Seismic noise suppression using weighted nuclear norm minimization method

Li, Juan; Wang, Daixiang; Ji, Shuai; Li, Yue; Qian, Zhihong

doi:10.1016/j.jappgeo.2017.09.013

Cited by 16 publications

(2 citation statements)

References 19 publications

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“…However, seismic data are usually subject to varying degrees of noise in eld acquisition at seismic monitoring stations, resulting in the reduction of data quality. Seismic noise suppression can bring more accuracy of subsequent seismic data analysis, so data denoising is of signi cance in increasing the quality of seismic data [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Seismic Signal Denoising Based on Adaptive Wavelet Modulus Maximum Method

Yao

Shen

et al. 2022

Mathematical Problems in Engineering

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Seismic noise suppression plays an important role in seismic data processing and interpretation. Aiming at remedying the problem of low quality of seismic data acquired by a seismometer, a novel denoising method based on wavelet maximum modulus and an adaptive threshold is designed. This adaptive wavelet maximum modulus (ATWMM) seismic signal noise reduction method is using the opposite polarity of the Lipschitz index with seismic signal and noise to extract the seismic signal of original data. Setting adaptive thresholding function related to wavelet decomposition scale to solve the problem of effective signal losing on a small decomposition scale. The experimental results show that the ATWMM method can extract more seismic signal from the noisy seismic data. Using RMSE and SNR evaluation, the synthetic Ricker seismic dataset experimental results show that the indexes are 0.0502 and 20.1617 dB. Compared with the wavelet modulus maximum (WMM) method, it has a 25.7% reduction and 14.6% increase. The real-field seismic data came from the JZW51 seismic monitoring station in China experimental results indicate that the proposed ATWMM method is effective in seismic signal denoising with SNR above 30.1855 dB of approximately 15.8% enhancement compared with the WMM method, that has improvement for quality of seismic data.

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Section: Introductionmentioning

confidence: 99%

Seismic Signal Denoising Based on Adaptive Wavelet Modulus Maximum Method

Yao

Shen

et al. 2022

Mathematical Problems in Engineering

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“…Weighted Nuclear Norm Minimization (WNNM) is a representative approach for solving low rank matrix approximation (LRMA) problems, which has been widely used in many fields, such as image denoising [21,22], image completion and repainting [23], seismic data denoising [24] and MIMO channel estimation [25]. WNNM outline several commonly used low-rank matrix methods, such as NNM, which can actually be viewed as a special counterpart of WNNM in which the weight vector of WNNM is set the same.…”

Section: Introductionmentioning

confidence: 99%

Extracting Seasonal Signals in GNSS Coordinate Time Series via Weighted Nuclear Norm Minimization

et al. 2020

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Global Navigation Satellite System (GNSS) coordinate time series contains obvious seasonal signals, which mainly manifest as a superposition of annual and semi-annual oscillations. Accurate extraction of seasonal signals is of great importance for understanding various geophysical phenomena. In this paper, a Weighted Nuclear Norm Minimization (WNNM) is proposed to extract the seasonal signals from the GNSS coordinate time series. WNNM assigns different weights to different singular values that enable us to estimate an approximate low rank matrix from its noisy matrix. To address this issue, the low rank characteristics of the Hankel matrix induced by GNSS coordinate time series was investigated first, and then the WNNM is applied to extract the seasonal signals in the GNSS coordinate time series. Meanwhile, the residuals have been analyzed, obtaining the estimation of the uncertainty of velocity. To demonstrate the effectiveness of the proposed algorithm, a number of tests have been carried out on both simulated and real GNSS dataset. Experimental results indicate that the proposed scheme offers preferable performances compared with many state-of-the-art methods.

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