Ali, W.H., M.S. Bhabra, P.F.J. Lermusiaux, A. March, J.R. Edwards, K. Rimpau, and P. Ryu, 2019. Stochastic Oceanographic-Acoustic Prediction and Bayesian Inversion for Wide Area Ocean Floor Mapping. In: OCEANS '19 MTS/IEEE Seattle, 27-31 October 2019, doi:10.23919/OCEANS40490.2019.8962870 Stochastic Oceanographic-Acoustic Prediction and Bayesian Inversion for Wide Area Ocean Floor Mapping Wael H. Ali1, Manmeet S. Bhabra1, Pierre F. J. Lermusiaux†, 1, Andrew March2, Joseph R. Edwards2, Katherine Rimpau2, Paul Ryu2 1Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 2Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA †Corresponding Author:
[email protected] Abstract—Covering the vast majority of our planet, the ocean The importance of an accurate knowledge of the seafloor is still largely unmapped and unexplored. Various imaging can hardly be overstated. It plays a significant role in aug- techniques researched and developed over the past decades, menting our knowledge of geophysical and oceanographic ranging from echo-sounders on ships to LIDAR systems in the air, have only systematically mapped a small fraction of the seafloor phenomena, including ocean circulation patterns, tides, sed- at medium resolution. This, in turn, has spurred recent ambitious iment transport, and wave action [16, 20, 41]. Ocean seafloor efforts to map the remaining ocean at high resolution. New knowledge is also critical for safe navigation of maritime approaches are needed since existing systems are neither cost nor vessels as well as marine infrastructure development, as in time effective. One such approach consists of a sparse aperture the case of planning undersea pipelines or communication mapping technique using autonomous surface vehicles to allow for efficient imaging of wide areas of the ocean floor.