranean strong motion database presented by Edwards and Fah (Geophys J Int 194(2):1190-1202, 2013a).
We introduce a method for computing instantaneous-polarization attributes from multicomponent signals. This is an improvement on the standard covariance method (SCM) because it does not depend on the window size used to compute the standard covariance matrix. We overcome the window-size problem by deriving an approximate analytical formula for the cross-energy matrix in which we automatically and adaptively determine the time window. The proposed method uses polarization analysis as applied to multicomponent seismic by waveform separation and filtering.
The estimation of minimum-misfit stochastic models from empirical ground-motion prediction equations
(2006)
In areas of moderate to low seismic activity there is commonly a lack of recorded strong ground motion. As a consequence, the prediction of ground motion expected for hypothetical future earthquakes is often performed by employing empirical models from other regions. In this context, Campbell's hybrid empirical approach (Campbell, 2003, 2004) provides a methodological framework to adapt ground-motion prediction equations to arbitrary target regions by using response spectral host-to-target-region-conversion filters. For this purpose, the empirical ground-motion prediction equation has to be quantified in terms of a stochastic model. The problem we address here is how to do this in a systematic way and how to assess the corresponding uncertainties. For the determination of the model parameters we use a genetic algorithm search. The stochastic model spectra were calculated by using a speed-optimized version of SMSIM (Boore, 2000). For most of the empirical ground-motion models, we obtain sets of stochastic models that match the empirical models within the full magnitude and distance ranges of their generating data sets fairly well. The overall quality of fit and the resulting model parameter sets strongly depend on the particular choice of the distance metric used for the stochastic model. We suggest the use of the hypocentral distance metric for the stochastic Simulation of strong ground motion because it provides the lowest-misfit stochastic models for most empirical equations. This is in agreement with the results of two recent studies of hypocenter locations in finite-source models which indicate that hypocenters are often located close to regions of large slip (Mai et al., 2005; Manighetti et al., 2005). Because essentially all empirical ground-motion prediction equations contain data from different geographical regions, the model parameters corresponding to the lowest-misfit stochastic models cannot necessarily be expected to represent single, physically realizable host regions but to model the generating data sets in an average way. In addition, the differences between the lowest-misfit stochastic models and the empirical ground-motion prediction equation are strongly distance, magnitude, and frequency dependent, which, according to the laws of uncertainty propagation, will increase the variance of the corresponding hybrid empirical model predictions (Scherbaum et al., 2005). As a consequence, the selection of empirical ground-motion models for host-to-target-region conversions requires considerable judgment of the ground-motion analyst