Optimization Issues in Ocean Acoustics

Abstract

Ocean acoustics, e.g., SONAR, has traditionally been used for the detection and localization of targets such as submarines or schools of fish. However, more recently the community has focused on the use of acoustics to probe the ocean itself and its boundaries. Ocean acoustic tomography for the purpose of estimating temperature structure throughout a large ocean volume is a fine example of such an application. This a a case where theory, propagation modeling, technology, and computer capabilities have all reached a sufficient level of maturity that such a large scale inverse problem becomes tractable. Other applications include the estimation of shallow water bottom properties such as sediment thicknesses and densities, of under-ice reflectivity, and more generally the estimation of any parameters which influence the acoustic propagation. However, the community continues to struggle with such basic issues as how to pose each problem properly so as to guarantee uniqueness for the solution and how to find that optimizing solution. The essential difficulty in finding solutions arises not only because the search space for the unknowns can be extremely large, but also because that space is usually highly non-convex thereby preventing the use of simple gradient based searches. Methods in use to find solutions include simulated annealing, genetic algorithms, and tailored search algorithms based on examinations of the solution space itself.