Meso-scale geoacoustic seabed quantification

Abstract We present an automated geoacoustic inversion method for novel autonomous underwater vehicle data that adapts model inference to seabed structure. Through parallelized trans-dimensional Bayesian inference, we infer seabed properties along a 12 km survey track on the scale of about 10 cm and 50 m in the vertical and horizontal, respectively. Using acoustic reflection coefficient data, the inferred physical parameters as a function of depth include sound speed, attenuation, density, and porosity. Parameter uncertainties are quantified, and the seabed properties agree closely with core samples at two control points and the layering structure with an independent sub-bottom seismic survey. Recovering high resolution seabed properties over large areas is shown to be feasible, which could become an important tool for marine industries, navies and oceanic research organizations.