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.
ASJC Scopus subject areas
- Geochemistry and Petrology