A method of random noise suppression based on compressed sensing

With the development of high precision seismic exploration technology, the use of high fidelity methods to improve the SNR of seismic data becomes the key to denoising. Curvelet threshold method can effectively suppress random noise, but at the same time the method is easy to produce pseudo Gibbs shock phenomenon, resulting in local distortion of the signal, thus affecting the processing effect. To solve this problem, a method of seismic signal denoising based on compressing sensing(CS) is presented in this paper. The method uses the difference between sparse representation of random noise and effective signal in curvelet sparse domain to suppress the separation of random noise. Seismic data are transformed into curvelet domain; Curvelet coefficients are reset by using the compression perception theory and the total variation regularization algorithm; Reconstructed seismic data after curvelet inversion are used to suppress noise. The theoretical model and practical data show that the proposed method can avoid the signal distortion caused by the pseudo-Gibbs phenomenon and further improve the signal-to-noise ratio of the data.