环境-声场不确定性传递过程中的代理建模方法

刘宗伟; 吕连港; 杨春梅; 姜 莹; 于晓林; 黄龙飞

doi:10.11993/j.issn.2096-3920.2018.05.004

环境-声场不确定性传递过程中的代理建模方法

doi: 10.11993/j.issn.2096-3920.2018.05.004

1.
国家海洋局第一海洋研究所, 山东青岛, 266061
2.
青岛海洋科学与技术国家实验室区域海洋动力学与数值模拟功能实验室, 山东青岛, 266237
3.
海洋环境科学与数值模拟国家海洋局重点实验室, 山东青岛, 266061

基金项目: 国家重点研发计划(2016YFC1400200); 国家自然科学基金项目(41706113)

详细信息

作者简介:
刘宗伟(1986-), 男, 博士, 助理研究员, 主要研究方向为海洋声学模型及信号处理.

中图分类号: TJ630.34; O427.3; TB566
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出版历程
- 刊出日期: 2018-10-31

Novel Surrogate Modeling Method in Uncertainty Propagation from Environment to Acoustic Field

1.
The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China
2.
Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
3.
Key Laboratory of Marine Sciences and Numerical Modeling, State Oceanic Administration, Qingdao 266061, China

摘要

摘要: 海洋环境参数的不确定性是声场预报不确定性的主要来源之一。海洋环境参数和声场之间通常具有非常强的非线性关系, 导致通过海洋环境参数的不确定性来计算声场的不确定性较为困难。传统使用的蒙特卡罗方法, 需要多次运行声场计算模型, 导致计算量过大。针对此, 文中提出了一种新的基于多项式-克里金方法(PC-Kriging)的代理建模方法, 可以高效地计算海洋环境参数至声场之间的不确定性传递过程。该方法使用多项式方法来提取系统响应的全局趋势, 使用克里金方法逼近局地响应。同时, 基于标准失配测试模型进行了计算机仿真, 从模型逼近精度以及传播损失概率密度函数(PDF)2个方面进行验证。结果表明: PC-Kriging代理建模方法在逼近精度上优于单独使用多项式或者克里金方法, 传播损失PDF也与蒙特卡罗方法结果吻合得较好, 适合用于环境-声场不确定性传递过程计算。
- 水下声场预报 /
- 环境-声场不确定性传递 /
- 代理建模 /
- 多项式-克里金方法 /
- 标准失配测试模型
Abstract: Uncertainty of ocean environmental parameters is one of the main sources of uncertainty in underwater acoustic field prediction. In addition, the relationship between the environmental parameters and the acoustic field may be highly nonlinear, so it is difficult to calculate the uncertainty of acoustic field through the uncertainty of ocean environmental parameters. The traditional Monte Carlo method needs to run the acoustic field calculation model many times, which results in too much calculation. To overcome the drawbacks of the existing methods, this paper proposes a new surrogate modeling method based on polynomial-chaos-Kriging(PC-Kriging) which can calculate the uncertainty transfer process efficiently from ocean environmental parameters to acoustic field. This method extracts the global trend of the system response using the polynomial chaos expansions(PCE) and approaches the local response using the Kriging method. Computer simulations using the general mismatched benchmark acoustic environmental model is performed to verify the proposed method in two aspects — the accuracy of the model approaching accuracy and the probability density function of the propagation loss. The results indicate that the surrogate modeling method based on the PC-Kriging is efficient and better in accuracy than the PCE or Kriging method. The probability density functions of propagation loss obtained by PC-Kriging and direct Monte Carlo are consistent, showing the suitability of PC-Kriging for the uncertainty propagation from environment to acoustic field.

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参考文献(16)

[1]	[1] Pace N G, Jensen F. Impact of Littoral Environmental Variability of Acoustic Predictions and Sonar Perfor-mance[J]. Springer Netherlands, 2002, 11(12): 213-218.
[2]	Pecknold S P, Masui K W, Hines P C. Transmission Loss Measurements and Geoacoustic Sensitivity Modeling at 1.2 kHz[J]. Journal of the Acoustical Society of America, 2008, 124(3): EL110-EL115.
[3]	Huang C F, Gerstoft P, Hodgkiss W S. Validation of Sta-tistical Estimation of Transmission Loss in the Presence of Geoacoustic Inversion Uncertainty[J]. Journal of the Acoustical Society of America, 2006, 119(5): 3224-3225.
[4]	Finette S. A Stochastic Representation of Environmental Uncertainty and Its Coupling to Acoustic Wave Propaga-tion in Ocean Waveguides[J]. Journal of the Acoustical Society of America, 2006, 120(5): 2567-2579.
[5]	James K R, Dowling D R. A Method for Approximating Acoustic-field-amplitude Uncertainty Caused by Envi-ronmental Uncertainties[J]. Journal of the Acoustical So-ciety of America, 2008, 124(3): 1465-1476.
[6]	James K R. Uncertainty in Underwater Acoustic Field Prediction[D]. Michigan: University of Michigan, 2009.
[7]	Finette S. Embedding Uncertainty into Ocean Acoustic Propagation Models[J]. The Journal of the Acoustical Society of America, 2005, 117(3): 997-1000.
[8]	Finette S. A Stochastic Response Surface Formulation of Acoustic Propagation Through an Uncertain Ocean Waveguide Environment[J]. The Journal of the Acoustical Society of America, 2009, 126(5): 2242-2247.
[9]	Liu Z W, Sun C, Du J Y. Efficient Environmental Uncer-tainty Propagation Using the Probabilistic Collocation Method[C]//Oceans Proceedings of MTS/IEEE. Hampton Roads, VA, USA: IEEE, 2012 Oceans, 2012 : 1-6.
[10]	Gerdes F, Finette S. A Stochastic Response Surface For-mulation for the Description of Acoustic Propagation Through an Uncertain Internal Wave Field[J]. The Journal of the Acoustical Society of America, 2012, 132(4): 2251-2264.
[11]	程广利, 张明敏, 胡金华. 一种更具普适性的浅海不确定声场快速算法[J]. 物理学报, 2014, 63(8): 084301-1- 084301-8. Cheng Guang-li, Zhang Ming-min, Hu Jin-hua. A Fast and More Universal Algorithm for an Uncertain Acoustic Filed in Shallow-water[J]. Acta Physica Sinica, 2014, 63(8): 084301-1-084301-8.
[12]	Audet C, Denni J, Moore D, et al. A Surrogate-model- based Method for Constrained Optimization[C]//8th Symposium on Multidisciplinary Analysis and Optimization. Long Beach, CA: AIAA, 2000: 4891.
[13]	Goel T, Thakur S, Haftka R T, et al. Surrogate ModelBased Strategy for Cryogenic Cavitation Model Validation and Sensitivity Evaluation[J]. International Journal for Numerical Methods in Fluids, 2008, 58(9): 969-1007.
[14]	Schobi R, Sudret B, Wiart J. Polynomial-chaos-based Kriging[J]. International Journal for Uncertainty Quanti-fication, 2015, 5(2): 55-63.
[15]	Marelli S, Sudret B. UQLab: A Framework for Uncertainty Quantification in Matlab[C]//SIAM Conference on Uncertainty Quantification. Savannah, GA, USA: ETH- Zürich, 2014: 2554.
[16]	Porter M B, Tolstoy A. The Matched Field Processing Benchmark Problems[J]. Journal of Computational Acoustics, 1994, 2(3): 161-185.