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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.
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.
Volcanic tremor extraction and earthquake detection using music information retrieval algorithms
(2021)
Volcanic tremor signals are usually observed before or during volcanic eruptions and must be monitored to evaluate the volcanic activity. A challenge in studying seismic signals of volcanic origin is the coexistence of transient signal swarms and long-lasting volcanic tremor signals. Separating transient events from volcanic tremors can, therefore, contrib-ute to improving upon our understanding of the underlying physical processes. Exploiting the idea of harmonic-percussive separation in musical signal processing, we develop a method to extract the harmonic volcanic tremor signals and to detect tran-sient events from seismic recordings. Based on the similarity properties of spectrogram frames in the time-frequency domain, we decompose the signal into two separate spec-trograms representing repeating (harmonic) and nonrepeating (transient) patterns, which correspond to volcanic tremor signals and earthquake signals, respectively. We reconstruct the harmonic tremor signal in the time domain from the complex spectrogram of the repeating pattern by only considering the phase components for the frequency range in which the tremor amplitude spectrum is significantly contribut-ing to the energy of the signal. The reconstructed signal is, therefore, clean tremor signal without transient events. Furthermore, we derive a characteristic function suitable for the detection of tran-sient events (e.g., earthquakes) by integrating amplitudes of the nonrepeating spectro-gram over frequency at each time frame. Considering transient events like earthquakes, 78% of the events are detected for signal-to-noise ratio = 0.1 in our semisynthetic tests. In addition, we compared the number of detected earthquakes using our method for one month of continuous data recorded during the Holuhraun 2014-2015 eruption in Iceland with the bulletin presented in Agustsdottir et al. (2019). Our single station event detection algorithm identified 84% of the bulletin events. Moreover, we detected a total of 12,619 events, which is more than twice the number of the bulletin events.
The variation of Rayleigh ellipticity versus frequency is gaining popularity in site characterization. It becomes a necessary observable to complement dispersion curves when inverting shear wave velocity profiles. Various methods have been proposed so far to extract polarization from ambient vibrations recorded on a single three-component station or with an array of three-component sensors. If only absolute values were recovered 10 yr ago, new array-based techniques were recently proposed with enhanced efficiencies providing also the ellipticity sign. With array processing, higher-order modes are often detected even in the ellipticity domain. We suggest to explore the properties of a high-resolution beamforming where radial and vertical components are explicitly included. If N is the number of three-component sensors, 2N x 2N cross-spectral density matrices are calculated for all presumed directions of propagation. They are built with N radial and N vertical channels. As a first approach, steering vectors are designed to fit with Rayleigh wave properties: the phase shift between radial and vertical components is either -Pi/2 or Pi/2. We show that neglecting the ellipticity tilt due to attenuation has only minor effects on the results. Additionally, we prove analytically that it is possible to retrieve the ellipticity value from the usual maximization of the high-resolution beam power. The method is tested on synthetic data sets and on experimental data. Both are reference sites already analysed by several authors. A detailed comparison with previous results on these cases is provided.
Ambient vibration techniques are promising methods for assessing the subsurface structure, in particular the shear-wave velocity profile (V-s). They are based on the dispersion property of surface waves in layered media. Therefore, the penetration depth is intrinsically linked to the energy content of the sources. For ambient vibrations, the spectral content extends in general to lower frequency when compared to classical artificial sources. Among available methods for processing recorded signals, we focus here on the spatial autocorrelation method. For stationary wavefields, the spatial autocorrelation is mathematically related to the frequency-dependent wave velocity c(omega). This allows the determination of the dispersion curve of traveling surface waves, which, in turn, is linked to the V-s profile. Here, we propose a direct inversion scheme for the observed autocorrelation curves to retrieve, in a single step, the V-s profile. The powerful neighborhood algorithm is used to efficiently search for all solutions in an n- dimensional parameter space. This approach has the advantage of taking into account the existing uncertainty over the measured curves, thus generating all V-s profiles that fit the data within their experimental errors. A preprocessing tool is also developed to estimate the validity of the autocorrelation curves and to reject parts of them if necessary before starting the inversion itself. We present two synthetic cases to test the potential of the method: one with ideal autocorrelation curves and another with autocorrelation curves computed from simulated ambient vibrations. The latter case is more realistic and makes it possible to figure out the problems that may be encountered in real experiments. The V-s profiles are correctly retrieved up to the depth of the first major velocity contrast unless low-velocity zones are accepted. We demonstrate that accepting low-velocity zones in the parameterization has a dramatic influence on the result of the inversion, with a considerable increase in the nonuniqueness of the problem. Finally, a real data set is processed with the same method
The resonance frequency of the transmission response in layered half-space model is important in the study of site effect because it is the frequency where the shake-ability of the ground is enhanced significantly. In practice, it is often determined by the H/V ratio technique in which the peak frequency of recorded H/V spectral ratio is interpreted as the resonance frequency. Despite of its importance, there has not been any formula of the resonance frequency of the layered half-space structure. In this paper, a simple approximate formula of the fundamental resonance frequency is presented after an exact formula in explicit form of the response function of vertically SH incident wave is obtained. The formula is in similar form with the one used in H/V ratio technique but it reflects several major effects of the model to the resonance frequency such as the arrangement of layers, the impedance contrast between layers and the half-space. Therefore, it could be considered as an improved formula used in H/V ratio technique. The formula also reflects the consistency between two approaches of the H/V ratio technique based on SH body waves or Rayleigh surface waves on the peak frequency under high impedance contrast condition. This formula is in explicit form and, therefore, may be used in the direct and inverse problem efficiently. A numerical illustration of the improved formula for an actual layered half-space model already investigated by H/V ratio technique is presented to demonstrate its new features and its improvement to the currently used formula.
We present an approach for rapidly estimating full moment tensors of earthquakes and their parameter uncertainties based on short time windows of recorded seismic waveform data by considering deep learning of Bayesian Neural Networks (BNNs). The individual neural networks are trained on synthetic seismic waveform data and corresponding known earthquake moment-tensor parameters. A monitoring volume has been predefined to form a three-dimensional grid of locations and to train a BNN for each grid point. Variational inference on several of these networks allows us to consider several sources of error and how they affect the estimated full moment-tensor parameters and their uncertainties. In particular, we demonstrate how estimated parameter distributions are affected by uncertainties in the earthquake centroid location in space and time as well as in the assumed Earth structure model. We apply our approach as a proof of concept on seismic waveform recordings of aftershocks of the Ridgecrest 2019 earthquake with moment magnitudes ranging from Mw 2.7 to Mw 5.5. Overall, good agreement has been achieved between inferred parameter ensembles and independently estimated parameters using classical methods. Our developed approach is fast and robust, and therefore, suitable for down-stream analyses that need rapid estimates of the source mechanism for a large number of earthquakes.
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.
In this paper we present densely sampled fumarole temperature data, recorded continuously at a high-temperature fumarole of Mt. Merapi volcano (Indonesia). These temperature time series are correlated with continuous records of rainfall and seismic waveform data collected at the Indonesian - German multi-parameter monitoring network. The correlation analysis of fumarole temperature and precipitation data shows a clear influence of tropical rain events on fumarole temperature. In addition, there is some evidence that rainfall may influence seismicity rates, indicating interaction of meteoric water with the volcanic system. Knowledge about such interactions is important, as lava dome instabilities caused by heavy-precipitation events may result in pyroclastic flows. Apart from the strong external influences on fumarole temperature and seismicity rate, which may conceal smaller signals caused by volcanic degassing processes, the analysis of fumarole temperature and seismic data indicates a statistically significant correlation between a certain type of seismic activity and an increase in fumarole temperature. This certain type of seismic activity consists of a seismic cluster of several high-frequency transients and an ultra-long-period signal (< 0.002 Hz), which are best observed using a broadband seismometer deployed at a distance of 600 m from the active lava dome. The corresponding change in fumarole temperature starts a few minutes after the ultra-long-period signal and simultaneously with the high-frequency seismic cluster. The change in fumarole temperature, an increase of 5 degreesC on average, resembles a smoothed step. Fifty-four occurrences of simultaneous high-frequency seismic cluster, ultra-long period signal and increase of fumarole temperature have been identified in the data set from August 2000 to January 2001. The observed signals appear to correspond to degassing processes in the summit region of Mt. Merapi. (C) 2004 Elsevier B.V. All rights reserved
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.
Merapi volcano is one of the most active and dangerous volcanoes of the earth. Located in central part of Java island (Indonesia), even a moderate eruption of Merapi poses a high risk to the highly populated area. Due to the close relationship between the volcanic unrest and the occurrence of seismic events at Mt. Merapi, the monitoring of Merapi's seismicity plays an important role for recognizing major changes in the volcanic activity. An automatic seismic event detection and classification system, which is capable to characterize the actual seismic activity in near real-time, is an important tool which allows the scientists in charge to take immediate decisions during a volcanic crisis. In order to accomplish the task of detecting and classifying volcano-seismic signals automatically in the continuous data streams, a pattern recognition approach has been used. It is based on the method of hidden Markov models (HMM), a technique, which has proven to provide high recognition rates at high confidence levels in classification tasks of similar complexity (e.g. speech recognition). Any pattern recognition system relies on the appropriate representation of the input data in order to allow a reasonable class-decision by means of a mathematical test function. Based on the experiences from seismological observatory practice, a parametrization scheme of the seismic waveform data is derived using robust seismological analysis techniques. The wavefield parameters are summarized into a real-valued feature vector per time step. The time series of this feature vector build the basis for the HMM-based classification system. In order to make use of discrete hidden Markov (DHMM) techniques, the feature vectors are further processed by applying a de-correlating and prewhitening transformation and additional vector quantization. The seismic wavefield is finally represented as a discrete symbol sequence with a finite alphabet. This sequence is subject to a maximum likelihood test against the discrete hidden Markov models, learned from a representative set of training sequences for each seismic event type of interest. A time period from July, 1st to July, 5th, 1998 of rapidly increasing seismic activity prior to the eruptive cycle between July, 10th and July, 19th, 1998 at Merapi volcano is selected for evaluating the performance of this classification approach. Three distinct types of seismic events according to the established classification scheme of the Volcanological Survey of Indonesia (VSI) have been observed during this time period. Shallow volcano-tectonic events VTB (h < 2.5 km), very shallow dome-growth related seismic events MP (h < 1 km) and seismic signals connected to rockfall activity originating from the active lava dome, termed Guguran. The special configuration of the digital seismic station network at Merapi volcano, a combination of small-aperture array deployments surrounding Merapi's summit region, allows the use of array methods to parametrize the continuously recorded seismic wavefield. The individual signal parameters are analyzed to determine their relevance for the discrimination of seismic event classes. For each of the three observed event types a set of DHMMs has been trained using a selected set of seismic events with varying signal to noise ratios and signal durations. Additionally, two sets of discrete hidden Markov models have been derived for the seismic noise, incorporating the fact, that the wavefield properties of the ambient vibrations differ considerably during working hours and night time. A total recognition accuracy of 67% is obtained. The mean false alarm (FA) rate can be given by 41 FA/class/day. However, variations in the recognition capabilities for the individual seismic event classes are significant. Shallow volcano-tectonic signals (VTB) show very distinct wavefield properties and (at least in the selected time period) a stable time pattern of wavefield attributes. The DHMM-based classification performs therefore best for VTB-type events, with almost 89% recognition accuracy and 2 FA/day. Seismic signals of the MP- and Guguran-classes are more difficult to detect and classify. Around 64% of MP-events and 74% of Guguran signals are recognized correctly. The average false alarm rate for MP-events is 87 FA/day, whereas for Guguran signals 33 FA/day are obtained. However, the majority of missed events and false alarms for both MP and Guguran events are due to confusion errors between these two event classes in the recognition process. The confusion of MP and Guguran events is interpreted as being a consequence of the selected parametrization approach for the continuous seismic data streams. The observed patterns of the analyzed wavefield attributes for MP and Guguran events show a significant amount of similarity, thus providing not sufficient discriminative information for the numerical classification. The similarity of wavefield parameters obtained for seismic events of MP and Guguran type reflect the commonly observed dominance of path effects on the seismic wave propagation in volcanic environments. The recognition rates obtained for the five-day period of increasing seismicity show, that the presented DHMM-based automatic classification system is a promising approach for the difficult task of classifying volcano-seismic signals. Compared to standard signal detection algorithms, the most significant advantage of the discussed technique is, that the entire seismogram is detected and classified in a single step.
We compute seismic velocity profiles by a combined inversion of surface-wave phase-velocity dispersion curves together with the full spectrum of the microtremor horizontal-to-vertical (H/V) spectral ratio at two sediment-covered sites in Germany. The sediment deposits are approximately 100 m thick at the first test site and approximately 400 m thick at the second test site. We have used an extended physical model based on the diffuse wavefield assumption for the interpretation of the observed microtremor H/V spectral ratio. The extension includes the interpretation of the microtremor H/V spectral ratio observed at depth (in boreholes). This full-wavefield approach accounts for the energy contribution from the body and surface waves, and thus it allows for inverting the properties of the shallow subsurface. We have obtained the multimode phase velocity dispersion curves from an independent study, and a description of the extracted branches and their interpretation was developed. The inversion results indicate that the combined approach using seismic ambient noise and actively generated surface-wave data will improve the accuracy of the reconstructed near-surface velocity model, a key step in microzonation, geotechnical engineering, seismic statics corrections, and reservoir imaging.
We present an integrated approach for deriving the 1D shear wave velocity (Vs) information at few tens to hundreds of meters down to the first strong impedance contrast in typical sedimentary environments. We use multiple small aperture seismic arrays in 1D and 2D configuration to record active and passive seismic surface wave data at two selected geotechnical sites in Germany (Horstwalde & Lobnitz). Standard methods for data processing include the Multichannel Analysis of Surface Waves (MASW) method that exploits the high frequency content in the active data and the sliding window frequency-wavenumber (f-k) as well as the spatial autocorrelation (SPAC) methods that exploit the low frequency content in passive seismic data. Applied individually, each of the passive methods might be influenced by any source directivity in the noise wavefield. The advantages of active shot data (known source location) and passive microtremor (low frequency content) recording may be combined using a correlation based approach applied to the passive data in the so called Interferometric Multichannel Analysis of Surface Waves (IMASW). In this study, we apply those methods to jointly determine and interpret the dispersion characteristics of surface waves recorded at Horstwalde and Lobnitz. The reliability of the dispersion curves is controlled by applying strict limits on the interpretable range of wavelengths in the analysis and further avoiding potentially biased phase velocity estimates from the passive f-k method by comparing to those derived from the SPatial AutoCorrelation method (SPAC). From our investigation at these two sites, the joint analysis as proposed allows mode extraction in a wide frequency range (similar to 0.6-35 Hz at Horstwalde and similar to 1.5-25 Hz at Lobnitz) and consequently improves the Vs profile inversion. To obtain the shear wave velocity profiles, we make use of a global inversion approach based on the neighborhood algorithm to invert the interpreted branches of the dispersion curves. Within the uncertainty given by the apparent spread of forward models we find that besides a well defined sediment velocity range also a reasonable minimum estimate of bedrock depth and bedrock velocity can be achieved. The Vs estimate for the best model in Horstwalde ranges from similar to 190 m/s at the surface up to similar to 390 m/s in the bottom of the soft sediment column. The bedrock starts earliest around 200 m depth and bedrock velocities are higher than 1000 m/s. In Lobnitz, we observe slightly lower velocities for the sediments (similar to 165-375 m/s for the best model) and a minimum thickness of 75 m. (C) 2016 Elsevier B.V. All rights reserved.
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.
Advances in the field of seismic interferometry have provided a basic theoretical interpretation to the full spectrum of the microtremor horizontal-to-vertical spectral ratio [H/V(f)]. The interpretation has been applied to ambient seismic noise data recorded both at the surface and at depth. The new algorithm, based on the diffuse wavefield assumption, has been used in inversion schemes to estimate seismic wave velocity profiles that are useful input information for engineering and exploration seismology both for earthquake hazard estimation and to characterize surficial sediments. However, until now, the developed algorithms are only suitable for on land environments with no offshore consideration. Here, the microtremor H/V(z, f) modelling is extended for applications to marine sedimentary environments for a 1-D layered medium. The layer propagator matrix formulation is used for the computation of the required Green’s functions. Therefore, in the presence of a water layer on top, the propagator matrix for the uppermost layer is defined to account for the properties of the water column. As an application example we analyse eight simple canonical layered earth models. Frequencies ranging from 0.2 to 50 Hz are considered as they cover a broad wavelength interval and aid in practice to investigate subsurface structures in the depth range from a few meters to a few hundreds of meters. Results show a marginal variation of 8 per cent at most for the fundamental frequency when a water layer is present. The water layer leads to variations in H/V peak amplitude of up to 50 per cent atop the solid layers.
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.
We test the capability of broadband arrays at teleseismic distances to image the spatio-temporal characteristics of the seismic energy release during the Dec 26, 2004 Sumatra earthquake at early observation times. Using a non-plane-wave array location technique previously reported values for rupture length (about 1150 km), duration (about 480 s), and average rupture velocity (2.4-2.7 km/s) are confirmed. Three dominant energy releases are identified: one near the hypocenter, a second at 6 degrees N94 degrees E about 130 s later and a third one after 300 s at 9 degrees N92-93 degrees E. The spatio-temporal distribution of the radiated seismic energy in the source region is calculated from the stacked broadband recordings of two arrays in Germany and Japan and results in rough estimates of the total seismic energy of 0.55.10(18) Nm (GRSN) and 1.53.10(18) Nm (FNET) respectively. Changes in the relative ratio of energy as function of spatio-temporal location indicate a rotation of the focal mechanism during the rupture process