Microseismic data denoising in the sychrosqueezed domain by integrating the wavelet coefficient thresholding and pixel connectivity

Abstract

Summary Microseismic monitoring is crucial for risk assessment in mining, fracturing, and excavation. In practice, microseismic records are often contaminated by undesired noise, which is an obstacle to high-precision seismic locating and imaging. In this study, we develop a new denoising method to improve the signal-to-noise ratio (SNR) of seismic signals by combining wavelet coefficient thresholding and pixel connectivity thresholding. First, the pure background noise range in the seismic record is estimated using the ratio of variance (ROV) method. Then, the synchrosqueezed continuous wavelet transform (SS-CWT) is employed to project the seismic records onto the time-frequency plane. After that, the wavelet coefficient threshold for each frequency is computed based on the empirical cumulative distribution function (ECDF) of the coefficients of the pure background noise. Next, hard thresholding is conducted to process the wavelet coefficients in the time-frequency domain. Finally, an image processing approach called pixel connectivity thresholding is introduced to further suppress isolated noise on the time-frequency plane. The wavelet coefficient threshold obtained by using pure background noise data is theoretically more accurate than that obtained by using the whole seismic record, because of the discrepancy in the power spectrum between seismic waves and background noise. After hard thresholding, the wavelet coefficients of residual noise exhibit isolated and lower pixel connectivity in the time-frequency plane, compared with those of seismic signals. Thus, pixel connectivity thresholding is utilized to deal with the residual noise and further improve the SNR of seismic records. The proposed new denoising method is tested by synthetic and real seismic data, and the results suggest its effectiveness and robustness when dealing with noisy data from different acquisition environments and sampling rates. The current study provides a useful tool for microseismic data processing.

Automatic P-Phase-Onset-Time-Picking Method of Microseismic Monitoring Signal of Underground Mine Based on Noise Reduction and Multiple Detection Indexes

“…The time-frequency analysis was carried out on the original microseismic monitoring data, and the threshold filtering of wavelet coefficient in the time-frequency domain was adopted [10]…”

“…The time-frequency analysis was carried out on the original microseismic monitoring data, and the threshold filtering of wavelet coefficient in the time-frequency domain was adopted [10] 5. The SNR of the mine microseismic waveform was increased from 9.0768 of the original waveform to the highest 21.2767 (SNR is the ratio of the amplitude root mean square of the waveform in a short time window with the first arrival time as the center), and the SNR of mine blasting waveform was increased from 14.06782 of the original waveform to the highest 27.1316.…”

“…Rock burst, collapse and other ground pressure disasters caused by deep high stress have become a major hidden danger restricting mine safety production. Microseismic monitoring technology is an important means of ground pressure risk prediction [1][2][3][4][5][6][7][8][9][10][11]. For the microseismic monitoring system, the automatic identification and accurate picking of the P-phase onset time of microseismic signals of the underground mine directly affects the timeliness and accuracy of microseismic source location, source mechanism inversion, and rock stability analysis [11][12][13].…”

“…Some researchers also use the Time-frequency Transform (TFT) to transform the seismic wave signal in the one-dimensional time domain into the two-dimensional time-frequency domain, so that the location of the microseismic signal can be clearly identified on the time-frequency spectrum, and then signal processing and denoising can be carried out. Wavelet coefficient threshold denoising method in time-frequency domain has been widely concerned due to its simple principle [26,27] and remarkable denoising effect [10,[28][29][30][31][32].…”

An Improved Synchrosqueezing S-Transform and Its Application in a GPR Detection Task

“…Figure 7e demonstrates that this method effectively preserved information in large connected regions of the T-F spectra while eliminating poorly connected interference. Details on pixel connectivity filtering can be found in [31]. Finally, Figure 7f presents the results from the SS-GPST inverse transform.…”