基于Allen算法微震信号P波初至及其自适应识别

Microseismic Signal of the First P wave Arrival and Its Adaptive Recognition Based on the Allen Algorithm

微震监测工程尺度是指监测范围在几米到几百米之间,该尺度下将Allen算法引入到微震领域时需调整该算法参数,以达到最佳拾取效果,从而提高微震定位精度。为此,提出一种基于Allen算法的微震信号P波初至及其自适应识别的方法:首先依据信噪比建立微震信号拾取信息数据库,再结合粒子群算法和拾取评价模型自动选取Allen关键参数;并建立了拾取过程中参数动态反馈修正机制,依靠拾取实例不断扩充和更新数据库Allen算法参数。研究结果表明:该方法能针对不同信号自适应选取微震信号Allen算法最优参数,能克服人工统计的耗时低效,更为准确的从实时监测数据中拾取微震信号及其P波初至,提高微震监测定位精度和数据处理效率,为岩爆、矿震等地质灾害的及时预报提供可靠的数据支持。

Project scale referred to microseismic monitoring in the range between a few meters to several hundred meters. In this scale, the parameters should be adjusted when Allen algorithm was introduced into the microseismic field, so asto achieve the best pick-up effect and improve the positioning accuracy. Therefore, a microseismic signal of the first P wave arrival and its adaptive recognition method based on the Allen algorithm was proposed. Firstly, a microseismic signal database was established according to the signal-to-noise ra- tio, and the Allen key parameters were automatically select- ed by combining with PSO and pick up evaluation model. Secondly, a dynamic feedback and correction mechanism for parameters was created in the pick-up process, and the Allen parameters in the database would be continuously expanded and updated in terms of pick up instances. Research indicated that this method could adaptively select the Allen Algorithm optimal parameters of microseismic signal, overcome the time-consuming and inefficient of artificial statistics, accu- rately pick up the microseismic signal and the first P wave arrival from monitoring data, and improve the accuracy of monitoring positioning and efficiency of data processing, which provided reliable data support for the timely prediction of geological disasters, such as rock burst, mining earth- quake and so on.