Frequency domain reflection waveform inversion with generalized internal multiple imaging


Wang, Guanchao
Guo, Qiang
Alkhalifah, Tariq Ali
Wang, Shangxu

KAUST Department
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Seismic Wave Analysis Group

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Full-waveform Inversion (FWI) has the potential to provide a high resolution detailed model of the earth’s subsurface, but it often fails to do so if the starting model is far from the true one. Reflection waveform inversion (RWI) is a popular method to build a sufficiently accurate initial model for FWI. In traditional RWI, the low-wavenumber updates are always computed and captured by smearing the data misfit along the reflection path with the help of migration/de-migration. However, the success of the RWI relies heavily on accurately reproducing the data in de-migration. Thus, we introduce a new generalized internal multiple imaging-based RWI implementation (GIMI-RWI), in which we avoid the Born modeling and update the primary reflection kernel directly. In the GIMI-RWI, we store one reflection kernel for each source-receiver pair, preserving the unique wave path for every single source-receiver trace. Subsequently, the convolution between the data residuals and the corresponding reflection kernel can build the tomographic velocity updates. In this situation, the long-wavelength tomographic updates are free of migration footprints, and will contribute a smoother background velocity to reduce the cycle-skipping risk and stabilize the followed full-waveform inversion process. Also, the GIMI-RWI method is source independent, as it entirely relies on the data. Using a synthetic example extracted from the Sigsbee2A model, we show the reliable performance of the GIMI-RWI technique.

Wang, G., Guo, Q., Alkhalifah, T., & Wang, S. (2021). Frequency domain reflection waveform inversion with generalized internal multiple imaging. GEOPHYSICS, 1–63. doi:10.1190/geo2020-0706.1

Society of Exploration Geophysicists



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