Sequential trans-dimensional Monte Carlo for range-dependent geoacoustic inversion

This paper develops a sequential trans-dimensional Monte Carlo algorithm for geoacoustic inversion in a strongly range-dependent environment. The algorithm applies advanced Markov chain Monte Carlo methods in combination with sequential techniques (particle filters) to carry out geoacoustic inversions for consecutive data sets acquired along a track. Changes in model parametrization along the track (e.g., number of sediment layers) are accounted for with trans-dimensional partition modeling, which intrinsically determines the amount of structure supported by the data information content. Challenging issues of rapid environmental change between consecutive data sets and high information content (peaked likelihood) are addressed by bridging distributions implemented using annealed importance sampling. This provides an efficient method to locate high-likelihood regions for new data which are distant and ∕ or disjoint from previous high-likelihood regions. The algorithm is applied to simulated reflection-coefficient data along a track, such as can be collected using a towed array close to the seabed. The simulated environment varies rapidly along the track, with changes in the number of layers, layer thicknesses, and geoacoustic parameters within layers. In addition, the seabed contains a geologic fault, where all layers are offset abruptly, and an erosional channel. Changes in noise level are also considered.     

Sequential Bayesian geoacoustic inversion for mobile and compact source-receiver configuration

Geoacoustic characterization of wide areas through inversion requires easily deployable configurations including free-drifting platforms, underwater gliders and autonomous vehicles, typically performing repeated transmissions during their course. In this paper, the inverse problem is formulated as sequential Bayesian filtering to take advantage of repeated transmission measurements. Nonlinear Kalman filters implement a random-walk model for geometry and environment and an acoustic propagation code in the measurement model. Data from MREA/BP07 sea trials are tested consisting of multitone and frequency-modulated signals (bands: 0.25-0.8 and 0.8-1.6 kHz) received on a shallow vertical array of four hydrophones 5-m spaced drifting over 0.7-1.6 km range. Space- and time-coherent processing are applied to the respective signal types. Kalman filter outputs are compared to a sequence of global optimizations performed independently on each received signal. For both signal types, the sequential approach is more accurate but also more efficient. Due to frequency diversity, the processing of modulated signals produces a more stable tracking. Although an extended Kalman filter provides comparable estimates of the tracked parameters, the ensemble Kalman filter is necessary to properly assess uncertainty. In spite of mild range dependence and simplified bottom model, all tracked geoacoustic parameters are consistent with high-resolution seismic profiling, core logging P-wave velocity, and previous inversion results with fixed geometries.     

Trans-dimensional geoacoustic inversion

This paper develops a general trans-dimensional Bayesian methodology for geoacoustic inversion. Trans-dimensional inverse problems are a generalization of fixed-dimensional inversion that includes the number and type of model parameters as unknowns in the problem. By extending the inversion state space to multiple subspaces of different dimensions, the posterior probability density quantifies the state of knowledge regarding inversion parameters, including effects due to limited knowledge about appropriate parametrization of the environment and error processes. The inversion is implemented here using a reversible-jump Markov chain Monte Carlo algorithm and the seabed is parametrized using a partition model. Unknown data errors are addressed by including a data-error model. Jumps between dimensions are implemented with a birth-death methodology that allows transitions between dimensions by adding or removing interfaces while maintaining detailed balance in the Markov chain. Trans-dimensional inversion results in an inherently parsimonious solution while partition modeling provides a naturally self-regularizing algorithm based on data information content, not on subjective regularization functions. Together, this results in environmental estimates that quantify appropriate seabed structure as supported by the data, allowing sharp discontinuities while approximating smooth transitions where needed. This approach applies generally to geoacoustic inversion and is illustrated here with seabed reflection-coefficient data.     

A hybrid scheme for geoacoustic inversion

A hybrid inversion scheme of geoacoustic parameters, which combines dispersion characteristics of normal modes with transmission loss, has been proposed based on the facts that different parameters have different effects on sound field. First, considering the dispersion characteristics of normal modes were sensitive to the density and the sound speed of seabottom, and not sensitive to the seabottom attenuation coefficient, the group time delays of different modes and different frequencies were acquired through adaptive optimal kernel time-frequency representation, then the group time delays were taken as the cost function, and using a global optimal algorithm, the seabottom density and the profile of seabottom sound speed were deduced. Successively, the validity of inversion results was evaluated by the a posteriori probability. Last, the attenuation coefficient of sediment is inverted using the transmission loss data recorded from the ship radiation noise. The validity of the total inversion results has been evaluated by making use of the matched field processing source localization.     

Trans-dimensional Bayesian geoacoustic inversion in shallow water

To deal with the inversion problem when the spatial structure of seaffoor sediments is unknown, a trans-dimensional particle filter method is proposed, where the cross-spectral density of the pressure field is used to estimate the sediment layering structure and geoacoustic parameters. The simulation results show that the number of sediment layers and geoacoustic parameters can be effectively estimated using the proposed method, and the parallel particle calculation makes this method more effici...     

Single-receiver geoacoustic inversion using modal reversal

This paper introduces a single-receiver geoacoustic-inversion method based on dispersion analysis and adapted to low-frequency impulsive sources in shallow-water environments. In this context, most existing methods take advantage of the modal dispersion curves in the time-frequency domain. Inversion is usually performed by matching estimated dispersion curves with simulated replicas. The method proposed here is different. It considers the received modes in the frequency domain. The modes are transformed using an operator called modal reversal, which is parameterized using environmental parameters. When modal reversal is applied using parameters that match the real environment, dispersion is compensated for in all of the modes. In this case, the reversed modes are in phase and add up constructively, which is not the case when modal reversal is ill-parameterized. To use this phenomenon, a criterion that adds up the reversed modes has been defined. The geoacoustic inversion is finally performed by maximizing this criterion. The proposed method is benchmarked against simulated data, and it is applied to experimental data recorded during the Shallow Water 2006 experiment.     

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。     

Data error covariance in matched-field geoacoustic inversion

Many approaches to geoacoustic inversion are based implicitly on the assumptions that data errors are Gaussian-distributed and spatially uncorrelated (i.e., have a diagonal covariance matrix). However, the latter assumption is often not valid due to theory errors, and can lead to reduced accuracy for geoacoustic parameter estimates and underestimation of parameter uncertainties. This paper examines the effects of data error (residual) covariance in matched-field geoacoustic inversion. An inversion approach is developed based on a nonparametric method of estimating the full covariance matrix (including off-diagonal terms) from the data residuals and explicitly including this covariance in the misfit function. Qualitative and quantitative statistical tests for Gaussianity and for correlations in complex residuals are considered to validate the inversion results. The approach is illustrated for Bayesian geoacoustic inversion of broadband, vertical-array acoustic data measured in the Mediterranean Sea.     

Multi-step geoacoustic inversion with a towed line array

Geoacoustic inversion is important for acoustic field predictions and matched field localizations in shallow water.Combing the Matched-field inversion(MFI) and the Reflectionloss inversion,a multi-step Bayesian inversion for geoacoustic parameters was presented.This method applied the posterior probability density(PPD) or inversion results from one inversion as prior information for subsequent inversion.First,the sensitive parameters were determined by the MFI.Second,the insensitive parameters were determined by the Reflection-loss inversion based on the PPD and inversion results from the MFI.The PPD results indicated that the multi-step inversion method was performed better than direct matched-field inversion,and the inversion results of some parameters were improved significantly.To demonstrate the advantages of the multi-step inversion method on the sound field prediction,the statistical properties of transmission loss based on the posterior probability were introduced.The transmission loss distribution showed that the predicted acoustic fields based on the multi-step inversion method had smaller errors.     

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.     

On the effect of error correlation on matched-field geoacoustic inversion

The effect of correlated data errors on matched-field geoacoustic inversion for vertical array data is examined. The correlated errors stem from the inability to model the inhomogeneities in the environment resulting in an additional error term beyond ambient noise. Simulated data with these correlated errors are generated and then inverted with or without using the proper covariance matrix. Results show that the correlated error has a negative impact on geoacoustic parameter estimation if not accounted for properly.     

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。     

Theoretical framework for geoacoustic inversion by adjoint method

Traditional geoacoustic inversions are generally solved by matched-field processing in combination with metaheuristic global searching algorithms which usually need massive computations. This paper proposes a new physical framework for geoacoustic retrievals. A parabolic approximation of wave equation with non-local boundary condition is used as the forward propagation model. The expressions of the corresponding tangent linear model and the adjoint operator are derived, respectively, by variational method. The analytical expressions for the gradient of the cost function with respect to the control variables can be formulated by the adjoint operator, which in turn can be used for optimization by the gradient-based method.     

Tabu for matched-field source localization and geoacoustic inversion

Tabu is a global optimization technique that has been very successful in operations research. In this paper, a Tabu-based method is developed for source localization and geoacoustic inversion with underwater sound data; the method relies on memory to guide the multiparameter search. Tabu is evaluated through a comparison to simulating annealing. Both methods are tested by inverting synthetic data for various numbers of unknown parameters. Tabu is found to be superior to the simulated annealing variant implemented here in terms both of accuracy and efficiency. Inversion results from the SWellEX-96 data set are also presented.     

Hybrid geoacoustic inversion of broadband Mediterranean Sea data

This paper describes an acoustic experiment (PROSIM'97) carried out to investigate inversion for seabed properties at a site off the west coast of Italy where previous acoustic and geophysical studies have been performed. Acoustic fields were measured at a vertical hydrophone array due to a swept-frequency source towed over weakly range-dependent bathymetry. Based on the known geology, the seabed is modeled as a sediment layer overlying a semi-infinite basement with unknown model parameters consisting of the sediment thickness, sediment and basement sound speeds, source range and depth, water depth at the source and array, and array tilt. A hybrid inversion algorithm is applied to determine the model values that minimize the mismatch with the measured acoustic fields. Multiple data sets are analyzed to examine the consistency of the inversion results. It is found that the low sound speed of the sediment layer, together with a large uncertainty in bathymetry, leads to strong correlations between the water depths and sediment thickness. This precludes reliable estimation of these parameters individually; however, the total depth to the basement can be estimated reliably. In addition, the basement speed and geometric parameters are estimated consistently, and all parameters compare favorably with the geophysical ground-truth information and with previous inversion results.     

Broadband sound propagation in shallow water and geoacoustic inversion

Part of an experiment to test a measurement package in a shallow water region in the Gulf of Mexico was designed to gather broadband acoustic data suitable for inversion to estimate seabed geoacoustic parameters. Continuous wave tow acoustic signals at multiple frequencies and broadband impulsive source signals were recorded on a horizontal line array in a high-noise environment. Simulated annealing with a normal mode forward propagation model is utilized to invert for a geoacoustic representation of the seabed. Several inversions are made from different data samples of two light bulb implosions, the measured sound speed profiles at the HLA and at the positions of the light bulb deployments, and for two different cost functions. The different cost functions, measured sound speed profiles, and measured time series result in different inverted geoacoustic profiles from which transmission loss is generated for comparison with measurements. On the basis of physical consistency and from the comparison of the transmission loss and time series, a best estimate geoacoustic profile is selected and compared to those obtained from previously reported inversions. Uncertainties in the sound speed profile are shown to affect the uncertainties of the estimated seabed parameters.     

利用海底反射信号进行地声参数反演的方法

针对现有反演方法的缺点,提出了一种基于海底反射信号的地声参数高分辨反演方法.它利用短距离声源在不同深度上发射宽带线性调频信号,采用垂直阵进行接收,首先通过匹配滤波方法提取多径到达信息,然后利用海底反射损失曲线,反演海底表层的声速和密度,最后利用浅底层反射信号估计沉积层参数.由于海水中直达波受到内波的强烈影响,选择海底表面反射作为参考,用以可靠地计算浅底层反射的相对到达时间和幅度,从而估计出沉积层的厚度、速度和衰减系数.通过海上实验,验证了利用浅底层反射信号反演参数的有效性. 关键词： 海底参数 反演 浅底层反射信号     

The effects of ignored seabed variability in geoacoustic inversion

Geoacoustic inversion using a matched-field inversion algorithm is a well-established technique for estimating the geoacoustic parameters of the seabed. This paper demonstrates how parameter estimation can be affected by unknown or wishfully ignored random range dependence of the true environment when the inversion model is--for practicality--assumed to be range independent. Simulations with controlled statistics were carried out using a simple shallow water model: an isospeed water column over a homogeneous elastic halfspace. The inversion parameters included water depth, compressional speed in the seabed, seabed density, and compressional wave attenuation. On average the environment is range independent: some parameters are constant while other parameters are random with range-independent means and variances. A Parabolic Equation underwater acoustic propagation model is used to calculate the simulated data fields for the range-dependent environment as well as to calculate the model fields for the range-independent inversion model. The Adaptive Simplex Simulated Annealing inversion algorithm is used to estimate the best-fit solution. It is found that ignoring the variability of even a single geoacoustic parameter leads to significant and correlated uncertainty (bias and variance) in the estimation of all inverted parameters. Results are presented for range variation of compressional sound speed and water depth.     

Bayesian geoacoustic inversion in a dynamic shallow water environment

This paper presents results for matched field Bayesian geoacoustic inversion of multitonal continuous wave data collected on the New Jersey continental shelf. To account for effects of significant spatial and temporal variation of the water column sound speed, the sound speed profile was represented by empirical orthogonal functions. Data error information for the inversion was estimated from multiple time windows of the data. Inversion results for the sediment sound speeds at three ranges are in excellent agreement with the ground truth.     

深海低声速沉积层简正波用于海底参数反演*

现有深海地声参数反演研究通常基于全波场理论,存在计算量大、多值性以及需要准确的水文环境信息等问题。针对这一问题,提出一种基于简正波频散特征的深海低声速沉积层海底参数反演方法。在南海北部大陆坡海域的一次实验中,坐底式水听器(深度约1740 m)接收的沉底弹信号中观察到一种低频成分先到而高频成分后至的到达结构,由被限制在低声速沉积层的简正波组成。通过匹配沉积层简正波60-220 Hz频段内的到达时间差对实验海域的低声速海底参数进行反演,得到沉积层厚度为16.4 m,沉积层声速为1450.5 m/s,与底质采样数据比较吻合,且具有较高的可信度,验证了所提方法的有效性。由于沉积层密度对简正波频散特征的敏感性较差,其反演结果可信度不是很高,需要进一步研究。