Optimizing the coefficients of the leading terms of the Born Series: FWI+MVA+more
AuthorsAlkhalifah, Tariq Ali
KAUST DepartmentEarth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Seismic Wave Analysis Group
Online Publication Date2017-05-26
Print Publication Date2017-06-12
Permanent link to this recordhttp://hdl.handle.net/10754/626977
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AbstractThe scattering series theoretically utilizes a model perturbation framework to explain the difference between the seismic modeled data corresponding to a background model and those measured in the field corresponding to the real Earth. These perturbations include short wavelength features like those predicted by full waveform inversion (FWI) gradients, and long wavelength features often constrained by migration velocity analysis (MVA) objectives. The Born series, however, is not a convergent series. If the perturbations are large, we probably will not be able to explain the data difference. Thus, using the leading terms of the Born in an iterative process, in which they are scaled properly, allows us to avoid such limitations and update the short and long wavelength components of the velocity model. In fact, the FWI update is manifested in the first term of the Born series, and the MVA update is represented by the transmission (first Fresnel zone) part of the second term. In this case, FWI and MVA are code names for dividing the optimized update to reflectivity based portions and those adequate for the background, respectively. Examples on synthetic and real data demonstrate this logic.
CitationAlkhalifah T (2017) Optimizing the coefficients of the leading terms of the Born Series: FWI+MVA+more. 79th EAGE Conference and Exhibition 2017 - Workshops. Available: http://dx.doi.org/10.3997/2214-4609.201701712.
SponsorsThis is a group effort the includes the whole Seismic wave analysis group at KAUST. To identify the contributors please visit our website: http://swag.kaust.edu.sa. I also thank Chevron for the SEG2014 Gulf of Mexico (GOM) blind data set.