Simultaneous-source shooting is a breakthrough in modern seismic acquisition, which can tremendously increase the acquisition efficiency and improve the data quality (Beasley et al., 1998; Abma and Yan, 2009; Berkhout, 2008). In blended acquisition, more than one source is shot simultaneously, regardless of the interference. When more than one source is involved in acquisition, either a denser or a wider shot coverage can be obtained for a given constant acquisition period. The wider coverage (Figure 1b) here refers to a higher acquisition efficiency while the denser coverage (Figure 1a) refers to a better-sampled seismic dataset. The attractive benefits are compromised by the challenges in dealing with strong interference from simultaneous sources in the acquired seismic data. We can either separate the blended sources into individual ones as if they were acquired independently, which is also called deblending (Gan et al., 2016; Chen, 2014), or directly migrate the blended data using newly-developed imaging schemes (Tang and Biondi, 2009; Verschuur and Berkhout, 2011). Deblending can provide similar data as the conventional acquisition and thus not require a change in post-processing and imaging algorithms, but need specific computationally expensive technique for the pre-processing (Abma and Yan, 2009; Abma, 2014). Direct imaging does not require any pre-processing steps for observed data and thus enjoys the benefit of high efficiency, but calls for a tremendously different processing workflow (Chen et al., 2015c; Xue et al., 2014).

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Figure 1.
Demonstration of the simultaneous-source geometry. (a) Two-source shooting for denser coverage. (b) Two-source shooting for wider coverage. Red points denote shot positions for source 1. Green points denote shot positions for source 2. Blue points denote receiver positions. Red and green strings denote the shooting rays. Arrows denote the shooting directions. Borrowed from Chen et al. (2014b).
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Because of many reported success of deblending, more and more focus is now moved towards the direct imaging of blended data. However, one of the most important components in the direct imaging of simultaneous-source data is the macro subsurface velocity model of the targeted area. In this paper, we focus on the velocity analysis of the simultaneous-source data. We demonstrate that it is possible to directly apply the common velocity scanning procedures to the blended data in the common-midpoint (CMP) domain. We also propose to use the newly developed similarity-weighted semblance (Gan et al., 2015a; Chen et al., 2015b) to perform the velocity analysis. Both synthetic and field data examples show that the similarity-weighted semblance can help obtain higher-resolution and more reliable velocity spectrum than the conventional semblance, especially in the case of simultaneous-source data. The direct imaging of simultaneous-source data based on the directly picked velocity is also carried out via the prestack kirchhoff time migration (PSKTM) approach. The performance shows that the migrated image from blended data based on the picked velocity from similarity-weighted semblance is very close to the migrated image from unblended data.