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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.
The SEIS (Seismic Experiment for Interior Structure) instrument onboard the InSight mission will be the first seismometer directly deployed on the surface of Mars. From studies on the Earth and the Moon, it is well known that site amplification in low-velocity sediments on top of more competent rocks has a strong influence on seismic signals, but can also be used to constrain the subsurface structure. Here we simulate ambient vibration wavefields in a model of the shallow sub-surface at the InSight landing site in Elysium Planitia and demonstrate how the high-frequency Rayleigh wave ellipticity can be extracted from these data and inverted for shallow structure. We find that, depending on model parameters, higher mode ellipticity information can be extracted from single-station data, which significantly reduces uncertainties in inversion. Though the data are most sensitive to properties of the upper-most layer and show a strong trade-off between layer depth and velocity, it is possible to estimate the velocity and thickness of the sub-regolith layer by using reasonable constraints on regolith properties. Model parameters are best constrained if either higher mode data can be used or additional constraints on regolith properties from seismic analysis of the hammer strokes of InSight’s heat flow probe HP3 are available. In addition, the Rayleigh wave ellipticity can distinguish between models with a constant regolith velocity and models with a velocity increase in the regolith, information which is difficult to obtain otherwise.
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.
In this paper, we propose a method of surface waves characterization based on the deformation of the wavelet transform of the analysed signal. An estimate of the phase velocity (the group velocity) and the attenuation coefficient is carried out using a model-based approach to determine the propagation operator in the wavelet domain, which depends nonlinearly on a set of unknown parameters. These parameters explicitly define the phase velocity, the group velocity and the attenuation. Under the assumption that the difference between waveforms observed at a couple of stations is solely due to the dispersion characteristics and the intrinsic attenuation of the medium, we then seek to find the set of unknown parameters of this model. Finding the model parameters turns out to be that of an optimization problem, which is solved through the minimization of an appropriately defined cost function. We show that, unlike time-frequency methods that exploit only the square modulus of the transform, we can achieve a complete characterization of surface waves in a dispersive and attenuating medium. Using both synthetic examples and experimental data, we also show that it is in principle possible to separate different modes in both the time domain and the frequency domain
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
This study presents results of ambient noise measurements from temporary single station and small-scale array deployments in the northeast of Basle. H/V spectral ratios were determined along various profiles crossing the eastern masterfault of the Rhine Rift Valley and the adjacent sedimentary rift fills. The fundamental H/V peak frequencies are decreasing along the profile towards the eastern direction being consistent with the dip of the tertiary sediments within the rift. Using existing empirical relationships between H/V frequency peaks and the depth of the dominant seismic contrast, derived on basis of the lambda/4-resonance hypothesis and a power law depth dependence of the S-wave velocity, we obtain thicknesses of the rift fill from about 155 m in the west to 280 in in the east. This is in agreement with previous studies. The array analysis of the ambient noise wavefield yielded a stable dispersion relation consistent with Rayleigh wave propagation velocities. We conclude that a significant amount of surface waves is contained in the observed wavefield. The computed ellipticity for fundamental mode Rayleigh waves for the velocity depth models used for the estimation of the sediment thicknesses is in agreement with the observed H/V spectra over a large frequency band
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 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.
Earthquake localization is both a necessity within the field of seismology, and a prerequisite for further analysis such as source studies and hazard assessment. Traditional localization methods often rely on manually picked phases. We present an alternative approach using deep learning that once trained can predict hypocenter locations efficiently. In seismology, neural networks have typically been trained with either single-station records or based on features that have been extracted previously from the waveforms. We use three-component full-waveform records of multiple stations directly. This means no information is lost during preprocessing and preparation of the data does not require expert knowledge. The first convolutional layer of our deep convolutional neural network (CNN) becomes sensitive to features that characterize the waveforms it is trained on. We show that this layer can therefore additionally be used as an event detector. As a test case, we trained our CNN using more than 2000 earthquake swarm events from West Bohemia, recorded by nine local three-component stations. The CNN successfully located 908 validation events with standard deviations of 56.4 m in east-west, 123.8 m in north-south, and 136.3 m in vertical direction compared to a double-difference relocated reference catalog. The detector is sensitive to events with magnitudes down to M-L = -0.8 with 3.5% false positive detections.
Avalanche activity is an important factor when estimating the regional avalanche danger. Moreover, a complete and detailed picture of avalanche activity is needed to understand the processes that lead to natural avalanche release. Currently, information on avalanche activity is mainly obtained through visual observations. However, this involves large uncertainties in the number and release times, influencing the subsequent analysis. Therefore, alternative methods for the remote detection of snow avalanches in particular in non-observed areas are highly desirable. In this study, we use the excited ground vibration to identify avalanches automatically. The specific seismic signature of avalanches facilitates the objective detection by a recently developed classification procedure. A probabilistic description of the signals, called hidden Markov models, allows the robust identification of corresponding signals in the continuous data stream. The procedure is based upon learning a general background model from continuous seismic data. Then, a single reference waveform is used to update an event-specific classifier. Thus, a minimum amount of training data is required by constructing such a classifier on the fly. In this study, we processed five days of continuous data recorded in the Swiss Alps during the avalanche winter 1999. With the restriction of testing large wet-snow avalanches only, the presented approach achieved very convincing results. We successfully detect avalanches over a large volume and distance range. Ninety-two percentage of all detections (43 out of 47) could be confirmed as avalanche events; only four false alarms are reported. We see a clear dependence of recognition capability on run-out distance and source-receiver distance of the observed events: Avalanches are detectable up to a source-receiver distance of eight times the avalanche length. Implications for analyzing a more comprehensive data set (smaller events and different flow regimes) are discussed in detail.
Volcanic eruptions are often preceded by seismic activity that can be used to quantify the volcanic activity. In order to allow consistent inference of the volcanic activity state from the observed seismicity patterns, objective and time-invariant classification results achievable by automatic systems should be preferred. Most automatic classification approaches need a large preclassified data set for training the system. However, in case of a volcanic crisis, we are often confronted with a lack of training data due to insufficient prior observations. In the worst case (e. g., volcanic crisis related reconfiguration of stations), there are even no prior observations available. Finally, due to the imminent crisis there might be no time for the time-consuming process of preparing a training data set. For this reason, we have developed a novel seismic-event spotting technique in order to be less dependent on previously acquired data bases and classification schemes. We are using a learning-while-recording approach based on a minimum number of reference waveforms, thus allowing for the build-up of a classification scheme as early as interesting events have been identified. First, short-term wave-field parameters (here, polarization and spectral attributes) are extracted from a continuous seismic data stream. The sequence of multidimensional feature vectors is then used to identify a fixed number of clusters in the feature space. Based on this general description of the overall wave field by a mixture of multivariate Gaussians, we are able to learn particular event classifiers (here, hidden Markov models) from a single waveform example. To show the capabilities of this new approach we apply the algorithm to a data set recorded at Soufriere Hills volcano, Montserrat. Supported by very high classification rates, we conclude that the suggested approach provides a valuable tool for volcano monitoring systems.
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.
Forecasting seismo-volcanic activity by using the dynamical behavior of volcanic earthquake rates
(2012)
We present a novel approach for short-term forecasting of volcano seismic activity. Volcanic earthquakes can be seen as a response mechanism of the earth crust to stresses induced by magma injection. From this point of view the temporal evolution of seismicity can be represented as a diffusion process which compensates pressure differences. By means of this dynamical approach we are able to estimate the system behavior in the near future which in turn allows us to forecast the evolution of the earthquake rate for the next time span from actual and past observations. For this purpose we model the earthquake rate as a random walk process embedded in a moving and deforming potential function. The center of the potential function is given by a moving average of the random walk's trace. We successfully apply this procedure to estimate the next day seismicity at Soufriere Hills volcano, Montserrat, over a time period of six years. When comparing the dynamical approach to the well known method of material failure forecast we find much better predictions of the critical stages of volcanic activity using the new approach.
Other than commonly assumed in seismology, the phase velocity of Rayleigh waves is not necessarily a single-valued function of frequency. In fact, a single Rayleigh mode can exist with three different values of phase velocity at one frequency. We demonstrate this for the first higher mode on a realistic shallow seismic structure of a homogeneous layer of unconsolidated sediments on top of a half-space of solid rock (LOH). In the case of LOH a significant contrast to the half-space is required to produce the phenomenon. In a simpler structure of a homogeneous layer with fixed (rigid) bottom (LFB) the phenomenon exists for values of Poisson's ratio between 0.19 and 0.5 and is most pronounced for P-wave velocity being three times S-wave velocity (Poisson's ratio of 0.4375). A pavement-like structure (PAV) of two layers on top of a half-space produces the multivaluedness for the fundamental mode. Programs for the computation of synthetic dispersion curves are prone to trouble in such cases. Many of them use mode-follower algorithms which loose track of the dispersion curve and miss the multivalued section. We show results for well established programs. Their inability to properly handle these cases might be one reason why the phenomenon of multivaluedness went unnoticed in seismological Rayleigh wave research for so long. For the very same reason methods of dispersion analysis must fail if they imply wave number k(l)(omega) for the lth Rayleigh mode to be a single-valued function of frequency.. This applies in particular to deconvolution methods like phase-matched filters. We demonstrate that a slant-stack analysis fails in the multivalued section, while a Fourier-Bessel transformation captures the complete Rayleigh-wave signal. Waves of finite bandwidth in the multivalued section propagate with positive group-velocity and negative phase-velocity. Their eigenfunctions appear conventional and contain no conspicuous feature.
Nowadays, an increasing amount of seismic data is collected by daily observatory routines. The basic step for successfully analyzing those data is the correct detection of various event types. However, the visually scanning process is a time-consuming task. Applying standard techniques for detection like the STA/LTAtrigger still requires the manual control for classification. Here, we present a useful alternative. The incoming data stream is scanned automatically for events of interest. A stochastic classifier, called hidden Markov model, is learned for each class of interest enabling the recognition of highly variable waveforms. In contrast to other automatic techniques as neural networks or support vector machines the algorithm allows to start the classification from scratch as soon as interesting events are identified. Neither the tedious process of collecting training samples nor a time-consuming configuration of the classifier is required. An approach originally introduced for the volcanic task force action allows to learn classifier properties from a single waveform example and some hours of background recording. Besides a reduction of required workload this also enables to detect very rare events. Especially the latter feature provides a milestone point for the use of seismic devices in alpine warning systems. Furthermore, the system offers the opportunity to flag new signal classes that have not been defined before. We demonstrate the application of the classification system using a data set from the Swiss Seismological Survey achieving very high recognition rates. In detail we document all refinements of the classifier providing a step-by-step guide for the fast set up of a well-working classification system.
Surface wave methods gained in the past decades a primary role in many seismic projects. Specifically, they are often used to retrieve a 1D shear wave velocity model or to estimate the V-s,V-30 at a site. The complexity of the interpretation process and the variety of possible approaches to surface wave analysis make it very hard to set a fixed standard to assure quality and reliability of the results. The present guidelines provide practical information on the acquisition and analysis of surface wave data by giving some basic principles and specific suggestions related to the most common situations. They are primarily targeted to non-expert users approaching surface wave testing, but can be useful to specialists in the field as a general reference. The guidelines are based on the experience gained within the InterPACIFIC project and on the expertise of the participants in acquisition and analysis of surface wave data.
In the present study, we investigated the dispersion characteristics of medium-to-long period Rayleigh waves (2 s < T < 20 s) using both single-station techniques (multiple-filter analysis, and phase-match filter) and multichannel techniques (horizontal slowness [p] and angular frequency [omega] stack, and cross-correlation) to determine the velocity structure for the Mt. Etna volcano. We applied these techniques to a dataset of teleseisms, as regional and local earthquakes recorded by two broad-band seismic arrays installed at Mt. Etna in 2002 and 2005, during two seismic surveys organized by the Istituto Nazionale di Geofisica e Vulcanologia (INGV), sezione di Napoli. The dispersion curves obtained showed phase velocities ranging from 1.5 km/s to 4.0 km/s in the frequency band 0.05 Hz to 0.45 Hz. We inverted the average phase velocity dispersion curves using a non-linear approach, to obtain a set of shear-wave velocity models with maximum resolution depths of 25 km to 30 km. Moreover, the presence of lateral velocity contrasts was checked by dividing the whole array into seven triangular sub-arrays and inverting the dispersion curves relative to each triangle.
Ambient vibration measurements with small, temporary arrays that produce estimates of surface wave dispersion have become increasingly popular as a low-cost, non-invasive tool for site characterisation. An important requirement for these measurements to be meaningful, however, is the temporal consistency and repeatability of the resulting dispersion and spatial autocorrelation curve estimates. Data acquired within several European research projects (NERIES task JRA4, SESAME, and other multinational experiments) offer the chance to investigate the variability of the derived data products. The dataset analysed here consists of repeated array measurements, with several years of time elapsed between them. The measurements were conducted by different groups in different seasons, using different instrumentations and array layouts, at six sites in Greece and Italy. Ambient vibration amplitude spectra and locations of dominant sources vary between the two measurements at each location. Still, analysis indicates that this does not influence the derived dispersion information, which is stable in time and neither influenced by the instrumentation nor the analyst. The frequency range over which the dispersion curves and spatial autocorrelation curves can be reliably estimated depends on the array dimensions (minimum and maximum aperture) used in the specific deployment, though, and may accordingly vary between the repeated experiments. The relative contribution of Rayleigh and Love waves to the wavefield can likewise change between repeated measurements. The observed relative contribution of Rayleigh waves is generally at or below 50%, with especially low values for the rural sites. Besides, the visibility of higher modes depends on the noise wavefield conditions. The similarity of the dispersion and autocorrelation curves measured at each site indicates that the curves are stable, mainly determined by the sub-surface structure, and can thus be used to derive velocity information with depth. Differences between velocity models for the same site derived from independently determined dispersion and autocorrelation curves-as observed in other studies-are consequently not adequately explained by uncertainties in the measurement part.
This study presents an unsupervised feature selection and learning approach for the discovery and intuitive imaging of significant temporal patterns in seismic single-station or network recordings. For this purpose, the data are parametrized by real-valued feature vectors for short time windows using standard analysis tools for seismic data, such as frequency-wavenumber, polarization, and spectral analysis. We use Self-Organizing Maps (SOMs) for a data-driven feature selection, visualization and clustering procedure, which is in particular suitable for high-dimensional data sets. Our feature selection method is based on significance testing using the Wald-Wolfowitz runs test for-individual features and on correlation hunting with SOMs in feature subsets. Using synthetics composed of Rayleigh and Love waves and real-world data, we show the robustness and the improved discriminative power of that approach compared to feature subsets manually selected from individual wavefield parametrization methods. Furthermore, the capability of the clustering and visualization techniques to investigate the discrimination of wave phases is shown by means of synthetic waveforms and regional earthquake recordings.