Thanks to the rapid growth in high-performance computing technology, full waveform inversion (FWI) has been successfully implemented in many field data applications. Nevertheless, it is still extremely expensive to perform a multi-parameter FWI over the whole subsurface model space that often needs to be discretized consistently using a fine grid, to delineate for example reservoir scale features. Building on the recent development of target-oriented imaging and inversion, we split the subsurface space into the overburden, above a datum level, and the target zone beneath the datum. Our objective is to retrieve the virtual data at a target level and then estimate a high-resolution model of the critical, possibly reservoir, zone. We first build an overburden velocity model using FWI with the data containing frequencies up to 20 Hz and then retrieve a virtual dataset at the datum survey from the data recorded at the Earth's surface. A least-squares optimization of the waveform redatuming is used for the virtual data retrieval. We finally invert for the target zone using the estimated highly reduced in size, but containing high-frequency, dataset. It will lead to an obvious boost in the convergence rate and bring down the memory and computational cost, even though a finer grid is used for the redatuming and the following inversion of the target zone. The Chevron 2014 blind test dataset is used to demonstrate the effectiveness of this strategy.