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.

在存在地震危险的采后区，及时制定风险管理决策面临着快速可靠地检测和定位地震事件的挑战。近年来，随着监测站密度的增加，产生大量地震数据，全球地震学已经开发了基于自动、完整波形的方法。这种方法通常不能直接应用于站点覆盖有限的采矿后监测，因为临时安装网络作为应急响应就是这种情况。在本文中，我们提出了一种新的方法来弥合这一差距，并使基于全波形的反向投影方法 (BackTrackBB) 能够应用于稀疏网络的数据。该方法已在法国东南部一座废弃和淹没的地下煤矿上成功测试。实施 BackTrackBB 应用程序之前的步骤是为了消除相干噪声，否则会导致大量错误检测。初步结果表明，研究区的地震活动受原房柱式矿山工程及其下方断层段（重新）激活内的水位变化控制。