Bayesian Ambient Noise Inversion for Geoacoustic Uncertainty Estimation

Bayesian Ambient Noise Inversion for Geoacoustic Uncertainty Estimation: Final report

Bayesian geoacoustic inversion using wind-driven ambient noise.

This paper applies Bayesian inversion to bottom-loss data derived from wind-driven ambient noise measurements from a vertical line array to quantify the information content constraining seabed geoacoustic parameters. The inversion utilizes a previously proposed ray-based representation of the ambient noise field as a forward model for fast computations of bottom loss data for a layered seabed. ...

Trans-dimensional geoacoustic inversion of wind-driven ambient noise.

This letter applies trans-dimensional Bayesian geoacoustic inversion to quantify the uncertainty due to model selection when inverting bottom-loss data derived from wind-driven ambient-noise measurements. A partition model is used to represent the seabed, in which the number of layers, their thicknesses, and acoustic parameters are unknowns to be determined from the data. Exploration of the par...

Uncertainty analysis in matched-field geoacoustic inversions.

Quantifying uncertainty for parameter estimates obtained from matched-field geoacoustic inversions using a Bayesian approach requires estimation of the uncertainties in the data due to ambient noise as well as modeling errors. In this study, the variance parameter of the Gaussian error model, hereafter called error variance, is assumed to describe the data uncertainty. In practice, this paramet...

Statistical estimation of source location in presence of geoacoustic inversion uncertainty.

A statistical estimation of source location incorporating uncertainty in ocean environmental model parameters is derived using a Bayesian approach. From a previous geoacoustic inversion, a posterior probability distribution of the environmental parameters that reflects uncertainty in the ocean environment is obtained. This geoacoustic uncertainty then is mapped into uncertainty in the acoustic ...