Deblending technology aims at separating simultaneous source seismic data between adjacent shots by allowing multiple sources to be shot simultaneously. Conventional deblending methods based on sparse inversion assume that the primary source is coherent, and the secondary source is randomized. The L2-norm minimization constraint can effectively minimize the Gaussian random noise while deblending in the transform domain. Nonetheless, the L2-norm misfit function is highly sensitive to outliers, negatively influencing the deblending performance. An effective optimization strategy is developed with deblending in pre-stack seismic data to eliminate outliers and enhance deblending accuracy. For this reason, we introduce the deblending algorithm in the morphological component analysis framework, modify the L2-norm misfit function to outlier-robust L1-norm and provide the corresponding derivation in detail via the alternating direction method of multipliers. Applications to synthetic and field data sets prove the improved robustness and efficiency of our deblending method.

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We propose a resolution-enhancement method based on the non-local similarity of the seismic profile. Because of the similarity in underground structures, the seismic data have similar event structures in different regions. We introduce the similarity as prior information to enhance the resolution of seismic data. The similarity, as a regularization term for enhancing the resolution, can improve the stability of the solution. The non-local similarity constraint is usually implemented based on nuclear norm minimization. We use the L1-L2 norm instead of the L1 norm in the nuclear norm, which will obtain a more accurate approximation of low rank. Compared with the deconvolution algorithm based on the L2 norm, we use non-local similarity as a constraint term. The proposed method can enhance the resolution with a low signalto-noise ratio, suppress random noise and improve lateral continuity. The synthetic and field data prove the superiority of the proposed method for low signal-to-noise ratio data.