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Noise suppression of distributed acoustic sensing vertical seismic profile data based on time-frequency analysis

100%

Shao D. , Li T. , Han L. , Li Y.

Acta Geophysica

2022

tom Vol. 70, no 4

1539--1549

Distributed acoustic sensing (DAS) technology is a novel technology applied in vertical seismic profile (VSP) exploration, which has many advantages, such as low cost, high precision, strong tolerance to harsh acquisition environment. However, the field DAS-VSP data are often disturbed by complex background noise and coupling noise with strong energy, affecting the quality of seismic data seriously. Therefore, we develop a time–frequency analysis method based on low-rank and sparse matrix decomposition (LSMD) and data position points distribution maps (DPM) to separate signals from noise. We adopt Multisynchrosqueezing Transform to construct the approximate ideal time–frequency representation of DAS data, which reduces the difficulty of signal to noise separation and avoids the loss of some effective information to a certain extent. The LSMD is performed to separate the signal component and noise component preliminarily. In addition, combined with the separated low-rank matrix and sparse matrix, we propose the DPM to improve the accuracy of signal component extraction and the recovery ability of the method for weak signals through the joint analysis of the maps in time domain and frequency domain. Both synthetic and field experiments show that the proposed method can suppress coupling noise and background noise and recover weak energy signals in DAS VSP data effectively.

Noise reduction for desert seismic data using spectral kurtosis adaptive bandpass filter

75%

Ma H. , Qian Z. , Li Y. , Lin H. , Shao D. , Yang B.

Acta Geophysica

2019

tom Vol. 67, no. 1

123--131

In view of the heterogeneity and week similarity of random noise in the desert seismic exploration, and lots of random noise focused on low frequency, the traditional bandpass filter and wavelet transform are used to separate the signal and noise. Although there are some denoising effects, the noise cannot be suppressed well, and effective signal is damaged to some extent. Because of the above shortcomings, we propose a bandpass filter denoising method based on spectral kurtosis in this paper. This method is based on the signal and the random noise’s energy distribution characteristics in the frequency domain. First, through short-time Fourier transform (STFT), the spectral kurtosis of noisy signals is obtained. Second, we design a new threshold by the obtained spectral kurtosis, the value of spectral kurtosis greater than the threshold is preserved, and the spectral kurtosis less than the threshold is set to 0. So, the method realises the adaptive choice of the filter passband, getting an adaptive bandpass filter. At the same time, the noise can be suppressed to a greater extent while the effective signal is retained very well. The noise removal results of synthetic data and actual data show that the proposed method has very good denoising performance and amplitude preserving capability.