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Sparsity-promoting Least-squares Migration with the Linearized Inverse Scattering Imaging ConditionNormal access

Authors: P.A. Witte, M. Yang and F.J. Herrmann
Event name: 79th EAGE Conference and Exhibition 2017
Session: Least-square and Marchenko Imaging
Publication date: 12 June 2017
DOI: 10.3997/2214-4609.201701125
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 2.6Mb )
Price: € 20

Summary:
Reverse-time migration (RTM) with the conventional cross-correlation imaging condition suffers from low-frequency artifacts that result from backscattered energy in the background velocity models. This problem translates to least-squares reverse-time migration (LS-RTM), where these artifacts slow down the convergence, as many of the initial iterations are spent on removing them. In RTM, this problem has been successfully addressed by the introduction of the so-called inverse scattering imaging condition, which naturally removes these artifacts. In this work, we derive the corresponding linearized forward operator of the inverse scattering imaging operator and incorporate this forward/adjoint operator pair into a sparsity-promoting (SPLS-RTM) workflow. We demonstrate on a challenging salt model, that LS-RTM with the inverse scattering imaging condition is far less prone to low-frequency artifacts than the conventional cross-correlation imaging condition, improves the convergence and does not require any type of additional image filters within the inversion. Through source subsampling and sparsity promotion, we reduce the computational cost in terms of PDE solves to a number comparable to conventional RTM, making our workflow applicable to large-scale problems.


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