Least squares Kirchhoff depth migration: implementation, challenges, and opportunities (2015)

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2015Least squares Kirchhoff depth migration: implementation, challenges, and opportunitiesAuthors: Daniel Trad*Daniel Trad*CGGhttps://doi.org/10.1190/segam2015-5833655.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Least squares migration (LSM), like interpolation, has the potential to address sampling issues on migrated images; it also generates images with better amplitudes than standard migration (Huang et al., 2014). Although both techniques share the same goal and often the same formulation, they differ in the nature of the forward model that is used to predict the data. While most interpolation algorithms use short linear events in windows, LSM is a powerful decomposition of data into physical basis functions, capable of using much more prior information than interpolation. However, in practice, the cost of the migration/modelling operator and its limited representation of reality limit its potential. Because of the size of real scale production problems, we can only afford a handful of iterations to invert a large migration/modelling operator. Therefore, an early stop in the iterations could compromise the final result, to the point of questioning the advantages of LSM over standard migration. In this abstract, I discuss some of these limitations and possible ways to overcome them. I also analyze similarities and differences with respect to seismic data interpolation. Finally, I present results using a least squares implementation of Kirchhoff pre-stack depth migration and applications of predictions from LSM for denoising. Keywords: depth migration, least squares, KirchhoffPermalink: https://doi.org/10.1190/segam2015-5833655.1FiguresReferencesRelatedDetailsCited byEfficient least-squares reverse time migration using local cross-correlation imaging condition1 June 2022 | Journal of Geophysics and Engineering, Vol. 19, No. 3Fast and Memory-Efficient Frequency-Domain Least-Squares Reverse-Time Migration Using Singular Value Decomposition (SVD)IEEE Transactions on Geoscience and Remote Sensing, Vol. 60Mitigating Velocity Errors in Least-Squares Imaging Using Angle-Dependent Forward and Adjoint Gaussian Beam Operators15 November 2021 | Surveys in Geophysics, Vol. 42, No. 6Combination of the common reflection surface-based prestack data regularization and reverse time migration: Application to real land dataGerman Garabito, Paul L. Stoffa, Yuri S. F. Bezerra, and João L. Caldeira15 February 2021 | GEOPHYSICS, Vol. 86, No. 2Assumptions and goals for least squares migration28 November 2019 | Geophysical Prospecting, Vol. 68, No. 3Improving the least-squares image by using angle information to avoid cycle skippingBin He, Yike Liu, and Yanbao Zhang23 October 2019 | GEOPHYSICS, Vol. 84, No. 6Challenges for Least-Squares Kirchhoff depth migrationDaniel Trad17 August 2017Seismic Processing: Migration I Complete Session17 August 2017Elastic least-squares reverse time migrationYuting Duan, Antoine Guitton, and Paul Sava30 May 2017 | GEOPHYSICS, Vol. 82, No. 4Prestack correlative least-squares reverse time migrationXuejian Liu, Yike Liu, Huiyi Lu, Hao Hu, and Majid Khan13 February 2017 | GEOPHYSICS, Vol. 82, No. 2Elastic least-squares reverse time migrationYuting Duan, Paul Sava, and Antoine Guitton1 September 2016 SEG Technical Program Expanded Abstracts 2015ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2015 Pages: 5634 publication data© 2015 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 19 Aug 2015 CITATION INFORMATION Daniel Trad*, (2015), "Least squares Kirchhoff depth migration: implementation, challenges, and opportunities," SEG Technical Program Expanded Abstracts : 4238-4242. https://doi.org/10.1190/segam2015-5833655.1 Plain-Language Summary Keywordsdepth migrationleast squaresKirchhoffPDF DownloadLoading ...