Volume 41 Issue 9
Aug.  2022
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Xiaoman Li, Biao Wang, Xuejie Bi, Hong Wu. A fast inversion method for ocean parameters based on dispersion curves with a single hydrophone[J]. Acta Oceanologica Sinica, 2022, 41(9): 71-85. doi: 10.1007/s13131-022-1999-z
Citation: Xiaoman Li, Biao Wang, Xuejie Bi, Hong Wu. A fast inversion method for ocean parameters based on dispersion curves with a single hydrophone[J]. Acta Oceanologica Sinica, 2022, 41(9): 71-85. doi: 10.1007/s13131-022-1999-z

A fast inversion method for ocean parameters based on dispersion curves with a single hydrophone

doi: 10.1007/s13131-022-1999-z
Funds:  The Scientific Research Foundation of Jiangsu University of Science and Technology for Recruited Talents under contract No. 1032931907; the Basic Science (Natural Science) General Program of Jiangsu Province Higher Education Institutions under contract No. 21KJD140001.
More Information
  • Corresponding author: E-mail: lixiaoman@just.edu.cn
  • Received Date: 2021-08-23
  • Accepted Date: 2021-11-21
  • Available Online: 2022-06-10
  • Publish Date: 2022-08-31
  • The dispersion characteristics of shallow water can be described by the dispersion curves, which contain substantial ocean parameter information. A fast ocean parameter inversion method based on dispersion curves with a single hydrophone is presented in this paper. The method is achieved through Bayesian theory. Several sets of dispersion curves extracted from measured data are used as the input function. The inversion is performed by matching a replica calculated with a dispersion formula. The bottom characteristics can be described by the bottom reflection phase shift parameter P. The propagation range and the depth can be inverted quickly when the seabed parameters are represented by on parameter P. The inversion results improve the inversion efficiency of the seabed parameters. Consequently, the inversion efficiency and accuracy are improved while the number of inversion parameters is decreased and the computational speed of replica is increased. The inversion results have lower error than the reference values, and the dispersion curves calculated with inversion parameters are also in good agreement with extracted curves from measured data; thus, the effectiveness of the inversion method is demonstrated.
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  • [1]
    Bao Qingliu, Zhang Youguang, Lin Mingsen, et al. 2017. An ocean current inversion accuracy analysis based on a Doppler spectrum model. Acta Oceanologica Sinica, 36(9): 101–107. doi: 10.1007/s13131-017-1115-y
    [2]
    Bonnel J, Dosso S E, Chapma N R. 2013. Bayesian geoacoustic inversion of single hydrophone light bulb data using warping dispersion analysis. The Journal of the Acoustical Society of America, 134(1): 120–130. doi: 10.1121/1.4809678
    [3]
    Bonnel J, Gervaise C, Nicolas B, et al. 2012. Single-receiver geoacoustic inversion using modal reversal. The Journal of the Acoustical Society of America, 131(1): 119–128. doi: 10.1121/1.3664083
    [4]
    Bonnel J, Nicolas B, Mars J I, et al. 2010. Estimation of modal group velocities with a single receiver for geoacoustic inversion in shallow water. The Journal of the Acoustical Society of America, 128(2): 719–727. doi: 10.1121/1.3459855
    [5]
    Bonnel J, Thode A, Wright D, et al. 2020. Nonlinear time-warping made simple: a step-by-step tutorial on underwater acoustic modal separation with a single hydrophone. The Journal of the Acoustical Society of America, 147(3): 1897–1926. doi: 10.1121/10.0000937
    [6]
    Cai Haiyan, Jiang Qingtang, Li Lin, et al. 2021. Analysis of adaptive short-time Fourier transform-based synchrosqueezing transform. Analysis and Applications, 19(1): 71–105. doi: 10.1142/S0219530520400047
    [7]
    Dosso S E. 2002. Quantifying uncertainty in geoacoustic inversion. I. A fast Gibbs sampler approach. The Journal of the Acoustical Society of America, 111(1): 129–142. doi: 10.1121/1.1419086
    [8]
    Dosso S E, Wilmut M J. 2008. Uncertainty estimation in simultaneous Bayesian tracking and environmental inversion. The Journal of the Acoustical Society of America, 124(1): 82–97. doi: 10.1121/1.2918244
    [9]
    Dosso S E, Wilmut M J. 2011. Bayesian multiple-source localization in an uncertain ocean environment. The Journal of the Acoustical Society of America, 129(6): 3577–3589. doi: 10.1121/1.3575594
    [10]
    Fallat M R, Dosso S E. 1999. Geoacoustic inversion via local, global, and hybrid algorithms. The Journal of the Acoustical Society of America, 105(6): 3219–3230. doi: 10.1121/1.424651
    [11]
    Gingras D F, Gerstoft P. 1995. Inversion for geometric and geoacoustic parameters in shallow water: experimental results. The Journal of the Acoustical Society of America, 97(6): 3589–3598. doi: 10.1121/1.412442
    [12]
    Heaney K D. 2004. Rapid geoacoustic characterization using a surface ship of opportunity. IEEE Journal of Oceanic Engineering, 29(1): 88–99. doi: 10.1109/JOE.2003.823286
    [13]
    Jensen F B, Kuperman W A, Porter M B, et al. 2011. Computational Ocean Acoustics. 2nd ed. New York: Springer, 354–356
    [14]
    Le Gac J C, Asch M, Stephan Y, et al. 2003. Geoacoustic inversion of broad-band acoustic data in shallow water on a single hydrophone. IEEE Journal of Oceanic Engineering, 28(3): 479–493. doi: 10.1109/JOE.2003.816689
    [15]
    Le Touze G, Nicolas B, Mars J I, et al. 2009. Matched representations and filters for guided waves. IEEE Transactions on Signal Processing, 57(5): 1783–1795. doi: 10.1109/TSP.2009.2013907
    [16]
    Li Xiaoman, Piao Shengchun, Zhang Minghui, et al. 2019a. A passive source location method in a shallow water waveguide with a single sensor based on Bayesian theory. Sensors (Basel), 19(6): 1452. doi: 10.3390/s19061452
    [17]
    Li Qianqian, Shi Juan, Li Zhenglin, et al. 2019b. Acoustic sound speed profile inversion based on orthogonal matching pursuit. Acta Oceanologica Sinica, 38(11): 149–157. doi: 10.1007/s13131-019-1505-4
    [18]
    Li Qianqian, Yang Fanlin, Zhang Kai, et al. 2016. Moving source parameter estimation in an uncertain environment. Acta Physica Sinica, 65(16): 164304. doi: 10.7498/aps.65.164304
    [19]
    Li Qianqian, Yang Fanlin, Zhang Kai. 2018. Multiple source localization using Bayesian theory in an uncertain environment. Haiyang Xuebao (in Chinese), 40(1): 39–46
    [20]
    Niu Haiqiang, Zhang Renhe, Li Zhenglin. 2014. Theoretical analysis of warping operators for non-ideal shallow water waveguides. The Journal of the Acoustical Society of America, 136(1): 53–65. doi: 10.1121/1.4883370
    [21]
    Porter M B. 1992. The KRAKEN normal mode program. Washington, DC: Naval Research Lab
    [22]
    Shang E C, Wu J R, Zhao Z D. 2012. Relating waveguide invariant and bottom reflection phase-shift parameter P in a Pekeris waveguide. The Journal of the Acoustical Society of America, 131(5): 3691–3697. doi: 10.1121/1.3699242
    [23]
    Walker S C, Roux P, Kuperman W A. 2005. Data-based mode extraction with a partial water column spanning array. The Journal of the Acoustical Society of America, 118(3): 1518–1525. doi: 10.1121/1.1993149
    [24]
    Wang Dong, Guo Lianghao, Liu Jianjun, et al. 2016. Passive impulsive source range estimation based on warping operator in shallow water. Acta Physica Sinica, 65(10): 104302. doi: 10.7498/aps.65.104302
    [25]
    Wang Dezhao, Shang Erchang. 2013. Underwater Acoustics. 2rd ed. Beijing: Science Press, 158–164
    [26]
    Whitley D. 1994. A genetic algorithm tutorial. Statistics and Computing, 4(2): 65–85
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