Automatic Detection and Location of Microseismic Events from Sparse Network and Its Application to Post-mining Monitoring

Abstract

In post-mining regions with seismic hazard, timely decision making for risk management faces the challenge of quick and reliable detection and location of seismic events. As a response to the increasing density of monitoring stations, generating large volumes of seismic data, automatic, full waveform-based methods have been developed in recent years in global seismology. Such methods often cannot be directly applied to post-mining monitoring with a limited station coverage, as it is the case when temporarily networks are installed as an emergency response. In this paper we propose a new methodology that bridges this gap and enables the application of a full waveform, backprojection based method (BackTrackBB) to data of sparse network. The methodology was successfully tested on an abandoned and flooded underground coalmine in South-eastern France. Steps preceding BackTrackBB application were implemented in order to remove the coherent noise that otherwise results in numerous false detections. First results indicate that seismic activity in the study area is controlled by water level variation within former room-and-pillar mine works and fault segments (re)activation below them.