Type
Conference PaperKAUST Department
Earth Science and Engineering ProgramPhysical Science and Engineering (PSE) Division
Date
2016-09Online Publication Date
2016-09Print Publication Date
2016-09Permanent link to this record
http://hdl.handle.net/10754/625273
Metadata
Show full item recordAbstract
We present a least-squares reverse time migration (LSRTM) method using Radon preconditioning to regularize noisy or severely undersampled data. A high resolution local radon transform is used as a change of basis for the reflectivity and sparseness constraints are applied to the inverted reflectivity in the transform domain. This reflects the prior that for each location of the subsurface the number of geological dips is limited. The forward and the adjoint mapping of the reflectivity to the local Radon domain and back are done through 3D Fourier-based discrete Radon transform operators. The sparseness is enforced by applying weights to the Radon domain components which either vary with the amplitudes of the local dips or are thresholded at given quantiles. Numerical tests on synthetic and field data validate the effectiveness of the proposed approach in producing images with improved SNR and reduced aliasing artifacts when compared with standard RTM or LSRTM.Citation
Dutta G, Agut C, Giboli M, Williamson P (2016) Least-squares reverse time migration with radon preconditioning. SEG Technical Program Expanded Abstracts 2016. Available: http://dx.doi.org/10.1190/segam2016-13943593.1.Publisher
Society of Exploration GeophysicistsConference/Event name
SEG International Exposition and 86th Annual Meeting, SEG 2016Additional Links
http://library.seg.org/doi/10.1190/segam2016-13943593.1ae974a485f413a2113503eed53cd6c53
10.1190/segam2016-13943593.1