不确定海洋环境中基于贝叶斯理论的声源运动参数估计方法

环境参数失配导致定位性能大幅度下降是匹配场定位所面临的难题之一.应用贝叶斯理论对环境聚焦,是当前解决该难题的研究热点.环境聚焦方法的实质是将未知环境参数和声源位置联合优化估计.然而,运动声源的位置时变性限制了观测时间长度和观测信息量,因此不得不利用很有限的观测信息来实现众多参数的估计.当航速较快或是环境信息的不确定性较大时,环境聚焦方法的效果迅速变差.借鉴卡尔曼滤波处理非平稳过程的参数估计思想,对航速较恒定的声源,本文将多个时刻的接收信号同时反演,引入能够描述声源位置随时间变化规律的时不变参数,以较少的时不变参数间接反演多个声源位置,从而有效降低待估参数维数.同时将当前估计结果作为下一次反演的先验信息,建立新的先验分布和代价函数,有效补偿个别异常数据,实现运动声源的连续定位.该方法在相同的环境不确定条件下,大幅度增加了观测时间和观测信息量,可以较好地改善环境聚焦方法的定位效果.     

一种基于简正波模态频散的远距离宽带海底参数反演方法

在浅海环境中, 海底环境参数对声传播有着重要的影响. 由于利用单个宽带声源进行海底参数反演时, 随着距离的增大, 误差变大, 本文提出利用warping变换对在浅海波导中传播的, 不同距离上的两个宽带爆炸声源进行简正波的有效分离, 实现了宽带爆炸声源的远距离海底参数反演. 采用全局寻优遗传算法对提取出的模态频散到达时间差与理论计算的模态频散到达时间差进行匹配处理, 并结合随距离连续变化的声传播损失, 实现了利用单水听器进行海底参数的反演. 实验结果表明: 运用反演出的海底参数提取模态频散时间差和实测数据提取出的模态频散时间差吻合得较好; 而通过传播损失反演得到的海底衰减系数与频率呈指数关系. 最后, 对反演结果进行了后验概率分析, 并将本组爆炸声源的反演结果用于另一组不同距离上爆炸声源时仍然有效, 来评价反演结果的有效性.     

半经验关系与匹配场联合处理的爆炸声源快速定位

爆炸声源位置的快速准确获取对声源级测量和声传播计算具有重要意义。为了解决利用单一水听器进行爆炸声源定位时难以获得较好的定位效率和精度的问题,提出了一种基于半经验关系与匹配场联合处理的爆炸声源快速定位方法。首先通过爆炸声源满足的半经验关系,对爆炸位置进行预估,缩小匹配参数的搜索范围;同时,在基于多途时延差匹配定位理论的基础上,利用爆炸声源的半经验关系建立联合匹配定位方法,引入气泡脉动周期和冲击波峰值增加匹配物理信息,实现爆炸声源深度和距离精确反演。仿真分析与2013年南海水下爆炸声试验数据分析结果表明,一次气泡脉动周期与多途时延差的联合匹配可提高对爆炸声源深度的估计精度;冲击波峰值与多途时延差的联合匹配可提高对距离的估计精度。额外匹配量的引入减少了估计精度对接收阵元个数的依赖,能够实现用单阵元快速准确地进行爆炸源位置的估计。      

舰船辐射噪声贝叶斯方法反演浅海底层结构及地声参数EI北大核心CSCD

针对以舰船辐射噪声为参考声源的浅海海底分层结构及地声参数反演问题,研究了一种基于贝叶斯理论的浅海多层海底地声参数反演方法。反演中以舰船辐射噪声的线谱成分为研究对象,进而采用非线性贝叶斯反演方法反演浅海底层结构、层中声速、声速衰减和密度,并对反演结果的不确定性进行分析。反演结果的最大后验概率估计值和边缘概率分布分别通过拨正模拟退火算法和Metropolis-Hastings采样法在各参数先验区间内计算获得,并根据贝叶斯信息准则确定最佳海底分层结构。海上实验表明:根据该方法反演获得海底分层结构及地声参数,计算得到的声压场与实测舰船辐射噪声传播损失误差不超过10%,反演结果能够准确表征实验海区海底特征。反演结果不确定性分析表明:海底纵波声速、横波声速以及密度的不确定性更小,对声压场变化更加敏感,反演结果更有效、准确。     

利用气枪声源数据的地声参数反演*

本文通过对东海采集的气枪声源数据进行处理,基于地声参数对不同声场观测量的敏感性差异,利用分步反演的策略进行分析。首先选择距离接收阵七个不同的距离点数据,通过warping变换准确提取第三阶至第八阶简正波的频散曲线;利用海底声速对频散曲线敏感的特性来反演声速;由Hamilton经验公式求得海底密度;通过传播损失拟合获得海底衰减。不同距离点数据反演的海底声速与密度一致性较高。实验提取的频散曲线和和反演参数仿真结果、实验获取的传播损失与反演参数获得传播损失均拟合较好。     

利用机会运动声源的水平阵地声反演EI北大核心CSCD

针对利用机会运动声源的反演问题,提出一种对水平阵的非相干波束输出进行重构并获得海底声学参数的反演方法。不同于匹配场反演,该方法利用了声场的平滑平均原理,并且将衰减简正波的影响比例提升。仿真分析表明所提方法相比于匹配场反演对海底衰减系数更加敏感并且对声源的空间位置误差更加宽容。实验数据的反演结果表明研究海域在30~160 Hz频率范围内海底衰减系数随频率的变化关系为(0.34±0.18) f^(1.59±0.27)dB/m(f的单位是kHz),并且给出了由反演出的参数计算的传播损失曲线与实验的声传播损失数据的比较,所提方法比匹配场反演方法更准确地表征了声场传播特征。     

浅海声速剖面与移动声源的跟踪定位

在水平非均匀分布的浅海环境中,针对移动声源跟踪时,声速剖面的变化会对声场产生影响,提出了一种利用集合卡尔曼滤波算法的声速剖面跟踪反演和移动声源跟踪定位的方法。首先,将声速剖面进行距离和深度的参数化表示,从而将对声速剖面的跟踪转化为对声速剖面前3阶经验正交函数系数的跟踪;其次,通过将声源状态信息和声速剖面信息表示为状态变量,而将垂直线列阵接收到的声场信息作为测量值建立状态-测量模型,然后利用集合卡尔曼滤波方法对模型状态变量进行跟踪。仿真结果得出:声速剖面跟踪反演的均方根误差和移动声源跟踪定位的绝对误差都非常小,对声源的跟踪定位精度很高。并且通过增加集合样本数、增加接收信号信噪比以及增加接收阵元数目都可以提高跟踪定位结果精度。最后,利用东海实验数据对本方法进行了验证。      

利用拖船自噪声进行浅海环境参数贝叶斯反演

研究了以拖船自噪声为参考声源的浅海环境参数反演问题,并针对反演结果不确定性快速量化评估问题,提出了一种基于自适应重要性抽样的贝叶斯反演新方法。反演利用了拖船自噪声低频线谱成分,并采用混合高斯推荐函数自适应推荐声场模型样本,使得样本集中于参数高概率密度区域,实现后验概率密度快速收敛计算。仿真试验结果表明:拖船自噪声反演能够准确估计水深、沉积层及阵列参数等。所提自适应重要性抽样贝叶斯反演方法的计算效率优于快速吉布斯抽样方法。利用试验数据处理验证,反演得到试验海域声学环境参数,计算传播损失与各阵元接收线谱强度变化吻合,说明反演最优环境模型能准确表征声场传播特征。      

一种地声参数的联合反演方法

根据地声参数对不同声场物理量影响不同,提出了一种利用简正波频散特征结合声传播损失反演地声参数的联合反演方法。首先,考虑到简正波的频散特性(群速度)对海底的密度和声速较为敏感,而对海底吸收系数不敏感,利用自适应时频分析方法,获得不同频率不同号数简正波的到达时间差,以此作为代价函数,采用全局优化算法,反演出海底密度和海底声速的分层结构,并用概率统计的方法评价反演结果的有效性。反演出海底密度和声速后,利用实验船辐射噪声得到随距离连续的声传播损失来反演出海底吸收系数。最后,把反演的参数很好的用于声源匹配定位验证了反演结果的有效性。      

利用拖船辐射噪声的深海声学参数近场匹配场反演方法

针对深海声学参数难以通过远距离合作声源反演获取的问题,提出了利用拖船低频噪声近场匹配场反演方法。首先,利用聚焦波束形成计算拖曳阵接收拖船噪声的方向性,获得传播路径特征;然后,构建多参数反演模型,由波数积分声传播模型计算拷贝场,采用遗传算法对多频匹配场目标函数进行反演。同时,采用蒙特卡罗方法分析参数后验概率密度。仿真与试验结果表明:深海环境中拖曳阵接收拖船噪声主要来自海底反射路径,利用该特性反演得到海水深度、噪声源距离、阵列深度、沉积层厚度等参数,多频联合反演可以提高沉积层厚度等参数反演准确性。宽带匹配场处理表明,利用反演最优参数模型能准确给出拖船噪声源的空间位置。      

Bayesian localization in an uncertain ocean environment

In order to improve the ability to localize a source in an uncertain acoustic environment,a Bayesian approach,referred to here as Bayesian localization is used by including the environment in the parameter search space.Genetic algorithms are used for the parameter optimization.This method integrates the a posterior probability density(PPD) over environmental parameters to obtain a sequence of marginal probability distributions over source range and depth,from which the most-probable source location and localization uncertainties can be extracted.Considering that the seabed density and attenuation are less sensitive to the objective function of matched field processing,we utilize the empirical relationship to invert those parameters indirectly.The broadband signals recorded by a vertical line array in a Yellow Sea experiment in 2000 are processed and analyzed.It was found that,the Bayesian localization method that incorporates the environmental variability into the processor,made it robust to the uncertainty in the ocean environment.In addition,using the empirical relationship could enhance the localization accuracy.     

Bayesian multiple-source localization in an uncertain ocean environment

This paper considers simultaneous localization of multiple acoustic sources when properties of the ocean environment (water column and seabed) are poorly known. A Bayesian formulation is developed in which the environmental parameters, noise statistics, and locations and complex strengths (amplitudes and phases) of multiple sources are considered to be unknown random variables constrained by acoustic data and prior information. Two approaches are considered for estimating source parameters. Focalization maximizes the posterior probability density (PPD) over all parameters using adaptive hybrid optimization. Marginalization integrates the PPD using efficient Markov-chain Monte Carlo methods to produce joint marginal probability distributions for source ranges and depths, from which source locations are obtained. This approach also provides quantitative uncertainty analysis for all parameters, which can aid in understanding of the inverse problem and may be of practical interest (e.g., source-strength probability distributions). In both approaches, closed-form maximum-likelihood expressions for source strengths and noise variance at each frequency allow these parameters to be sampled implicitly, substantially reducing the dimensionality and difficulty of the inversion. Examples are presented of both approaches applied to single- and multi-frequency localization of multiple sources in an uncertain shallow-water environment, and a Monte Carlo performance evaluation study is carried out.     

Bayesian Tracking in an Uncertain Shallow Water Environment

A Bayesian source tracking approach is developed to track a moving acoustic source in an uncertain ocean environment.This approach treats the environmental parameters(e.g.,water depth,sediment and bottom parameters) at the source location and the source parameters(e.g.,source depth,range and speed) as unknown random variables that evolve as the source moves.To track a target with low signal-to-noise ratio(SNR),acoustic signals from a series of observations are treated in a simultaneous inversion.This allows real-time updating of the environment and accurate tracking of the moving source.The noise signals radiated from a surface ship target are processed and analyzed.It is found that the Bayesian source tracking method could enhance the localization accuracy in an uncertain water environment and low SNR.     

Uncertainty estimation in simultaneous Bayesian tracking and environmental inversion

This paper develops a Bayesian approach for two related inverse problems: tracking an acoustic source when ocean environmental parameters are unknown, and determining environmental parameters using acoustic data from an unknown (moving) source. The formulation considers source and environmental parameters as unknown random variables constrained by noisy acoustic data and by prior information on parameter values (e.g., physical limits for environmental properties) and on inter-parameter relationships (limits on radial and vertical source speed). The goal is not simply to estimate parameter values, but to rigorously determine parameter uncertainty distributions, thereby quantifying the information content of the data/prior to resolve source and environmental parameters. Results are presented as marginal posterior probability densities (PPDs) for environmental parameters and joint marginal PPDs for source ranges and depths. Given the numerically intensive inversion, an efficient Markov-chain Monte Carlo importance-sampling approach is developed which combines Metropolis and heat-bath Gibbs' sampling, employs efficient proposal distributions based on a linearized PPD approximation, and considers nonunity sampling temperatures to ensure a complete parameter search. The approach is illustrated with two simulated examples representing tracking a quiet submerged source and geoacoustic inversion using noise from an unknown ship of opportunity. In both cases, source, seabed, and water-column parameters are unknown.     

海洋—声学耦合过程不确定性快速预报

针对海洋声速空间非均匀和时变条件下的声场不确定性快速预报问题,根据海洋-声学耦合模式预报声速场时空变化过程,获取声速垂直结构不确定性分布规律,提出经验正交函数-随机多项式展开方法,以降低不确定参数维度,得到声场不确定性分布.数值计算表明该方法在保证同等精度的同时可大幅减少计算量,相比常规随机多项式展开方法计算效率可提高2个数量级,且计算量不随声速剖面复杂程度变化而改变.经海上实验验证,结果表明该方法可实现环境不确定性条件下的声场及其不确定性的快速预报。      

Uncertainty analysis in matched-field geoacoustic inversions

Quantifying uncertainty for parameter estimates obtained from matched-field geoacoustic inversions using a Bayesian approach requires estimation of the uncertainties in the data due to ambient noise as well as modeling errors. In this study, the variance parameter of the Gaussian error model, hereafter called error variance, is assumed to describe the data uncertainty. In practice, this parameter is not known a priori, and choosing a particular value is often difficult. Hence, to account for the uncertainty in error variance, several methods are introduced for implementing both the full and empirical Bayesian approaches. A full Bayesian approach that permits uncertainty of the error variance to propagate through the parameter estimation processes is a natural way of incorporating the uncertainty of error variance. Due to the large number of unknown parameters in the full Bayesian uncertainty analysis, an alternative, the empirical Bayesian approach, is developed, in which the posterior distributions of model parameters are conditioned on a point estimate of the error variance. Comparisons between the full and empirical Bayesian inferences of model parameters are presented using both synthetic and experimental data.     

Geoacoustic Inversion for Bottom Parameters via Bayesian Theory in Deep Ocean

We develop a new approach to estimating bottom parameters based on the Bayesian theory in deep ocean.The solution in a Bayesian inversion is characterized by its posterior probability density(PPD),which combines prior information about the model with information from an observed data set.Bottom parameters are sensitive to the transmission loss(TL)data in shadow zones of deep ocean.In this study,TLs of different frequencies from the South China Sea in the summer of 2014 are used as the observed data sets.The interpretation of the multidimensional PPD requires the calculation of its moments,such as the mean,covariance,and marginal distributions,which provide parameter estimates and uncertainties.Considering that the sensitivities of shallowzone TLs vary for different frequencies of the bottom parameters in the deep ocean,this research obtains bottom parameters at varying frequencies.Then,the inversion results are compared with the sampling data and the correlations between bottom parameters are determined.Furthermore,we show the inversion results for multifrequency combined inversion.The inversion results are verified by the experimental TLs and the numerical results,which are calculated using the inverted bottom parameters for different source depths and receiver depths at the corresponding frequency.     

A Bayesian Approach to the Estimation of Parameters and Their Interdependencies in Environmental Modeling

We present a case study for Bayesian analysis and proper representation of distributions and dependence among parameters when calibrating process-oriented environmental models. A simple water quality model for the Elbe River (Germany) is referred to as an example, but the approach is applicable to a wide range of environmental models with time-series output. Model parameters are estimated by Bayesian inference via Markov Chain Monte Carlo (MCMC) sampling. While the best-fit solution matches usual least-squares model calibration (with a penalty term for excessive parameter values), the Bayesian approach has the advantage of yielding a joint probability distribution for parameters. This posterior distribution encompasses all possible parameter combinations that produce a simulation output that fits observed data within measurement and modeling uncertainty. Bayesian inference further permits the introduction of prior knowledge, e.g., positivity of certain parameters. The estimated distribution shows to which extent model parameters are controlled by observations through the process of inference, highlighting issues that cannot be settled unless more information becomes available. An interactive interface enables tracking for how ranges of parameter values that are consistent with observations change during the process of a step-by-step assignment of fixed parameter values. Based on an initial analysis of the posterior via an undirected Gaussian graphical model, a directed Bayesian network (BN) is constructed. The BN transparently conveys information on the interdependence of parameters after calibration. Finally, a strategy to reduce the number of expensive model runs in MCMC sampling for the presented purpose is introduced based on a newly developed variant of delayed acceptance sampling with a Gaussian process surrogate and linear dimensionality reduction to support function-valued outputs.