Seismic Data Denoising Based On Non-Local Means Algorithm

Seismic exploration is an important means of petroleum exploration.Seismic data generated by surface excitation can be used to invert the underground geological structure and oil reserves.However,the collected seismic data contain a large amount of noise,which is not conducive to subsequent data analysis.Non-local means(NLM)algorithm has been widely used in color image denoising and has achieved satisfactory results since it was proposed.However,its application in seismic data denoising still needs further study.In this paper,the characteristics of noisy seismic data are fully analyzed,and the research on noise reduction of seismic data is carried out based on NLM framework.The main research contents are as follows:(1)Non-Local Means Seismic Data Denoising by Combination of Edge DetectionAiming at the problem that the events are smoothed when the non-local means algorithm is used for seismic data denoising,a non-local mean seismic data denoising algorithm(Sobel8-NLM)based on edge detection is proposed.Firstly,the eight-directions Sobel operator is used to detect the events of noisy seismic data,and then the detected events information is used to improve the weight of NLM,so that the neighborhood weights with large difference are reduced,and the events of seismic data is well recovered while denoising is realized.The experiment takes synthetic,marine and land seismic data as samples,and compares them with NLM,non-local means denoising algorithm(Sobel2-NLM)combined with classical Sobel operator and non-local means noise reduction algorithm combined with Canny operator to verify the advantages of the algorithm in seismic data denoising.(2)Seismic Data Denoising Based on Adaptive Fast Improvement of Non-local MeansAiming at the two problems of Sobel8-NLM,that the filtering parameter cannot change adaptively with the noise level and the calculation amount is large,an adaptive fast improved non-local means seismic data denoising algorithm is proposed.Sobel8-NLM noise reduction is applied to noisy seismic data twice,filtering parameters are improved,and cross-correlation function is used instead of convolution operation to improve the noise reduction effect of seismic data and reduce operation time.The experiment takes synthetic,marine and land seismic data as samples,compares the noise reduction effect and time of NLM,Sobel8-NLM and non-local means algorithm based on nearest neighbor selection strategy,and verifies the denoising performance of the algorithm on seismic data.Finally,through the noise reduction experiment of the seismic data collected in the field,the robustness of the algorithm is verified.(3)Total Variational Regularization Based on Non-local Means Seismic Data Denoising Total variation regularization constraint can effectively maintain the events in seismic data.Therefore,a total variation regularization based on non-local means seismic data denoising algorithm is proposed.Using the NLM denoising results of seismic data,the weights are updated to remove the jitter effect,and the NLM noise reduction results after updating the weights are subjected to total variational regularization constraints.The experiment takes synthetic,marine and land seismic data as samples,and compares the noise reduction effect of NLM,the non-local means algorithm based on the nearest neighbor selection strategy,and the efficiency of the algorithm is compared at the end,which verifies the advantages of the algorithm in seismic denoising.