


 Velocity analysis using similarityweighted semblance  

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Building a subsurface velocity model is one of the most important issues in exploration geophysics. There are generally four ways for building the velocity model. The first one is normalmoveout (NMO) based velocity analysis, which requires picking peaks in the velocity spectra (Luo and Hale, 2012; Taner and Koehler, 1969; Fomel, 2009). The velocity spectra is obtained by applying a number of NMO corrections with different velocities and then calculating their corresponding semblances. The second type is Bornapproximationbased wave equation migration velocity analysis (WEMVA), which is a nonlinear optimization process that aims at estimating the migration velocity using the Bornapproximated wave equation in the image domain and is less vulnerable to the local minimum and more effective for reflection signals (Sava and Biondi, 2004a; Li, 2013; Sava and Biondi, 2004b). The third type is raybased migration velocity analysis, also known as traveltime tomography. The velocity model is built by updating the velocity model so that the misfit between predicted and observed firstbreak traveltimes is minimized (Chen et al., 2013; Noble et al., 2010; Zhu et al., 1992; Osypov, 2000; Li et al., 2013). The fourth type is the recently popular full waveform inversion (FWI), which minimizes the leastsquares misfit between the measured data and the synthesized data predicted from the current velocity model in the data domain (Zhou et al., 2012; Virieux and Operto, 2009; Guitton et al., 2012) and can improve the resolution of velocity structures. In this paper, we focus on the NMO based velocity analysis , which provides the important initial velocity model for the following more complicated velocity analysis procedures.
NMO based velocity analysis using semblance has been an indispensable step for building the initial subsurface velocity model since its introduction by Taner and Koehler (1969). There exist several modifications of traditional semblance that better meet the requirements of specific datasets (Luo and Hale, 2012; Fomel, 2009; Sarkar et al., 2001). Sarkar et al. (2001) and Fomel (2009) modified the traditional semblance formulation and proposed amplitudeversusoffset (AVO) adaptive semblances in different ways. Luo and Hale (2012) increase the resolution of the semblance map in order to distinguish the peaks between primary and multiple reflections.
Increasing the resolution of the semblance spectra is beneficial for picking the true NMO velocity, especially important when multiples are existing in the CMP gathers (Luo and Hale, 2012). We can increase the resolution by weighting terms in the traditional semblance calculation. Hale (2009) defined a weighted semblance that conformed to structural features. Luo and Hale (2012) chose an offsetdependent weighting function that effectively increased the semblance spectra resolution. In this paper, we use a different weighting scheme, by utilizing the local similarity between each trace and a reference trace. The reference trace can be a zerooffset trace, or the stacked trace using a traditional stacking technique. Both synthetic and field data examples demonstrate a successful performance of the proposed approach.



 Velocity analysis using similarityweighted semblance  

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