Simultaneous sources acquisition is a new acquisition to reduce acquisition cost. With more than one sources or air guns fired simultaneously, acquisition time and cost can be reduced significantly. As the recorded dataset are blended together in simultaneous acquisition, a following deblending process is usually needed before any further denoise and analysis.
Deblending methods are different due to the differences of sources in land and marine acquisition system.
Land simultaneous acquisition
Land simultaneous acquisition are usually configured by several vibroseises, which are fired with signatures coded differently (orthogonally). The recorded blended dataset are correlated with each signature and yield the related clean deblending result.
Marine Simultaneous acquisition
For marine acquisition, it is not easy to "code" the signature for airguns. Thus, airguns are usually shooted at a random firing time, which makes the interfering shots becoming random spikes in common receiver, offset or CMP domain (as shown in Figure 1). Despiking methods such as median filter (Wang et al., 2012 ), space-varying median filter (SVMF) (Chen, 2015 ), structure-oriented median filter (Gan et al., 2016 ), and singular spectral analysis (Cheng and Sacchi, 2015 ) can be used for the deblending marine simultaneous data.
It has been shown by a lot of researchers that inversion based deblending methods are more powerful than the filtering based methods as mentioned above. Iterative inversion methods include shaping regularization based iterative seislet thresholding method (Chen et al., 2014 ), iterative curvelet thresholding method (Zu et al., 2016 ; Qu et al., 2016 ), iterative orthogonalization and seislet thresholding (Chen, 2015 ), iterative seislet frame thresholding method (Gan et al., 2016 ), iterative amplitude-preserving Radon thresholding (Xue et al., 2017 ), iterative rank-increasing method (Xue et al., 2017 ), etc.
Direct imaging and inversion
The simultaneous source data can be either first-separated and second-processed, or directly used for imaging and inversion.
Least-squares reverse time migration (LSRTM) is currently the dominant way to directly migrate simultaneous-source data. Due to the strong migration artifacts, effective anti-noise regularization operator needs to applied, such as the smoothing filter constrained LSRTM (Chen et al., 2015 ; Xue et al, 2016 ), lowrank low-rank constrained edge-preserving LSRTM (Chen et al., 2017 ). However, one of the biggest problems in direct imaging is a fairly acceptable macro velocity model of subsurface structure, which requires sophisticated velocity analysis techniques (Gan et al., 2016 ).
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