微震事件的信号识别算法研究及参数优化

微震监测系统主要用来监测和分析各种生产及开采活动中由岩石破坏所引起的微震。微震监测原理与地震监测相同，主要包括数据采集，事件的信号识别和定位，其中信号识别主要是指P渡震相的识别，它直接影响微震定位的可靠性。目前在地震监测中，广泛使用的识别算法主要是pick—ew和FilterPicker算法。使用实际数据对这两个算法进行研究并优化其参数，使它们能够更好地应用在微震事件的监测中。由于pick—eW配置参数的复杂性，主要是依据前人使用的历史经验对参数进行优化；而FiherPicker算法只需要调整一个参数就可以将其更好的应用在微震监测中，主要使用0．618法来找出这个参数值。

Research and parameter optimization on microseismic event signal recognition algorithm

Microseismic monitoring system is mainly used to monitor and analyze the microseisms caused by rock destruc- tion of various productions and mining activities. Microseismic monitoring principle is the same as earthquake monitoring, which includes data acquisition, signal identification and location of events. The signal identification mainly refers to the P-wave seis- mic phase identification, which directly affects the reliability of microseismic monitoring system. Currently the recognition algo- rithms widely used in seismic monitoring are pick_ew algorithm and FilterPicker algorithms. Actual data is used to study these two algorithms and optimize their parameters, to enable them to better use in monitoring microseismic events. Since the complexity of the parameter configuration of pick_ew, the historical experience of our predecessors are mainly used to optimize the parame- ters, while it only need to adjust one parameter in FiherPicker algorithm, that can make it better application in microseismic monitoring. The parameter value is found out by 0.618 method.