One-step data-domain least-squares reverse time migration

Qiancheng Liu, Daniel Peter

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Least-squares reverse time migration (LSRTM) is an iterative inversion algorithm for estimating the broadband-wavenumber reflectivity model. Although it produces superior results compared with conventional reverse time migration (RTM), LSRTM is computationally expensive. We have developed a one-step LSRTM method by considering the demigrated and observed data to design a deblurring preconditioner in the data domain using the Wiener filter. For the Wiener filtering, we further use a stabilized division algorithm via the Taylor expansion. The preconditioned observed data are then remigrated to obtain a deblurred image. The total cost of this method is about two RTMs. Through synthetic and real data experiments, we see that one-step LSRTM is able to enhance image resolution and balance source illumination at low computational costs.

Original languageEnglish (US)
Pages (from-to)R361-R368
Issue number4
StatePublished - Jul 1 2018

Bibliographical note

Funding Information:
We are grateful to editors S. Operto and A. Chen and reviewers M. Wong, L. Xu, and an anonymous reviewer for improving the initial manuscript. The research reported in this publication is supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research under award no. UAPN#2605-CRG4. For computer time, this research used the resources of the Information Technology Division and Extreme Computing Research Center at KAUST.

Publisher Copyright:
© 2018 Society of Exploration Geophysicists.


  • Inversion
  • Least-squares migration
  • Reverse time migration

ASJC Scopus subject areas

  • Geochemistry and Petrology


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