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


  • Inversion
  • Least-squares migration
  • Reverse time migration

ASJC Scopus subject areas

  • Geochemistry and Petrology


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