We process a bathymetry survey from the Sea of Galilee. This dataset is contaminated with non-Gaussian noise in the form of glitches and spikes inside the lake and at the track ends. Drift on the depth measurements leads to vessel tracks in the preliminary depth images. We derive an inversion scheme that produces a much reduced noise map of the Sea of Galilee. This inversion scheme includes preconditioning and Iteratively Reweighted Least-Squares with the proper weighting function to get rid of the non-Gaussian noise. We remove the ship tracks by adding a modeling operator inside the inversion that accounts for the drift in the data. We then approximate the model covariance matrix with a prediction-error filter to enhance details in the middle of the lake. Unfortunately, the prediction-error filter has the property of degrading the resolution of the depth map at the edges of the lake. Our images of the Sea of Galilee show ancient shorelines and rifting features inside the lake.