Suppressing non-stationary random noise in microseismic data by using ensemble empirical mode decomposition and permutation entropy

• A de-noising method of non-stationary random noise was proposed.
• The method is composed of ensemble empirical mode decomposition and permutation entropy.
• Signals and non-stationary noise can be distinguished by using permutation entropy.
• Simulation and case analysis show that this method can effectively suppress the noise.

Microseismic signal is inevitably mixed with non-stationary random noise in the process of acquisition, which is difficult to be separated from non-stationary random noise by using the traditional methods of linear filtering and spectrum analysis. Thus a suppressing method of non-stationary random noise is proposed. It firstly conducts the multi-scale decomposition of microseismic signal containing noises based on ensemble empirical mode decomposition (EEMD). Several components of Intrinsic Mode Functions (IMFs) are obtained and they are arranged in descending order according to their frequencies. In order to accurately identify the signals and noises in these IMF components and compare the normal microseismic signals with noises, the quantity of permutation entropy is introduced to describe the characteristics of normal microseismic signal. The threshold value of permutation entropy is used to extract the IMF components conforming to the characteristics of microseismic signal. These IMF components are reconstructed to suppress the noise. Through simulation and the test for the practical microseismic monitoring data, it is indicated that the method has a better treatment effect for non-stationary random noise in microseismic signal.