ABSTRACT Dip moveout is presented as a data conditioning process in which a diffraction surface in midpoint-offset-time space is transformed into a surface of revolution. Pre-stack imaging operates on constant-time slices of the data, after dip moveout has been applied, in which diffractions appear as circles, adds data along these circles, and plots the sum at the largest offset point of each circle. After both operations, a standard velocity analysis and stacking algorithm produces a migrated section. Both operations are independent of velocity. INTRODUCTION Two problems commonly associated with CDP stacking are (1) a loss of resolution because the reflection point moves updip as the offset increases and (2) the stacking velocity depends on the dip angle of the reflector. The first problem is particularly severe if the reflection point moves across a fault as it moves updip, as indicated in Figure 1. The second problem means that stacking velocities are not appropriate for migration after stack. Dip moveout (DMO) helps with both problems, as first explained by Judson et al. (1976). There is less loss of resolution because the gathers, after DMO, have events with common reflection points. Also, the stacking velocity is not so dependent on the angle of dip and is more useful for selecting velocities to migrate the data after stack. However, the stacking velocity is estimated at the slant travel time T2 and not at the vertical travel time To, as one would wish. DMO can be followed by another processing step called pre-stack imaging (PSI). After PSI, the data are migrated but not yet stacked. In other words, gathers formed after DMO and PSI have been applied can be stacked using a standard velocity analysis and stack program. In this case, there is no loss of resolution, the stacking velocity gives the root-mean-square velocity at time To, and the stacked traces are fully migrated. Both DMO and PSI are independent of velocity. The processes do not have to be repeated if the velocity is changed. The selection of velocity is made at the last step when the migrated traces are being stacked. Thus the velocity analysis may be more robust because the traces used in the analysis have a better signal to noise ratio. This paper gives an overview of both processes and illustrates the algorithms by showing how a diffraction in multi-fold data is summed and plotted at a point. THREE-STEP PRE-STACK MIGRATION The first step is DMO. Viewing a 2D multi-fold line as a three-dimensional space with axes m (the midpoint coordinate), h (half the offset), and t (time), as shown in Figure 2, we can see that DMO operates on vertical sections with constant h. The original data cube is transformed into a new cube by processing the constant-offset sections one by one.

Summary Converted wave (C-wave) dip moveout (DMO) is one of the main procedures for multi-component seismic data processing. In this work, we derive a C-wave DMO formula based upon a second-order approximate trajectory equation of conversion-points, and further propose a fast algorithm in frequency-wavenumber ( f-k ) domain. With Cwave DMO process, the common-conversion-point (CCP) migrates to the common-middle-point (CMP). Impuse responses and a real OBC data example are presented. Introduction Compared with traditional single-component seismic data, multi-component data, in which both P- and S-waves may exist, can provide much more valuable information of subsurface structure and lithology. Processing of multicomponent data is substantially more complex than P-wave processing (Tatham,1993).

ABSTRACT Seismic reflection data exhibiting the effects of azimuthal velocity variation can be generated by waves propagating through geologic layers containing intrinsic horizontal transverse isotropic (HTI) velocity anisotropy, or through isotropic layers deformed into complex 3D velocity structures, or through both. Most current approaches to processing such data attempt to compensate separately for one physical mechanism or the other, but not both simultaneously. Many azimuthal anisotropy (AA) processing flows initially assume zero structural dip to calculate an azimuthal residual moveout (RMO) correction that flattens CMP gathers after standard isotropic normal moveout. Other approaches initially assume no intrinsic HTI velocity anisotropy in order to generate isotropic migration image gathers, which subsequently are flattened through an azimuthal RMO operator. Both approaches address intrinsic anisotropy and complex 3D structure separately, where in fact both effects may be coupled in the data. This leads to velocity estimation errors and degraded image quality, especially for large offsets, strong velocity anisotropy, and steep geologic dips. The most accurate solution to this problem is to perform iterative full anisotropic prestack depth migration velocity analysis, but this is impractical with current computational resources. For elliptical HTI media and complex 3D structure, we develop a computationally efficient anisotropic elliptical moveout (EMO) operator to precondition and regularize seismic wavefields by incorporating azimuthal velocity profile ellipticity. Forward and adjoint elliptical DMO operators are cascaded together to form a single EMO operation,which has a skewed saddle-like impulse response resembling that of the isotropic azimuthal moveout (AMO) operator, and can correct structural dip image errors commonly 10–20° or more for typical far offset and ellipticity values. EMO can be used to improve imaging results by data regularization to interpolate the azimuthally anisotropic seismic wavefield, and/or by applying data preconditioning to create an approximately equivalent isotropic data set.

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