Application of Dual-Source Modal Dispersion and Variational Bayesian Monte Carlo Method for Local Geoacoustic Inversion in Weakly Range-Dependent Shallow Water

Acoustics Australia - Tập 51 - Trang 23-38 - 2022
Wang Hao1,2, Rui Duan1,2, Kunde Yang1,2
1School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, China
2Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi’an, China

Tóm tắt

Most of the continental shelf area is a weakly range-dependent shallow-water environment. Compared with range-independent Bayesian geoacoustic inversion, range-dependent inversion usually has problems with the complex forward model and low efficiency for posterior analysis. According to the adiabatic normal-mode theory, the weakly range-dependent shallow-water environment can be divided into a series of range-independent segments; thus, this paper proposes a dual-source modal dispersion inversion method for local geoacoustic parameters of a segment based on a range-independent forward model. In addition, considering that the computational cost of the forward model limits the application of sampling-based methods for posterior analysis, a novel approximate variational inference, namely variational Bayesian Monte Carlo, is applied in this study. It has superior efficiency and shows similar accuracy compared with Markov Chain Monte Carlo sampling. This work is demonstrated in the shallow-water experiment in the continental shelf area of the East China Sea, and the results indicate that the local and range-dependent geoacoustic parameters are well-estimated.

Tài liệu tham khảo

Chapman, N.R., Shang, E.C.: Review of geoacoustic inversion in underwater acoustics. J. Theor. Comput. Acoust. 29, 2130004 (2021). https://doi.org/10.1142/S259172852130004X Yardim, C., Gerstoft, P., Hodgkiss, W.S.: Tracking of geoacoustic parameters using Kalman and particle filters. J. Acoust. Soc. Am. 125, 746–760 (2009). https://doi.org/10.1121/1.3050280 Siderius, M., Nielsen, P.L., Gerstoft, P.: Range-dependent seabed characterization by inversion of acoustic data from a towed receiver array. J. Acoust. Soc. Am. 112, 13 (2002) Dettmer, J., Dosso, S.E., Holland, C.W.: Sequential trans-dimensional Monte Carlo for range-dependent geoacoustic inversion. J. Acoust. Soc. Am. 129, 1794–1806 (2011). https://doi.org/10.1121/1.3557052 Ross Chapman, N., Chin-Bing, S., King, D., Evans, R.B.: Benchmarking geoacoustic inversion methods for range-dependent waveguides. IEEE J. Oceanic Eng. 28, 320–330 (2003). https://doi.org/10.1109/JOE.2003.816737 Yardim, C., Gerstoft, P., Hodgkiss, W.S.: Geoacoustic and source tracking using particle filtering: experimental results. J. Acoust. Soc. Am. 128, 75–87 (2010). https://doi.org/10.1121/1.3438475 Yardim, C., Gerstoft, P., Hodgkiss, W.S.: Sequential geoacoustic inversion at the continental shelfbreak. J. Acoust. Soc. Am. 131, 12 (2012) Jensen, F.B., Kuperman, W.A., Porter, M.B., Schmidt, H.: Computational Ocean Acoustic, 2nd edn. Springer, New York (2011) Bonnel, J., Dosso, S.E., Ross Chapman, N.: Bayesian geoacoustic inversion of single hydrophone light bulb data using warping dispersion analysis. J. Acoust. Soc. Am. 134, 120–130 (2013). https://doi.org/10.1121/1.4809678 Duan, R., Ross Chapman, N., Yang, K., Ma, Y.: Sequential inversion of modal data for sound attenuation in sediment at the New Jersey Shelf. J. Acoust. Soc. Am. 139, 70–84 (2016). https://doi.org/10.1121/1.4939122 Guo, X., Yang, K., Duan, R., Ma, Y.: Sequential inversion for geoacoustic parameters in the south China sea using modal dispersion curves. Acoust. Aust. 45, 119–129 (2017). https://doi.org/10.1007/s40857-017-0082-y Bonnel, J., Dosso, S.E., Goff, J.A., Lin, Y.-T., Miller, J.H., Potty, R.G., Wilson, P.S., Knobles, D.P.: Transdimensional geoacoustic inversion using prior information on range-dependent seabed layering. IEEE J. Ocean. Eng. (2021). https://doi.org/10.1109/JOE.2021.3062719 Tollefsen, D., Dosso, S.E., Wilmut, M.J.: Matched-field geoacoustic inversion with a horizontal array and low-level source. J. Acoust. Soc. Am. 120, 221–230 (2006). https://doi.org/10.1121/1.2205132 Dosso, S.E., Dettmer, J.: Bayesian matched-field geoacoustic inversion. Inverse Prob. 27, 055009 (2011). https://doi.org/10.1088/0266-5611/27/5/055009 Shen, Y., Pan, X., Zheng, Z., Gerstoft, P.: Matched-field geoacoustic inversion based on radial basis function neural network. J. Acoust. Soc. Am. 148, 3279–3290 (2020). https://doi.org/10.1121/10.0002656 Yang, K., Xu, L., Yang, Q., Li, G.: Two-step inversion of geoacoustic parameters with bottom reverberation and transmission loss in the deep ocean. Acoust. Aust. 46, 131–142 (2018). https://doi.org/10.1007/s40857-018-0130-2 Xu, L., Yang, K., Yang, Q.: geoacoustic inversion using physical-statistical bottom reverberation model in the deep ocean. Acoust. Aust. 47, 261–269 (2019). https://doi.org/10.1007/s40857-019-00164-3 Yu, S., Liu, B., Yu, K., Yang, Z., Kan, G., Zong, L.: Inversion of bottom parameters using a backscattering model based on the effective density fluid approximation. Appl. Acoust. 182, 108187 (2021). https://doi.org/10.1016/j.apacoust.2021.108187 Dosso, S.E., Holland, C.W.: Geoacoustic uncertainties from viscoelastic inversion of seabed reflection data. IEEE J. Ocean. Eng. 31, 657–671 (2006). https://doi.org/10.1109/JOE.2005.858358 Chapman, N.: Perspectives on geoacoustic inversion of ocean bottom reflectivity data. J. Mar. Sci. Eng. 4, 61 (2016). https://doi.org/10.3390/jmse4030061 Yang, K., Xiao, P., Duan, R., Ma, Y.: Bayesian inversion for geoacoustic parameters from ocean bottom reflection loss. J. Comput. Acoust. 25, 1750019 (2017). https://doi.org/10.1142/S0218396X17500199 Dosso, S.E., Wilmut, M.J., Lapinski, A.-L.S.: An adaptive-hybrid algorithm for geoacoustic inversion. IEEE J. Ocean. Eng. 26, 324–336 (2001). https://doi.org/10.1109/48.946507 Liu, H., Yang, K., Yang, Q.: Sequential parameter estimation of modal dispersion curves with an adaptive particle filter in shallow water: experimental results. Remote Sens. 13, 2387 (2021). https://doi.org/10.3390/rs13122387 Blei, D.M., Kucukelbir, A., McAuliffe, J.D.: Variational inference: a review for statisticians. J. Am. Stat. Assoc. 112, 859–877 (2017). https://doi.org/10.1080/01621459.2017.1285773 Acerbi, L.: Variational bayesian monte carlo. In: Advances in neural information processing systems. http://arxiv.org/abs/1810.05558 (2018) Haario, H., Laine, M., Mira, A., Saksman, E.: DRAM: efficient adaptive MCMC. Stat. Comput. 16, 339–354 (2006). https://doi.org/10.1007/s11222-006-9438-0 Bonnel, J., Chapman, N.R.: Geoacoustic inversion in a dispersive waveguide using warping operators. J. Acoust. Soc. Am. 130, EL101–EL107 (2011). https://doi.org/10.1121/1.3611395 Bonnel, J., Thode, A., Wright, D., Chapman, R.: Nonlinear time-warping made simple: a step-by-step tutorial on underwater acoustic modal separation with a single hydrophone. J. Acoust. Soc. Am. 147, 1897–1926 (2020). https://doi.org/10.1121/10.0000937 Daubechies, I., Lu, J., Wu, H.-T.: Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl. Comput. Harmon. Anal. 30, 243–261 (2011). https://doi.org/10.1016/j.acha.2010.08.002 Oberlin, T., Meignen, S.: The second-order wavelet synchrosqueezing transform. In: 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). pp. 3994–3998. IEEE, New Orleans, LA (2017) Porter, M.B.: The KRAKEN Normal Mode Program. SACLANTCEN Memorandum, SM-245, La Spezia (1991) Dosso, S.E., Nielsen, P.L., Wilmut, M.J.: Data error covariance in matched-field geoacoustic inversion. J. Acoust. Soc. Am. 119, 208–219 (2006). https://doi.org/10.1121/1.2139625 Che, Y., Wu, X., Pastore, G., Li, W., Shirvan, K.: Application of kriging and variational bayesian monte carlo method for improved prediction of doped UO2 fission gas release. Ann. Nucl. Energy 153, 108046 (2021). https://doi.org/10.1016/j.anucene.2020.108046 Collins, M.D.: A split-step Padé solution for the parabolic equation method. J. Acoust. Soc. Am. 93, 1736–1742 (1993). https://doi.org/10.1121/1.406739 Berné, S., Vagner, P., Guichard, F., Lericolais, G., Liu, Z., Trentesaux, A., Yin, P., Yi, H.I.: Pleistocene forced regressions and tidal sand ridges in the East China Sea. Mar. Geol. 188, 293–315 (2002). https://doi.org/10.1016/S0025-3227(02)00446-2 Wu, Z., Jin, X., Cao, Z., Li, J., Zheng, Y., Shang, J.: Distribution, formation, and evolution of sand ridges on the East China Sea shelf. Sci. China Earth Sci. 53, 101–112 (2010). ((In Chinese)) Li, L., Wang, X., Cao, B., Shen, W., Yang, L.: 3D seismic geomorphology, evolution and genesis of shelf sand ridge, East China Sea. Geoscience 27, 783–790 (2013). ((In Chinese))