Villarrica Volcano (Chile) is one of the most active volcanoes in South America. Its low-frequency (≤5 Hz) seismicity consists of a continuous tremor, overlain by impulsive transient events of higher amplitude in 60-s intervals. This signal was recorded in March 2012 by an extensive local network, comprising 75 stations and including 6 subarrays. It allowed us to apply and compare three techniques to locate the origin of the seismicity: intersection of propagation directions determined by array analysis, mapping amplitudes, and modeling of amplitude decay. All methods yield almost identical, temporally stable, epicenters inside the summit crater, which confirms earlier attributions of the seismicity to volcanic activity inside the conduit. The discrete transients and the interevent tremor share the same source location. From the dominance of surface waves and the obvious scattering, we infer a source near the surface. For two arrays at the northern and western flank, a dispersion relation was derived, which allowed for the determination of S wave velocity-depth functions. At both locations, the velocity structure can be modeled by three layers with interfaces at 100 and 400m depths. The velocities (300 to 3,000 m/s) correspond to pyroclastic material at different states of consolidation. The modeling of the amplitude decay reveals a quality factor around 50.
<p>Villarrica is one of the most active and <span>dangerous</span> volcanoes in Chile. During the last decade it consisted of a single open vent hosting an active lava lake which produced mild stombolian explosions, persistent tremor and continuous degassing.</p><p>We present an analysis of the seismic activity of Villarrica between 2010 and 2012. Periods of increased lava lake activity are characterized by numerous small transient events which exibit a variety of waveforms and spectral characteristics. Statistical analysis of interevent times revealed a periodic occurrence. At comparable volcanic systems (Stromboli, Erebus), such distributions of events indicated unusual periods of activity corresponding to magma injection. Methods of blind signal separation (ICA, PCA) were used to analyse the wavefield. While regional and local tectonic earthquakes can easily be separated, the tremor and transient events from the crater can not.</p>
Summary The massive eruption of the Hunga Volcano on 15 January 2022 provides an ideal test case for reviewing established methods to discriminate and analyse source processes. Discriminating source mechanisms and identifying their origins is a key task when analysing suspicious events in the frame of the Comprehensive Nuclear-Test-Ban Treaty. Earthquakes and explosions can be distinguished in some cases using well established methods such as inversion for the seismic moment tensor. In more complex cases the combination of analyses of the seismic, infrasonic and hydroacoustic waveform content can be of help. More challenging is the discrimination of the specific kind of explosive source such as a nuclear test and a volcano eruption based on the data from the three waveform technologies alone. Here, we apply standard techniques destined to analyse relevant events in the frame of the CTBT, i. e. all three waveform technologies (seismology, infrasound, and hydroacoustic) and atmospheric transport modelling of radionuclides. We investigate the potential of standard analysis methods to discriminate a source and identify their possible weaknesses. We show that the methods applied here work very well to identify, investigate, and discriminate an explosive event. During discrimination we could not only exclude a shear-source (i. e. earthquake) but also distinguish the volcanic explosion in contrast to a man-made explosion. However, some tasks remain difficult with the available methods. These tasks include the reliable estimation of the strength of a non-shear event and thereupon a yield estimation of a possibly CTBT relevant event. In addition to evaluating our methods, we could relate our results with specific phases of the eruption process providing a more detailed insight of what happened. Our investigations of the eruption details only provide a starting point for further in-depth analysis. However, they underline the importance of the Hunga eruption event for science. The huge amount of observations provide a unique opportunity for knowledge gain in several sub-disciplines of the geosciences. In addition, although not being a nuclear test, it also provides a useful and important data set for further developing multi-technology analyses in the frame of the CTBT.
<p class="md-end-block md-p md-focus"><span class="md-plain md-expand">Continuous monitoring of data quality is a major issue in seismology because the achievement of robust scientific results depends on the reliability of the underlying data resources. We present a Python package which provides means to perform a systematic analysis of noise data in the time and frequency domain. The tool is designed to process large amounts of channels and years of data. In a first step, average amplitude levels and power spectral densities are computed for large parts - preferably the whole available time range - of the data of a station. Depending on the size of the data set, this processing takes minutes to hours. Therefore, the results are stored in rapidly accessible HDF5-files. Subsequently, they are visualized using color-coded matrix displays (spectrograms) and interactive 3D-figures. The resulting figures give insight to characteristic noise patterns at the station and possible noise sources, like various forms of anthropogenic noise or wind generated noise. Furthermore, changes in noise levels or noise patterns are easily detectable. Such changes either indicate changes in the environmental conditions at the recording site or changes in the recording hardware improperly reflected by the station metadata, often signaling a problem with the metadata. Furthermore, the processed data can easily be restricted to selected times, e.g. to investigate the influence of day/night cycles or to obtain wind-speed dependent spectrograms. In this manner, a comprehensive picture of relevant characteristics at a station site may be acquired.</span></p> <p class="md-end-block md-p"><span class="md-plain">The package is build from established Python libraries like obspy, scipy and h5py. Matplotlib and plotly are used for data visualization. The core functionalities are accessible via command line interface while the underlying API allows for more customized workflows.</span></p>
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