Closed-loop surface-related multiple estimation with full-wavefield migration-reconstructed near offsets for shallow water

Abstract

Reliably separating primary and multiple reflections in a shallow water environment (i.e., 50 m to 200 m water depth) still remains a challenge. The success of previously published closed-loop surface-related multiple estimation (CL-SRME) depends heavily on the data coverage, i.e., the near-offset reconstruction. Therefore, we propose the integrated framework of CL-SRME and full-wavefield migration (FWM). Multiples recorded in the data are capable of helping infill the acquisition imprint of the FWM image. With this image as a strong constraint, we are able to reconstruct the data at near-offsets, which is essential for better primary and multiple estimation during CL-SRME. FWM applied in a non-linear way can avoid the negative influences from the missing data, and at the same time bring in more physics between primaries and multiples. The FWM image of the top part of the subsurface is also used to back-project the information from multiples to primaries with the physical constraint of all this information belongs to the same earth model, provided that a good description of the source wavefield and a reasonable velocity model are available. The proposed integrated framework first reconstructs near-offsets via the closed-loop imaging process of FWM and then feeds the complete reconstructed data to CL-SRME for better primary and multiple estimation. A good performance is demonstrated on both 2D synthetic and field data examples in a challenging shallow water environment.

Full-Wavefield Migration Using an Imaging Condition of Global Normalization Multi-Order Wavefields: Application to a Synthetic Dataset

“…The generalized internal multiple imaging (GIMI) method can simulate high-order internal scattering through interference and can subsequently develop least-squares suppression of artifacts [29,30]. Full-wavefield migration (FWM) offers various strategies for combining primary and multiple imaging [31][32][33][34][35] and uses closed-loop one-way wave operators and inversion strategies to simultaneously process data containing surface-related multiples and internal multiples. Another approach involves least-squares reverse time migration (LSRTM) for separately extracted multiples.…”

Surface‐related multiple prediction for ocean‐bottom node data based on demigration using downgoing wave imaging data

“…With the development of marine threedimensional seismic exploration, scholars have carried out research on the surface-related multiple elimination (SRME) method for three-dimensional conventional streamer seismic data, which solved some problems for multiple prediction, such as sparse source spacing (i.e. navigation line spacing) in the direction of the connecting line, less receiver points and limited offset range, and developed a variety of practical methods finally (Baumstein & Hadidi, 2006;Hokstad & Sollie, 2006;Lin et al, 2004;Pica et al, 2005;Van Dedem & Verschuur, 2005;Zhang & Verschuur, 2021).…”