Abstract: 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.

Key words: microseism, expectation maximization (EM) algorithm, Kalman filter, signal to noise ratio