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PS Azimuth Moveout (PS-AMO) transforms the offset and azimuth of multicomponent data. To do this, we transform the data from the CMP domain to the CRP domain, where we compute the new offset and azimuth, and then transform back from the CRP domain to the CMP domain. Theoretically, the cascade operation of any 3-D prestack imaging operator with its inverse produces AMO Rosales and Biondi (2006)

PS-AMO has several potential applications for 3-D multicomponent processing. One is geometry regularization, through which PS-AMO helps to fill in the acquisition gaps using the information of surrounding traces. Another is data-reduction through partial stacking, which combines PS-AMO and partial stacking to reduce the computational cost of 3-D prestack depth imaging. A third application is the interpolation of unevenly sampled traces, which differs from the first application in the sense that PS-AMO is the main interpolation operator. In this paper, we will use PS-AMO to reduce the dimensionality of the 3-D prestack data, that is, to go from a five-dimensional prestack cube ($P(t,{\bf x},{\bf h})$) to a four-dimensional cube ($\widetilde{P}(t,{\bf x},h_x)$).

Multicomponent ocean-bottom seismometer (OBS) technology brings new problems to the table with converted-wave data. One of the main problems with OBS data is the irregularity in the acquisition geometry. Irregular geometries are a serious impediment to accurate subsurface imaging Beasley (1994); Chemingui (1996); Gardner and Canning (1994). Irregularly sampled data affect the image with amplitude artifacts and phase distortions if the missing data are assumed to be zero traces. Irregular geometry problems are more evident in cases in which the amplitude information is one of the main goals of study. Typical OBS seismic-data acquisition presents processing problems similar to those of land data. Gardner and Canning (1994) demonstrate some of the effects of irregular sampling on 3-D prestack migration, through synthetic examples using real 3-D land-acquisition geometry. For converted waves, the problem of irregular sampling is especially crucial, since most of the PS processing focuses on the estimation of rock properties from seismic amplitudes.

To solve the problem of reorganizing irregular geometries, there are two distinct approaches that can be applied: 1) data regularization before migration Duijndam et al. (2000), and 2) irregular-geometry correction during migration Albertin et al. (1999); Audebert (2000); Bloor et al. (1999); Duquet et al. (1998); Nemeth et al. (1999); Prucha and Biondi (2002); Rousseau et al. (2000). Biondi and Vlad 2002 combine the advantages of the previous two approaches. Their methodology regularizes the data geometry before migration, filling in the acquisition gaps with an AMO operator that preserves the amplitudes in the frequency-wavenumber log-stretch domain.

A methodology that involves the PS-AMO operator can be used to solve for geometry irregularities of OBS PS data. Due to the asymmetry of ray trajectories in PS data, there are more elements to consider in PS-data regularization than in PP-data regularization. Our method for PS-data regularization uses the PS-AMO operator to preserve the resolution of dipping events and correct for the lateral shift between the common midpoint and the common reflection point.

The 3-D OBS data set acquired above the Alba reservoir in the North Sea serves as test data for our PS geometry-regularization methodology. We show how our methodology fills the acquisition gaps using information from neighboring traces and the physics of the converted-wave propagation phenomena, as we reduce the dimensionality of our data from five dimensions to four dimensions.

Throughout this paper, we will first describe the PS-AMO operator used for this experiment and its implementation; then we will describe its application to reduce the dimensionality of the data set in preparation for converted-wave common-azimuth migration of the 3-D OBS data set from the Alba oil field.

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Next: f-k log-stretch PS-AMO Up: Rosales and Clapp: PS-AMO Previous: Rosales and Clapp: PS-AMO
Stanford Exploration Project