Passive seismic has recently attracted a great deal of attention because non-artificial source is used in subsurface imaging. The utilization of passive source is low cost compared with artificial-source exploration. In general, constructing virtual shot gathers by using cross-correlation is a preliminary step in passive seismic data processing, which provides the basis for applying conventional seismic processing methods. However, the subsurface structure is not uniformly illuminated by passive sources, which leads to that the ray path of passive seismic does not fit the hyperbolic hypothesis. Thereby, travel time is incorrect in the virtual shot gathers. Besides, the cross-correlation results are contaminated by incoherent noise since the passive sources are always natural. Such noise is kinematically similar to seismic events and challenging to be attenuated, which will inevitably reduce the accuracy in the subsequent process. Although primary estimation for transient-source seismic data has already been proposed, it is not feasible to noise-source seismic data due to the incoherent noise. To overcome the above problems, we proposed to combine focal transform and local similarity into a highly integrated operator and then added it into the closed-loop surface-related multiple elimination based on the 3D L1-norm sparse inversion framework. Results proved that the method was capable of reliably estimating noise-free primaries and correcting travel time at far offsets for a foresaid virtual shot gathers in a simultaneous closed-loop inversion manner.

Blended acquisition has become popular due to its high efficiency and low cost. In conventional processing procedure, overlapping seismic data need to be separated as if they were acquired through the unblended way before primary estimation which is time-consuming. Given this situation, we utilize the recently introduced algorithm closed-loop SRME based on 3D L1-norm sparse inversion methodology, in which primaries of the deblended shot records can be directly estimated from the blended data. With the introduction of the blended operator to this technique, a link is made between the blended and conventional acquisition. A one-step inversion is proposed, achieving simultaneous seismic deblending and primary estimation. Preliminary results of synthetic data are promising.

Ghost is unavoidable in marine seismic data acquisition, limiting the bandwidth of useful information and reducing the resolution of imaging results. In the deghosting method, the 3D algorithms can better eliminate the 3D effect compared with the conventional 2D algorithm. However, the 3D algorithm requires dense sampling in the crossline direction, which means greater storage space and computational cost. In this paper, we propose a pseudo-3D deghosting method to get rid of the limitation of dense spatial sampling. We arrange the conventional 2D multi-shot gathers in the form of 3D data cube by time, offset and shot number to achieve simultaneous deghosting for multi-shot gathers; We also introduce a sparse transform based L1 constraint to avoid local minimum. The basic idea of our method is including the information of the common offset gathers (COGs) into the deghosting to improve the inversion accuracy. The proposed method is easy to implemented without any pre-processing, and field example demonstrates the effectiveness of the proposed method.