Velocity model building from seismic reflection data by full-waveform inversion

Full-waveform inversion is re-emerging as a powerful data-fitting procedure for quantitative seismic imaging of the subsurface from wide-azimuth seismic data. This method is suitable to build high-resolution velocity models provided that the targeted area is sampled by both diving waves and reflected waves. However, the conventional formulation of full-waveform inversion prevents the reconstruction of the small wavenumber components of the velocity model when the subsurface is sampled by reflected waves only. This typically occurs as the depth becomes significant with respect to the length of the receiver array. This study first aims to highlight the limits of the conventional form of full-waveform inversion when applied to seismic reflection data, through a simple canonical example of seismic imaging and to propose a new inversion workflow that overcomes these limitations. The governing idea is to decompose the subsurface model as a background part, which we seek to update and a singular part that corresponds to some prior knowledge of the reflectivity. Forcing this scale uncoupling in the full-waveform inversion formalism brings out the transmitted wavepaths that connect the sources and receivers to the reflectors in the sensitivity kernel of the full-waveform inversion, which is otherwise dominated by the migration impulse responses formed by the correlation of the downgoing direct wavefields coming from the shot and receiver positions. This transmission regime makes full-waveform inversion amenable to the update of the long-to-intermediate wavelengths of the background model from the wide scattering-angle information. However, we show that this prior knowledge of the reflectivity does not prevent the use of a suitable misfit measurement based on cross-correlation, to avoid cycle-skipping issues as well as a suitable inversion domain as the pseudo-depth domain that allows us to preserve the invariant property of the zero-offset time. This latter feature is useful to avoid updating the reflectivity information at each non-linear iteration of the full-waveform inversion, hence considerably reducing the computational cost of the entire workflow. Prior information of the reflectivity in the full-waveform inversion formalism, a robust misfit function that prevents cycle-skipping issues and a suitable inversion domain that preserves the seismic invariant are the three key ingredients that should ensure well-posedness and computational efficiency of full-waveform inversion algorithms for seismic reflection data.