基于EM-KF算法的微地震信号去噪方法

Microseismic Signal Denoising Method Based on EM-KF Algorithm

针对微地震信号能量较弱,噪声较强,使微地震弱信号难以提取问题,提出了一种基于EM-KF(Expectation Maximization Kalman Filter)的微地震信号去噪方法。通过建立一个符合微地震信号规律的状态空间模型,并利用EM(Expectation Maximization)算法获取卡尔曼滤波的参数最优解,结合卡尔曼滤波,可以有效地提升微地震信号的信噪比,同时保留有效信号。通过合成和真实数据实验结果表明,与传统的小波滤波和卡尔曼滤波相比,该方法具有更高的效率和更好的精度。

Microseismic monitoring technology has been widely used in unconventional oil and gas development.The microseismic signal has weak energy and strong noise,which makes the follow-up work difficult and requires high-precision and accurate data.To solve the problem of extracting weak microseismic signals,an EM-KF(Expectation Maximization Kalman Filter)-based method is proposed for denoising microseismic signals.By establishing a state space model that conforms to the laws of microseismic signals and using the EM(Expectation Maximization)algorithm to obtain the optimal solution of the parameters for the Kalman filter,the signal-to-noise ratio of microseismic signals can be effectively improved while retaining the effective signals.The experimental results of synthetic data and real data show that this method has higher efficiency and better accuracy than traditional wavelet filtering and Kalman filtering.