Comprehensive study of micro-seismicity by using an automatic monitoring platform

Abstract

A modern digital seismic network, with many stations optimally distributed on the earthquake causative seismic zone, enables detection of very low magnitude earthquakes and determination of their source parameters. It is essential to associate to such kind of networks procedures to analyze the huge amount of continuously recorded data for monitoring the space-time-magnitude evolution of natural and/or induced seismicity. Hence, the demand for near-real-time, automated data collection and analysis procedures for assisting seismic network operators in carrying out microearthquake monitoring is growing. In response to this need, we designed a computational software platform, TREMOR, for fast and reliable detection and characterization of seismicity recorded by a dense local seismic network. TREMOR integrates different open-source seismological algorithms for earthquake signal detection, location, and source characterizations in a fully automatic workflow. We applied the platform in play-back mode to the continuous waveform data recorded during 1 month at the Japanese Hi-net seismic network in the Nagano region (Japan) and compared the resulting catalog with the Japan Meteorological Agency bulletin in terms of number of detections, location pattern and magnitudes. The results show that the completeness magnitude of the new seismic catalog decreased by 0.35 units of the local magnitude scale and consequently the number of events increased by about 60% with respect to the available catalog. Moreover, the fault plane solutions resulted coherent with the stress regime of the region, and the Vp/Vs ratio well delineated the main structural features of the area. According to our results, TREMOR has shown to be a valid tool for investigating and studying earthquakes, especially to identify and monitor natural or induced micro-seismicity.