Velocity Building by Reflection Waveform Inversion without Cycle-skipping

Qiang Guo, Tariq Ali Alkhalifah, Zedong Wu

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

9 Scopus citations

Abstract

Reflection waveform inversion (RWI) provides estimation of low wavenumber model components using reflections generated from a migration/demigration process. The resulting model tends to be a good initial model for FWI. In fact, the optimization images to combine the migration velocity analysis (MVA) objectives (given here by RWI) and the FWI ones. However, RWI may still encounter cycle-skipping at far offsets if the velocity model is highly inaccurate. Similar to MVA, RWI is devoted to focusing reflection data to its true image positions, yet because of the cycle skipping potential we tend to initially use only near offsets. To make the inversion procedure more robust, we introduce the extended image into our RWI. Extending the model perturbations (or image) allows us to better fit the data at larger offsets even with an inaccurate velocity. Thus, we implement a nested approach to optimize the velocity and extended image simultaneously using the objective function of RWI. We slowly reduce the extension, as the image becomes focused, to allow wavepath updates from far offsets to near as a natural progression from long wavelength updates to shorter ones. Applications on synthetic data demonstrate the effectiveness of our method without much additional cost to RWI.
Original languageEnglish (US)
Title of host publication79th EAGE Conference and Exhibition 2017
PublisherEAGE Publications
ISBN (Print)9789462822177
DOIs
StatePublished - May 26 2017

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