Sparse least-squares reverse time migration using seislets

Gaurav Dutta, Gerard T. Schuster

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


We propose sparse least-squares reverse time migration (LSRTM) using seislets as a basis for the reflectivity distribution. This basis is used along with a dip-constrained preconditioner that emphasizes image updates only along prominent dips during the iterations. These dips can be estimated from the standard migration image or from the gradient using plane-wave destruction filters or structural tensors. Numerical tests on synthetic datasets demonstrate the benefits of this method for mitigation of aliasing artifacts and crosstalk noise in multisource least-squares migration.
Original languageEnglish (US)
Pages (from-to)4232-4237
Number of pages6
JournalSEG Technical Program Expanded Abstracts 2015
StatePublished - Aug 19 2015

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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