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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.
A multidisciplinary study has been carried out to contribute to the understanding of the geologic evolution of the largest known occurrence of ultra-high-pressure (UHP) rocks on Earth, the Dabie Shan of eastern China. Geophysical data, collected along a ca. 20 km E-W trending seismic line in the eastern Dabie Shan, indicate that the crust comprises three layers. The upper crust has a homogeneously low reflectivity and exhibits roughly subhorizontal reflectors down to ca. 15 km. It is therefore interpreted to portray a crustal UHP slab thrust over non-UHP crust. An aprubt change in intensity and geometry of observed reflectors marks the boundary of a mid- to lower crustal zone which is present down to ca. 33 km. This crustal zone likely represents cratonal Yangtze crust that was unaffected by the Triassic UHP event and which has acted as the footwall during exhumation of the crustal wedge. Strong and continuous reflectors occurring at ca. 33-40 km depth most likely trace the Moho at the base of the crust. Any trace of a crustal root, that may have formed in response to collision tectonics, is therefore not preserved. A shollow tomographic velocity modell based on inversion of the first arrivals is constructed additionally. This model clearly images the distinct lithologies on both sides of the Tan Lu fault. Sediments to the east exhibit velocities of about 3.4 - 5.0 km* s^-1, whereas the gneisses have 5.2 - 6.0 km*s^-1. Geometry of velocity isolines may trace the structures present in the rocks. Thus the sediments dip shallowly towards the fault, whereas isoclinal folds are imaged to occur in the gneisses. Field data from the UHP unit of the Dabie Shan enables definition of basement-cover sequences that represent sections of the former passive margin of the Yangtze craton. One of the cover sequences, the Changpu unit, still displays a stratigraphic contact with basement gneisses, while the other, the Ganghe unit, includes no relative basement exposure. The latter unit is in tectonic contact with the basement of the former unit via a greenschist-facies blastomylonite. The Changpu unit is chiefly constituted by calc-arenitic metasediments intercalated with meta-basalts, whereas the Ganghe unit contains arenitic-volcanoclastic metasediments that are likewise associated with meta-basalts. The basement comprises a variety of felsic gneisses, ranging from preserved eclogitic- to greenschist-facies paragenesis, and locally contains mafic-ultramafic meta-plutons in addition to minor basaltic rocks. Metabasites of all lithologies are eclogite-facies or are retrogressed equivalents, which, with the exception of those from the Ganghe unit, bear coesite and thus testify to an UHP metamorphic overprint. Mineral chemistry of the analysed samples reveal large compositional variations among the main minerals, i.e. garnet and omphacite, indicating either distinct protoliths or different degrees of interaction with their host-rocks. Contents of ferric iron in low Fetot omphacites are determined by wet chemical titration and found to be rather high, i.e. 30-40 %. However, a even more conservative estimate of 50% is applied in the corresponding calculations, in order to be comparable with previous studies. Textural constraints and compositional zonation pattern are compatible with equilibrium conditions during peak metamorphism followed by a retrogressive overprint. P-T data are calculated with special focus on the application of the garnet-omphacite-phengite barometer, combined with Fe-Mg exchange thermometers. Maximum pressures range from 42-48 kbar (for the Changpu unit) to ~37 kbar (for the Ganghe unit and basement rocks). Temperatures during the eclogite metamorphism reached ca. 750 °C. Although the sample suite reveals variable peak-pressures, temperatures are in reasonable agreement. Pressure differences are interpreted to be due to strongly Ca-dominated garnet (up to 50 mol % grossular in the Changpu unit) and modification of peak-compositions during retrogressive metamorphism. The integrated geological data presented in this thesis allow it to be concluded that, i) basement and cover rocks are present in the Dabie Shan and both experienced UHP conditions ii) the Dabie Shan is the metamorphic equivalent of the former passive margin of the Yangtze craton iii) felsic gneisses undergoing UHP metamorphism are affected by volume changes due to phase transitions (qtz <-> coe), which directly influence the tectono-metamorphic processes iv) initial differences in temperature may account for the general lack of lower crustal rocks in UHP-facies