We propose a novel method for seismic noise attenuation by applying nonstationary polynomial fitting （NPF）, which can estimate coherent components with amplitude variation along the event. The NPF with time-varying ...We propose a novel method for seismic noise attenuation by applying nonstationary polynomial fitting （NPF）, which can estimate coherent components with amplitude variation along the event. The NPF with time-varying coefficients can adaptively estimate the coherent components. The smoothness of the polynomial coefficients is controlled by shaping regularization. The signal is coherent along the offset axis in a common midpoint （CMP） gather after normal moveout （NMO）. We use NPF to estimate the effective signal and thereby to attenuate the random noise. For radial events-like noise such as ground roll, we first employ a radial trace （RT） transform to transform the data to the time-velocity domain. Then the NPF is used to estimate coherent noise in the RT domain. Finally, the coherent noise is adaptively subtracted from the noisy dataset. The proposed method can effectively estimate coherent noise with amplitude variations along the event and there is no need to propose that noise amplitude is constant. Results of synthetic and field data examples show that, compared with conventional methods such as stationary polynomial fitting and low cut filters, the proposed method can effectively suppress seismic noise and preserve the signals.展开更多
The ground penetrating radar （GPR） detection data is a wide band signal, always disturbed by some noise, such as ambient random noise and muhiple refleetion waves. The noise affects the target identification of unde...The ground penetrating radar （GPR） detection data is a wide band signal, always disturbed by some noise, such as ambient random noise and muhiple refleetion waves. The noise affects the target identification of underground medium seriously. A method based on principal component analysis （PCA） was proposed to ex- tract the target signal and remove the uncorrelated noise. According to the correlation of signal, the authors get the eigenvalues and corresponding eigenvectors by decomposing the covariance matrix of GPR data and make linear transformation for the GPR data to get the principal components （PCs）. The lower-order PCs stand h^r the strong correlated target signals of the raw data, and the higher-order ones present the uneorrelated noise. Thus the authors can extract the target signal and filter uncorrelated noise effectively by the PCA. This method was demonstrated on real ultra-wideband through-wall radar data and simulated GPR data. Both of the results show that the PCA method can effectively extract the GPR target signal and remove the uncorrelated noise.展开更多
In the acoustic detection process of buried non-metallic pipelines,the echo signal is often interfered by a large amount of noise,which makes it extremely difficult to effectively extract useful signals.An denoising a...In the acoustic detection process of buried non-metallic pipelines,the echo signal is often interfered by a large amount of noise,which makes it extremely difficult to effectively extract useful signals.An denoising algorithm based on empirical mode decomposition(EMD)and wavelet thresholding was proposed.This method fully considered the nonlinear and non-stationary characteristics of the echo signal,making the denoising effect more significant.Its feasibility and effectiveness were verified through numerical simulation.When the input SNR(SNRin)is between-10 dB and 10 dB,the output SNR(SNRout)of the combined denoising algorithm increases by 12.0%-34.1%compared to the wavelet thresholding method and by 19.60%-56.8%compared to the EMD denoising method.Additionally,the RMSE of the combined denoising algorithm decreases by 18.1%-48.0%compared to the wavelet thresholding method and by 22.1%-48.8%compared to the EMD denoising method.These results indicated that this joint denoising algorithm could not only effectively reduce noise interference,but also significantly improve the positioning accuracy of acoustic detection.The research results could provide technical support for denoising the echo signals of buried non-metallic pipelines,which was conducive to improving the acoustic detection and positioning accuracy of underground non-metallic pipelines.展开更多
基金supported by the National Basic Research Program of China (973 program, grant 2007CB209606) the National High Technology Research and Development Program of China (863 program, grant 2006AA09A102-09)
文摘We propose a novel method for seismic noise attenuation by applying nonstationary polynomial fitting （NPF）, which can estimate coherent components with amplitude variation along the event. The NPF with time-varying coefficients can adaptively estimate the coherent components. The smoothness of the polynomial coefficients is controlled by shaping regularization. The signal is coherent along the offset axis in a common midpoint （CMP） gather after normal moveout （NMO）. We use NPF to estimate the effective signal and thereby to attenuate the random noise. For radial events-like noise such as ground roll, we first employ a radial trace （RT） transform to transform the data to the time-velocity domain. Then the NPF is used to estimate coherent noise in the RT domain. Finally, the coherent noise is adaptively subtracted from the noisy dataset. The proposed method can effectively estimate coherent noise with amplitude variations along the event and there is no need to propose that noise amplitude is constant. Results of synthetic and field data examples show that, compared with conventional methods such as stationary polynomial fitting and low cut filters, the proposed method can effectively suppress seismic noise and preserve the signals.
基金Supported by project of Natural Science Foundation of China(No.41174097)
文摘The ground penetrating radar （GPR） detection data is a wide band signal, always disturbed by some noise, such as ambient random noise and muhiple refleetion waves. The noise affects the target identification of underground medium seriously. A method based on principal component analysis （PCA） was proposed to ex- tract the target signal and remove the uncorrelated noise. According to the correlation of signal, the authors get the eigenvalues and corresponding eigenvectors by decomposing the covariance matrix of GPR data and make linear transformation for the GPR data to get the principal components （PCs）. The lower-order PCs stand h^r the strong correlated target signals of the raw data, and the higher-order ones present the uneorrelated noise. Thus the authors can extract the target signal and filter uncorrelated noise effectively by the PCA. This method was demonstrated on real ultra-wideband through-wall radar data and simulated GPR data. Both of the results show that the PCA method can effectively extract the GPR target signal and remove the uncorrelated noise.
基金supported by Nanchong Southwest Petroleum University Science and Technology Strategic Cooperation Project(Nos.23XNSYSX0022,23XNSYSX0026)Provincial Science and Technology Plan Project(No.2023ZHCG0020)Southwest Petroleum University Natural Science“Sailing Plan”Project(No.2023QHZ003)。
文摘In the acoustic detection process of buried non-metallic pipelines,the echo signal is often interfered by a large amount of noise,which makes it extremely difficult to effectively extract useful signals.An denoising algorithm based on empirical mode decomposition(EMD)and wavelet thresholding was proposed.This method fully considered the nonlinear and non-stationary characteristics of the echo signal,making the denoising effect more significant.Its feasibility and effectiveness were verified through numerical simulation.When the input SNR(SNRin)is between-10 dB and 10 dB,the output SNR(SNRout)of the combined denoising algorithm increases by 12.0%-34.1%compared to the wavelet thresholding method and by 19.60%-56.8%compared to the EMD denoising method.Additionally,the RMSE of the combined denoising algorithm decreases by 18.1%-48.0%compared to the wavelet thresholding method and by 22.1%-48.8%compared to the EMD denoising method.These results indicated that this joint denoising algorithm could not only effectively reduce noise interference,but also significantly improve the positioning accuracy of acoustic detection.The research results could provide technical support for denoising the echo signals of buried non-metallic pipelines,which was conducive to improving the acoustic detection and positioning accuracy of underground non-metallic pipelines.
