Evaluating the potential of reflection-based waveform inversion

Khalid Almuteri, Yu Geng and Kris Innanen

ABSTRACT

Full waveform inversion (FWI) is a powerful tool to build high-resolution velocity models, from recorded seismic data. However, a major issue with FWI is that it fails at reconstructing the low-wavenumber components in the absence of low-frequency information in the data. Generally, for a limited-offset acquisition geometry, deep targets are only sampled by reflected waves with narrow scattering angles, which makes such failure inevitable. In this paper, we point out the limitation of conventional FWI when applied to reflection data, and review an alternative approach to overcome this limitation. The new waveform inversion formalism relies on decomposing the subsurface model into a background part that we seek to resolve, and a reflectivity part that we assume to be known. We show that separating the decoupled velocity model into long-wavelength and shortwavelength components permit us to extract the contribution of the reflected data to the background part of the velocity model.

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