3D angle gathers from reverse time migration

Abstract

Common-image gathers are an important output of prestack depth migration. They provide information needed for velocity model building and amplitude and phase information for subsurface attribute interpretation. Conventionally, common-image gathers are computed using Kirchhoff migration on commonoffset/azimuth data volumes. When geologic structures are complex and strong contrasts exist in the velocity model, the complicated wave behaviors will create migration artifacts in the image gathers. As long as the gather output traces are indexed by any surface attribute, such as source location, receiver location, or surface plane-wave direction, they suffer from the migration artifacts caused by multiple raypaths. These problems have been addressed in a significant amount of work, resulting in common-image gathers computed in the reflection angle domain, whose traces are indexed by the subsurface reflection angle and/or the subsurface azimuth angle.Most of these efforts have concentrated on Kirchhoff and oneway wave-equation migration methods. For reverse time migration, subsurface angle gathers can be produced using the same approach as that used for one-way wave-equation migration. However, these approaches need to be revisited when producing high-quality subsurface angle gathers in three dimensions (reflection angle/azimuth angle), especially for wide-azimuth data. We have developed a method for obtaining 3D subsurface reflection angle/azimuth angle common-image gathers specifically for the amplitude-preserved reverse time migration. The method builds image gathers with a highdimensional convolution of wavefields in the wavenumber domain. We have found a windowed antileakage Fourier transform method that leads to an efficient and practical implementation. This approach has generated high-resolution angle-domain gathers on synthetic 2.5D data and 3D wideazimuth real data.

Angle-domain Migration Velocity Analysis using Wave-equation Reflection Traveltime Inversion

“…The angle-domain CIG provides a new opportunity for migration velocity analysis since it is less sensitive to migration artifacts in the presence of the multiples (Xu et al, 2001;Xu et al, 2011). There are several types of angle-domain CIGs decomposition methods available now.…”

“…However, surface-related CIGs suffer from migration artifacts due to multipath of wave propagation and can lead to erroneous results for MVA. Angledomain CIGs are proposed to eliminate artifacts present in offset-domain or shot-domain CIGs (Xu et al, 2001;Xu et al, 2011). This is compared to Zhang et al(2011) who estimate reflection traveltime residuals by correlating extrapolated data traces, and then update the velocity by smearing them along the reflection wavepaths.…”

Least-squares Reverse-time Migration

“…Current research focuses on four main issues: wavefield extrapolation (Stork, 2013;Zhang and Yao, 2013), alternative imaging conditions (Valenciano and Biondi, 2003;Zhang and Sun, 2008;Liu et al, 2011), amplitude preservation (Zhang et al, 2007a;Zhang and Sun, 2008), and how to efficiently generate common image gathers (CIGs) (Xu et al, 2011). We will consider each of these issues in turn.…”

Shot- and angle-domain wave-equation traveltime inversion of reflection data: Theory

“…Because this conversion method is valid for 3D only in the absence of crossline structure dip (Biondi and Symes, 2004) and the computation is very complex in 3D (Fomel, 2004), some alternatives can be used to form the angle-domain crosscorrelation. Other methods can be used to create the angle gathers in 3D if Sava and Fomel's method is too expensive or too difficult to implement in a particular application (Xu et al, 2011). For a variation of the subsurface offset parameter h within a certain range, p s ðx − h; z þ h tan θ; tjx s Þ represents a local downgoing plane wave and p g ðx þ h; z þ h tan θ; tjx s Þ denotes a local upgoing plane wave as shown in Figure 2.…”

“…Reflection-based FWI aims to invert for the long-wavelength components of the velocity model by separating out the tomography term in the conventional FWI kernel (Xu et al, 2011;Zhou et al, 2012;Wang et al, 2013), whereas the velocity update of conventional FWI is dominated by the high-wavenumber migration term in the absence of low-frequency and long-offset data (Tang et al, 2013). Therefore, a hybrid FWI scheme is often used to alternatively update the long and short wavelengths of the model using separate tomography and migration gradients at each iteration.…”