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

Zhiyi Zeng1,2,3; Tianxin Lu3; Peng Han1,2,3; Da Zhang4; Xiao-Hui Yang1,2,3; Yaqian Shi4; Ying Chang4; Jianzhong Zhang5; Rui Dai4; Hu Ji4

2022-10-28

10.1093/gji/ggac378

GEOPHYSICAL JOURNAL INTERNATIONAL   影响因子和分区

0956-540X

1365-246X

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 used 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.

Wavelet transform Induced seismicity Seismic noise Microseismic denoising Wavelet thresholding

SCI ; EI

英语

第一 ; 通讯

Science and Technology Program of Shenzhen[JCYJ20210324104602006] ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)[GML2019ZD0203] ; Shenzhen Key Laboratory of Deep Offshore Oil and Gas Exploration Technology[ZDSYS20190902093007855]

Geochemistry & Geophysics

Geochemistry & Geophysics

WOS:000875229100006

OXFORD UNIV PRESS

20225213314878

Data handling ; Distribution functions ; Frequency domain analysis ; Pixels ; Risk assessment ; Seismic waves ; Seismology ; Signal to noise ratio

Seismology:484 ; Earthquake Measurements and Analysis:484.1 ; Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2 ; Accidents and Accident Prevention:914.1 ; Mathematical Transformations:921.3 ; Probability Theory:922.1

GEOSCIENCES

Web of Science

1.Shenzhen Key Laboratory of Deep Offshore Oil and Gas Exploration Technology, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
2.Southern Marine Science and Engineering Guangdong Laboratory, Shenzhen, Guangdong Province, Zhuhai 519000, China
3.Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
4.Institute of Mining Engineering, BGRIMM Technology Group, Beijing 102600, China
5.College of Marine Geosciences, Ocean University of China, Qingdao 266100, China

Zhiyi Zeng,Tianxin Lu,Peng Han,et al. Microseismic data denoising in the sychrosqueezed domain by integrating the wavelet coefficient thresholding and pixel connectivity[J]. GEOPHYSICAL JOURNAL INTERNATIONAL,2022,232(2):1113-1128.

Zhiyi Zeng.,Tianxin Lu.,Peng Han.,Da Zhang.,Xiao-Hui Yang.,...&Hu Ji.(2022).Microseismic data denoising in the sychrosqueezed domain by integrating the wavelet coefficient thresholding and pixel connectivity.GEOPHYSICAL JOURNAL INTERNATIONAL,232(2),1113-1128.

Zhiyi Zeng,et al."Microseismic data denoising in the sychrosqueezed domain by integrating the wavelet coefficient thresholding and pixel connectivity".GEOPHYSICAL JOURNAL INTERNATIONAL 232.2(2022):1113-1128.