Gradient representations in seabed geoacoustic inversion by Bernstein polynomials

A seabed parameterization approach that represents continuous geoacoustic gradients as a sum of Bernstein polynomial basis functions weighted by unknown coefficients which are estimated by Bayesian inversion of seabed acoustic reflectivity data is discussed. Simulated BL data with added zero-mean Gaussian noise (0.5 dB standard deviation) were computed for a sediment profile that mimics core measurements, with a steep positive depth-dependent gradient for the density and a mild negative gradient for the sound speed. At most depths, the PPD overlaps the true sound speeds and densities, capturing some of the fine scale features of the profiles but requiring only a small number of parameters. Attempting such inversion with a layered modeled would substantially enlarge the dimensionality of the parameter space.