Abstract:

Long-range acoustic propagation in the ocean is often well modeled by a discrete set of propagating normal modes. When this is the case, the acoustic field measured at an array of sensors can be decomposed into its modal components providing the basis for matched-mode processing (MMP) methods. Modal decomposition represents a discrete linear inverse problem. For vertical arrays which do not adequately sample the water column or for horizontal arrays, the inverse problem is ill-conditioned and modal decomposition can lead to poor results for noisy measurements. Regularization is a technique for stabilizing inverse problems based on trading off minimizing the data residuals (misfit) with minimizing some function of the solution. In the case of modal decomposition for MMP, an appropriate choice for the regularizing function is the deviation of the modal excitations of the solution from those of the replica field at each trial source location. This results in the best possible match between the measured and replica mode excitations given the noise and array characteristics and can lead to substantial improvements in MMP.