Compressive geoacoustic inversion using ambient noise.

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. ...

Bayesian Ambient Noise Inversion for Geoacoustic Uncertainty Estimation

Estimation of seabed geoacoustic parameters in shallow water by remote sensing remains a challenging task due to constraints on hardware, data collection and analysis, and cost of maritime surveys. This work focuses on the application of two techniques that might offer a solution to those constraints: the use of ambient noise to probe the seabed, and Bayesian inversion of these data to estimate...

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...

Bayesian Ambient Noise Inversion for Geoacoustic Uncertainty Estimation: Final report

On the effect of error correlation on matched-field geoacoustic inversion.

The effect of correlated data errors on matched-field geoacoustic inversion for vertical array data is examined. The correlated errors stem from the inability to model the inhomogeneities in the environment resulting in an additional error term beyond ambient noise. Simulated data with these correlated errors are generated and then inverted with or without using the proper covariance matrix. Re...