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Technical Abstract

High resolution multi-modal surface wave inversion for shallow S-wave velocity model building

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Shallow S-wave velocity model building is very important to land and Ocean Bottom Seismic (OBS) PS data processing. Surface wave, propagating along near surface, brings lots of useful information of shallow lithology which is good for near surface S-wave velocity estimation. In this study, multi-modal surface wave inversion (MM-SWI) was used to build high resolution shallow S-wave velocity model for one of the recently acquired Ocean Bottom Node (OBN) surveys. To generate high signal-to-noise ratio (S/N) dispersion spectrum for MM-SWI, super gathers based on the extracted surface waves were constructed. Aperture (within 400 m) was carefully chosen to ensure dispersion spectrum is of high spatial resolution as well. Auto-picking of multi-modal dispersion curves was implemented after the dispersion analysis. To overcome the strong nonlinear problem during multi-modal inversion, the combined Levenberg-Marquard (LM) and differential evolution (DE) inversion method was used. High resolution dispersion analysis and robust auto-picking of multi-modal dispersion curves enhance productivity significantly which allows a high resolution shallow S wave velocity model to be built efficiently for this survey. The inverted MM-SWI model correlates well with both the shallow geological structures and the seafloor map. It is able to reveal the fine layers, small faults, small channels and other lithological variations. PS statics was then computed from the inverted high resolution Vs model and applied to improve the continuity and coherency of the PS time image. The MM-SWI model was also incorporated into the S-wave velocity model building process for PS depth imaging. The evidences from the model comparison, statics application and PS depth image support the reliability of the high resolution shallow S-wave velocity model from MM-SWI.
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Publications

EAGE - European Association of Geoscientists and Engineers

Authors

X. Li, Y. Chen, X. Miao, L. Li, F.C. Loh, B. Hung (CGG) ; S. Wolfarth, D. Priyambodo (BP)

Month

June

Copyright

© 2019 EAGE