An Effective Denoising Method Based on Cumulative Distribution Function Thresholding and its Application in the Microseismic Signal of a Metal Mine With High Sampling Rate (6 kHz)

Microseismic events can be used to analyze the risk of tunnel collapse, rock burst, and other mine hazards in space and time. In practice, the artificial activities and other signals at the mining site can seriously interfere with the microseismic waveforms, reducing the signal-to-noise ratio. In this study, we propose a denoising method based on the threshold of the cumulative distribution function (CDF) of the wavelet coefficients in the wavelet domain using synchrosqueezed continuous wavelet transform (SS-CWT). First, the ratio of microseismic signal variance between two adjacent time windows is used to determine the range of background noise. Then, the microseismic signal is transformed into a wavelet domain using SS-CWT, and the threshold of wavelet coefficients at each scale is estimated based on the cumulative distribution function (CDF) of background noise. At last, a post-processing step is applied by utilizing an amplitude smoothing function, to further suppress the noise. The proposed denoising method is tested by both synthetic and filed microseismic data recorded in a metal mine. The results show that the method is effective in denoising and can improve the SNR of mine microseismic data with a high sampling rate.