Geoacoustic Inversion and Uncertainty Analysis with $\mathcal{MAX-MIN}$ Ant System

Abstract

Inverse problems in ocean acoustics are generally solved by means of matched field processing in combination with metaheuristic global search algorithms. Solutions that describe acoustical properties of the bed and subbottom in a shallow water environment are typically approximations that require uncertainty analysis. This work compares Ant Colony Optimization with other metaheuristics for geoacoustic inversion, particularly Genetic Algorithms. It is demonstrated that a $\mathcal{MAX}$-$\mathcal{MIN}$ Ant System can find good estimates and provide uncertainty analysis. In addition, the algorithm can easily be tuned, but proper tuning does not guarantee that every run will converge given a limited processing time. Another concern is that a single optimization run may find a solution while there is no clear indication on the accuracy. Both issues can be solved when probability distributions are based on parallel $\mathcal{MAX-MIN}$ Ant System runs.