摘要
提出一种结合时间序列季节趋势离散(seasonal trend dispersion,STD)分解和递推最小二乘(recursive least squares,RLS)法的自适应去除工频干扰方法。该方法利用STD分解提取含工频干扰微震波的季节项,作为RLS的参考信号,并动态更新算法系数,使计算信号接近工频干扰信号。设计仿真实验,将1组无工频干扰的微震波与3种不同类型工频干扰叠加,分别使用有限冲击响应(finite impulse response,FIR)滤波、小波阈值(wavelet threshold,WT)滤波和本文方法进行处理。结果表明,本文方法能有效去除工频干扰,同时完整保留微震波的关键时频特征。此外,将本文方法应用于桃园煤矿微震监测数据处理,验证了其工程应用的可行性与可靠性。
An adaptive method for removing power frequency interference is proposed by combining seasonal trend dispersion(STD)decomposition with recursive least squares(RLS).This method utilizes STD decomposition to extract the seasonal component containing power frequency interference from microseismic waves,which serves as the reference signal for RLS,and dynamically updates the algorithm coefficients to make the calculated signal approach the power frequency interference signal.Simulation experiments were designed by superimposing 1 set of microseismic waves without power frequency interference with 3 different types of power frequency interference.The finite impulse response(FIR)filtering,wavelet threshold(WT)filtering,and the method presented in this paper were used for processing.The results indicate that the method in this paper can effectively remove power frequency interference while fully preserving the key time-frequency characteristics of microseismic waves.Furthermore,the application of this method to the microseismic monitoring data processing of Taoyuan coal mine has verified its feasibility and reliability in engineering applications.
作者
刘宝霖
张明伟
袁国涛
田壮才
LIU Baolin;ZHANG Mingwei;YUAN Guotao;TIAN Zhuangcai(School of Mechanics and Civil Engineering,China University of Mining and Technology,1 Daxue Road,Xuzhou 221116,China;State Key Laboratory of Deep Geotechnical Mechanics and Underground Engineering,China University of Mining and Technology,1 Daxue Road,Xuzhou 221116,China)
出处
《大地测量与地球动力学》
北大核心
2025年第9期954-963,共10页
Journal of Geodesy and Geodynamics
基金
淮北矿业集团打造原创技术策源地重点项目(淮矿计合字2024第217号)。
关键词
微震波
工频干扰
季节趋势离散分解
递推最小二乘法
自适应算法
microseismic wave
power frequency interference
seasonal trend discrete decomposition
recursive least squares method
adaptive algorithm
