@inproceedings{OhrnbergerWassermannRichter2006,
  author    = {Ohrnberger, Matthias and Wassermann, Joachim and Richter, Gudrun},
  title     = {Automatic detection and classification of seismic signals for monitoring purposes},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-7294},
  year      = {2006},
  abstract  = {Interdisziplin{\"a}res Zentrum f{\"u}r Musterdynamik und Angewandte Fernerkundung Workshop vom 9. - 10. Februar 2006},
  language  = {en}
}
@article{HolschneiderDialloKuleshetal.2005,
  author    = {Holschneider, Matthias and Diallo, Mamadou Sanou and Kulesh, Michail and Ohrnberger, Matthias and Luck, E. and Scherbaum, Frank},
  title     = {Characterization of dispersive surface waves using continuous wavelet transforms},
  issn      = {0956-540X},
  year      = {2005},
  abstract  = {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},
  language  = {en}
}
@article{WatheletJongmansOhrnberger2005,
  author    = {Wathelet, M. and Jongmans, D. and Ohrnberger, Matthias},
  title     = {Direct inversion of spatial autocorrelation curves with the neighborhood algorithm},
  issn      = {0037-1106},
  year      = {2005},
  abstract  = {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},
  language  = {en}
}
@article{KrugerOhrnberger2005,
  author    = {Kruger, Frank and Ohrnberger, Matthias},
  title     = {Tracking the rupture of the M-w=9.3 Sumatra earthquake over 1,150 km at teleseismic distance},
  issn      = {0028-0836},
  year      = {2005},
  abstract  = {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.},
  language  = {en}
}
@article{KruegerOhrnberger2005,
  author    = {Kr{\"u}ger, Frank and Ohrnberger, Matthias},
  title     = {Spatio-temporal source characteristics of the 26 December 2004 Sumatra earthquake as imaged by teleseismic broadband arrays},
  year      = {2005},
  abstract  = {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},
  language  = {en}
}
@article{KohlerOhrnbergerScherbaumetal.2004,
  author    = {Kohler, A. and Ohrnberger, Matthias and Scherbaum, Frank and Stange, S. and Kind, F.},
  title     = {Ambient vibration measurements in the Southern Rhine Graben close to Basle},
  issn      = {1593-5213},
  year      = {2004},
  abstract  = {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},
  language  = {en}
}
@article{RichterWassermannZimmeretal.2004,
  author    = {Richter, Gudrun and Wassermann, J{\"u}rgen and Zimmer, Martin and Ohrnberger, Matthias},
  title     = {Correlation of seismic activity and fumarole temperature at the Mt. Merapi volcano (Indonesia) in 2000},
  issn      = {0377-0273},
  doi       = {10.1016/j.jvolgeores.2004.03.006},
  year      = {2004},
  abstract  = {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},
  language  = {en}
}
@phdthesis{Ohrnberger2001,
  author    = {Ohrnberger, Matthias},
  title     = {Continuous automatic classification of seismic signals of volcanic origin at Mt. Merapi, Java, Indonesia},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-0000028},
  school      = {Universit{\"a}t Potsdam},
  year      = {2001},
  abstract  = {Aufgrund seiner nahezu kontinuierlichen eruptiven Aktivit{\"a}t z{\"a}hlt der Merapi zu den gef{\"a}hrlichsten Vulkanen der Welt. Der Merapi befindet sich im Zentralteil der dicht bev{\"o}lkerten Insel Java (Indonesien). Selbst kleinere Ausbr{\"u}che des Merapi stellen deswegen eine große Gefahr f{\"u}r die ans{\"a}ssige Bev{\"o}lkerung in der Umgebung des Vulkans dar. Die am Merapi beobachtete enge Korrelation zwischen seismischer und vulkanischer Aktivit{\"a}t erlaubt es, mit Hilfe der {\"U}berwachung der seismischen Aktivit{\"a}t Ver{\"a}nderungen des Aktivit{\"a}tszustandes des Merapi zu erkennen. Ein System zur automatischen Detektion und Klassifizierung seismischer Ereignisse liefert einen wichtigen Beitrag f{\"u}r die schnelle Analyse der seismischen Aktivit{\"a}t. Im Falle eines bevorstehenden Ausbruchszyklus bedeutet dies ein wichtiges Hilfsmittel f{\"u}r die vor Ort ans{\"a}ssigen 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 {\"A}hnlichkeit von seismischen Signalen vulkanischen Ursprunges und Sprachaufzeichnungen und den großen Erfolg, den HMM-basierte Erkennungssysteme in der automatischen Spracherkennung erlangt haben. F{\"u}r 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 k{\"o}nnen, werden die Mustervektoren durch eine lineare Transformation und nachgeschaltete Vektor Quantisierung in eine diskrete Symbolsequenz {\"u}berf{\"u}hrt. Als Klassifikator kommt eine Maximum-Likelihood Testfunktion zwischen dieser Sequenz und den, in einem {\"u}berwachten Lernverfahren trainierten, DHMMs zum Einsatz. Die am Merapi kontinuierlich aufgezeichneten seismischen Daten im Zeitraum vom 01.07. und 05.07.1998 sind besonders f{\"u}r einen Test dieses Klassifikationssystems geeignet. In dieser Zeit zeigte der Merapi einen rapiden Anstieg der Seismizit{\"a}t 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. F{\"u}r die Parametrisierung des Wellenfeldes werden deswegen seismische Array-Verfahren eingesetzt. Die individuellen Wellenfeld Parameter wurden hinsichtlich ihrer Relevanz f{\"u}r den Klassifikationsprozess detailliert analysiert. F{\"u}r jede der drei Signalklassen wurde ein Satz von DHMMs trainiert. Zus{\"a}tzlich 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 G{\"u}te der Klassifikationsergebnisse zeigt starke Variationen zwischen den individuellen Signalklassen. Flache vulkanisch-tektonische Beben (VTB) zeigen sehr ausgepr{\"a}gte Wellenfeldeigenschaften und, zumindest im untersuchten Zeitraum, sehr stabile Zeitmuster der individuellen Wellenfeldparameter. Das DHMM-basierte Klassifizierungssystem erlaubte f{\"u}r 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 {\"a}hnlichen Wellenfeldeigenschaften beider Signalklassen erkl{\"a}rt werden, deren Ursache vermutlich in den bekannt starken Einfl{\"u}ssen 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 {\"u}blicherweise 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.},
  language  = {en}
}
@article{WassermannBudiOhrnbergeretal.1999,
  author    = {Wassermann, Joachim and Budi, E. N. and Ohrnberger, Matthias and Gossler, J.},
  title     = {Long term seismicity and source changes during different activity stages of Mt. Merapi (Indonesia) using a two scale seismic array},
  year      = {1999},
  language  = {en}
}
@article{OhrnbergerWassermannScherbaumetal.1999,
  author    = {Ohrnberger, Matthias and Wassermann, J{\"u}rgen and Scherbaum, Frank and Budi, E. N. and Gossler, J.},
  title     = {Detection and classification of seismic signals of volcanic origin at Mt. Merapi (Indonesia)},
  year      = {1999},
  language  = {en}
}