Geoacoustic inversion using combustive sound source signals

Abstract

Combustive sound source (CSS) data collected on single hydrophone receiving units, in water depths ranging from 65to110m, during the Shallow Water 2006 experiment clearly show modal dispersion effects and are suitable for modal geoacoustic inversions. CSS shots were set off at 26m depth in 100m of water. The inversions performed are based on an iterative scheme using dispersion-based short time Fourier transform in which each time-frequency tiling is adaptively rotated in the time-frequency plane, depending on the local wave dispersion. Results of the inversions are found to compare favorably to local core data.

Bayesian geoacoustic inversion of single hydrophone light bulb data using warping dispersion analysis

“…The process has been successfully applied in underwater acoustics to infer seabed sound speed from water-borne mode dispersion using a single receiver and an impulsive low-frequency source. 5,[13][14][15][16][17][18] In this context, dispersion curves are estimated using TF analysis of the received signal. However, modal resolution (i.e., the ability to isolate individual modes) in the TF domain is range dependent: Because of dispersion, modal separation increases with range.…”

“…Dispersion curve estimation is straightforward only when range is large enough, 5,13 but requires advanced signal processing for shorter ranges. [15][16][17][18][19][20][21] In this case, one solution is to transform the signal using warping 9,10 so that it becomes adapted to the intrinsic limitations of TF analysis. 16,17 Note that dispersion inversion for shear wave speed profiles has also been applied to interface waves (Rayleigh waves on land 22 and Scholte waves on the seabed 23,24 ).…”

Geoacoustic inversions using Combustive Sound Source data from Shallow Water-2006 experiment

“…A new estimate of the modal group speeds is made and the steps are then repeated to get a new estimate until the group speed values converge. [12].). The dashed line is the geoacoustic model proposed by Jiang et al [11].…”

Estimates of the temporal and spatial variability of ocean sound speed on the New Jersey shelf

“…A number of signal processing techniques have been applied for this purpose. 9,[16][17][18] However, these approaches are user-intensive, involving algorithm tuning and/or manual peak-picking. In order to handle the high volume of data associated with the acoustic sensing network, it was desirable to automate the modal travel time estimation process.…”