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Die Eifel ist eines der jüngsten vulkanischen Gebiete Mitteleuropas. Die letzte Eruption ereignete sich vor ungefähr 11000 Jahren. Bisher ist relativ wenig bekannt über die tieferen Mechanismen, die für den Vulkanismus in der Eifel verantwortlich sind. Erdbebenaktivität deutet ebenso darauf hin, dass die Eifel eines der geodynamisch aktivsten Gebiete Mitteleuropas ist. In dieser Arbeit wird die Receiver Function Methode verwendet, um die Strukturen des oberen Mantels zu untersuchen. 96 teleseismische Beben (mb > 5.2) wurden ausgewertet, welche von permanenten und mobilen breitbandigen und kurzperiodischen Stationen aufgezeichnet wurden. Das temporäre Netzwerk registrierte von November 1997 bis Juni 1998 und überdeckte eine Fläche von ungefähr 400x250 km². Das Zentrum des Netzwerkes befand sich in der Vulkaneifel. Die Auswertung der Receiver Function Analyse ergab klare Konversionen von der Moho und den beiden Manteldiskontinuitäten in 410 km und 660 km Tiefe, sowie Hinweise auf einen Mantel-Plume in der Region der Eifel. Die Moho wurde bei ungefähr 30 km Tiefe beobachtet und zeigt nur geringe Variationen im Bereich des Netzwerkes. Die beobachteten Variationen der konvertierten Phasen der Moho können mit lateralen Schwankungen in der Kruste zu tun haben, die mit den Receiver Functions nicht aufgelöst werden können. Die Ergebnisse der Receiver Function Methode deuten auf eine Niedriggeschwindigkeitszone zwischen 60 km bis 90 km in der westlichen Eifel hin. In etwa 200 km Tiefe werden im Bereich der Eifel amplitudenstarke positive Phasen von Konversionen beobachtet. Als Ursache hierfür wird eine Hochgeschwindigkeitszone vorgeschlagen, welche durch mögliches aufsteigendes, dehydrierendes Mantel-Material verursacht wird. Die P zu S Konversionen an der 410 km Diskontinuität zeigen einen späteren Einsatz als nach dem IASP91-Modell erwartet wird. Die migrierten Daten weisen eine Absenkung der 410 km Diskontinuität um bis zu 20 km Tiefe auf, was einer Erhöhung der Temperatur von bis zu etwa 140° Celsius entspricht. Die 660 km Diskontinuität weist keine Aufwölbung auf. Dies deutet darauf hin, dass kein Mantelmaterial direkt von unterhalb der 660 km Diskontinuität in der Eifel-Region aufsteigt oder, dass der Ursprung des Eifel-Plumes innerhalb der Übergangszone liegt.
Assuming that liquid iron alloy from the outer core interacts with the solid silicate-rich lower mantle the influence on the core-mantle reflected phase PcP is studied. If the core-mantle boundary is not a sharp discontinuity, this becomes apparent in the waveform and amplitude of PcP. Iron-silicate mixing would lead to regions of partial melting with higher density which in turn reduces the velocity of seismic waves. On the basis of the calculation and interpretation of short-period synthetic seismograms, using the reflectivity and Gauss Beam method, a model space is evaluated for these ultra-low velocity zones (ULVZs). The aim of this thesis is to analyse the behaviour of PcP between 10° and 40° source distance for such models using different velocity and density configurations. Furthermore, the resolution limits of seismic data are discussed. The influence of the assumed layer thickness, dominant source frequency and ULVZ topography are analysed. The Gräfenberg and NORSAR arrays are then used to investigate PcP from deep earthquakes and nuclear explosions. The seismic resolution of an ULVZ is limited both for velocity and density contrasts and layer thicknesses. Even a very thin global core-mantle transition zone (CMTZ), rather than a discrete boundary and also with strong impedance contrasts, seems possible: If no precursor is observable but the PcP_model /PcP_smooth amplitude reduction amounts to more than 10%, a very thin ULVZ of 5 km with a first-order discontinuity may exist. Otherwise, if amplitude reductions of less than 10% are obtained, this could indicate either a moderate, thin ULVZ or a gradient mantle-side CMTZ. Synthetic computations reveal notable amplitude variations as function of the distance and the impedance contrasts. Thereby a primary density effect in the very steep-angle range and a pronounced velocity dependency in the wide-angle region can be predicted. In view of the modelled findings, there is evidence for a 10 to 13.5 km thick ULVZ 600 km south-eastern of Moscow with a NW-SE extension of about 450 km. Here a single specific assumption about the velocity and density anomaly is not possible. This is in agreement with the synthetic results in which several models create similar amplitude-waveform characteristics. For example, a ULVZ model with contrasts of -5% VP , -15% VS and +5% density explain the measured PcP amplitudes. Moreover, below SW Finland and NNW of the Caspian Sea a CMB topography can be assumed. The amplitude measurements indicate a wavelength of 200 km and a height of 1 km topography, previously also shown in the study by Kampfmann and Müller (1989). Better constraints might be provided by a joined analysis of seismological data, mineralogical experiments and geodynamic modelling.
The shallow Earth’s layers are at the interplay of many physical processes: some being driven by atmospheric forcing (precipitation, temperature...) whereas others take their origins at depth, for instance ground shaking due to seismic activity. These forcings cause the subsurface to continuously change its mechanical properties, therefore modulating the strength of the surface geomaterials and hydrological fluxes. Because our societies settle and rely on the layers hosting these time-dependent properties, constraining the hydro-mechanical dynamics of the shallow subsurface is crucial for our future geographical development. One way to investigate the ever-changing physical changes occurring under our feet is through the inference of seismic velocity changes from ambient noise, a technique called seismic interferometry. In this dissertation, I use this method to monitor the evolution of groundwater storage and damage induced by earthquakes. Two research lines are investigated that comprise the key controls of groundwater recharge in steep landscapes and the predictability and duration of the transient physical properties due to earthquake ground shaking. These two types of dynamics modulate each other and influence the velocity changes in ways that are challenging to disentangle. A part of my doctoral research also addresses this interaction. Seismic data from a range of field settings spanning several climatic conditions (wet to arid climate) in various seismic-prone areas are considered. I constrain the obtained seismic velocity time-series using simple physical models, independent dataset, geophysical tools and nonlinear analysis. Additionally, a methodological development is proposed to improve the time-resolution of passive seismic monitoring.
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).
The Tien-Shan and the neighboring Pamir region are two of the largest mountain belts in the world. Their deformation is dominated by intermontane basins bounded by active thrust and reverse faulting. The Tien-Shan mountain belt is characterized by a very high rate of seismicity along its margins as well as within the Tien-Shan interior. The study area of the here presented thesis, the western part of the Tien-Shan region, is currently seismically active with small and moderate sized earthquakes. However, at the end of the 19th beginning of the 20th century, this region was struck by a remarkable series of large magnitude (M>7) earthquakes, two of them reached magnitude 8.
Those large earthquakes occurred prior to the installation of the global digital seismic network and therefore were recorded only by analog seismic instruments. The processing of the analog data brings several difficulties, for example, not always the true parameters of the recording system are known. Another complicated task is the digitization of those records - a very time-consuming and delicate part. Therefore a special set of techniques is developed and modern methods are adapted for the digitized instrumental data analysis.
The main goal of the presented thesis is to evaluate the impact of large magnitude M≥7.0 earthquakes, which occurred at the turn of 19th to 20th century in the Tien-Shan region, on the overall regional tectonics. A further objective is to investigate the accuracy of previously estimated source parameters for those earthquakes, which were mainly based on macroseismic observations, and re-estimate them based on the instrumental data. An additional aim of this study is to develop the tools and methods for faster and more productive usage of analog seismic data in modern seismology.
In this thesis, the ten strongest and most interesting historical earthquakes in Tien-Shan region are analyzed. The methods and tool for digitizing and processing the analog seismic data are presented. The source parameters of the two major M≥8.0 earthquakes in the Northern Tien-Shan are re-estimated in individual case studies. Those studies are published as peer-reviewed scientific articles in reputed journals. Additionally, the Sarez-Pamir earthquake and its connection with one of the largest landslides in the world, Usoy landslide, is investigated by seismic modeling. These results are also published as a research paper.
With the developed techniques, the source parameters of seven more major earthquakes in the region are determined and their impact on the regional tectonics was investigated. The large magnitudes of those earthquakes are confirmed by instrumental data. The focal mechanism of these earthquakes were determined providing evidence for responsible faults or fault systems.
Modern acquisition of seismic data on receiver networks worldwide produces an increasing amount of continuous wavefield recordings. Hence, in addition to manual data inspection, seismogram interpretation requires new processing utilities for event detection, signal classification and data visualization. Various machine learning algorithms, which can be adapted to seismological problems, have been suggested in the field of pattern recognition. This can be done either by means of supervised learning using manually defined training data or by unsupervised clustering and visualization. The latter allows the recognition of wavefield patterns, such as short-term transients and long-term variations, with a minimum of domain knowledge. Besides classical earthquake seismology, investigations of temporal patterns in seismic data also concern novel approaches such as noise cross-correlation or ambient seismic vibration analysis in general, which have moved into focus within the last decade. In order to find records suitable for the respective approach or simply for quality control, unsupervised preprocessing becomes important and valuable for large data sets. Machine learning techniques require the parametrization of the data using feature vectors. Applied to seismic recordings, wavefield properties have to be computed from the raw seismograms. For an unsupervised approach, all potential wavefield features have to be considered to reduce subjectivity to a minimum. Furthermore, automatic dimensionality reduction, i.e. feature selection, is required in order to decrease computational cost, enhance interpretability and improve discriminative power. This study presents an unsupervised feature selection and learning approach for the discovery, imaging and interpretation of significant temporal patterns in seismic single-station or network recordings. In particular, techniques permitting an intuitive, quickly interpretable and concise overview of available records are suggested. For this purpose, the data is parametrized by real-valued feature vectors for short time windows using standard seismic analysis tools as feature generation methods, such as frequency-wavenumber, polarization, and spectral analysis. The choice of the time window length is dependent on the expected durations of patterns to be recognized or discriminated. We use Self-Organizing Maps (SOMs) for a data-driven feature selection, visualization and clustering procedure, which is particularly suitable for high-dimensional data sets. Using synthetics composed of Rayleigh and Love waves and three different types of real-world data sets, we show the robustness and reliability of our unsupervised learning approach with respect to the effect of algorithm parameters and data set properties. Furthermore, we approve the capability of the clustering and imaging techniques. For all data, we find improved discriminative power of our feature selection procedure compared to feature subsets manually selected from individual wavefield parametrization methods. In particular, enhanced performance is observed compared to the most favorable individual feature generation method, which is found to be the frequency spectrum. The method is applied to regional earthquake records at the European Broadband Network with the aim to define suitable features for earthquake detection and seismic phase classification. For the latter, we find that a combination of spectral and polarization features favor S wave detection at a single receiver. However, SOM-based visualization of phase discrimination shows that clustering applied to the records of two stations only allows onset or P wave detection, respectively. In order to improve the discrimination of S waves on receiver networks, we recommend to consider additionally the temporal context of feature vectors. The application to continuous recordings of seismicity close to an active volcano (Mount Merapi, Java, Indonesia) shows that two typical volcano-seismic events (VTB and Guguran) can be detected and distinguished by clustering. In contrast, so-called MP events cannot be discriminated. Comparable results are obtained for selected features and recognition rates regarding a previously implemented supervised classification system. Finally, we test the reliability of wavefield clustering to improve common ambient vibration analysis methods such as estimation of dispersion curves and horizontal to vertical spectral ratios. It is found, that in general, the identified short- and long-term patterns have no significant impact on those estimates. However, for individual sites, effects of local sources can be identified. Leaving out the corresponding clusters, yields reduced uncertainties or allows for improving estimation of dispersion curves.
Die genauen Einsatzzeiten seismischer P-Phasen von Erdbeben werden in SeisComP3 und anderen Auswerteprogrammen standardmäßig und in Echtzeit automatisch bestimmt. S-Phasen stellen dagegen eine weit größere Herausforderung dar. Nur mit genauen Picks der P- bzw. S-Phasen können die Erdbebenlokationen korrekt und stabil bestimmt werden. Darum besteht erhebliches Interesse, diese mit hoher Genauigkeit zu bestimmen. Das Ziel der vorliegenden Bachelorarbeit war es, vier verschiedene, bereits vorhandene S-Phasenpicker auf ausgewählte Parameter optimal zu konfigurieren, auf Testdaten anzuwenden und deren Leistungsfähigkeit objektiv zu bewerten. Dazu wurden ein S-Picker (S-L2) aus dem OpenSource SeisComp3-Programmpaket, zwei S-Picker (S-AIC, S-AIC-V) als kommerzielles Modul der Firma gempa GmbH für SeisComP3 und ein S-Picker (Frequenzband) aus dem OpenSource PhasePaPy-Paket ausgewählt. Die Bewertung erfolgte durch Vergleich automatischer Picks mit manuell bestimmten Einsatzzeiten. Alle vier Picker wurden separat konfiguriert und auf drei verschiedene Datensätze von Erdbeben in N-Chile und im Vogtland, Deutschland, angewandt. Dazu wurden regional bzw. lokal typische Erdbeben zufällig ausgewählt und die P- und S-Phasen manuell bestimmt. Mit den zu testenden S-Pickeralgorithmen wurden dieselben Daten durchsucht und die Picks automatisch bestimmt. Die Konfigurationen der Picker wurden gleichzeitig automatisch und objektiv durch iterative Anpassung optimiert. Ein neu erstelltes Bewertungssystem vergleicht die manuellen und die automatisch gefundenen S-Picks anhand von definierten Qualitätsfaktoren. Die Qualitätsfaktoren sind: der Mittelwert und die Standardabweichung der zeitlichen Differenzen zwischen den S-Picks, die Anzahl an übereinstimmenden S-Picks, die Prozentangaben über mögliche S-Picks und die benötigt Rechenzeit. Die objektive Bewertung erfolgte anhand eines Scores. Der Scorewert ergibt sich aus der gewichteten Summe folgender normierter Qualitätsfaktoren: Standardabweichung (20%), Mittelwert (20%) und Prozentangabe über mögliche S-Picks (60%). Konfigurationen mit hohem Score werden bevorzugt. Die bevorzugten Konfigurationen der verschiedenen Picker wurden miteinander verglichen, um den am besten geeigneten S-Pickeralgorithmus zu bestimmen. Allgemein zeigt sich, dass der S-AIC Picker für jeden der drei Datensätze die höchsten Scores und damit die besten Ergebnisse liefert. Dabei wurde für jeden Datensatz ein andere Konfiguration der Parameter des S-AIC Pickers als die am besten geeignete bezeichnet. Daher ist für jede Erdbebenregion eine andere Konfigurationen erforderlich, um optimale Ergebnisse mit diesem S-Picker zu bekommen.
Rapidly growing seismic and macroseismic databases and simplified access to advanced machine learning methods have in recent years opened up vast opportunities to address challenges in engineering and strong motion seismology from novel, datacentric perspectives. In this thesis, I explore the opportunities of such perspectives for the tasks of ground motion modeling and rapid earthquake impact assessment, tasks with major implications for long-term earthquake disaster mitigation.
In my first study, I utilize the rich strong motion database from the Kanto basin, Japan, and apply the U-Net artificial neural network architecture to develop a deep learning based ground motion model. The operational prototype provides statistical estimates of expected ground shaking, given descriptions of a specific earthquake source, wave propagation paths, and geophysical site conditions. The U-Net interprets ground motion data in its spatial context, potentially taking into account, for example, the geological properties in the vicinity of observation sites. Predictions of ground motion intensity are thereby calibrated to individual observation sites and earthquake locations.
The second study addresses the explicit incorporation of rupture forward directivity into ground motion modeling. Incorporation of this phenomenon, causing strong, pulse like ground shaking in the vicinity of earthquake sources, is usually associated with an intolerable increase in computational demand during probabilistic seismic hazard analysis (PSHA) calculations. I suggest an approach in which I utilize an artificial neural network to efficiently approximate the average, directivity-related adjustment to ground motion predictions for earthquake ruptures from the 2022 New Zealand National Seismic Hazard Model. The practical implementation in an actual PSHA calculation demonstrates the efficiency and operational readiness of my model. In a follow-up study, I present a proof of concept for an alternative strategy in which I target the generalizing applicability to ruptures other than those from the New Zealand National Seismic Hazard Model.
In the third study, I address the usability of pseudo-intensity reports obtained from macroseismic observations by non-expert citizens for rapid impact assessment. I demonstrate that the statistical properties of pseudo-intensity collections describing the intensity of shaking are correlated with the societal impact of earthquakes. In a second step, I develop a probabilistic model that, within minutes of an event, quantifies the probability of an earthquake to cause considerable societal impact. Under certain conditions, such a quick and preliminary method might be useful to support decision makers in their efforts to organize auxiliary measures for earthquake disaster response while results from more elaborate impact assessment frameworks are not yet available.
The application of machine learning methods to datasets that only partially reveal characteristics of Big Data, qualify the majority of results obtained in this thesis as explorative insights rather than ready-to-use solutions to real world problems. The practical usefulness of this work will be better assessed in the future by applying the approaches developed to growing and increasingly complex data sets.
Receiver functions are a good tool to investigate the seismotectonic structure beneath the a seismic station. In this study we apply the method to stations situated on or near Sumatra to find constraints on a more detailed velocity model which should improve earthquake localisation. We estimate shallow Moho-depths (~ 21 km) close to the trench and depths of ~30 km at greater distances. First evidences for the dip direction of the slab of ~60° are provided. Receiver functions were calculated for 20 stations for altogether 110 earthquakes in the distance range between 30° and 95° from the receiver. However the number of receiver functions per station is strongly variable as it depends on the installation date, the signal-to-noise-ratio of the station and the reliability of the acquisition.
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.