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.

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<p>The dense coverage of Europe with seismological stations offers a large variety of advanced seismological data processing options. With thousands of stations available a manual quality check of the data becomes more and more unfeasible. Moreover, with rising network traffic, increasing amounts of users, data requests and data size, errors are more likely to occur and the requested data will not always be available for various reasons. Identifying stations and networks that are more likely to be unavailable during data requests is also part of a data quality control. We show how randomized tests can be used to evaluate the data availability for the European stations and how very simple data processing routines calculating average noise levels at stations can be used to identify erroneous data or metadata.</p>