Sound propagation predictions for ice-covered ocean acoustic environments do not match observational data: received levels in nature are less than expected, suggesting that the effects of the ice are substantial. Effects due to elasticity in overlying ice can be significant enough that low-shear approximations, such as effective complex density treatments, may not be appropriate. Building on recent elastic seafloor modeling developments, a range-dependent parabolic equation solution that treats the ice as an elastic medium is presented.
View Article and Find Full Text PDFParabolic equation solutions use various techniques for approximating range-dependent interfaces. One is a mapping approach [M. D.
View Article and Find Full Text PDFThe seafloor is considered to be a thin surface layer overlying an elastic half space. In addition to layers of this type being thin, they may also have shear wave speeds that can be small (order 100 m/s). Both the thin and low-shear properties, viewed as small parameters, can cause mathematical and numerical singularities to arise.
View Article and Find Full Text PDFParabolic equation methods for fluid and elastic media are extended to layered poro-elastic media, including some shallow-water sediments. A previous parabolic equation solution for one model of range-independent poro-elastic media [Collins et al., J.
View Article and Find Full Text PDFShallow-water environments typically include sediments containing thin or low-shear layers. Numerical treatments of these types of layers require finer depth grid spacing than is needed elsewhere in the domain. Thin layers require finer grids to fully sample effects due to elasticity within the layer.
View Article and Find Full Text PDFThe parabolic equation method with a single-scattering correction allows for accurate modeling of range-dependent environments in elastic layered media. For problems with large contrasts, accuracy and efficiency are gained by subdividing vertical interfaces into a series of two or more single-scattering problems. This approach generates several computational parameters, such as the number of interface slices, an iteration convergence parameter τ, and the number of iterations n for convergence.
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