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We present a scheme for multiscale phase inversion (MPI) of seismic data that is less sensitive than full-waveform inversion (FWI) to the unmodeled physics of wave propagation and to a poor starting model. To avoid cycle skipping, the multiscale strategy temporally integrates the traces several times, i.e., high-order integration, to produce low-boost seismograms that are used as input data for the initial iterations of MPI. As the iterations proceed, lower frequencies in the data are boosted by using integrated traces of lower order as the input data. The input data are also filtered into different narrow frequency bands for the MPI implementation. Numerical results with synthetic acoustic data indicate that, for the Marmousi model, MPI is more robust than conventional multiscale FWI when the initial model is moderately far from the true model. Results from synthetic viscoacoustic and elastic data indicate that MPI is less sensitive than FWI to some of the unmodeled physics. Inversion of marine data indicates that MPI is more robust and produces modestly more accurate results than FWI for this data set.

Wave-equation migration velocity analysis (WEMVA) based on subsurface-offset, angle domain, or time-lag common-image gathers (CIGs) requires significant computational and memory resources because it computes higher dimensional migration images in the extended image domain. To mitigate this problem, we have developed a WEMVA method using plane-wave CIGs. Plane-wave CIGs reduce computational cost and memory storage because they are directly calculated from prestack plane-wave migration and the number of plane waves is often much smaller than the number of shots. In the case of an inaccurate migration velocity, the moveout of plane-wave CIGs is automatically picked by a semblance analysis method, which is then linked to the migration velocity update by a connective function. Numerical tests on two synthetic data sets and a field data set validate the efficiency and effectiveness of this method.