ALBERT

Beschreibung: 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 Soufrière Hills volcano, Montserrat. Supported by very high classification rates, we conclude that the suggested approach provides a valuable tool for volcano monitoring systems.

Beschreibung: Ambient vibrations are nowadays considerably used worldwide for numerous types of engineering applications and scientific research. Geopsy and its companion tools are part of that landscape. Since the first release of the program package in 2005, as outcome of the European Union project Site Effects aSsessment from AMbient noisE, Geopsy has become a mature multiplatform open-source package (released under GNU Public License version 3) that has already been recognized as a reference tool for analyzing ambient vibration data in the context of site characterization studies. The community of users has grown from a core group of researchers up to thousands of seismologists and engineers on every career level and on all continents. The versatility of geopsy allows for the processing of all kinds of data needed in site characterization studies, that is, from single station single trace to three-component array recordings. In all of the aforementioned cases, the steps from field acquisition to the production of publication-ready figures are covered and supported by user-friendly graphical user interfaces or corresponding command-line tools for the automation of the complete processing chain. To avoid black-box usage, a number of lower-level tools guarantee maximum flexibility in accessing and controlling processing results at any stage of the analysis.

Beschreibung: 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

Beschreibung: 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 wavefield 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 North-West 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

Beschreibung: 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.

Beschreibung: We analyze data from passive and active seismic experiments conducted in the Adventdalen valley of Svalbard in the Norwegian Arctic. Our objective is to characterize the ambient wavefield of the region and to investigate permafrost dynamics through estimates of seismic velocity variations. We are motivated by a need for early geophysical detection of potentially hazardous changes to permafrost stability. We draw upon several data sources to constrain various aspects of seismic wave propagation in Adventdalen. We use f-k analysis of five years of continuous data from the Spitsbergen seismic array (SPITS) to demonstrate that ambient seismic noise on Svalbard consists of continuously present body waves and intermittent surface waves appearing at regular intervals. A change in wavefield direction accompanies the sudden onset of surface waves when the average temperature rises above the freezing point, suggesting a cryogenic origin. This hypothesis is supported further by our analysis of records from a temporary broadband network, which indicates that the background wavefield is dominated by icequakes. Synthetic Green’s functions calculated from a 3D velocity model match well with empirical Green’s functions constructed from the recorded ambient seismic noise. We use a shallow shear-wave velocity model, obtained from active seismic measurements, to estimate the maximum depth of Rayleigh wave sensitivity to changes in shear velocity to be in the 50–100 m range. We extract seasonal variations in seismic velocities from ambient noise cross-correlation functions computed over three years of SPITS data. We attribute relative velocity variations to changes in the ice content of the shallow (2–4 m depth) permafrost, which is sensitive to seasonal temperature changes. A linear decreasing trend in seismic velocity is observed over the years, most likely due to permafrost warming.

Beschreibung: 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, contribute 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 transient events from seismic recordings. Based on the similarity properties of spectrogram frames in the time–frequency domain, we decompose the signal into two separate spectrograms 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 contributing 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 transient events (e.g., earthquakes) by integrating amplitudes of the nonrepeating spectrogram 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 Ágústsdóttir 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.