Bayesian inference for water column and sediment inversion in ocean acoustics

Bayesian methods provide inference on unknown parameters given prior beliefs and a statistical model for the observations in a noisy environment. It was shown in “Sequential filtering and linearization for inversion in the Seabed Characterization Experiment,” J. Acoust. Soc. Am. 144, 1913 (2018) how geometry and sediment parameter estimation in an oceanic experiment can be achieved with a two-stage Bayesian approach. Here, we demonstrate the potential of the method for inversion with a different data set presenting challenges in identifying features of the acoustic field necessary for the second stage of the estimation process. The water column sound speed is complex in this experiment, necessitating a complex inverse model. The transmitted signals are lfm pulses and probability densities of multipath arrival times are calculated at the receivers. For the forward and inverse calculations, we compute the Jacobian matrix for sound speed estimation using EOF coefficients. The inversion, employing the direct and surface and bottom paths, leads to inference on the geometry and the water column sound speed profile. Using the computed probability densities and introducing one more arrival, sediment property densities are calculated. Results are in excellent agreement with expected values for the site. [Work supported by ONR.]Bayesian methods provide inference on unknown parameters given prior beliefs and a statistical model for the observations in a noisy environment. It was shown in “Sequential filtering and linearization for inversion in the Seabed Characterization Experiment,” J. Acoust. Soc. Am. 144, 1913 (2018) how geometry and sediment parameter estimation in an oceanic experiment can be achieved with a two-stage Bayesian approach. Here, we demonstrate the potential of the method for inversion with a different data set presenting challenges in identifying features of the acoustic field necessary for the second stage of the estimation process. The water column sound speed is complex in this experiment, necessitating a complex inverse model. The transmitted signals are lfm pulses and probability densities of multipath arrival times are calculated at the receivers. For the forward and inverse calculations, we compute the Jacobian matrix for sound speed estimation using EOF coefficients. The inversion, employing the direct ...