TY - GEN A1 - Zali, Zahra A1 - Rein, Teresa A1 - Krüger, Frank A1 - Ohrnberger, Matthias A1 - Scherbaum, Frank T1 - Ocean bottom seismometer (OBS) noise reduction from horizontal and vertical components using harmonic–percussive separation algorithms T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Records from ocean bottom seismometers (OBSs) are highly contaminated by noise, which is much stronger compared to data from most land stations, especially on the horizontal components. As a consequence, the high energy of the oceanic noise at frequencies below 1 Hz considerably complicates the analysis of the teleseismic earthquake signals recorded by OBSs. Previous studies suggested different approaches to remove low-frequency noises from OBS recordings but mainly focused on the vertical component. The records of horizontal components, which are crucial for the application of many methods in passive seismological analysis of body and surface waves, could not be much improved in the teleseismic frequency band. Here we introduce a noise reduction method, which is derived from the harmonic–percussive separation algorithms used in Zali et al. (2021), in order to separate long-lasting narrowband signals from broadband transients in the OBS signal. This leads to significant noise reduction of OBS records on both the vertical and horizontal components and increases the earthquake signal-to-noise ratio (SNR) without distortion of the broadband earthquake waveforms. This is demonstrated through tests with synthetic data. Both SNR and cross-correlation coefficients showed significant improvements for different realistic noise realizations. The application of denoised signals in surface wave analysis and receiver functions is discussed through tests with synthetic and real data. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1320 Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-588828 SN - 1866-8372 IS - 1320 ER - TY - GEN A1 - Kriegerowski, Marius A1 - Cesca, Simone A1 - Ohrnberger, Matthias A1 - Dahm, Torsten A1 - Krüger, Frank T1 - Event couple spectral ratio Q method for earthquake clusters BT - application to northwest Bohemia T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - We develop an amplitude spectral ratio method for event couples from clustered earthquakes to estimate seismic wave attenuation (Q-1) in the source volume. The method allows to study attenuation within the source region of earthquake swarms or aftershocks at depth, independent of wave path and attenuation between source region and surface station. We exploit the high-frequency slope of phase spectra using multitaper spectral estimates. The method is tested using simulated full wave-field seismograms affected by recorded noise and finite source rupture. The synthetic tests verify the approach and show that solutions are independent of focal mechanisms but also show that seismic noise may broaden the scatter of results. We apply the event couple spectral ratio method to northwest Bohemia, Czech Republic, a region characterized by the persistent occurrence of earthquake swarms in a confined source region at mid-crustal depth. Our method indicates a strong anomaly of high attenuation in the source region of the swarm with an averaged attenuation factor of Qp < 100. The application to S phases fails due to scattered P-phase energy interfering with S phases. The Qp anomaly supports the common hypothesis of highly fractured and fluid saturated rocks in the source region of the swarms in northwest Bohemia. However, high temperatures in a small volume around the swarms cannot be excluded to explain our observations. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 683 KW - west bohemia KW - attenuation tomography KW - swarm earthquakes KW - focal zone KW - parameters KW - locations KW - fault Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-426029 IS - 683 ER - TY - GEN A1 - Rößler, Dirk A1 - Krüger, Frank A1 - Ohrnberger, Matthias T1 - Rupture Propagation of the 2008/05/12 Ms8.0 Wenchuan Earthquake N2 - We study the rupture propagation of the 2008/05/12 Ms8.0 Wenchuan Earthquake. We apply array techniques such as semblance vespagram analysis to P waves recorded at seismic broadband station within 30-100° epicentral distance. By combination of multiple large aperture station groups spatial and temporal resolution is enhanced and problems due source directivity and source mechanism are avoided. We find that seismic energy was released for at least 110 s. Propagating unilaterally at sub-shear rupture velocity of about 2.5 km/s in NE direction, the earthquake reaches a lateral extent of more than 300 km. Whereas high semblance during within 70 s from rupture start indicates simple propagation more complex source processes are indicated thereafter by decreases coherency in seismograms. At this stage of the event coherency is low but significantly above noise level. We emphasize that first result of our computations where obtain within 30 minutes after source time by using an atomized algorithm. This procedure has been routinely and globally applied to major earthquakes. Results are made public through internet. KW - Sichuan KW - Wenchuan KW - Erdbeben KW - Bruchverfolgung KW - Arrayseismologie KW - Sichuan KW - Wenchuan KW - earthquake KW - rupture KW - propagation KW - array seismology Y1 - 2008 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-29195 ER - TY - GEN A1 - Rößler, Dirk A1 - Hiemer, Stephan A1 - Bach, Christoph A1 - Delavaud, Elise A1 - Krüger, Frank A1 - Ohrnberger, Matthias A1 - Sauer, David A1 - Scherbaum, Frank A1 - Vollmer, Daniel T1 - Small-aperture seismic array monitors Vogtland earthquake swarm in 2008/09 N2 - The most recent intense earthquake swarm in the Vogtland lasted from 6 October 2008 until January 2009. Greatest magnitudes exceeded M3.5 several times in October making it the greatest swarm since 1985/86. In contrast to the swarms in 1985 and 2000, seismic moment release was concentrated near swarm onset. Focal area and temporal evolution are similar to the swarm in 2000. Work hypothysis: uprising upper-mantle fluids trigger swarm earthquakes at low stress level. To monitor the seismicity, the University of Potsdam operated a small aperture seismic array at 10 km epicentral distance between 18 October 2008 and 18 March 2009. Consisting of 12 seismic stations and 3 additional microphones, the array is capable of detecting earthquakes from larger to very low magnitudes (M<-1) as well as associated air waves. We use array techniques to determine properties of the incoming wavefield: noise, direct P and S waves, and converted phases. KW - Vogtland KW - Erdbebenschwarm 2008 KW - Arrayseismologie KW - Vogtland KW - West Bohemia KW - earthquake swarm KW - array seismology Y1 - 2009 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-29185 ER - TY - GEN A1 - Rößler, Dirk A1 - Krüger, Frank A1 - Ohrnberger, Matthias A1 - Ehlert, Lutz T1 - Automatic near real-time characterisation of large earthquakes N2 - An der Universität Potsdam wird seit 2008 ein automatisiertes Verfahren angewandt, um Bruchparamter großer Erdbeben in quasi-Echtzeit, d.h. wenige Minuten nachdem sich das Beben ereignet hat, zu bestimmen und der Öffentlichkeit via Internet zur Verfügung zu stellen. Es ist vorgesehen, das System in das Deutsch-Indonesische Tsunamifrühwarnsystem (GITEWS) zu integrieren, für das es speziell konfiguriert ist. Wir bestimmen insbesondere die Dauer und die Ausdehnung des Erdbebens, sowie dessen Bruchgeschwindigkeit und -richtung. Dabei benutzen wir die Seismogramme der zuerst eintreffenden P Wellen vom Breitbandstationen in teleseimischer Entfernung vom Beben sowie herkömmliche Arrayverfahren in teilweise modifizierter Form. Die Semblance wir als Ähnlichkeitsmaß verwendet, um Seismogramme eines Stationsnetzes zu vergleichen. Im Falle eines Erdbebens ist die Semblance unter Berücksichtigung des Hypozentrums zur Herdzeit und während des Bruchvorgangs deutlich zeitlich und räumlich erhöht und konzentriert. Indem wir die Ergebnisse verschiedener Stationsnetzwerke kombinieren, erreichen wir Unabhängigkeit von der Herdcharakteristik und eine raum-zeitliche Auflösung, die es erlaubt die o.g. Parameter abzuleiten. In unserem Beitrag skizzieren wir die Methode. Anhand der beiden M8.0 Benkulu Erdbeben (Sumatra, Indonesien) vom 12.09.2007 und dem M8.0 Sichuan Ereignis (China) vom 12.05.2008 demonstrieren wir Auflösungsmöglichkeiten und vergleichen die Ergebnisse der automatisierten Echtzeitanwendung mit nachträglichen Berechnungen. Weiterhin stellen wir eine Internetseite zur Verfügung, die die Ergebnisse präsentiert und animiert. Diese kann z.B. in geowissenschaftlichen Einrichtungen an Computerterminals gezeigt werden. Die Internetauftritte haben die folgenden Adressen: http://www.geo.uni-potsdam.de/arbeitsgruppen/Geophysik_Seismologie/forschung/ruptrack/openday http://www.geo.uni-potsdam.de/arbeitsgruppen/Geophysik_Seismologie/forschung/ruptrack KW - Erdbeben KW - Arrayseismologie KW - Echtzeitanwendung KW - teleseismische Bruchverfolgung KW - earthquake KW - array seismology KW - real-time application KW - teleseismic rupture tracking Y1 - 2008 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-20191 ER - TY - GEN A1 - Krüger, Frank A1 - Ohrnberger, Matthias A1 - Rößler, Dirk T1 - Rupture imaging of large earthquakes with a poststack isochrone migration method N2 - Rapid and robust characterization of large earthquakes in terms of their spatial extent and temporal duration is of high importance for disaster mitigation and early warning applications. Backtracking of seismic P-waves was successfully used by several authors to image the rupture process of the great Sumatra earthquake (26.12.2004) using short period and broadband arrays. We follow here an approach of Walker et al. to backtrack and stack broadband waveforms from global network stations using traveltimes for a global Earth model to obtain the overall spatio-temporal development of the energy radiation of large earthquakes in a quick and robust way. We present results for selected events with well studied source processes (Kokoxili 14.11.2001, Tokachi-Oki 25.09.2003, Nias 28.03.2005). Further, we apply the technique in a semi-real time fashion to broadband data of earthquakes with a broadband magnitude >= 7 (roughly corresponding to Mw 6.5). Processing is based on first automatic detection messages from the GEOFON extended virtual network (GEVN). KW - Seismologie KW - Erdbeben KW - Array Seismologie KW - Migration KW - Seismology KW - Earthquake KW - Array Seismology KW - Migration Y1 - 2008 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-18395 ER - TY - GEN A1 - Rößler, Dirk A1 - Krüger, Frank A1 - Ohrnberger, Matthias T1 - Automatic near real-time characterisation of large earthquakes N2 - We use seismic array methods (semblance analysis) to image areas of seismic energy release in the Sunda Arc region and world-wide. Broadband seismograms at teleseismic distances (30° ≤ Δ ≤ 100°) are compared at several subarrays. Semblance maps of different subarrays are multiplied. High semblance tracked over long time (10s of second to minutes) and long distances indicate locations of earthquakes. The method allows resolution of rupture characteristics important for tsunami early warning: start and duration, velocity and direction, length and area. The method has been successfully applied to recent and historic events (M>6.5) and is now operational in real time. Results are obtained shortly after source time, see http://www.geo.uni-potsdam.de/Forschung/Geophysik/GITEWS/tsunami.htm). Comparison of manual and automatic processing are in good agreement. Computational effort is small. Automatic results may be obtained within 15 - 20 minutes after event occurrence. KW - Seismology KW - Earthquake KW - Tsunami KW - Array Seismology Y1 - 2008 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-18382 ER - TY - JOUR A1 - Lipke, Katrin A1 - Zitzmann, Max A1 - Amberger, Manuel A1 - Ehlert, Carsten A1 - Rößler, Dirk A1 - Krüger, Frank A1 - Ohrnberger, Matthias T1 - Traveltime residuals at regional and teleseismic distances for SE-Asia N2 - Traveltime residuals for worldwide seismic stations are calculated. We use P and S waves from earthquakes in SE-Asia at teleseismic and regional distances. The obtained station residuals help to enhance earthquake localisation. Furthermore we calculated regional source dependent station residuals. They show a systematic dependence of the locality of the source. These source dependent residuals reflect heterogenities along the path and can be used for a refinement of earthquake localisation. N2 - Laufzeitresiduen für weltweite seismische Stationen werden berechnet. Wir nutzen P - und S-Wellen von Erdbeben in Südostasien in teleseismischen und regionalen Distanzen. Die so erhaltenen Stationsresiduen helfen, die Lokaliesierung von Erdbeben zu verbessern. Außerdem berechnen wir regional quellabhängige Stationsresiduen. Diese zeigen eine systematische Abhänbgigkeit vom Ort der Quelle. Sie spiegeln Heterogenitäten entlang des Strahlweges wieder und können für eine Verfeinerung der Ersbebenlokaliesierung genutzt werden. KW - Seismologie KW - Südostasien KW - Laufzeitresiduen KW - GITEWS KW - Wellengeschwindigkeiten KW - seismology KW - Southeast Asia KW - traveltime KW - residuals KW - GITEWS Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-14117 ER - TY - GEN A1 - Rößler, Dirk A1 - Krüger, Frank A1 - Ohrnberger, Matthias T1 - Rupture propagation of recent large TsE off-coast Sumatra and Java N2 - The spatio-temporal evolution of the three recent tsunamogenic earthquakes (TsE) off-coast N-Sumatra (Mw9.3), 28/03/2005 (Mw8.5) off-coast Nias, on 17/07/2006 (Mw7.7) off-coast Java. Start time, duration, and propagation of the rupture are retrieved. All parameters can be obtained rapidly after recording of the first-arrival phases in near-real time processing. We exploit semblance analysis, backpropagation and broad-band seismograms within 30°-95° distance. Image enhancement is reached by stacking the semblance of arrays within different directions. For the three events, the rupture extends over about 1150, 150, and 200km, respectively. The events in 2004, 2005, and 2006 had source durations of at least 480s, 120s, and 180s, respectively. We observe unilateral rupture propagation for all events except for the rupture onset and the Nias event, where there is evidence for a bilateral start of the rupture. Whereas average rupture speed of the events in 2004 and 2005 is in the order of the S-wave speed (≈2.5-3km/s), unusually slow rupturing (≈1.5 km/s) is indicated for the July 2006 event. For the July 2006 event we find rupturing of a 200 x 100 km wide area in at least 2 phases with propagation from NW to SE. The event has some characteristics of a circular rupture followed by unilateral faulting with change in slip rate. Fault area and aftershock distribution coincide. Spatial and temporal resolution are frequency dependent. Studies of a Mw6.0 earthquake on 2006/09/21 and one synthetic source show a ≈1° limit in resolution. Retrieved source area, source duration as well as peak values for semblance and beam power generally increase with the size of the earthquake making possible an automatic detection and classification of large and small earthquakes. KW - Tsunami KW - Erdbeben KW - Indischer Ozean KW - Bruchausbreitung KW - Seismologie KW - Tsunami KW - Earthquake KW - Indonesia KW - Indian Ocean KW - Rupture Propagation KW - Seismology Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-13039 ER - TY - JOUR A1 - Rößler, Dirk A1 - Krüger, Frank A1 - Ohrnberger, Matthias T1 - Rupture propagation of the TsE (Mw7.7) on 17 July 2006 off-coast Java N2 - The Mw=7.7 tsunamogenic earthquake (TsE) on 17 July 2006, 08:19:28 shock the Indian Ocean at about 15 km depth off-coast Java, Indonesia. It caused a local tsunami with wave heights exceeding 2 m. The death toll reached several hundred. Thousands of people were displaced. By means of standard array methods, we have investigated the propagation and the extent of the rupture front of the causative earthquake. Waveform similarity is expressed by means of the semblance. We back-propagate the semblance for first-arrival phases recorded at broad-band stations within teleseismic distances (30°-95°). Image enhancement is realised by stacking the semblance of 8 arrays within different epicentral and azimuthal directions. From teleseismic observations we find rupturing of a 200 x 100 km wide area in at least 2 phases with propagation from NW to SE and source duration >125 s. The event has some characteristics of a circular rupture followed by unilateral faulting with change in slip rate. Unusually slow rupturing (≈1.5 km/s) is indicated. Fault area and aftershock distribution coincide. Spatial and temporal resolution are frequency dependent. Studies of a Mw6.0 earthquake on 2006/09/21 and one synthetic source show a ≈1° limit in resolution. Retrieved source area, source duration as well as peak values for semblance and beam power increase with the size of the earthquake making possible an automatic detection and classification of large and small earthquakes. KW - Seismologie KW - Erdbeben KW - Tsunami KW - Indischer Ozean Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-12964 ER - 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 - 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 -