A Phase-unwrapped Solution for Overcoming a Poor Starting Model in Full-wavefield Inversion

We present a new phase-unwrapped full-wavefield inversion (FWI) methodology for applying the technique to seismic data directly from a poor or simple starting model in an automated, robust manner. The well-known difficulty that arises with a poor starting model is a ‘cycle-skipped’ relationship between predicted and observed data at useable inversion frequencies. The local minimum convergence of cycle-skipped data is one of the root causes for inaccurately recovered models in practical applications of FWI. Further it is why practical applications to date have focussed on favourable datasets possessing very low frequencies and an accurate velocity model already known prior to applying FWI. Here we tackle the cycle-skipping problem by inverting the lowest useable frequency of the data using an unwrapped phase-only objective function. We minimise a smooth, phase-unwrapped residual, extracted from the data by exploiting the spatial continuity existing between adjacent traces. The majority of field datasets acquired today are spatially well enough sampled to be manipulated in this way. An application to highly cycle-skipped synthetic data from the Marmousi model shows the benefit of applying phase-unwrapped inversion to a dataset prior to starting conventional FWI.