Full waveform inversion (FWI) despite it's potential suffers from the ability to converge to the desired solution due to the high nonlinearity of the objective function at conventional seismic frequencies. Even if frequencies necessary for the convergence are available, the high number of iterations required to approach a solution renders FWI as very expensive (especially in 3D). A spectral implementation in which the wavefields are extrapolated and gradients are calculated in the wavenumber domain allows for a cleaner more efficient implementation (no finite difference dispersion errors). In addition, we use not only an up and down going wavefield decomposition of the gradient to access the smooth background update, but also a right and left propagation decomposition to allow us to do that for large dips. To insure that the extracted smooth component of the gradient has the right decent direction, we solve an optimization problem to search for the smoothest component that provides a negative (decent) gradient. Application to the Marmousi model shows that this approach works well with linear increasing initial velocity model and data with frequencies above 2Hz.