Effect of ocean sound speed uncertainty on matched-field geoacoustic inversion

Abstract

The effect of ocean sound speed uncertainty on matched-field geoacoustic inversion is investigated using data from the SW06 experiment along a nearly range-independent bathymetric track. Significant sound speed differences were observed at the source and receiving array and several environmental parameterizations were investigated for the inversion including representing the ocean sound speed at both source and receivers with empirical orthogonal function (EOF) coefficients. A genetic algorithm-based global optimization method was applied to the candidate environmental models. Then, a Bayesian inversion technique was used to quantify uncertainty in the environmental parameters for the best environmental model, which included an EOF description of the ocean sound speed.

Short range travel time geoacoustic inversion with vertical line array

“…The estimates are also consistent with the results of matched-field inversion of longer range data along the same radial path from MPL-VLA1. 2,3 The inverted sound speeds represent the average sound speeds in the layers. The SW06 experiment site has been extensively surveyed previously so that a large amount of geological and geophysical ground truth information and oceanographic observations are available.…”

“…However, the ocean SSP has significant impact on matched-field geoacoustic inversion (MFI), 1 and MFI performance is improved by including ocean sound speed variability in the inversion. 2,3 The objective of this work is to report travel time geoacoustic inversion in a time varying ocean environment. The data were collected on the Marine Physical Laboratory vertical line array (MPL-VLA1) during the Shallow Water 2006 (SW06) experiments.…”

Compressive acoustic sound speed profile estimation

“…[2][3][4] This reduction in resolution causes SSP uncertainty, especially when internal waves or currents generate strong, temporally varying SSP anomalies. [5][6][7] This uncertainty can severely effect the accuracy of inversion for other parameters. 6,7 We show that SSPs in range-independent shallow ocean environments can be resolved using compressive sensing 8,9 (CS).…”

“…[5][6][7] This uncertainty can severely effect the accuracy of inversion for other parameters. 6,7 We show that SSPs in range-independent shallow ocean environments can be resolved using compressive sensing 8,9 (CS). CS asserts that parameters can be recovered robustly for certain highly underdetermined linear problems via sparse regularization of a least-squares cost function, provided that the solutions are sparse, i.e., few nonzero parameters (out of many) explain the observations.…”

“…Larger SSP variation is modeled using EOFs. 2,5,7 The pressure p obs 2 C M received at an M element VLA is modeled as…”