Data Uncertainty Estimation in Matched-Field Geoacoustic Inversion

The received acoustic field generated by a single passage of a research vessel on the New Jersey continental shelf is employed to infer probability distributions for the parameter values representing the frequency dependence of the seabed attenuation and the source levels of the ship. The statistical inference approach employed in the analysis is a maximum entropy methodology. The average value of the error function, needed to uniquely specify a conditional posterior probability distribution, is estimated with data samples from time periods in which the ship-receiver geometry is dominated by either the stern or bow aspect. The existence of ambiguities between the source levels and the environmental parameter values motivates an attempt to partially decouple these parameter values. The main result is the demonstration that parameter values for the attenuation (α and the frequency exponent), the sediment sound speed, and the source levels can be resolved through a model space reduction technique. The results of this multi-step statistical inference developed for ship radiated noise is then tested by processing towed source data over the same bandwidth and source track to estimate continuous wave source levels that were measured independently with a reference hydrophone on the tow body.

Section: A Maximum Entropymentioning

confidence: 99%

“…However, for a large number of frequencies, the sampling of the error function with Monte Carlo (which is used here) becomes prohibitive. 20 In the event that one does have prior information about the source levels, the approach can then include explicit sampling of SL i in the error function (see Sec. IV F).…”

Section: Stepmentioning

confidence: 99%

Maximum entropy inference of seabed attenuation parameters using ship radiated broadband noise

Knobles

2015

“…An unknown source can be treated by maximizing the likelihood over A, , and ͑i.e., setting ‫ץ‬L / ‫ץ‬A = ‫ץ‬L / ‫ץ‬ = ‫ץ‬L / ‫ץ‬ =0͒ yielding 35,36 …”

Section: ͑5͒mentioning

confidence: 99%

Uncertainty estimation in simultaneous Bayesian tracking and environmental inversion

Dosso

Wilmut

2008

This paper develops a Bayesian approach for two related inverse problems: tracking an acoustic source when ocean environmental parameters are unknown, and determining environmental parameters using acoustic data from an unknown (moving) source. The formulation considers source and environmental parameters as unknown random variables constrained by noisy acoustic data and by prior information on parameter values (e.g., physical limits for environmental properties) and on inter-parameter relationships (limits on radial and vertical source speed). The goal is not simply to estimate parameter values, but to rigorously determine parameter uncertainty distributions, thereby quantifying the information content of the data/prior to resolve source and environmental parameters. Results are presented as marginal posterior probability densities (PPDs) for environmental parameters and joint marginal PPDs for source ranges and depths. Given the numerically intensive inversion, an efficient Markov-chain Monte Carlo importance-sampling approach is developed which combines Metropolis and heat-bath Gibbs' sampling, employs efficient proposal distributions based on a linearized PPD approximation, and considers nonunity sampling temperatures to ensure a complete parameter search. The approach is illustrated with two simulated examples representing tracking a quiet submerged source and geoacoustic inversion using noise from an unknown ship of opportunity. In both cases, source, seabed, and water-column parameters are unknown.

“…[1][2][3][4][5] Variability of the acoustic environment is one of the major obstacles to model based processing frameworks, such as matched field processing [6][7][8] and matched field inversion. 9 These variabilities usually span a wide range of spatial and temporal scales. It is not realistic for conventional oceanographic measurements to provide the ability to synoptically observe all these dynamic processes in shallow water, especially those with sub-mesoscale processes which are short in time and space.…”

Section: Introductionmentioning

confidence: 99%

“…It also is one of the most important parameters in determining acoustic waveguide propagation, for example, the existence of duct propagation or the fraction of energy interacting with the bottom. Numerous approaches have been developed to invert for the sound speed in the water column by combining acoustic models with in situ measurements, such as the travel time approach in the early stages of the OAT, 12 and matched field inversion 9,[13][14][15][16][17] in late years. Oceanographic variabilities cause the SSP to evolve in time and space.…”

Section: Introductionmentioning

confidence: 99%

Tracking of time-evolving sound speed profiles in shallow water using an ensemble Kalman-particle filter

Zhou

2013

This paper presents a tracking technique for performing sequential geoacoustic inversion monitoring range-independent environmental parameters in shallow water. The inverse problem is formulated in a state-space model with a state equation for the time-evolving sound speed profile (SSP) and a measurement equation that incorporates acoustic measurements via a hydrophone array. The particle filter (PF) is an ideal algorithm to perform tracking of environmental parameters for nonlinear systems with non-Gaussian probability densities. However, it has the problem of the mismatch between the proposal distribution and the a posterior probability distribution (PPD). The ensemble Kalman filter (EnKF) can obtain the PPD based on the Bayes theorem. A tracking algorithm improves the performance of the PF by employing the PPD of the EnKF as the proposal distribution of the PF. Tracking capabilities of this filter, the EnKF and the PF are compared with synthetic acoustic pressure data and experimental SSP data. Simulation results show the proposed method enables the continuous tracking of the range-independent SSP and outperforms the PF and the EnKF. Moreover, the complexity analysis is performed, and the computational complexity of the proposed method is greatly increased because of the combination of the PF and the EnKF.