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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。
The microseismic monitoring engineering scale means that the monitoring range is between a few meters and a few hundred meters. When the Allen algorithm is introduced into the micro-seismic area at this scale, the algorithm parameters need to be adjusted to achieve the best pick-up effect so as to improve the microseismic positioning accuracy. Therefore, a method based on Allen algorithm for initial arrival of P wave and adaptive recognition of P wave is proposed. Firstly, a database of microseismic signal pick-up information is established based on signal-to-noise ratio, and then the Allen key parameters are automatically selected by combining particle swarm optimization with picking evaluation model ; And set up a dynamic feedback correction mechanism of the parameters in the process of picking up, continuously expanding and updating the database Allen algorithm parameters by picking up instances. The results show that this method can adaptively select the optimal parameters of the Allen algorithm for different signals and can overcome the time-consuming and inefficient manual statistics, and more accurately pick up the microseismic signals and their P-wave first arrival, Improve the precision of micro-seismic monitoring and positioning and data processing efficiency, and provide reliable data support for timely prediction of geological disasters such as rockburst and mine earthquake.