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
It＇ s a problem to be solved how to de-noise the signal of blast shock wave overpressure. In the conventional methods, the high frequency of the signal is cut directly by some mathematics algorithms, such as Fourier ...It＇ s a problem to be solved how to de-noise the signal of blast shock wave overpressure. In the conventional methods, the high frequency of the signal is cut directly by some mathematics algorithms, such as Fourier Transform, but some of the useful signal will be cut together. We adopt a new method for the signal de-noising of shock wave overpressure by wavelet analysis, There are four steps in this method. Firstly, the original signal is de-compoed. Then the time-frequency features of the signal and noise are analyzed. Thirdly, the noise is separated from the signal by only cutting its frequency while the useful signal frequency is reserved as much as possible. Lastly, the useful signal with least loss of information is recovered by reconstruction process. To verify this method, a blast shock wave signal is de-noised with FFF to make a comparison. The results show that the signal de-noised by wavelet analysis approximates the ideal signal well.展开更多
Attenuation of noise is a persistent problem in seismic exploration. The authors use conventional denoising method to remove noise which may cause vibration near the discontinuity called pseudo-Gibbs artifact.In order...Attenuation of noise is a persistent problem in seismic exploration. The authors use conventional denoising method to remove noise which may cause vibration near the discontinuity called pseudo-Gibbs artifact.In order to remove the artifact,the study proposed a method combining the seislet transform and total variation minimization. Firstly,the data are converted into the seislet transform domain. Secondly,the hard threshold was used for eliminating the noise and keep useful signal,which is the initial input for the next step. Finally,total variation minimization dealed with denoised data to recover boundary information and further eliminated the noise. Synthetic data examples show that the method has feasibility in eliminating random noise and protecting detailed signal,and also shows better results than the classic f-x deconvolution. The field data example also shows effective in practice. It can remove the noise and preserve the discontinuity signal at the same time.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.
文摘It＇ s a problem to be solved how to de-noise the signal of blast shock wave overpressure. In the conventional methods, the high frequency of the signal is cut directly by some mathematics algorithms, such as Fourier Transform, but some of the useful signal will be cut together. We adopt a new method for the signal de-noising of shock wave overpressure by wavelet analysis, There are four steps in this method. Firstly, the original signal is de-compoed. Then the time-frequency features of the signal and noise are analyzed. Thirdly, the noise is separated from the signal by only cutting its frequency while the useful signal frequency is reserved as much as possible. Lastly, the useful signal with least loss of information is recovered by reconstruction process. To verify this method, a blast shock wave signal is de-noised with FFF to make a comparison. The results show that the signal de-noised by wavelet analysis approximates the ideal signal well.
文摘Attenuation of noise is a persistent problem in seismic exploration. The authors use conventional denoising method to remove noise which may cause vibration near the discontinuity called pseudo-Gibbs artifact.In order to remove the artifact,the study proposed a method combining the seislet transform and total variation minimization. Firstly,the data are converted into the seislet transform domain. Secondly,the hard threshold was used for eliminating the noise and keep useful signal,which is the initial input for the next step. Finally,total variation minimization dealed with denoised data to recover boundary information and further eliminated the noise. Synthetic data examples show that the method has feasibility in eliminating random noise and protecting detailed signal,and also shows better results than the classic f-x deconvolution. The field data example also shows effective in practice. It can remove the noise and preserve the discontinuity signal at the same time.
