High-resolution velocity models generated by full-waveform inversion (FWI) can be effectively used in seismic reservoir characterization. However, FWI in anisotropic elastic media is hampered by the nonlinearity of inversion and parameter trade-offs. Here, we propose a robust way to constrain the inversion workflow using per-facies rock-physics relationships derived from borehole information (well logs). A probabilistic approach in the Bayesian domain is used to create the spatial distribution of this prior information and constrain the model updating at each iteration of FWI. The advantages of the facies-based FWI are demonstrated on 2D and 3D VTI (transversely isotropic with a vertical symmetry axis) elastic models with substantial structural complexity. In particular, the tests show that our algorithm improves the spatial resolution of the medium parameters without using ultra-low-frequency data required by conventional FWI.
Singh, S., Tsvankin, I., & Naeini, E. Z. (2019). Bayesian approach to facies-constrained waveform inversion for VTI media. SEG Technical Program Expanded Abstracts 2019. doi:10.1190/segam2019-3214511.1
This work was supported by the Consortium Project on Seismic Inverse Methods for Complex Structures at the Center for Wave Phenomena (CWP) and competitive research funding from the King Abdullah University of Science and Technology (KAUST).