I will present a methodology for 1-D imaging of upper mantle structure using a Bayesian approach that incorporates a novel combination of seismic data types and an adaptive parameterisation based on piecewise discontinuous splines. This inversion algorithm lays the groundwork for improved seismic velocity models of the lithosphere and asthenosphere by harnessing the recent expansion of large seismic arrays and computational power alongside sophisticated data analysis. Careful processing of P- and S-wave arrivals isolates converted phases generated at velocity gradients between the mid-crust and 300 km depth. This data is allied with ambient noise Love and Rayleigh wave phase velocities, Rayleigh wave ellipticity ratios, and longer period Rayleigh wave speeds from earthquake data to obtain detailed VS and VP velocity models. Synthetic tests demonstrate that converted phases are necessary to accurately constrain velocity gradients, and S-p phases are particularly important for resolving mantle structure, while surface waves are necessary for capturing absolute velocities and pinning down Vp/Vs ratio. We apply the method to several stations in the northwest and north-central United States, finding that the imaged structure improves upon existing models by sharpening the vertical resolution of absolute velocity profiles, offering robust uncertainty estimates, and revealing mid-lithospheric velocity gradients indicative of thermochemical cratonic layering. This flexible method holds promise for increasingly detailed understanding of the upper mantle.