Dreamlet source‐receiver survey sinking prestack depth migration

Abstract

Survey sinking migration downward continues the entire surface observed multi‐shot data to the subsurface step by step recursively. Reflected energy from reflectors at current depth appear at zero time and zero offset in the extrapolated wavefield. The data (seismic records) of t > 0 at this depth are equivalent to the data acquired by a survey system deployed at this depth. This is the reason to name the process ‘survey sinking’. The records of negative time need not to be further propagated since they carry no information to image structures beneath the new survey system. In this paper, we combine survey sinking with dreamlet migration. The dreamlet migration method decomposes the seismic wavefield and one‐way wave propagator by complete time‐space localized bases. The localization on time gives flexibility on time‐varying operations during depth extrapolation. In dreamlet survey sinking migration, it only keeps the data for imaging the structures beneath the sunk survey system and gets rid of the data already used to image structures above it. The deeper the depth is, the shorter is the valid time records of the remaining data and less computation is needed for one depth step continuation. For data decomposition, in addition to time axis, dreamlet survey sinking also decomposes the data for source and receiver gathers, which is a fully localized decomposition of prestack seismic data. A three‐scatter model is first used to demonstrate the computational feature and principle of this method. Tests on the two‐dimensional SEG/EAGE salt model show that with reduced data sets the proposed method can still obtain good imaging quality on complex geology structures and a strong velocity contrast environment.

Seismic-Wave Scattering, Imaging, and Inversion

“…Imaging using dreamlets with selected significant coefficients improves the computational efficiency because the number of significant coefficients are small. Wu and his colleagues successfully employed the dreamlet concepts in prestack depth migration with source-receiver survey sinking [33], in Gabor-frame-based Gaussian packet migration [18], and in migration with Gaussian wave packets based on Poincare wavelets [19].…”

Chapter 2. Wavefield Representation, Propagation and Imaging Using Localized Waves: Beamlet, Curvelet and Dreamlet

“…27 Number variation of dreamlet coefficients during migration. The black and green lines are for the survey sinking dreamlet coefficients using sunken data and the full data, respectively (SeeWu et al, 2013).…”