TY - JOUR A1 - Fischer, Tomáš A1 - Hrubcova, Pavla A1 - Dahm, Torsten A1 - Woith, Heiko A1 - Vylita, Tomáš A1 - Ohrnberger, Matthias A1 - Vlček, Josef A1 - Horalek, Josef A1 - Dedecek, Petr A1 - Zimmer, Martin A1 - Lipus, Martin P. A1 - Pierdominici, Simona A1 - Kallmeyer, Jens A1 - Krüger, Frank A1 - Hannemann, Katrin A1 - Korn, Michael A1 - Kaempf, Horst A1 - Reinsch, Thomas A1 - Klicpera, Jakub A1 - Vollmer, Daniel A1 - Daskalopoulou, Kyriaki T1 - ICDP drilling of the Eger Rift observatory BT - magmatic fluids driving the earthquake swarms and deep biosphere JF - Scientific drilling : reports on deep earth sampling and monitoring N2 - The new in situ geodynamic laboratory established in the framework of the ICDP Eger project aims to develop the most modern, comprehensive, multiparameter laboratory at depth for studying earthquake swarms, crustal fluid flow, mantle-derived CO2 and helium degassing, and processes of the deep biosphere. In order to reach a new level of high-frequency, near-source and multiparameter observation of earthquake swarms and related phenomena, such a laboratory comprises a set of shallow boreholes with high-frequency 3-D seismic arrays as well as modern continuous real-time fluid monitoring at depth and the study of the deep biosphere. This laboratory is located in the western part of the Eger Rift at the border of the Czech Republic and Germany (in the West Bohemia–Vogtland geodynamic region) and comprises a set of five boreholes around the seismoactive zone. To date, all monitoring boreholes have been drilled. This includes the seismic monitoring boreholes S1, S2 and S3 in the crystalline units north and east of the major Nový Kostel seismogenic zone, borehole F3 in the Hartoušov mofette field and borehole S4 in the newly discovered Bažina maar near Libá. Supplementary borehole P1 is being prepared in the Neualbenreuth maar for paleoclimate and biological research. At each of these sites, a borehole broadband seismometer will be installed, and sites S1, S2 and S3 will also host a 3-D seismic array composed of a vertical geophone chain and surface seismic array. Seismic instrumenting has been completed in the S1 borehole and is in preparation in the remaining four monitoring boreholes. The continuous fluid monitoring site of Hartoušov includes three boreholes, F1, F2 and F3, and a pilot monitoring phase is underway. The laboratory also enables one to analyze microbial activity at CO2 mofettes and maar structures in the context of changes in habitats. The drillings into the maar volcanoes contribute to a better understanding of the Quaternary paleoclimate and volcanic activity. Y1 - 2022 U6 - https://doi.org/10.5194/sd-31-31-2022 SN - 1816-8957 SN - 1816-3459 VL - 31 SP - 31 EP - 49 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Fischer, Tomas A1 - Hrubcova, Pavla A1 - Dahm, Torsten A1 - Woith, Heiko A1 - Vylita, Tomas A1 - Ohrnberger, Matthias A1 - Vlcek, Josef A1 - Horalek, Josef A1 - Dedecek, Petr A1 - Zimmer, Martin A1 - Lipus, Martin P. A1 - Pierdominici, Simona A1 - Kallmeyer, Jens A1 - Krüger, Frank A1 - Hannemann, Katrin A1 - Korn, Michael A1 - Kämpf, Horst A1 - Reinsch, Thomas A1 - Klicpera, Jakub A1 - Vollmer, Daniel A1 - Daskalopoulou, Kyriaki T1 - ICDP drilling of the Eger Rift observatory BT - magmatic fluids driving the earthquake swarms and deep biosphere JF - Scientific Drilling N2 - The new in situ geodynamic laboratory established in the framework of the ICDP Eger project aims to develop the most modern, comprehensive, multiparameter laboratory at depth for studying earthquake swarms, crustal fluid flow, mantle-derived CO2 and helium degassing, and processes of the deep biosphere. In order to reach a new level of high-frequency, near-source and multiparameter observation of earthquake swarms and related phenomena, such a laboratory comprises a set of shallow boreholes with high-frequency 3-D seismic arrays as well as modern continuous real-time fluid monitoring at depth and the study of the deep biosphere. This laboratory is located in the western part of the Eger Rift at the border of the Czech Republic and Germany (in the West Bohemia-Vogtland geodynamic region) and comprises a set of five boreholes around the seismoactive zone. To date, all monitoring boreholes have been drilled. This includes the seismic monitoring boreholes S1, S2 and S3 in the crystalline units north and east of the major Novy Kostel seismogenic zone, borehole F3 in the Hartousov mofette field and borehole S4 in the newly discovered Bazina maar near Liba. Supplementary borehole P1 is being prepared in the Neualbenreuth maar for paleoclimate and biological research. At each of these sites, a borehole broadband seismometer will be installed, and sites S1, S2 and S3 will also host a 3-D seismic array composed of a vertical geophone chain and surface seismic array. Seismic instrumenting has been completed in the S1 borehole and is in preparation in the remaining four monitoring boreholes. The continuous fluid monitoring site of Hartousov includes three boreholes, F1, F2 and F3, and a pilot monitoring phase is underway. The laboratory also enables one to analyze microbial activity at CO2 mofettes and maar structures in the context of changes in habitats. The drillings into the maar volcanoes contribute to a better understanding of the Quaternary paleoclimate and volcanic activity. Y1 - 2022 U6 - https://doi.org/10.5194/sd-31-31-2022 SN - 1816-8957 SN - 1816-3459 VL - 31 SP - 31 EP - 49 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Esfahani, Reza Dokht Dolatabadi A1 - Vogel, Kristin A1 - Cotton, Fabrice Pierre A1 - Ohrnberger, Matthias A1 - Scherbaum, Frank A1 - Kriegerowski, Marius T1 - Exploring the dimensionality of ground-motion data by applying autoencoder techniques JF - Bulletin of the Seismological Society of America : BSSA N2 - In this article, we address the question of how observed ground-motion data can most effectively be modeled for engineering seismological purposes. Toward this goal, we use a data-driven method, based on a deep-learning autoencoder with a variable number of nodes in the bottleneck layer, to determine how many parameters are needed to reconstruct synthetic and observed ground-motion data in terms of their median values and scatter. The reconstruction error as a function of the number of nodes in the bottleneck is used as an indicator of the underlying dimensionality of ground-motion data, that is, the minimum number of predictor variables needed in a ground-motion model. Two synthetic and one observed datasets are studied to prove the performance of the proposed method. We find that mapping ground-motion data to a 2D manifold primarily captures magnitude and distance information and is suited for an approximate data reconstruction. The data reconstruction improves with an increasing number of bottleneck nodes of up to three and four, but it saturates if more nodes are added to the bottleneck. Y1 - 2021 U6 - https://doi.org/10.1785/0120200285 SN - 0037-1106 SN - 1943-3573 VL - 111 IS - 3 SP - 1563 EP - 1576 PB - Seismological Society of America CY - El Cerito, Calif. ER - TY - JOUR A1 - Zali, Zahra A1 - Ohrnberger, Matthias A1 - Scherbaum, Frank A1 - Cotton, Fabrice A1 - Eibl, Eva P. S. T1 - Volcanic tremor extraction and earthquake detection using music information retrieval algorithms JF - Seismological research letters N2 - Volcanic tremor signals are usually observed before or during volcanic eruptions and must be monitored to evaluate the volcanic activity. A challenge in studying seismic signals of volcanic origin is the coexistence of transient signal swarms and long-lasting volcanic tremor signals. Separating transient events from volcanic tremors can, therefore, contrib-ute to improving upon our understanding of the underlying physical processes. Exploiting the idea of harmonic-percussive separation in musical signal processing, we develop a method to extract the harmonic volcanic tremor signals and to detect tran-sient events from seismic recordings. Based on the similarity properties of spectrogram frames in the time-frequency domain, we decompose the signal into two separate spec-trograms representing repeating (harmonic) and nonrepeating (transient) patterns, which correspond to volcanic tremor signals and earthquake signals, respectively. We reconstruct the harmonic tremor signal in the time domain from the complex spectrogram of the repeating pattern by only considering the phase components for the frequency range in which the tremor amplitude spectrum is significantly contribut-ing to the energy of the signal. The reconstructed signal is, therefore, clean tremor signal without transient events. Furthermore, we derive a characteristic function suitable for the detection of tran-sient events (e.g., earthquakes) by integrating amplitudes of the nonrepeating spectro-gram over frequency at each time frame. Considering transient events like earthquakes, 78% of the events are detected for signal-to-noise ratio = 0.1 in our semisynthetic tests. In addition, we compared the number of detected earthquakes using our method for one month of continuous data recorded during the Holuhraun 2014-2015 eruption in Iceland with the bulletin presented in Agustsdottir et al. (2019). Our single station event detection algorithm identified 84% of the bulletin events. Moreover, we detected a total of 12,619 events, which is more than twice the number of the bulletin events. KW - algorithms KW - body waves KW - earthquakes KW - elastic waves KW - eruptions KW - geologic hazards KW - natural hazards KW - P-waves KW - S-waves KW - seismic waves KW - signal-to-noise ratio KW - swarms KW - volcanic earthquakes Y1 - 2021 U6 - https://doi.org/10.1785/0220210016 SN - 0895-0695 SN - 1938-2057 VL - 92 IS - 6 SP - 3668 EP - 3681 PB - Seismological Society of America CY - Boulder, Colo. ER - TY - JOUR A1 - Forbriger, Thomas A1 - Gao, Lingli A1 - Malischewsky, Peter A1 - Ohrnberger, Matthias A1 - Pan, Yudi T1 - A single Rayleigh mode may exist with multiple values of phase-velocity at one frequency JF - Geophysical journal international N2 - Other than commonly assumed in seismology, the phase velocity of Rayleigh waves is not necessarily a single-valued function of frequency. In fact, a single Rayleigh mode can exist with three different values of phase velocity at one frequency. We demonstrate this for the first higher mode on a realistic shallow seismic structure of a homogeneous layer of unconsolidated sediments on top of a half-space of solid rock (LOH). In the case of LOH a significant contrast to the half-space is required to produce the phenomenon. In a simpler structure of a homogeneous layer with fixed (rigid) bottom (LFB) the phenomenon exists for values of Poisson's ratio between 0.19 and 0.5 and is most pronounced for P-wave velocity being three times S-wave velocity (Poisson's ratio of 0.4375). A pavement-like structure (PAV) of two layers on top of a half-space produces the multivaluedness for the fundamental mode. Programs for the computation of synthetic dispersion curves are prone to trouble in such cases. Many of them use mode-follower algorithms which loose track of the dispersion curve and miss the multivalued section. We show results for well established programs. Their inability to properly handle these cases might be one reason why the phenomenon of multivaluedness went unnoticed in seismological Rayleigh wave research for so long. For the very same reason methods of dispersion analysis must fail if they imply wave number k(l)(omega) for the lth Rayleigh mode to be a single-valued function of frequency.. This applies in particular to deconvolution methods like phase-matched filters. We demonstrate that a slant-stack analysis fails in the multivalued section, while a Fourier-Bessel transformation captures the complete Rayleigh-wave signal. Waves of finite bandwidth in the multivalued section propagate with positive group-velocity and negative phase-velocity. Their eigenfunctions appear conventional and contain no conspicuous feature. KW - Surface waves and free oscillations KW - Theoretical seismology KW - Wave KW - propagation Y1 - 2020 U6 - https://doi.org/10.1093/gji/ggaa123 SN - 0956-540X SN - 1365-246X VL - 222 IS - 1 SP - 582 EP - 594 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Esfahani, Reza Dokht Dolatabadi A1 - Gholami, Ali A1 - Ohrnberger, Matthias T1 - An inexact augmented Lagrangian method for nonlinear dispersion-curve inversion using Dix-type global linear approximation JF - Geophysics N2 - Dispersion-curve inversion of Rayleigh waves to infer subsurface shear-wave velocity is a long-standing problem in seismology. Due to nonlinearity and ill-posedness, sophisticated regularization techniques are required to solve the problem for a stable velocity model. We have formulated the problem as a minimization problem with nonlinear operator constraint and then solve it by using an inexact augmented Lagrangian method, taking advantage of the Haney-Tsai Dix-type relation (a global linear approximation of the nonlinear forward operator). This replaces the original regularized nonlinear problem with iterative minimization of a more tractable regularized linear problem followed by a nonlinear update of the phase velocity (data) in which the update can be performed accurately with any forward modeling engine, for example, the finite-element method. The algorithm allows discretizing the medium with thin layers (for the finite-element method) and thus omitting the layer thicknesses from the unknowns and also allows incorporating arbitrary regularizations to shape the desired velocity model. In this research, we use total variation regularization to retrieve the shear-wave velocity model. We use two synthetic and two real data examples to illustrate the performance of the inversion algorithm with total variation regularization. We find that the method is fast and stable, and it converges to the solution of the original nonlinear problem. Y1 - 2020 U6 - https://doi.org/10.1190/GEO2019-0717.1 SN - 0016-8033 SN - 1942-2156 VL - 85 IS - 5 SP - EN77 EP - EN85 PB - Society of Exploration Geophysicists CY - Tulsa ER - TY - JOUR 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 JF - Solid earth 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. Y1 - 2023 U6 - https://doi.org/10.5194/se-14-181-2023 SN - 1869-9529 VL - 14 IS - 2 SP - 181 EP - 195 PB - Coepernicus Publ. CY - Göttingen ER - 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 - JOUR A1 - Steinberg, Andreas A1 - Vasyura-Bathke, Hannes A1 - Gaebler, Peter Jost A1 - Ohrnberger, Matthias A1 - Ceranna, Lars T1 - Estimation of seismic moment tensors using variational inference machine learning JF - Journal of geophysical research : Solid earth N2 - We present an approach for rapidly estimating full moment tensors of earthquakes and their parameter uncertainties based on short time windows of recorded seismic waveform data by considering deep learning of Bayesian Neural Networks (BNNs). The individual neural networks are trained on synthetic seismic waveform data and corresponding known earthquake moment-tensor parameters. A monitoring volume has been predefined to form a three-dimensional grid of locations and to train a BNN for each grid point. Variational inference on several of these networks allows us to consider several sources of error and how they affect the estimated full moment-tensor parameters and their uncertainties. In particular, we demonstrate how estimated parameter distributions are affected by uncertainties in the earthquake centroid location in space and time as well as in the assumed Earth structure model. We apply our approach as a proof of concept on seismic waveform recordings of aftershocks of the Ridgecrest 2019 earthquake with moment magnitudes ranging from Mw 2.7 to Mw 5.5. Overall, good agreement has been achieved between inferred parameter ensembles and independently estimated parameters using classical methods. Our developed approach is fast and robust, and therefore, suitable for down-stream analyses that need rapid estimates of the source mechanism for a large number of earthquakes. KW - seismology KW - machine learning KW - earthquake source KW - moment tensor KW - full KW - waveform Y1 - 2021 U6 - https://doi.org/10.1029/2021JB022685 SN - 2169-9313 SN - 2169-9356 VL - 126 IS - 10 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Hammer, Conny A1 - Fäh, Donat A1 - Ohrnberger, Matthias T1 - Automatic detection of wet-snow avalanche seismic signals JF - Natural hazards : journal of the International Society for the Prevention and Mitigation of Natural Hazards N2 - Avalanche activity is an important factor when estimating the regional avalanche danger. Moreover, a complete and detailed picture of avalanche activity is needed to understand the processes that lead to natural avalanche release. Currently, information on avalanche activity is mainly obtained through visual observations. However, this involves large uncertainties in the number and release times, influencing the subsequent analysis. Therefore, alternative methods for the remote detection of snow avalanches in particular in non-observed areas are highly desirable. In this study, we use the excited ground vibration to identify avalanches automatically. The specific seismic signature of avalanches facilitates the objective detection by a recently developed classification procedure. A probabilistic description of the signals, called hidden Markov models, allows the robust identification of corresponding signals in the continuous data stream. The procedure is based upon learning a general background model from continuous seismic data. Then, a single reference waveform is used to update an event-specific classifier. Thus, a minimum amount of training data is required by constructing such a classifier on the fly. In this study, we processed five days of continuous data recorded in the Swiss Alps during the avalanche winter 1999. With the restriction of testing large wet-snow avalanches only, the presented approach achieved very convincing results. We successfully detect avalanches over a large volume and distance range. Ninety-two percentage of all detections (43 out of 47) could be confirmed as avalanche events; only four false alarms are reported. We see a clear dependence of recognition capability on run-out distance and source-receiver distance of the observed events: Avalanches are detectable up to a source-receiver distance of eight times the avalanche length. Implications for analyzing a more comprehensive data set (smaller events and different flow regimes) are discussed in detail. KW - Snow avalanche recognition KW - Automatic detection KW - Avalanche forecasting KW - Hidden Markov model Y1 - 2016 U6 - https://doi.org/10.1007/s11069-016-2707-0 SN - 0921-030X SN - 1573-0840 VL - 86 SP - 601 EP - 618 PB - Springer CY - New York ER -