Local Seismicity: Matched Filter Detection Routine with Synthetic Templates using 1D velocity model

This study evaluates the performance of Synthetic Template Matching for seismic event detection in the West Bohemia region (Czechia), comparing it with two established methods: the automated detector-locator PEPiN and an Artificial Neural Network. Synthetic templates are generated using a 1D velocity model and span a grid of five fundamental focal mechanisms (FMs), independent of any prior waveform or FM knowledge. The resulting catalog includes origin time, similarity, magnitude, location, number of detecting templates, and interpreted focal mechanism. In WEBNET data, Synthetic Template Matching with a cross-correlation threshold of 0.4 detected 264 events with a completeness magnitude of Mc​=−0.1. All the detected seismicity is real and local, and the interpreted FMs (within the seismic network) predominantly align with strike-slip events. Although the method does not outperform PEPiN or the Artificial Neural Network in terms of Mc​, it reliably estimates focal mechanisms and epicentral locations.