Fig. 6

a The accuracy of underwater geolocalization predictions across the globe is significantly improved using a deep neural network (shown as a solid line) compared to a parametric model (shown as a dashed line). The global map illustrates the mean (shown as a diamond) and first standard deviation (shown as either a solid or dashed line) of the particle filter estimate for geolocation at the end of a day. The large errors observed in the mean and standard deviation of the estimated geolocation using the parametric approach are primarily due to a lack of understanding of the various physical phenomena that contribute to underwater polarization. b–e The close-up maps display the errors in the network model at a scale that allows the resolution of the covariance