Estimation of mesoscale sound speed perturbations via underwater acoustic tomography

1983 ◽  
Vol 74 (S1) ◽  
pp. S98-S98
Author(s):  
Ching‐Sang Chiu ◽  
Yves Desaubies
Keyword(s):  
Sound Speed ◽  
Acoustic Tomography ◽  
Underwater Acoustic

scholarly journals Travel-time correction and preliminary results for ocean acoustic tomography in South China Sea

2019 ◽  
Vol 283 ◽  
pp. 04003
Author(s):  
Xingyu Ji ◽  
Hangfang Zhao
Keyword(s):  
Kalman Filter ◽  
South China Sea ◽  
Travel Time ◽  
South China ◽  
Sound Speed ◽  
Acoustic Tomography ◽  
Speed Profile ◽  
Travel Times ◽  
Sound Speed Profile ◽  
Hydrophone Array

An acoustic tomography trial experiment was conducted in South China Sea during May to August in 2016. Two moorings are installed apart from about 56.94 km, while each consists of one low frequency source, 20 hydrophones deployed from the depth of about 400 m to 1600 m, total 32 depth sensors and 3 compass and tilt sensors. Due to internal waves and currents in this area, as a typical value, horizontal drift of a mooring can reach 300 m, thus moorings drift need to be considered to correct ray travel-time. In this paper, the shape of a mooring is estimated firstly and locations of all hydrophone array elements are then calculated and finally used to determine travel-time perturbation of acoustic arrivals. The mooring is modelled as 2 curves, while the end of the mooring is fixed at the cement anchor on the sea floor. Optimization is used to acquire hydrophone location inferential solution. The inferred shape of hydrophone array and element locations are used to correct the travel-times measured in the experiment. We find that corrected travel-times match the trend of the change of sound speed profile better in the sea. Finally, the corrected travel-times are used to tomography of sound speed profile. AR (Autoregressive) process is used to describe the dynamic evolution of sound speed profile and Kalman filter is applied in the sequential estimation. The performances of the time-independent method and the method using AR process and Kalman filter are compared, reasonably the latter is better than the former in particular with abundant measured data.

Underwater Acoustic Tomography

1985 ◽  
Vol 35 (2) ◽  
pp. 151-161 ◽  
Author(s):  
V. K. Aatre
Keyword(s):  
Acoustic Tomography ◽  
Underwater Acoustic

Vertical underwater acoustic tomography in an experimental basin

2017 ◽  
Vol 141 (5) ◽  
pp. 3656-3656 ◽  
Author(s):  
Guangming Li ◽  
David Ingram ◽  
Arata Kaneko ◽  
Minmo Chen ◽  
Noriaki Gohda ◽  
...  
Keyword(s):  
Acoustic Tomography ◽  
Underwater Acoustic ◽  
Experimental Basin

scholarly journals An Acoustic Tomography Technique for Concurrently Observing the Structure of the Atmosphere and Water Bodies

2017 ◽  
Vol 34 (3) ◽  
pp. 617-629 ◽  
Author(s):  
Anthony Finn ◽  
Kevin Rogers
Keyword(s):  
Theoretical Analysis ◽  
Sound Speed ◽  
Water Surface ◽  
Low Frequency ◽  
Sound Field ◽  
Acoustic Signals ◽  
Acoustic Tomography ◽  
Radio Waves ◽  
Surface Errors ◽  
Underwater Sensors

AbstractThe opacity of water to radio waves means there are few, if any, techniques for remotely sensing it and the atmosphere concurrently. However, both these media are transparent to low-frequency sound (<300 Hz), which makes it possible to contemplate systems that take advantage of the natural integration along acoustic paths of signals propagating through both media. This paper proposes—and examines with theoretical analysis—a method that exploits the harmonics generated by the natural signature of a propeller-driven aircraft as it overflies an array of surface and underwater sensors. Correspondence of the projected and observed narrowband acoustic signals, which are monitored synchronously on board the aircraft and by both sensor sets, allows the exact travel time of detected rays to be related to a linear model of the constituent terms of sound speed. These observations may then be inverted using tomography to determine the inhomogeneous structures of both regions. As the signature of the aircraft comprises a series of harmonics between 50 Hz and 1 kHz, the horizontal detection limits of such a system may be up to a few hundred meters, depending on the depth of the sensors, roughness of the water surface, errors due to refraction, and magnitude of the sound field generated by the source aircraft. The approach would permit temperature, wind, and current velocity profiles to be observed both above and below the water’s surface.

Experiments on underwater acoustic tomography

2007 ◽  
Vol 53 (3) ◽  
pp. 393-416 ◽  
Author(s):  
Yu. A. Chepurin
Keyword(s):  
Acoustic Tomography ◽  
Underwater Acoustic

A Comparative Study of Moored/Point and Acoustic Tomography/Integral Observations of Sound Speed in Fram Strait Using Objective Mapping Techniques

2016 ◽  
Vol 33 (10) ◽  
pp. 2079-2093 ◽  
Author(s):  
Brian D. Dushaw ◽  
Hanne Sagen
Keyword(s):  
Sound Speed ◽  
Heat Content ◽  
Ocean Model ◽  
The Arctic ◽  
Mooring Line ◽  
Acoustic Tomography ◽  
Fram Strait ◽  
Data Types ◽  
Parameterized Model ◽  
Ocean Environment

AbstractEstimation of the exchange of seawater of various properties between the Arctic and North Atlantic Oceans presents a challenging observational problem. The strong current systems within Fram Strait induce recirculations and a turbulent ocean environment dominated by mesoscale variations of 4–10-km scale. By employing a simple parameterized model for mesoscale variability within Fram Strait, the authors examine the ability of a line array of closely spaced moorings and an acoustic tomography line to measure the average sound speed, a proxy variable for ocean temperature or heat content. Objective maps are employed to quantify the uncertainties resulting from the different measurement approaches. While measurements by a mooring line and tomography result in similar uncertainties in estimations of range- and depth-averaged sound speed, the combination of the two approaches gives uncertainties 3 times smaller. The two measurements are sufficiently different as to be complementary; one measurement provides resolution for the aspects of the temperature section that the other misses. The parameterized model and its assumptions as to the magnitudes and scales of variability were tested by application to a hydrographic section across Fram Strait measured in 2011. This study supports the deployment of the 2013–16 Arctic Ocean under Melting Ice (UNDER-ICE) network of tomographic transceivers spanning the ongoing moored array line across Fram Strait. Optimal estimation for this ocean environment may require combining disparate data types as constraints on a numerical ocean model using data assimilation.

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