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Introduction

Seismic velocity-analysis methods can be divided into two major groups. First, there are techniques that aim to minimize the misfit in the data domain, such as full waveform inversion (Luo and Schuster, 1991; Tarantola, 1984). Second, there are other techniques that aim to improve the quality in the image domain such as migration velocity analysis (MVA)(Symes and Carazzone, 1991; Biondi and Sava, 1999; Shen, 2004). These techniques try to measure the quality of the image and then invert the estimated image perturbation using a linearized wave-equation operator.

There are several advantages to minimizing the residual in the image space, such as increasing the signal-to-noise ratio and decreasing the complexity of the data (Tang et al., 2008). However, the biggest challenge in WEMVA techniques is that the true image is unknown. Therefore, each technique uses a certain attribute of the background image and tries to estimate the residual using that attribute. The stack-power-maximization technique maximizes the angle stack, and differential semblance optimization (DSO) minimizes the difference between neighboring traces in angle gathers. However, these assumptions can cause some problems, such as cycle-skipping in stack power maximization and edge effects in DSO.

In this paper, I present a new method of measuring the image residual that is based on the cross-correlation of the observed image with a reference image in reflection angle gathers. The reference image can be any image with the desired kinematics, i.e. flat angle gathers. Therefore, it is possible to choose an angle stack as the reference image. However, angle stacks do not take into account the limited acquisition, which can result in anomalies in the gradient if the angle gathers are not muted properly. In order to take acquision into account, I create my reference image by computing Born-modeling data with the background slowness and a reference reflectivity. This reference reflectivity could come from the angle-stack image or from non-seismic data such as well-logs or geologic models. Therefore, modeling and migrating a dataset gives us more flexibility than just using the angle stack. The derivation of this method is based on traveltime inversion by Luo and Schuster (1991) but in the image domain instead of the data domain. After deriving the objective function and the gradient of this method, I provide some synthetic examples and compare the gradient to the optimum WEMVA gradient.

This technique is similar to differential residual migration (DRM) (Sava, 2004) in the sense that it uses the kinematics of a reference image. However, there are a few advantages in using correlation over DRM. First, the correlation method gives us more flexibility in choosing the reference image. Second, picking correlation lags could be automated more easily than picking DRM panels. Finally, the objective function of the correlation method could include the full correlation function as opposed to just maximum lags, which will eliminate picking and fully automate the inversion.


next up previous [pdf]

Next: Method Up: Almomin: WEMVA Previous: Almomin: WEMVA

2011-05-24