Seismic Signal Denoising Based on the Improved MP
Xi Li · Computing Technology and Automation · 2013
The noise of the seismic data is divided into coherent noise and random noise.Aiming at the random noise,using the matching pursuit(MP)algorithm to denoise can obtain certain denoising effect,but the computational burden of the MP algorithm is too large,which seriously affects the efficiency of denoising.In order to solve this problem,this paper uses the genetic algorithm(GA)to search for the optimal atom,greatly reducing the computational complexity of the algorithm and improving the operation speed of the algorithm.In this paper,the Ricker wavelet was improved,through joining the scaling parameters,displacement and phase parameters.it can obtain better effect that using the improved Ricker wavelet to construct a over-completed dictionary to denoise.This article use the adjacent residual ratio threshold as the terminating condition,compared with using hard threshold as the terminating conditions which enhances the robustness of the algorithm.Firstly,Compared with the MP algorithm,the improved method in this paper is used to denoise synthetic seismic signal added random noise in the experimental simulation.Simulation results proved that the improved denoising method has obvious superiority in noise ratio,the mean square error and calculation speed,then the improved method can be applied to practical seismic signal denoising,and obtain good denoising effect.