Making the most out of least-squares migration

Yunsong Huang, Gaurav Dutta, Wei Dai, Xin Wang, Gerard T. Schuster, Jianhua Yu

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Standard migration images can suffer from (1) migration artifacts caused by an undersampled acquisition geometry, (2) poor resolution resulting from a limited recording aperture, (3) ringing artifacts caused by ripples in the source wavelet, and (4) weak amplitudes resulting from geometric spreading, attenuation, and defocusing. These problems can be remedied in part by least-squares migration (LSM), also known as linearized seismic inversion or migration deconvolution (MD), which aims to linearly invert seismic data for the reflectivity distribution. Given a sufficiently accurate migration velocity model, LSM can mitigate many of the above problems and can produce more resolved migration images, sometimes with more than twice the spatial resolution of standard migration. However, LSM faces two challenges: The computational cost can be an order of magnitude higher than that of standard migration, and the resulting image quality can fail to improve for migration velocity errors of about 5% or more. It is possible to obtain the most from least-squares migration by reducing the cost and velocity sensitivity of LSM.
Original languageEnglish (US)
Pages (from-to)954-960
Number of pages7
JournalThe Leading Edge
Issue number9
StatePublished - Sep 2014

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KAUST Repository Item: Exported on 2020-10-01


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