MATEC Web Conf.
Volume 283, 2019The 2nd Franco-Chinese Acoustic Conference (FCAC 2018)
|Number of page(s)||5|
|Published online||28 June 2019|
Travel-time correction and preliminary results for ocean acoustic tomography in South China Sea
1 Department of Information Science and Electronic Engineering, Zhejiang University, 310027, Hangzhou, China
2 Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province, 316021, Zhoushan, China
* Corresponding author: email@example.com
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.
© The Authors, published by EDP Sciences, 2019
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