Application Of Curvelet Transform Method In Improving The Signal To Noise Ratio Of Seismic Signals

Noise suppression is a very important step in seismic data processing.Limited by the complex working conditions and acquisition technology of the exploration block,the quality of the seismic data acquired in the field is low,mixed with a variety of noise signals,which seriously affects the signal-to-noise ratio and fidelity of the seismic profile,making it difficult to come to the next seismic interpretation.The current denoising methods have their own limitations.In order to maximize the signal-to-noise ratio of seismic signals while protecting effective signals,the mathematical tool of curvelet transform is applied,and other algorithms are combined to suppress seismic noise.This article is based on the theory of Curvelet transform and mainly studies the suppression of seismic random noise by Curvelet threshold algorithms.At the same time,it compares and analyzes the denoising effects of wavelet soft hard compromise threshold algorithms and empirical mode decomposition algorithms.In response to the shortcomings of several algorithms(incomplete noise suppression and excessive suppression of effective signals),a Curvelet adaptive threshold denoising algorithm is proposed,which adds a threshold factor that varies with the scale of curvelet,Satisfied the requirements of different decomposition scales for curvelet transform and improved the problem of signal coefficient aliasing;In order to improve computational efficiency and data redundancy,a joint denoising algorithm based on Curvelet adaptive threshold and adaptive complete empirical mode decomposition(CEEMDAN)is proposed.This algorithm performs Curvelet adaptive threshold processing on some IMF components obtained from CEEMDAN decomposition,and then reconstructs the processed IMF components and remaining components to obtain the final processed signal.By conducting random noise suppression on seismic forward modeling and actual seismic data,and comparing and analyzing the four denoising results,it can be seen that the proposed joint denoising algorithm has obvious advantages,improving computational efficiency and accuracy.The denoised seismic records have clear and visible events,good edge smoothness,and protect weak effective signals while minimizing random noise.The overall signal-to-noise ratio is high.Finally,the joint denoising algorithm was applied to suppress linear interference,and the denoising results were analyzed to verify the effectiveness and universality of the proposed joint method.