An Adaptive 6-Dimensional Floating-Search Multi-Station Seismic-Event Detector (A6-DFMSD) and its Application to Low Frequency Earthquakes in the East Eifel Volcanic Field, Germany

Abstract We introduce a new seismic event detector that applies signal analysis in the frequency domain while searching matches coincidences at neighbouring recording stations. Unlike for the template matching based methods and the supervised and some unsupervised machine learning based methods, no a priori waveform information is needed for our Adaptive 6-Dimensional Floating-search Multi-station Seismic-event Detector (A6-DFMSD). As a challenging test example, the new detector is applied to a dataset with 5 months of micro-earthquake ( ML < 2) recordings in the East Eifel Volcanic Field (EEVF), Germany. There both, magmatic and tectonic, earthquakes occur in a depth range between 3 km and 43 km. In summary, we detect 401 micro-earthquakes in this period without missing any of the 87 events listed in the existing catalogues of the region. This means that overall A6-DFMSD detected 4.6 times as many events were already known. After a manual location and classification of the events, we show that A6-DFMSD finds events of different origins (tectonic, magmatic, atmospheric, and anthropogenic). In particular, low-frequency earthquakes of magmatic origin with a complicated coda are very well identified. We suggest that seismological networks monitoring local seismicity with similar target zones should benefit from the use of A6-DFMSD to allow the detection of a wide range of seismic signals.