TY  - CHAP
A1  - Ohrnberger, Matthias
A1  - Wassermann, Joachim
A1  - Richter, Gudrun
T1  - Automatic detection and classification of seismic signals for monitoring purposes
N2  - Interdisziplinäres Zentrum für Musterdynamik und Angewandte Fernerkundung Workshop vom 9. - 10. Februar 2006
Y1  - 2006
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-7294
N1  - [Poster]
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  - Kruger, Frank
A1  - Ohrnberger, Matthias
T1  - Tracking the rupture of the M-w=9.3 Sumatra earthquake over 1,150 km at teleseismic distance
N2  - On 26 December 2004, a moment magnitude M-w = 9.3 earthquake occurred along Northern Sumatra, the Nicobar and Andaman islands, resulting in a devastating tsunami in the Indian Ocean region(1). The rapid and accurate estimation of the rupture length and direction of such tsunami-generating earthquakes is crucial for constraining both tsunami wave- height models as well as the seismic moment of the events. Compressional seismic waves generated at the hypocentre of the Sumatra earthquake arrived after about 12 min at the broadband seismic stations of the German Regional Seismic Network (GRSN)(2,3), located approximately 9,000 km from the event. Here we present a modification of a standard array- seismological approach and show that it is possible to track the propagating rupture front of the Sumatra earthquake over a total rupture length of 1,150 km. We estimate the average rupture speed to be 2.3-2.7 km s(-1) and the total duration of rupture to be at least 430 s, and probably between 480 and 500 s.
Y1  - 2005
SN  - 0028-0836
ER  - 
TY  - JOUR
A1  - Krüger, Frank
A1  - Ohrnberger, Matthias
T1  - Spatio-temporal source characteristics of the 26 December 2004 Sumatra earthquake as imaged by teleseismic broadband arrays
N2  - 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
Y1  - 2005
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  - JOUR
A1  - Richter, Gudrun
A1  - Wassermann, Jürgen
A1  - Zimmer, Martin
A1  - Ohrnberger, Matthias
T1  - Correlation of seismic activity and fumarole temperature at the Mt. Merapi volcano (Indonesia) in 2000
N2  - 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
Y1  - 2004
UR  - http://www.sciencedirect.com/science/journal/03770273
U6  - https://doi.org/10.1016/j.jvolgeores.2004.03.006
SN  - 0377-0273
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  - Wassermann, Joachim
A1  - Budi, E. N.
A1  - Ohrnberger, Matthias
A1  - Gossler, J.
T1  - Long term seismicity and source changes during different activity stages of Mt. Merapi (Indonesia) using a two scale seismic array
Y1  - 1999
ER  - 
TY  - JOUR
A1  - Ohrnberger, Matthias
A1  - Wassermann, Jürgen
A1  - Scherbaum, Frank
A1  - Budi, E. N.
A1  - Gossler, J.
T1  - Detection and classification of seismic signals of volcanic origin at Mt. Merapi (Indonesia)
Y1  - 1999
ER  -