Separation of seismic blended data by sparse inversion over dictionary learning

Recent development of blended acquisition calls for the new procedure to process blended seismic measurements. Presently, deblending and reconstructing unblended data followed by conventional processing is the most practical processing workflow. We study seismic deblending by advanced sparse inversion with a learned dictionary in this paper. To make our method more effective, hybrid acquisition and time-dithering sequential shooting are introduced so that clean single-shot records can be used to train the dictionary to favor the sparser representation of data to be recovered. Deblending and dictionary learning with l₁-norm based sparsity are combined to construct the corresponding problem with respect to unknown recovery, dictionary, and coefficient sets. A two-step optimization approach is introduced. In the step of dictionary learning, the clean single-shot data are selected as trained data to learn the dictionary. For deblending, we fix the dictionary and employ an alternating scheme to update the recovery and coefficients separately. Synthetic and real field data were used to verify the performance of our method. The outcome can be a significant reference in designing high-efficient and low-cost blended acquisition.