Fast sparsity-promoting least-squares migration with multiples in time domain

TitleFast sparsity-promoting least-squares migration with multiples in time domain
Publication TypeConference
Year of Publication2017
AuthorsMengmeng Yang, Emmanouil Daskalakis, Felix J. Herrmann
Conference NameSEG Technical Program Expanded Abstracts
Page4828-4832
Month09
Keywordsleast-squares RTM, multiples, SEG, sparsity, time domain
Abstract

Based on the latest developments of research in inversion technology with optimization, researchers have made significant progress in the implementation of least-squares reverse-time migration (LS-RTM) of primaries. In Marine data however, these applications rely on the success of a pre-imaging separation of primaries and multiples, which can be modeled as a multi-dimensional convolution between the vertical derivative of the surface-free Green’s function and the down-going receiver wavefield. Instead of imaging the primaries and multiples separately, we implement the LS-RTM of the total down-going wavefield by combining areal source injection and linearized Born modelling, where strong surface related multiples are generated from a strong density variation at the ocean bottom. The advantage including surface related multiples in LS-RTM is the extra illumination we obtain from these multiples without incurring additional computational costs related to carrying out multi-dimensional convolutions part of conventional multiple prediction procedures. Even though we are able to avert these computational costs, our approach shares the large costs of LSRTM. We reduce these costs by combining randomized source subsampling with our sparsity-promoting imaging technology, which produces artifact-free, high-resolution images, with the surface-related multiples migrated properly.

Notes

(SEG, Houston)

URLhttps://www.slim.eos.ubc.ca/Publications/Public/Conferences/SEG/2017/yang2017SEGfsp/yang2017SEGfsp.html
DOI10.1190/segam2017-17741608.1
Presentation

https://www.slim.eos.ubc.ca/Publications/Public/Conferences/SEG/2017/yan...

Citation Keyyang2017SEGfsp