KAUST DepartmentCenter for Subsurface Imaging and Fluid Modeling
Earth Science and Engineering Program
Environmental Science and Engineering Program
Physical Science and Engineering (PSE) Division
Online Publication Date2012-06-15
Print Publication Date2012-07
Permanent link to this recordhttp://hdl.handle.net/10754/562219
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AbstractLeast-squares migration has been shown to improve image quality compared to the conventional migration method, but its computational cost is often too high to be practical. In this paper, we develop two numerical schemes to implement least-squares migration with the reverse time migration method and the blended source processing technique to increase computation efficiency. By iterative migration of supergathers, which consist in a sum of many phase-encoded shots, the image quality is enhanced and the crosstalk noise associated with the encoded shots is reduced. Numerical tests on 2D HESS VTI data show that the multisource least-squares reverse time migration (LSRTM) algorithm suppresses migration artefacts, balances the amplitudes, improves image resolution and reduces crosstalk noise associated with the blended shot gathers. For this example, the multisource LSRTM is about three times faster than the conventional RTM method. For the 3D example of the SEG/EAGE salt model, with a comparable computational cost, multisource LSRTM produces images with more accurate amplitudes, better spatial resolution and fewer migration artefacts compared to conventional RTM. The empirical results suggest that multisource LSRTM can produce more accurate reflectivity images than conventional RTM does with a similar or less computational cost. The caveat is that the LSRTM image is sensitive to large errors in the migration velocity model. © 2012 European Association of Geoscientists & Engineers.
CitationDai, W., Fowler, P., & Schuster, G. T. (2012). Multi-source least-squares reverse time migration. Geophysical Prospecting, 60(4), 681–695. doi:10.1111/j.1365-2478.2012.01092.x
SponsorsWe thank the sponsors of Center for Subsurface Imaging and Fluid Modeling (CSIM) consortium for their financial support, and we thank the HPC and IT research computing group of King Abdullah University of Science and Technology for providing us their clusters and technical support. This work was started at WesternGeco when the first author was an intern there. We are grateful to WesternGeco for providing computation resources for early numerical tests and for permission to publish this paper.