KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Earth Science and Engineering Program
Extreme Computing Research Center
Physical Science and Engineering (PSE) Division
KAUST Grant NumberUAPN#2605-CRG4
Online Publication Date2018-07-02
Print Publication Date2018-07
Permanent link to this recordhttp://hdl.handle.net/10754/631334
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AbstractLeast-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.
CitationLiu Q, Peter D (2018) One-step data-domain least-squares reverse time migration. GEOPHYSICS 83: R361–R368. Available: http://dx.doi.org/10.1190/geo2017-0622.1.
SponsorsWe 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.
PublisherSociety of Exploration Geophysicists