Range-dependent seabed characterization by inversion of acoustic data from a towed receiver array

Abstract

The MAPEX2000 experiments were conducted in the Mediterranean Sea in March, 2000 to determine seabed properties using a towed acoustic source and receiver array. Towed systems are advantageous because they are easy to deploy from a ship and the moving platform offers the possibility for estimating spatially variable ͑range-dependent͒ seabed properties. In this paper, seabed parameters are determined using a matched-field geoacoustic inversion approach with measured, towed array data. Previous research has successfully applied matched-field geoacoustic inversion techniques to measured acoustic data. However, in nearly all cases the inverted data were collected on moored, vertical receiver arrays. Results here show that seabed parameters can also be extracted by inverting acoustic measurements from a towed array of receivers, and these agree with those inverted using data received simultaneously on a vertical array. These findings imply that a practical technique could be developed to map range-dependent seabed parameters over large areas using a towed acoustic system. An example of such a range-dependent inversion is given using measurements from the MAPEX2000 experiments.

Change-point detection for recursive Bayesian geoacoustic inversions

“…Using Bayes rule, the PPD of the possible change-points, conditioned on all the measurements, is 1 pðrjy 1:L Þ / pðy 1:L jrÞpðrÞ; (12) where pðy 1:L jrÞ ¼ …”

“…III, the method is demonstrated in simulation with parameters corresponding to the low SNR, 100-900 Hz linear frequency modulation (LFM) pulses observed in the Shallow Water 2006 experiment. The change-point approach also could be applied to local matched-field geoacoustic inversions using towed arrays, [12][13][14][15][16] but this scenario is not considered here.…”

Model-Based Ocean Acoustic Signal Processing

“…Inversion for the characteristics of the propagation medium subsequently expanded with estimation of geoacoustic parameters in highly complex environments. [10][11][12][13][14][15][16] Within a Bayesian framework, but with dynamic models in mind-namely Bayesian filtering, Candy and Sullivan 17 sought to remedy the mismatch problem by embedding the propagation model into a Kalman filter (KF). This has the advantage of allowing the troublesome parameters to be included as part of the state vector of unknowns, a procedure known as "augmentation. "…”

Phenomenological and global optimization inversion

“…A simple configuration would be to have a ship sailing over the intrusion towing a horizontal array and a source [39]. In such a configuration, it would be possible to determine geoacoustic parameters.…”