TY  - JOUR
A1  - Wathelet, Marc
A1  - Guillier, B.
A1  - Roux, P.
A1  - Cornou, C.
A1  - Ohrnberger, Matthias
T1  - Rayleigh wave three-component beamforming
BT  - signed ellipticity assessment from high-resolution frequency-wavenumber processing of ambient vibration arrays
JF  - Geophysical journal international
N2  - 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.
KW  - Fourier analysis
KW  - Time-series analysis
KW  - Site effects
KW  - Surface waves and free oscillations
KW  - Wave propagation
Y1  - 2018
U6  - https://doi.org/10.1093/gji/ggy286
SN  - 0956-540X
SN  - 1365-246X
VL  - 215
IS  - 1
SP  - 507
EP  - 523
PB  - Oxford Univ. Press
CY  - Oxford
ER  - 
TY  - JOUR
A1  - Knapmeyer-Endrun, Brigitte
A1  - Golombek, Matthew P.
A1  - Ohrnberger, Matthias
T1  - Rayleigh Wave Ellipticity Modeling and Inversion for Shallow Structure at the Proposed InSight Landing Site in Elysium Planitia, Mars
JF  - Space science reviews
N2  - 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.
KW  - Mars
KW  - Interior
KW  - Seismology
KW  - Regoliths
Y1  - 2017
U6  - https://doi.org/10.1007/s11214-016-0300-1
SN  - 0038-6308
SN  - 1572-9672
VL  - 211
SP  - 339
EP  - 382
PB  - Springer
CY  - Dordrecht
ER  - 
TY  - JOUR
A1  - Overduin, Pier Paul
A1  - Haberland, Christian
A1  - Ryberg, Trond
A1  - Kneier, Fabian
A1  - Jacobi, Tim
A1  - Grigoriev, Mikhail N.
A1  - Ohrnberger, Matthias
T1  - Submarine permafrost depth from ambient seismic noise
JF  - Geophysical research letters
N2  - 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.
KW  - submarine permafrost
KW  - ambient noise
KW  - Siberia
KW  - continental shelf
Y1  - 2015
U6  - https://doi.org/10.1002/2015GL065409
SN  - 0094-8276
SN  - 1944-8007
VL  - 42
IS  - 18
SP  - 7581
EP  - 7588
PB  - American Geophysical Union
CY  - Washington
ER  - 
TY  - JOUR
A1  - Holschneider, Matthias
A1  - Diallo, Mamadou Sanou
A1  - Kulesh, Michail
A1  - Ohrnberger, Matthias
A1  - Luck, E.
A1  - Scherbaum, Frank
T1  - Characterization of dispersive surface waves using continuous wavelet transforms
N2  - 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
Y1  - 2005
SN  - 0956-540X
ER  - 
TY  - JOUR
A1  - Wathelet, M.
A1  - Jongmans, D.
A1  - Ohrnberger, Matthias
T1  - Direct inversion of spatial autocorrelation curves with the neighborhood algorithm
N2  - 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
Y1  - 2005
SN  - 0037-1106
ER  - 
TY  - JOUR
A1  - Kohler, A.
A1  - Ohrnberger, Matthias
A1  - Scherbaum, Frank
A1  - Stange, S.
A1  - Kind, F.
T1  - Ambient vibration measurements in the Southern Rhine Graben close to Basle
N2  - 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
Y1  - 2004
SN  - 1593-5213
ER  - 
TY  - THES
A1  - Ohrnberger, Matthias
T1  - Continuous automatic classification of seismic signals of volcanic origin at Mt. Merapi, Java, Indonesia
N2  - Aufgrund seiner nahezu kontinuierlichen eruptiven Aktivität zählt der Merapi zu den gefährlichsten Vulkanen der Welt. Der Merapi befindet sich im Zentralteil der dicht bevölkerten Insel Java (Indonesien). Selbst kleinere Ausbrüche des Merapi stellen deswegen eine große Gefahr für die ansässige Bevölkerung in der Umgebung des Vulkans dar. Die am Merapi beobachtete enge Korrelation zwischen seismischer und vulkanischer Aktivität erlaubt es, mit Hilfe der Überwachung der seismischen Aktivität Veränderungen des Aktivitätszustandes des Merapi zu erkennen. Ein System zur automatischen Detektion und Klassifizierung seismischer Ereignisse liefert einen wichtigen Beitrag für die schnelle Analyse der seismischen Aktivität. Im Falle eines bevorstehenden Ausbruchszyklus bedeutet dies ein wichtiges Hilfsmittel für die vor Ort ansässigen Wissenschaftler. In der vorliegenden Arbeit wird ein Mustererkennungsverfahren verwendet, um die Detektion und Klassifizierung seismischer Signale vulkanischen Urprunges aus den kontinuierlich aufgezeichneten Daten in Echtzeit zu bewerkstelligen. Der hier verwendete A nsatz der hidden Markov Modelle (HMM) wird motiviert durch die große Ähnlichkeit von seismischen Signalen vulkanischen Ursprunges und Sprachaufzeichnungen und den großen Erfolg, den HMM-basierte Erkennungssysteme in der automatischen Spracherkennung erlangt haben.  Für eine erfolgreiche Implementierung eines Mustererkennungssytems ist es notwendig, eine geeignete Parametrisierung der Rohdaten vorzunehmen. Basierend auf den Erfahrungswerten seismologischer Observatorien wird ein Vorgehen zur Parametrisierung des seismischen Wellenfeldes auf Grundlage von robusten Analyseverfahren vorgeschlagen. Die Wellenfeldparameter werden pro Zeitschritt in einen reell-wertigen Mustervektor zusammengefasst. Die aus diesen Mustervektoren gebildete Zeitreihe ist dann Gegenstand des HMM-basierten Erkennungssystems. Um diskrete hidden Markov Modelle (DHMM) verwenden zu können, werden die Mustervektoren durch eine lineare Transformation und nachgeschaltete Vektor Quantisierung in eine diskrete Symbolsequenz überführt. Als Klassifikator kommt eine Maximum-Likelihood Testfunktion zwischen dieser Sequenz und den, in einem überwachten Lernverfahren trainierten, DHMMs zum Einsatz. Die am Merapi kontinuierlich aufgezeichneten seismischen Daten im Zeitraum vom 01.07. und 05.07.1998 sind besonders für einen Test dieses Klassifikationssystems geeignet. In dieser Zeit zeigte der Merapi einen rapiden Anstieg der Seismizität kurz bevor dem Auftreten zweier Eruptionen am 10.07. und 19.07.1998. Drei der bekannten, vom Vulkanologischen Dienst in Indonesien beschriebenen, seimischen Signalklassen konnten in diesem Zeitraum beobachtet werden. Es handelt sich hierbei um flache vulkanisch-tektonische Beben (VTB, h < 2.5 km), um sogenannte MP-Ereignisse, die in direktem Zusammenhang mit dem Wachstum des aktiven Lavadoms gebracht werden, und um seismische Ereignisse, die durch Gesteinslawinen erzeugt werden (lokaler Name: Guguran). Die spezielle Geometrie des digitalen seismischen Netzwerkes am Merapi besteht aus einer Kombination von drei Mini-Arrays an den Flanken des Merapi. Für die Parametrisierung des Wellenfeldes werden deswegen seismische Array-Verfahren eingesetzt. Die individuellen Wellenfeld Parameter wurden hinsichtlich ihrer Relevanz für den Klassifikationsprozess detailliert analysiert. Für jede der drei Signalklassen wurde ein Satz von DHMMs trainiert. Zusätzlich wurden als Ausschlussklassen noch zwei Gruppen von Noise-Modellen unterschieden. Insgesamt konnte mit diesem Ansatz eine Erkennungsrate von 67 % erreicht werden. Im Mittel erzeugte das automatische Klassifizierungssystem 41 Fehlalarme pro Tag und Klasse. Die Güte der Klassifikationsergebnisse zeigt starke Variationen zwischen den individuellen Signalklassen. Flache vulkanisch-tektonische Beben (VTB) zeigen sehr ausgeprägte Wellenfeldeigenschaften und, zumindest im untersuchten Zeitraum, sehr stabile Zeitmuster der individuellen Wellenfeldparameter. Das DHMM-basierte Klassifizierungssystem erlaubte für diesen Ereignistyp nahezu 89% richtige Entscheidungen und erzeugte im Mittel 2 Fehlalarme pro Tag. Ereignisse der Klassen MP und Guguran sind mit dem automatischen System schwieriger zu erkennen. 64% aller MP-Ereignisse und 74% aller Guguran-Ereignisse wurden korrekt erkannt. Im Mittel kam es bei MP-Ereignissen zu 87 Fehlalarmen und bei Guguran Ereignissen zu 33 Fehlalarmen pro Tag. Eine Vielzahl der Fehlalarme und nicht detektierten Ereignisse entstehen jedoch durch eine Verwechslung dieser beiden Signalklassen im automatischen Erkennnungsprozess. Dieses Ergebnis konnte aufgrund der ähnlichen Wellenfeldeigenschaften beider Signalklassen erklärt werden, deren Ursache vermutlich in den bekannt starken Einflüssen des Mediums entlang des Wellenausbreitungsweges in vulkanischen Gebieten liegen.  Insgesamt ist die Erkennungsleistung des entwickelten automatischen Klassifizierungssystems als sehr vielversprechend einzustufen. Im Gegensatz zu Standardverfahren, bei denen in der Seismologie üblicherweise nur der Startzeitpunkt eines seismischen Ereignisses detektiert wird, werden in dem untersuchten Verfahren seismische Ereignisse in ihrer Gesamtheit erfasst und zudem im selben Schritt bereits klassifiziert.
N2  - 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.
KW  - volcanic seismology
KW  - Merapi
KW  - monitoring
KW  - classification
KW  - pattern recognition
KW  - Hidden Markov Model (HMM)
KW  - Seismic Array Methods
Y1  - 2001
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-0000028
ER  - 
TY  - JOUR
A1  - Steinberg, Andreas
A1  - Vasyura-Bathke, Hannes
A1  - Gaebler, Peter Jost
A1  - Ohrnberger, Matthias
A1  - Ceranna, Lars
T1  - Estimation of seismic moment tensors using variational inference machine learning
JF  - Journal of geophysical research : Solid earth
N2  - 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.
KW  - seismology
KW  - machine learning
KW  - earthquake source
KW  - moment tensor
KW  - full
KW  - waveform
Y1  - 2021
U6  - https://doi.org/10.1029/2021JB022685
SN  - 2169-9313
SN  - 2169-9356
VL  - 126
IS  - 10
PB  - American Geophysical Union
CY  - Washington
ER  - 
TY  - JOUR
A1  - Kriegerowski, Marius
A1  - Petersen, Gesa Maria
A1  - Vasyura-Bathke, Hannes
A1  - Ohrnberger, Matthias
T1  - A Deep Convolutional Neural Network for Localization of Clustered Earthquakes Based on Multistation Full Waveforms
JF  - Seismological research letters
N2  - 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.
Y1  - 2018
U6  - https://doi.org/10.1785/0220180320
SN  - 0895-0695
SN  - 1938-2057
VL  - 90
IS  - 2
SP  - 510
EP  - 516
PB  - Seismological Society of America
CY  - Albany
ER  - 
TY  - JOUR
A1  - Cristiano, Luigia
A1  - Petrosino, Simona
A1  - Saccorotti, Gilberto
A1  - Ohrnberger, Matthias
A1  - Scarpa, Roberto
T1  - Shear-wave velocity structure at Mt. Etna from inversion of Rayleigh-wave dispersion patterns (2 s < T < 20 s)
N2  - 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.
Y1  - 2010
UR  - http://annalsofgeophysics.ingv.it/index.html
U6  - https://doi.org/10.4401/Ag-4574
SN  - 1593-5213
ER  -