P. Gerstoft
Saclant Undersea Res. Ctr., La Spezia, Italy
An inversion of sound fields for determining unknown environmental parameters can be separated into four parts: (1) discretization of the environment and discretization or transformation of the data, (2) efficient and accurate forward modeling, (3) efficient optimization procedures, and (4) uncertainty analysis. While much work had been done on the first two, much less has been done on the latter two, especially for object functions with several local minima. Global optimization methods accept the multiple minima and try to find the global minimum, without doing an exhaustive search. These methods are based on directed Monte Carlo search, and two promising methods are simulated annealing and genetic algorithms (GA). These have been compared and it has been found that GA's often are superior. The global methods are time demanding and in order to speed up the convergence, gradient steps can be taken during the iteration process. The examples here will be based on a horizontally stratified environment where all the material and geometrical parameters can be taken as unknown parameters. The solution is presented in terms of a posteriori probability function describing the parameters. From this the most likely model parameters can be found and their uncertainty and importance can be assessed.