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由于微地震信号能量微弱、信噪比低,需要对采集到的微地震数据进行去噪处理,从而提高微地震记录的信噪比,提高震源定位的精度.目前存在许多基于模型的先进滤波方法,如基于机理模型的卡尔曼滤波已经成功应用在微地震信号去噪中.为了建立微地震信号的数学模型,改善卡尔曼滤波效果,本文通过数据辨识方法,对微地震信号建立了ARMA模型,并进一步转化为适用于卡尔曼滤波算法的状态空间模型.在此基础上研究了卡尔曼滤波方法,设计了适用于微地震去噪的卡尔曼滤波实现算法.理论模型和实际微地震监测数据处理结果表明,基于辨识模型的卡尔曼滤波算法能够有效抑制微地震信号中的随机噪声,显著提高微地震监测信号的信噪比,从而验证了该辨识模型的准确性和滤波算法的可行性.
Due to the weak energy and low signal-to-noise ratio of the microseismic signal, it is necessary to de-noise the collected microseismic data so as to improve the signal-to-noise ratio of the microseismic record and improve the accuracy of the hypocenter positioning.Now there are many advanced model-based filtering methods For example, based on the mechanism model, Kalman filter has been successfully applied in the microseismic signal denoising.In order to establish a mathematical model of microseismic signals and improve the Kalman filter effect, this paper establishes an ARMA model for microseismic signals by data identification method, And further transformed into a state space model suitable for Kalman filter algorithm.On this basis, the Kalman filter method is studied, and a Kalman filter algorithm is designed for microseismic denoising.The theoretical model and actual micro-seismic monitoring data processing The results show that the Kalman filter algorithm based on the recognition model can effectively suppress the random noise in the microseismic signal and significantly improve the signal-to-noise ratio of the microseismic monitoring signal, thus verifying the accuracy of the identification model and the feasibility of the filtering algorithm.