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Unlike the conventional seismic acquisition, simultaneous-source acquisition allows the overlap in the record, which can enhance acquisition efficiency and improve image quality. To further explore the advantage of simultaneous-source technology, a double blending survey is proposed, which contains the self-interference and cross-interference. In the designed survey, the self- and cross-interference are controlled by two factors (inline shot interval and source number). When the inline shot interval is larger than the efficient record length (ERL), the blending scheme is degraded to the cross simultaneous-source survey. When the source number is equal to one, the blending scheme is degraded to the self simultaneous-source survey. To suppress intense hybrid interference, the mixed regularization term integrating sparse constraint and rank reduction constraint is applied to provide stronger regularization ability. Compared with the individual penalty term, the mixed constraint can further suppress hybrid interference and obtain better deblending performance. Deblended results on synthetic and field data examples confirm the performance of mixed constraint and demonstrate that the designed hybrid simultaneous-source survey can further enhance the efficiency of acquisition. [ABSTRACT FROM AUTHOR]

Sparse representation (SR)-based denoising method attenuates random seismic noise trace-by-trace, which may not be able to employ the coherency between neighboring traces. To address this, we propose a multiple measurement vector (MMV)-based algorithm for robust denoising of multichannel seismic gathers. Seismic reflectors in common offset gather (COG) is characterized by horizontal events, which satisfy the common sparsity assumption, making it ideal for the MMV approach. ℓ2,1-norm regularization, which enforces two constraints on source matrix, i.e., temporal sparsity and the horizontal continuity, is then adopted in the MMV model to stabilize the lateral variation between channels. Besides, ℓ2,1-norm regularization provides a reasonable intrinsic structure to reduce the multisolution of the algorithm. Celebrating the strengths of iteratively reweighted (IR), we present a novel MMV algorithm, IR ℓ2,1 norm minimization (IR-ℓ2,1), that further improves the performance. The formulated IR-ℓ2,1 can be minimized by the alternating direction method of multipliers. Both synthetic and field data applications confirm the effectiveness of the proposed denoising method. [ABSTRACT FROM AUTHOR]