Seismic data regularization is an important preprocessing step in seismic signal processing. Traditional seismic acquisition methods follow the Shannon–Nyquist sampling theorem, whereas compressive sensing（CS） prov...Seismic data regularization is an important preprocessing step in seismic signal processing. Traditional seismic acquisition methods follow the Shannon–Nyquist sampling theorem, whereas compressive sensing（CS） provides a fundamentally new paradigm to overcome limitations in data acquisition. Besides the sparse representation of seismic signal in some transform domain and the 1-norm reconstruction algorithm, the seismic data regularization quality of CS-based techniques strongly depends on random undersampling schemes. For 2D seismic data, discrete uniform-based methods have been investigated, where some seismic traces are randomly sampled with an equal probability. However, in theory and practice, some seismic traces with different probability are required to be sampled for satisfying the assumptions in CS. Therefore, designing new undersampling schemes is imperative. We propose a Bernoulli-based random undersampling scheme and its jittered version to determine the regular traces that are randomly sampled with different probability, while both schemes comply with the Bernoulli process distribution. We performed experiments using the Fourier and curvelet transforms and the spectral projected gradient reconstruction algorithm for 1-norm（SPGL1）, and ten different random seeds. According to the signal-to-noise ratio（SNR） between the original and reconstructed seismic data, the detailed experimental results from 2D numerical and physical simulation data show that the proposed novel schemes perform overall better than the discrete uniform schemes.展开更多
Aim To find an effective and fast algorithm to analyze undersampled signals. Methods\ The advantage of high order ambiguity function(HAF) algorithm is that it can analyze polynomial phase signals by phase rank reduct...Aim To find an effective and fast algorithm to analyze undersampled signals. Methods\ The advantage of high order ambiguity function(HAF) algorithm is that it can analyze polynomial phase signals by phase rank reduction. In this paper, it was first used to analyze the parameters of undersampled signals. When some conditions are satisfied, the problem of frequency confusion can be solved. Results and Conclusion\ As an example, we analyze undersampled linear frequency modulated signal. The simulation results verify the effectiveness of HAF algorithm. Compared with time frequency distribution, HAF algorithm reduces computation burden to a great extent, needs weak boundary conditions and doesn't have boundary effect.展开更多
To achieve restoration of high frequency information for an undersampled and degraded low-resolution image, a nonlinear and real-time processing method-the radial basis function (RBF) neural network based super-resolu...To achieve restoration of high frequency information for an undersampled and degraded low-resolution image, a nonlinear and real-time processing method-the radial basis function (RBF) neural network based super-resolution method of restoration is proposed. The RBF network configuration and processing method is suitable for a high resolution restoration from an undersampled low-resolution image. The soft-competition learning scheme based on the k-means algorithm is used, and can achieve higher mapping approximation accuracy without increase in the network size. Experiments showed that the proposed algorithm can achieve a super-resolution restored image from an undersampled and degraded low-resolution image, and requires a shorter training time when compared with the multiplayer perception (MLP) network.展开更多
The phenomenon of frequency ambiguity may appear in radar or communication systems. S. Barbarossa(1991) had unwrapped the frequency ambiguity of single component undersampled signals by Wigner-Ville distribution(WVD)....The phenomenon of frequency ambiguity may appear in radar or communication systems. S. Barbarossa(1991) had unwrapped the frequency ambiguity of single component undersampled signals by Wigner-Ville distribution(WVD). But there has no any effective algorithm to analyze multicomponent undersampled signals by now. A new algorithm to analyze multicomponent undersampled signals by high-order ambiguity function (HAF) is proposed hera HAF analyzes polynomial phase signals by the method of phase rank reduction, its advantage is that it does not have boundary effect and is not sensitive to the cross-items of multicomponent signals.The simulation results prove the effectiveness of HAF algorithm.展开更多
基金financially supported by The 2011 Prospective Research Project of SINOPEC(P11096)
文摘Seismic data regularization is an important preprocessing step in seismic signal processing. Traditional seismic acquisition methods follow the Shannon–Nyquist sampling theorem, whereas compressive sensing（CS） provides a fundamentally new paradigm to overcome limitations in data acquisition. Besides the sparse representation of seismic signal in some transform domain and the 1-norm reconstruction algorithm, the seismic data regularization quality of CS-based techniques strongly depends on random undersampling schemes. For 2D seismic data, discrete uniform-based methods have been investigated, where some seismic traces are randomly sampled with an equal probability. However, in theory and practice, some seismic traces with different probability are required to be sampled for satisfying the assumptions in CS. Therefore, designing new undersampling schemes is imperative. We propose a Bernoulli-based random undersampling scheme and its jittered version to determine the regular traces that are randomly sampled with different probability, while both schemes comply with the Bernoulli process distribution. We performed experiments using the Fourier and curvelet transforms and the spectral projected gradient reconstruction algorithm for 1-norm（SPGL1）, and ten different random seeds. According to the signal-to-noise ratio（SNR） between the original and reconstructed seismic data, the detailed experimental results from 2D numerical and physical simulation data show that the proposed novel schemes perform overall better than the discrete uniform schemes.
文摘Aim To find an effective and fast algorithm to analyze undersampled signals. Methods\ The advantage of high order ambiguity function(HAF) algorithm is that it can analyze polynomial phase signals by phase rank reduction. In this paper, it was first used to analyze the parameters of undersampled signals. When some conditions are satisfied, the problem of frequency confusion can be solved. Results and Conclusion\ As an example, we analyze undersampled linear frequency modulated signal. The simulation results verify the effectiveness of HAF algorithm. Compared with time frequency distribution, HAF algorithm reduces computation burden to a great extent, needs weak boundary conditions and doesn't have boundary effect.
文摘To achieve restoration of high frequency information for an undersampled and degraded low-resolution image, a nonlinear and real-time processing method-the radial basis function (RBF) neural network based super-resolution method of restoration is proposed. The RBF network configuration and processing method is suitable for a high resolution restoration from an undersampled low-resolution image. The soft-competition learning scheme based on the k-means algorithm is used, and can achieve higher mapping approximation accuracy without increase in the network size. Experiments showed that the proposed algorithm can achieve a super-resolution restored image from an undersampled and degraded low-resolution image, and requires a shorter training time when compared with the multiplayer perception (MLP) network.
文摘The phenomenon of frequency ambiguity may appear in radar or communication systems. S. Barbarossa(1991) had unwrapped the frequency ambiguity of single component undersampled signals by Wigner-Ville distribution(WVD). But there has no any effective algorithm to analyze multicomponent undersampled signals by now. A new algorithm to analyze multicomponent undersampled signals by high-order ambiguity function (HAF) is proposed hera HAF analyzes polynomial phase signals by the method of phase rank reduction, its advantage is that it does not have boundary effect and is not sensitive to the cross-items of multicomponent signals.The simulation results prove the effectiveness of HAF algorithm.
