Target-oriented Redatuming and Inversion on the Waveform Nature of Seismic Reﬂections
ProgramEarth Science and Engineering
KAUST DepartmentPhysical Sciences and Engineering (PSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/662833
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AbstractThe process of full-waveform inversion (FWI) seeks a model of the Earth’s sub surface that produces simulated data to ﬁt the observed data. The resolution of the model can be both complex and costly to meet such an objective. Resolving the reservoir is even more challenging as it requires an accurate representation of the physics throughout. Although FWI for diving waves has been successfully applied, the reservoir located at depth requires FWI to take advantage of the reﬂections. How ever, major issues are alongside the value of reﬂections, such as limited illumination, diﬃculties in recovering lowwavenumbers of the model, trade-oﬀs between the model parameters, etc, which hinders its applications so far. Recent studies on reﬂection waveform inversion (RWI) revealed the unique po tential of reﬂections in illuminating the deep model building. RWI identiﬁes the nonzero-oﬀset data mismatch and produces low-to-middle wavenumber model up dates along the reﬂection wavepath, which brings unprecedented robustness to FWI. FWI is therefore exposed to a better chance of resolving the deep targets within its own framework. However, RWI makes FWI even more computationally intractable. Alternatively, we introduce redatuming to FWI applications, aiming at retrieving survey-sinking virtual data to focus our inversion on the target zones. It improves the eﬃciency of entire loop of our inversion and, meanwhile, reduce the trade-oﬀs of FWI implemented on the entire model. Hereby, we split FWI into sequential optimization problems consisting of overburden estimation, virtual data retrieval and target inver sion. We exploit the advantages of the reﬂections produced by our datum modeling 5 to improve the robustness of the overburden inversion. The resulting macro model is reﬁned by follow-up FWI on relatively low-frequency bands to save the computation. The virtual dataset is calculated using an extended imaging condition. We specially summarize the reﬂection modeling and imaging process in terms of datuming. Higher frequencies are involved in retrieving the virtual data that are substituted into the target inversion, which allows adoption of a ﬁner grid and some enhanced treatment to satisfy the demand for high-resolution and multi-parameter delineation. The po tential applications are demonstrated by examples, with limitations and future work suggested in the last chapter.