TY - JOUR A1 - Kriegerowski, Marius A1 - Petersen, Gesa Maria A1 - Vasyura-Bathke, Hannes A1 - Ohrnberger, Matthias T1 - A Deep Convolutional Neural Network for Localization of Clustered Earthquakes Based on Multistation Full Waveforms JF - Seismological research letters N2 - Earthquake localization is both a necessity within the field of seismology, and a prerequisite for further analysis such as source studies and hazard assessment. Traditional localization methods often rely on manually picked phases. We present an alternative approach using deep learning that once trained can predict hypocenter locations efficiently. In seismology, neural networks have typically been trained with either single-station records or based on features that have been extracted previously from the waveforms. We use three-component full-waveform records of multiple stations directly. This means no information is lost during preprocessing and preparation of the data does not require expert knowledge. The first convolutional layer of our deep convolutional neural network (CNN) becomes sensitive to features that characterize the waveforms it is trained on. We show that this layer can therefore additionally be used as an event detector. As a test case, we trained our CNN using more than 2000 earthquake swarm events from West Bohemia, recorded by nine local three-component stations. The CNN successfully located 908 validation events with standard deviations of 56.4 m in east-west, 123.8 m in north-south, and 136.3 m in vertical direction compared to a double-difference relocated reference catalog. The detector is sensitive to events with magnitudes down to M-L = -0.8 with 3.5% false positive detections. Y1 - 2018 U6 - https://doi.org/10.1785/0220180320 SN - 0895-0695 SN - 1938-2057 VL - 90 IS - 2 SP - 510 EP - 516 PB - Seismological Society of America CY - Albany ER - TY - JOUR A1 - Lontsi, Agostiny Marrios A1 - Garcia-Jerez, Antonio A1 - Camilo Molina-Villegas, Juan A1 - Jose Sanchez-Sesma, Francisco A1 - Molkenthin, Christian A1 - Ohrnberger, Matthias A1 - Krüger, Frank A1 - Wang, Rongjiang A1 - Fah, Donat T1 - A generalized theory for full microtremor horizontal-to-vertical [H/V(z,f)] spectral ratio interpretation in offshore and onshore environments JF - Geophysical journal international N2 - Advances in the field of seismic interferometry have provided a basic theoretical interpretation to the full spectrum of the microtremor horizontal-to-vertical spectral ratio [H/V(f)]. The interpretation has been applied to ambient seismic noise data recorded both at the surface and at depth. The new algorithm, based on the diffuse wavefield assumption, has been used in inversion schemes to estimate seismic wave velocity profiles that are useful input information for engineering and exploration seismology both for earthquake hazard estimation and to characterize surficial sediments. However, until now, the developed algorithms are only suitable for on land environments with no offshore consideration. Here, the microtremor H/V(z, f) modelling is extended for applications to marine sedimentary environments for a 1-D layered medium. The layer propagator matrix formulation is used for the computation of the required Green’s functions. Therefore, in the presence of a water layer on top, the propagator matrix for the uppermost layer is defined to account for the properties of the water column. As an application example we analyse eight simple canonical layered earth models. Frequencies ranging from 0.2 to 50 Hz are considered as they cover a broad wavelength interval and aid in practice to investigate subsurface structures in the depth range from a few meters to a few hundreds of meters. Results show a marginal variation of 8 per cent at most for the fundamental frequency when a water layer is present. The water layer leads to variations in H/V peak amplitude of up to 50 per cent atop the solid layers. KW - Numerical modelling KW - Earthquake hazards KW - Seismic interferometry KW - Site effects KW - Theoretical seismology KW - Wave propagation Y1 - 2019 U6 - https://doi.org/10.1093/gji/ggz223 SN - 0956-540X SN - 1365-246X VL - 218 IS - 2 SP - 1276 EP - 1297 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Riggelsen, Carsten A1 - Ohrnberger, Matthias T1 - A machine learning approach for improving the detection capabilities at 3C Seismic Stations JF - Pure and applied geophysics N2 - We apply and evaluate a recent machine learning method for the automatic classification of seismic waveforms. The method relies on Dynamic Bayesian Networks (DBN) and supervised learning to improve the detection capabilities at 3C seismic stations. A time-frequency decomposition provides the basis for the required signal characteristics we need in order to derive the features defining typical "signal" and "noise" patterns. Each pattern class is modeled by a DBN, specifying the interrelationships of the derived features in the time-frequency plane. Subsequently, the models are trained using previously labeled segments of seismic data. The DBN models can now be compared against in order to determine the likelihood of new incoming seismic waveform segments to be either signal or noise. As the noise characteristics of seismic stations varies smoothly in time (seasonal variation as well as anthropogenic influence), we accommodate in our approach for a continuous adaptation of the DBN model that is associated with the noise class. Given the difficulty for obtaining a golden standard for real data (ground truth) the proof of concept and evaluation is shown by conducting experiments based on 3C seismic data from the International Monitoring Stations, BOSA and LPAZ. Y1 - 2014 U6 - https://doi.org/10.1007/s00024-012-0592-3 SN - 0033-4553 SN - 1420-9136 VL - 171 IS - 3-5 SP - 395 EP - 411 PB - Springer CY - Basel ER - TY - JOUR A1 - Hammer, Conny A1 - Beyreuther, Moritz A1 - Ohrnberger, Matthias T1 - A seismic-event spotting system for volcano fast-response systems JF - Bulletin of the Seismological Society of America N2 - Volcanic eruptions are often preceded by seismic activity that can be used to quantify the volcanic activity. In order to allow consistent inference of the volcanic activity state from the observed seismicity patterns, objective and time-invariant classification results achievable by automatic systems should be preferred. Most automatic classification approaches need a large preclassified data set for training the system. However, in case of a volcanic crisis, we are often confronted with a lack of training data due to insufficient prior observations. In the worst case (e. g., volcanic crisis related reconfiguration of stations), there are even no prior observations available. Finally, due to the imminent crisis there might be no time for the time-consuming process of preparing a training data set. For this reason, we have developed a novel seismic-event spotting technique in order to be less dependent on previously acquired data bases and classification schemes. We are using a learning-while-recording approach based on a minimum number of reference waveforms, thus allowing for the build-up of a classification scheme as early as interesting events have been identified. First, short-term wave-field parameters (here, polarization and spectral attributes) are extracted from a continuous seismic data stream. The sequence of multidimensional feature vectors is then used to identify a fixed number of clusters in the feature space. Based on this general description of the overall wave field by a mixture of multivariate Gaussians, we are able to learn particular event classifiers (here, hidden Markov models) from a single waveform example. To show the capabilities of this new approach we apply the algorithm to a data set recorded at Soufriere Hills volcano, Montserrat. Supported by very high classification rates, we conclude that the suggested approach provides a valuable tool for volcano monitoring systems. Y1 - 2012 U6 - https://doi.org/10.1785/0120110167 SN - 0037-1106 VL - 102 IS - 3 SP - 948 EP - 960 PB - Seismological Society of America CY - Albany 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 - Kohler, A. A1 - Ohrnberger, Matthias A1 - Scherbaum, Frank A1 - Stange, S. A1 - Kind, F. T1 - Ambient vibration measurements in the Southern Rhine Graben close to Basle N2 - 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 Y1 - 2004 SN - 1593-5213 ER - TY - JOUR A1 - Tran Thanh Tuan, A1 - Pham Chi Vinh, A1 - Ohrnberger, Matthias A1 - Malischewsky, Peter A1 - Aoudia, Abdelkrim T1 - An Improved Formula of Fundamental Resonance Frequency of a Layered Half-Space Model Used in H/V Ratio Technique JF - Pure and applied geophysics N2 - The resonance frequency of the transmission response in layered half-space model is important in the study of site effect because it is the frequency where the shake-ability of the ground is enhanced significantly. In practice, it is often determined by the H/V ratio technique in which the peak frequency of recorded H/V spectral ratio is interpreted as the resonance frequency. Despite of its importance, there has not been any formula of the resonance frequency of the layered half-space structure. In this paper, a simple approximate formula of the fundamental resonance frequency is presented after an exact formula in explicit form of the response function of vertically SH incident wave is obtained. The formula is in similar form with the one used in H/V ratio technique but it reflects several major effects of the model to the resonance frequency such as the arrangement of layers, the impedance contrast between layers and the half-space. Therefore, it could be considered as an improved formula used in H/V ratio technique. The formula also reflects the consistency between two approaches of the H/V ratio technique based on SH body waves or Rayleigh surface waves on the peak frequency under high impedance contrast condition. This formula is in explicit form and, therefore, may be used in the direct and inverse problem efficiently. A numerical illustration of the improved formula for an actual layered half-space model already investigated by H/V ratio technique is presented to demonstrate its new features and its improvement to the currently used formula. KW - Response function KW - H/V ratio technique KW - Orthotropy KW - SH waves KW - Quarter-wavelength principle Y1 - 2016 U6 - https://doi.org/10.1007/s00024-016-1313-0 SN - 0033-4553 SN - 1420-9136 VL - 173 SP - 2803 EP - 2812 PB - Springer CY - Basel 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 - 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 - TY - JOUR A1 - Holschneider, Matthias A1 - Diallo, Mamadou Sanou A1 - Kulesh, Michail A1 - Ohrnberger, Matthias A1 - Luck, E. A1 - Scherbaum, Frank T1 - Characterization of dispersive surface waves using continuous wavelet transforms N2 - 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 Y1 - 2005 SN - 0956-540X ER -