We present 4D seismic inversion to reservoir pressure and saturation changes applied to data from the Catcher fields. The inversion workflow integrates data from reservoir simulation, well logs and production volumes, time-lapse time shifts and angle-stacked 4D seismic amplitudes as well as machine learning and Bayesian inversion methods. It begins with a petro-elastic model and reservoir pressure sensitivity calibration step, using well log data and time-lapse time-shifts. A machine learning inversion is then used to create an initial estimate of the reservoir property changes. This estimate is then used as prior information, in conjunction with reservoir simulation pressure data, for a stochastic Bayesian inversion workflow. We show that the Bayesian inversion benefits from the use of machine learning prior, leading to improvements in the match to the observed 4D seismic signal as well as the injected and produced water volumes. In addition to a most probable solution, stochastic sampling of the Bayesian posterior distribution also produces uncertainty estimates which are valuable in such inversion problems. With this result, we extract multiple equiprobable realisations and define conditional bounds for the pressure and saturation changes, which we recognise as helpful for reservoir management and 4D seismic data assimilation into reservoir simulation models.