Making the most out of the least (squares migration)

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations


Standard migration images can suffer from migration artifacts due to 1) poor source-receiver sampling, 2) weak amplitudes caused by geometric spreading, 3) attenuation, 4) defocusing, 5) poor resolution due to limited source-receiver aperture, and 6) ringiness caused by a ringy source wavelet. To partly remedy these problems, least-squares migration (LSM), also known as linearized seismic inversion or migration deconvolution (MD), proposes to linearly invert seismic data for the reflectivity distribution. If the migration velocity model is sufficiently accurate, then LSM can mitigate many of the above problems and lead to a more resolved migration image, sometimes with twice the spatial resolution. However, there are two problems with LSM: the cost can be an order of magnitude more than standard migration and the quality of the LSM image is no better than the standard image for velocity errors of 5% or more. We now show how to get the most from least-squares migration by reducing the cost and velocity sensitivity of LSM.
Original languageEnglish (US)
Title of host publicationSEG Technical Program Expanded Abstracts 2014
PublisherSociety of Exploration Geophysicists
Number of pages6
StatePublished - Aug 5 2014

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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