Simultaneous-Source Data Separation Based On The Sparsity-Promoting Constraints

Simultaneous-source seismic data acquisition technology can significantly improve the efficiency of the acquisition by "simultaneously" stimulating multiple sources,thereby reducing the acquisition time and cost,while improving the quality of the underground structure of the lighting to enhance the quality of seismic data.However,although the amount of information acquired is doubled,data interference between seismic records also occurs.For the interference of seismic records,it is necessary to carry out detailed analysis and to formulate the appropriate strategy to deal with it,among which the more intuitionistic method is by separating the simultaneous-source seismic data to obtain the single shot seismic records without interference,which is also known as deblending.Based on the summary of the status and development trend of simultaneous-source data separation domestically and abroad,this paper analyzes the application of sparsity-promoting constraints in simultaneous-source data separation and processing.And studies the separation of simultaneous-source seismic data in two cases.The first case is the separation of the seismic data acquired based on the random time delay in the common detective gathers.The application of the iterative shrinkage thresholding algorithm based on the sparsity-promoting seislet transform is discussed in the simultaneous-source data separation.It is pointed out that the sparsity-promoting seislet transform is better than other transformations The second case is to separate the simultaneous-source seismic data in the common shot gathers,and a new iterative separation algorithm different from the dip filter is proposed,which will express the separation problem of the multi-source data as a class of linear inversion problems,and using the seislet framework to construct the sparsity-promoting transform of the conflicting dip components,and then use the iterative shrinkage thresholding algorithm to achieve the separation,the synthetic data and field data examples verify the feasibility of the method.