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.     

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.     

不确知海洋环境下的贝叶斯声源定位法

为了提高不确知海洋环境下的声源定位性能,贝叶斯声源定位法将环境参数与声源位置同时反演。该方法利用遗传算法在参数空间中寻优,将后验概率密度在环境参数起伏变化范围内积分,得到声源距离和深度的边缘概率分布,从中求得声源位置的最优值,并进行定位结果的不确定性分析。考虑到海底密度和衰减系数对匹配场处理代价函数的敏感性较弱,利用海底参数之间的经验关系实现这两个参数的间接反演。处理并分析了2000年的一次黄海声传播实验数据,研究表明,贝叶斯声源定位法对环境失配有较好的宽容性。采用经验公式可减少待反演参量维数,进一步提高定位的精度。      

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.     

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。     

Full wave-field reflection coefficient inversion

This paper develops a Bayesian inversion for recovering multilayer geoacoustic (velocity, density, attenuation) profiles from a full wave-field (spherical-wave) seabed reflection response. The reflection data originate from acoustic time series windowed for a single bottom interaction, which are processed to yield reflection coefficient data as a function of frequency and angle. Replica data for inversion are computed using a wave number-integration model to calculate the full complex acoustic pressure field, which is processed to produce a commensurate seabed response function. To address the high computational cost of calculating short range acoustic fields, the inversion algorithms are parallelized and frequency averaging is replaced by range averaging in the forward model. The posterior probability density is interpreted in terms of optimal parameter estimates, marginal distributions, and credibility intervals. Inversion results for the full wave-field seabed response are compared to those obtained using plane-wave reflection coefficients. A realistic synthetic study indicates that the plane-wave assumption can fail, producing erroneous results with misleading uncertainty bounds, whereas excellent results are obtained with the full-wave reflection inversion.     

Macroscopic Time Evolution and MaxEnt Inference for Closed Systems with Hamiltonian Dynamics

MaxEnt inference algorithm and information theory are relevant for the time evolution of macroscopic systems considered as problem of incomplete information. Two different MaxEnt approaches are introduced in this work, both applied to prediction of time evolution for closed Hamiltonian systems. The first one is based on Liouville equation for the conditional probability distribution, introduced as a strict microscopic constraint on time evolution in phase space. The conditional probability distribution is defined for the set of microstates associated with the set of phase space paths determined by solutions of Hamilton’s equations. The MaxEnt inference algorithm with Shannon’s concept of the conditional information entropy is then applied to prediction, consistently with this strict microscopic constraint on time evolution in phase space. The second approach is based on the same concepts, with a difference that Liouville equation for the conditional probability distribution is introduced as a macroscopic constraint given by a phase space average. We consider the incomplete nature of our information about microscopic dynamics in a rational way that is consistent with Jaynes’ formulation of predictive statistical mechanics, and the concept of macroscopic reproducibility for time dependent processes. Maximization of the conditional information entropy subject to this macroscopic constraint leads to a loss of correlation between the initial phase space paths and final microstates. Information entropy is the theoretic upper bound on the conditional information entropy, with the upper bound attained only in case of the complete loss of correlation. In this alternative approach to prediction of macroscopic time evolution, maximization of the conditional information entropy is equivalent to the loss of statistical correlation, and leads to corresponding loss of information. In accordance with the original idea of Jaynes, irreversibility appears as a consequence of gradual loss of information about possible microstates of the system.     

Parameter Estimation as a Problem in Statistical Thermodynamics

In this work, we explore the connections between parameter fitting and statistical thermodynamics using the maxent principle of Jaynes as a starting point. In particular, we show how signal averaging may be described by a suitable one particle partition function, modified for the case of a variable number of particles. These modifications lead to an entropy that is extensive in the number of measurements in the average. Systematic error may be interpreted as a departure from ideal gas behavior. In addition, we show how to combine measurements from different experiments in an unbiased way in order to maximize the entropy of simultaneous parameter fitting. We suggest that fit parameters may be interpreted as generalized coordinates and the forces conjugate to them may be derived from the system partition function. From this perspective, the parameter fitting problem may be interpreted as a process where the system (spectrum) does work against internal stresses (non-optimum model parameters) to achieve a state of minimum free energy/maximum entropy. Finally, we show how the distribution function allows us to define a geometry on parameter space, building on previous work[1, 2]. This geometry has implications for error estimation and we outline a program for incorporating these geometrical insights into an automated parameter fitting algorithm.     

Bayesian geoacoustic inversion using wind-driven ambient noise

This paper applies Bayesian inversion to bottom-loss data derived from wind-driven ambient noise measurements from a vertical line array to quantify the information content constraining seabed geoacoustic parameters. The inversion utilizes a previously proposed ray-based representation of the ambient noise field as a forward model for fast computations of bottom loss data for a layered seabed. This model considers the effect of the array's finite aperture in the estimation of bottom loss and is extended to include the wind speed as the driving mechanism for the ambient noise field. The strength of this field relative to other unwanted noise mechanisms defines a signal-to-noise ratio, which is included in the inversion as a frequency-dependent parameter. The wind speed is found to have a strong impact on the resolution of seabed geoacoustic parameters as quantified by marginal probability distributions from Bayesian inversion of simulated data. The inversion method is also applied to experimental data collected at a moored vertical array during the MAPEX 2000 experiment, and the results are compared to those from previous active-source inversions and to core measurements at a nearby site.     

Range-dependent seabed characterization by inversion of acoustic data from a towed receiver array

The MAPEX2000 experiments were conducted in the Mediterranean Sea in March, 2000 to determine seabed properties using a towed acoustic source and receiver array. Towed systems are advantageous because they are easy to deploy from a ship and the moving platform offers the possibility for estimating spatially variable (range-dependent) seabed properties. In this paper, seabed parameters are determined using a matched-field geoacoustic inversion approach with measured, towed array data. Previous research has successfully applied matched-field geoacoustic inversion techniques to measured acoustic data. However, in nearly all cases the inverted data were collected on moored, vertical receiver arrays. Results here show that seabed parameters can also be extracted by inverting acoustic measurements from a towed array of receivers, and these agree with those inverted using data received simultaneously on a vertical array. These findings imply that a practical technique could be developed to map range-dependent seabed parameters over large areas using a towed acoustic system. An example of such a range-dependent inversion is given using measurements from the MAPEX2000 experiments.     

考虑噪声源深度分布的海洋环境噪声模型及地声参数反演

考虑到海洋环境噪声源深度分布不集中,建立了噪声源随深度分布的海洋环境噪声模型,分析了源深度对噪声场垂向特征的影响并从简正波角度予以解释,发现海底声阻抗和声源深度都显著影响由海洋环境噪声获得的等效海底反射损失大掠射角部分,进而将该模型用于地声参数反演.两段实测噪声数据200—525 Hz频段的反演结果表明:基于海洋环境噪声的地声参数反演最优值与声传播的反演结果相近;源平均深度最优值随频率增加有变小的趋势,说明随频率增加环境噪声主要贡献源逐渐由航船转为风浪;当海况大于3级时,400 Hz以上频段噪声源深度平均值很小,与Monahan气泡理论的描述一致.     

Subexponentially Growing Hilbert Space and Nonconcentrating Distributions in a Constrained Spin Model

Motivated by recent experiments with two-component Bose–Einstein condensates, we study fully-connected spin models subject to an additional constraint. The constraint is responsible for the Hilbert space dimension to scale only linearly with the system size. We discuss the unconventional statistical physical and thermodynamic properties of such a system, in particular the absence of concentration of the underlying probability distributions. As a consequence, expectation values are less suitable to characterize such systems, and full distribution functions are required instead. Sharp signatures of phase transitions do not occur in such a setting, but transitions from singly peaked to doubly peaked distribution functions of an “order parameter” may be present.     

An iterative implementation of rotated coordinates for inverse problems

A generalized inversion method is presented that uses a rotated coordinates technique [Collins and Fishman, J. Acoust. Soc. Am. 98, 1637-1644 (1995)] in simulated annealing to invert for both the location of an acoustic source and parameters that describe the ocean seabed. The rotated coordinates technique not only aids in the inversion process but also indicates the coupling of the source and environmental parameters and the relative sensitivities of the cost function to changes in the various parameters. The information obtained from the rotated coordinates provides insights into how the inversion problem can be effectively decoupled. An iterative process consisting of multiple simulated annealing runs that each use a different set of rotated coordinates is demonstrated. This multistep algorithm is called systematic decoupling using rotated coordinates and is especially helpful when inverting for a large number of unknown parameters. The cost function minimized in the inversion algorithm is model-data cross-hydrophone spectra summed coherently over frequency and receiver pairs. The results of applying this inversion method to simulated data are presented in this paper.     

海洋声场中分布式无源定位系统的节点配置方法

为了提高海洋声场中分布式无源定位系统的定位精度,提出一种基于自适应遗传算法的节点配置方法。首先,基于到达时间TOA(Time of Arrival)定位算法推导出均匀物理场与海洋声场中定位误差的CRLB(Cramer Rao Lower Bound)。之后,利用BELLHOP模型对海洋声场进行建模,获得任意位置处目标声源与节点接收信号相关的传递函数并对等效声速进行计算.以目标声源在观测区域内服从均匀分布为例,将定位误差的平均CRLB最小为优化准则,采用自适应遗传算法对节点进行优化配置。结果表明,该方法能够有效降低海洋声场中分布式无源定位系统的定位误差,并给出定位误差随节点个数增加呈非线性递减的变化趋势,可为工程应用提供理论指导。      

Fisher and Shannon information entropies for a noncentral inversely quadratic plus exponential Mie-type potential

In this work, we determine the Fisher and Shannon entropies, the expectation values and the squeeze state for a noncentral inversely quadratic plus exponential Mie-type potential analytically.The proposed potential is solved under the Schr?dinger equation using a special Greene Aldrich approximation to the centrifugal term to obtain a normalised wave function within the framework of the Nikiforov–Uvarov method. Numerical results are obtained for different screening parameters:α?=?0.1, 0.12 and 0.13 for varying real constant parameter(B). The numerical solutions are obtained only for ground state. The numerical results of Fisher entropy both for position and momentum spaces are in good agreement with existing literature. The normalisation constant, wave function, and probability density plots are carried out using a well designed Mathematica algorithm.The Fourier transform of position space entropy gives the momentum space entropy.     

Effects of incoherent and coherent source spectral information in geoacoustic inversion

This paper examines the effect on matched-field geoacoustic inversion of including source spectral information, as can be available in controlled-source acoustic surveys. Source information can consist of relative or absolute knowledge of the source amplitude and/or phase spectra, and can allow frequency-coherent processing of spatial acoustic-field data. A number of multi-frequency acoustic processors, appropriate for specific types of source information, are defined based on the likelihood function for complex acoustic-field data with Gaussian noise. The information content of the various processors is quantified in terms of marginal probability distributions and highest-probability density intervals for the unknown geoacoustic and geometric parameters, which define the accuracy expected in inversion. Marginal distributions are estimated using a fast Gibbs sampler approach to Bayesian inversion, which provides an efficient, unbiased sampling of the multi-dimensional posterior probability density. The analysis is illustrated for incoherent and coherent processors corresponding to several types of source knowledge ranging from complete information to no information, and the results are considered as a function of the spatial and frequency sampling of the acoustic fields.     

Extraction of physical laws from joint experimental data

The extraction of a physical law y=y_o(x) from joint experimental data about x and y is treated. The joint, the marginal and the conditional probability density functions (PDF) are expressed by given data over an estimator whose kernel is the instrument scattering function. As an optimal estimator of y_o(x) the conditional average is proposed. The analysis of its properties is based upon a new definition of prediction quality. The joint experimental information and the redundancy of joint measurements are expressed by the relative entropy. With the number of experiments the redundancy on average increases, while the experimental information converges to a certain limit value. The difference between this limit value and the experimental information at a finite number of data represents the discrepancy between the experimentally determined and the true properties of the phenomenon. The sum of the discrepancy measure and the redundancy is utilized as a cost function. By its minimum a reasonable number of data for the extraction of the law y_o(x) is specified. The mutual information is defined by the marginal and the conditional PDFs of the variables. The ratio between mutual information and marginal information is used to indicate which variable is the independent one. The properties of the introduced statistics are demonstrated on deterministically and randomly related variables.     

Geoacoustic inversion for bottom parameters in a thermocline environment in the northern area of the South China Sea

Bottom acoustic parameters have important influence on the application of underwater acoustic propagation and source location.The acoustic parameters of the seabed in the northern of the South China Sea(SCS) were inversed using the experiment data from an acoustic experiment in 2015.Based on the comprehensive analysis of the influence of the sound speed fluctuation and the geoacoustic model on seabed inversion,the multi-parameter hybrid acoustic inversion scheme is improved by selecting the equivalent mean sound speed profile(SSP) and half-infinite liquid bottom model to save the inversion dimensions in the matched field processing(MFP) inversion.The inverted bottom sound speed and density are in good agreement with the core sampling measurements.The nonlinear empirical relationship of the attenuation coefficient with frequency is given out.The inversion results are meaningful to the sound propagation research and application in the northern area of the SCS.     

基于拖曳倾斜线列阵的海底反射损失提取方法

在浅海环境中,海底声学参数对水下声场的精确预报十分重要.现有的海底声学参数反演方法大多数是采用固定垂直阵进行的,其缺点是不能实施大面积、高分辨的走航式反演.在已提出的垂直阵海底反射损失反演方法的基础上,研究了基于三种线列阵阵形的海底反射损失提取方法及其特点,提出了基于拖曳倾斜线列阵与三个声源组合的走航式海底参数快速获取方法,并对该方法进行了误差分析.研究结果表明：覆盖同样的掠射角范围,垂直线列阵需与多个距离的声源组合,拖曳水平线列阵只需一个声源组合但需要采用较大的物理孔径,拖曳倾斜线列阵综合了垂直阵和水平 关键词： 水下声场预报 海底声学参数 海底反射损失 拖曳倾斜线列阵     

Information Theory Applied to Parameter Inference of Pake Doublet Spectra

Magnetic resonance absorption lineshapes can have subtle dependencies on the model parameters that specify the lineshape. To quantify how the model parameters influence the lineshape, it is useful to study simple model systems for which analytical expressions are available. We propose that information theory is a useful tool to quantify how well model parameters may be inferred from a noisy signal. Information theory also allows us to assess the importance of missing parameters from an incomplete model. We do this by monitoring the magnitude of a partition function determined from a suitably defined probability mass function as the model parameters are varied. The optimum parameter set makes the partition function a maximum, which establishes a computable criterion for determining the best model parameter set. Given the availability of a partition function, one may define thermodynamic functions such as the entropy. The optimum parameter set in this interpretation corresponds to the state of maximum entropy. In this work, we observe that at sufficiently low signal to noise ratio, the entropy landscape has no clear maximum, while a related quantity, the Fisher information, always has a clear minimum at the optimum parameter set. The qualitative information we are able to gather from the entropy landscapes is also difficult to assess when the parameters are far from their optimum values, at least for the model system studied here.