Multi-parameter full waveform inversion (FWI) suffers from the complex nonlinearity in the objective function, compounded by the eventual tradeoff between the model parameters. A hierarchical approach based on frequency and arrival time data decimation to maneuver the complex nonlinearity associated with this problem usually falls short in anisotropic media. In place of data decimation, I use a model gradient filter approach to access the parts of the gradient more suitable to combat the potential nonlinearity and parameter trade off. The filter is based on representing the gradient in the time-lag normalized domain in which the small scattering angles of the gradient update is initially muted out. A model update hierarchical filtering strategy includes applying varying degree of filtering to the different parameter updates. A feature not easily accessible to simple data decimation. Using both FWI and reection based FWI (RFWI), two strategies to combat the tradeoff between anisotropic parameters are outlined.