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

Abstract We introduce a seismic event detector that applies signal analysis in the time and frequency domains. Signals are searched for with matching coincidences at neighbouring recording stations in space and time. No a priori waveform information is needed for the Adaptive 6-Dimensional Floating-search Multi-station Seismic-event Detector (A6-DFMSD). It combines a short / long time average algorithm (STA/LTA), frequency range selection, energy envelope matching, and backprojection techniques to find a robust detection model. As a challenging test example, the new detector is tuned and applied to a dataset with five months of microearthquake (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. A6-DFMSD detected 4.3 times as many events as were already known and it discovered a previously unknown event cluster. After manual localization and classification of the events, we show that A6-DFMSD finds events of different origins: tectonic, magmatic, atmospheric, and anthropogenic. In particular, low-frequency (LF) earthquakes of magmatic origin with a complicated waveform coda are very well identified. We suggest that seismological networks monitoring local seismicity in similar target zones would benefit from the use of A6-DFMSD to allow the detection of a wide range of different seismic signals.

Keywords

• Automatic seismic event detection
• Primary classification
• Magmatic deep-low-frequency microearthquakes
• Local seismological networks