Measurements and modeling of low-frequency near-surface scattering statistics

High-resolution acoustic measurements of low-frequency near-surface backscattering at low grazing angles have been made in the open ocean using vertical arrays of coherent sources. Over the range of wind speeds (4-18 m/s) encountered, the normalized data amplitudes exhibited variable non-Rayleigh behavior, from near Rayleigh in the highest sea states to near lognormal in low-to-moderate sea states. Seven probability density function (pdf) models were fit to the data, with the three-component Rayleigh mixture providing the most consistent fits and the least errors. One pdf model, the Poisson-Rayleigh, provided not only good fits to many data sets, but also physical insights into the scattering process. This model's estimates of the expected number of discrete scatterers ranged from 200/km/sup 2/ at low wind speeds to 2000/km/sup 2/ at high wind speeds, consistent with the expected densities of fish and subsurface bubble clouds, respectively. These results are encouraging with regard to developing physical models capable of using local results (such as these) to accurately predict long-range reverberation and clutter statistics.     

Statistics of broad-band bottom reverberation predictions in shallow-water waveguides

A new coherent reverberation model developed at the Naval Research Laboratory, Washington, DC, and the Supreme Allied Commander Atlantic Undersea Research Centre, La Spezia, Italy, is exercised in the 17-750-Hz band to estimate the degree of non-Rayleighness of shallow-water reverberation envelopes as a function of waveguide multipath, system bandwidth, directivity, and frequency. Findings suggest that reverberation from diffuse, but non-Gaussian, scatterer distributions is significantly more Rayleigh for multipath environments than for equivalent environments excited by a single or small number of modes or for broadside receiver array processing that extracts narrow angles of reception. These findings suggest that the problem of non-Rayleigh reverberation in shallow-water waveguides can be ameliorated through the use of tuned ensonification and reception schemes, which retain high probabilities of detection while reducing the associated probability of false alarm.     

An adaptive-hybrid algorithm for geoacoustic inversion

This paper presents an adaptive hybrid algorithm to invert ocean acoustic field measurements for seabed geoacoustic parameters. The inversion combines a global search (simulated annealing) and a local method (downhill simplex), employing an adaptive approach to control the trade off between random variation and gradient-based information in the inversion. The result is an efficient and effective algorithm that successfully navigates challenging parameter spaces including large numbers of local minima, strongly correlated parameters, and a wide range of parameter sensitivities. The algorithm is applied to a set of benchmark test cases, which includes inversion of simulated measurements with and without noise, and cases where the model parameterization is known and where the parameterization most be determined as part of the inversion. For accurate data, the adaptive inversion often produces a model with a Bartlett mismatch lower than the numerical error of the propagation model used to compute the replica fields. For noisy synthetic data, the inversion produces a model with a mismatch that is lower than that for the true parameters. Comparison with previous inversions indicates that the adaptive hybrid method provides the best results to date for the benchmark cases     

Use of the kurtosis statistic in the frequency domain as an aid in detecting random signals

Power spectral density estimation is often employed as a method for signal detection. For signals which occur randomly, a frequency domain kurtosis estimate supplements the power spectral density estimate and, in some cases, can be employed to detect their presence. This has been verified from experiments with real data of randomly occurring signals. In order to better understand the detection of randomly occurring signals, sinusoidal and narrow-band Gaussian signals are considered, which when modeled to represent a fading or multipath environment, are received as non-Ganssian in terms of a frequency domain kurtosis estimate. Several fading and muitipath propagation probability density distributions of practical interest are considered, including Rayleigh and log-normal. The model is generalized to handle transient and frequency modulated signals by taking into account the probability of the signal being in a specific frequency range over the total data interval. It is shown that this model produces kurtosis values consistent with real data measurements. The ability of the power spectral density estimate and the frequency domain kurtosis estimate to detect randomly occurring signals, generated from the model, is compared using the deflection criterion. It is shown, for the cases considered, that over a large range of conditions, the power spectral density estimate is a better statistic based on the deflection criterion. However, there is a small range of conditions over which it appears that the frequency domain kurtosis estimate has an advantage. The real data that initiated this analytical investigation are also presented.     

Simulation of non-Rayleigh reverberation and clutter

The simulation of active sonar reverberation time series has traditionally been done using either a computationally intensive point-scatterer model or a Rayleigh-distributed reverberation-envelope model with a time-varying power level. Although adequate in scenarios where reverberation arises from a multitude of scatterers, the Rayleigh model is not representative of the target-like non-Rayleigh reverberation or clutter commonly observed with modern high-resolution sonar systems operating in shallow-water environments. In this paper, techniques for simulating non-Rayleigh reverberation are developed within the context of the finite-number-of-scatterers representation of K-distributed reverberation, which allows control of the reverberation-envelope statistics as a function of system (beamwidth and bandwidth) and environmental (scatterer density and size) parameters. To avoid the high computational effort of the point-scatterer model, reverberation is simulated at the output of the matched filter and is generated using efficient approximate methods for forming K-distributed random variables. Finite impulse response filters are used to introduce the effects of multipath propagation and the shape of the reverberation power spectrum, the latter of which requires the development of a prewarping of the K distribution parameters to control the reverberation-envelope statistics. The simulation methods presented in this paper will be useful in the testing and evaluation of active sonar signal processing algorithms, as well as for simulation-based research on the effects of the sonar system and environment on the reverberation-envelope probability density function.     

Bayesian geoacoustic inversion for the Inversion Techniques 2001 Workshop

This paper applies a Bayesian formulation to range-dependent geoacoustic inverse problems. Two inversion methods, a hybrid optimization algorithm and a Bayesian sampling algorithm, are applied to some of the 2001 Inversion Techniques Workshop benchmark data. The hybrid inversion combines the local (gradient-based) method of downhill simplex with the global search method of simulated annealing in an adaptive algorithm. The Bayesian inversion algorithm uses a Gibbs sampler to estimate properties of the posterior probability density, such as mean and maximum a posteriori parameter estimates, marginal probability distributions, highest-probability density intervals, and the model covariance matrix. The methods are applied to noise-free and noisy benchmark data from shallow ocean environments with range-dependent geophysical and geometric properties. An under-parameterized approach is applied to determine the optimal model parameterization consistent with the resolving power of the acoustic data. The Bayesian inversion method provides a complete solution including quantitative uncertainty estimates and correlations, while the hybrid inversion method provides parameter estimates in a fraction of the computation time.     

Improvements in navigation using SeaBeam crossing errors

Adjustments to satellite constrained navigation are required to match SeaBeam bathymetric data at track crossings due to errors in dead reckoning and inaccuracies in satellite fixes. By shifting one of the SeaBeam swaths involved in a track crossing relative to the other and calculating the sum of the squares of the differences in bathymetry within the area of overlapping coverage, we map a two-dimensional error surface whose minimum corresponds to the best estimate of the correction to navigation required at the crossing point. Estimates of the covariance of this correction are derived from the error surface. We employ the curve fitting technique of Tarantola and Valette (1982) to invert for a smooth correction function to a starting model of the position of the ship as a function of time. This technique incorporates formal errors assigned to dead reckoning, satellite fixes, and the shifts required to match bathymetric swaths at crossing points in a simultaneous inversion for the correction function for all tracks within the study area.In a test of the method in a study area on the southern Mid-Atlantic Ridge, a data set involving two cruises, 30 days of SeaBeam data, and 753 track crossings, we found that crossing SeaBeam swaths can potentially resolve the relative position of the ship on the two tracks to within 30 to 70 m. The inversion procedure yielded a much better constrained navigation function and much improved match of bathymetry. The final model of the navigation fit crossing shifts about as well as satellite data (with respect to their assigned data errors) with the RMS value of the crossing shifts decreasing from 1200 m in the original satellite-constrained navigation to 200 m in the final solution. However, the potential resolution of position using SeaBeam swaths was not fully achieved in the solution because there are systematic bathymetric artifacts in SeaBeam data, multiple local minima in the error surfaces in highly lineated topography, inadequate dead reckoning data, occasional bad satellite fixes, and limitations on the short period corrections allowed in the model.     

基于正交匹配追踪的声层析方法

声速剖面的变化会对声传播产生较大的影响,经验正交函数模型经常用来实现对声速剖面数据的简化描述。然而在内波、湍流等海水不均匀性存在时,这种正则化操作会造成声速重构精度的大幅降低。本文利用字典学习生成声速剖面的非正交原子,在稀疏编码时采用正交匹配追踪（OMP,Orthogonal Matching Pursuit）算法,更新字典则使用KSVD （Kernel Singular Value Decomposition）的字典更新算法。由于字典学习不需要强制使用正交条件,对于训练数据更加灵活,从而可以使用少数的原子组合达到更高的重构精度。利用一次浅海声学实验多次测量的声速剖面研究了海水声速剖面的经验正交函数表示和字典学习,研究表明：相比于正交函数表示,学习字典可以利用少数原子（甚至一个原子）更好的解释声速剖面扰动。字典学习可以提高声速剖面的稀疏性,从而提高声速剖面的反演精度。     

WRF模式中台风"启德"模拟对 参数化方案的敏感性分析

为准确模拟台风路径和强度,采用WRF模式比较不同微物理过程和积云对流过程参数化方案对台风路径和强度模拟的影响,并基于集合预报方法考虑对台风预报系统误差进行优化。选用4种微物理过程方案和3种积云对流参数化方案,针对1213号台风“启德”进行模拟,结果表明不同的参数化方案对台风路径和强度的预报结果有明显影响,积云对流过程参数化方案相对于微物理过程参数化方案更加敏感。基于不同参数化方案扰动成员的集合平均预报方法,对于台风路径和强度的模拟误差均有明显改善,台风路径误差随时间增幅较小,其结果优于全部12个单方案试验的模拟结果;从台风强度方面来看,基于集合预报方法模拟得到的台风强度变化趋势与实况结果一致,且误差较小,优于大多数试验方案。结果表明:采用不同微物理过程和积云对流过程参数化方案的组合构建的集合预报模型,对于台风路径和强度的模拟均有一定程度改善,减小了采用不同参数化方案产生的路径不确定性,使其在台风“启德”的路径模拟上与实况更为接近,可为提升台风预报能力提供科学参考。     

集合调整卡尔曼滤波方法在M2分潮数值模拟中的水深估计研究

数据同化利用观测信息对模型状态场调整的同时也可以对数值模型中的不确定参数进行估计,从而改进数值模型,提高数值模拟的精度。本文基于集合调整卡尔曼滤波方法,采用广义坐标系统的美国普林斯顿大学海洋模式的外模式开展了渤海和部分黄海海域M₂分潮模拟中的水深估计研究。理想数据同化试验结果表明,集合调整卡尔曼滤波方法能很好地降低模式模拟的水位误差并反演出“真实”的水深参数。而在NAO.99Jb和验潮站数据的实际数据同化试验中,与验潮站数据相比较,水深参数估计后,模式模拟的M₂分潮振幅与迟角误差分别降低了40.27%和49.19%。     

北冰洋浮冰区湍流通量观测试验及参数化研究

利用2008年8月21～29日我国第3次北极考察期间在北冰洋海区(84°27′N,143°37′W～85°13′N,147°20′W)冰站观测的湍流资料及相关资料,对海冰近地层湍流通量及其特征参数进行了研究.结果表明:观测期间浮冰近地层始终存在逆温和逆湿层.这与我们以前(1999年在75°N和2003年在78°N)的观...     

基于多波束声线传播的声速剖面反演法

以测得的误差声速剖面作为初始猜测值,利用多波束记录到的波束传播时间和波束角等信息,通过广义线性反演得到一个与实际声速剖面比较接近的声速剖面,这有助于减少声速剖面的误差。通过理论模型计算,验证了该方法的可行性和正确性。     

基于高光谱遥感的渤海海冰厚度半经验模型

Sea ice thickness is one of the most important input parameters for the prevention and mitigation of sea ice disasters and the prediction of local sea environments and climates. Estimating the sea ice thickness is currently the most important issue in the study of sea ice remote sensing. With the Bohai Sea as the study area, a semiempirical model of the sea ice thickness(SEMSIT) that can be used to estimate the thickness of first-year ice based on existing water depth estimation models and hyperspectral remote sensing data according to an optical radiative transfer process in sea ice is proposed. In the model, the absorption and scattering properties of sea ice in different bands(spectral dimension information) are utilized. An integrated attenuation coefficient at the pixel level is estimated using the height of the reflectance peak at 1 088 nm. In addition, the surface reflectance of sea ice at the pixel level is estimated using the 1 550–1 750 nm band reflectance. The model is used to estimate the sea ice thickness with Hyperion images. The first validation results suggest that the proposed model and parameterization scheme can effectively reduce the estimation error associated with the sea ice thickness that is caused by temporal and spatial heterogeneities in the integrated attenuation coefficient and sea ice surface. A practical semi-empirical model and parameterization scheme that may be feasible for the sea ice thickness estimation using hyperspectral remote sensing data are potentially provided.     

Nitrogen isotopic fractionation of particulate organic matter production and remineralization in the Prydz Bay and its adjacent areas

During the 29 th Chinese National Antarctic Research Expedition, spatial variations in nitrogen isotopic composition of particulate nitrogen(δ¹⁵N_PN) and their controlling factors were examined in detail with regard to nitrate drawdown by phytoplankton and particulate nitrogen(PN) remineralization in the Prydz Bay and its adjacent areas. To better constrain the nitrogen transformations, the physical and chemical parameters, including temperature, salinity, nutrients, PN and ...     

测量海底声学参数的匹配场反演方法

在测量海底声学参数的实际海洋环境中，声源和接收位置的距离这两个参数常常无法准确测量，在这种情况下，需要采用匹配场反演方法来估计海底的声学参数。一般情况下，匹配场反演方法可以归纳为2个组成部分，即海洋声场的声学预报模型和搜索控制策略。文中采用受控制的穷举方法作为搜索控制策略，对1996年中美远黄海试验的实验数据进行了匹配场反演试验，用以测定海底参数，由此得到的海底声学参数与实验中测量的声场衰减进行对比，一致性很好。     

柘林湾近岸水产养殖区水域叶绿素a浓度反演

海水养殖已成为近海水体环境的重要污染源, 叶绿素a作为水体浮游植物生物量的一个重要参数, 是水质评价的重要指标。本文以广东省柘林湾为研究区域, 采用2018年9月4日的哨兵2号(Sentinel-2)影像与海水养殖区水体中实测的叶绿素a浓度数据构建了叶绿素a浓度的单波段模型、比值模型、三波段模型与归一化叶绿素a指数模型(Normalized Difference Chlorophyll Index, NDCI)等估算模型; 通过对比评价, 以反演精度高的模型估算了2018年多个月份的叶绿素a浓度, 并分析其分布特征。结果显示: 1) NDCI模型的反演精度明显高于其他模型, 其可决系数R²为0.8, 均方根误差(Root Mean Square Error, RMSE)为9.7, 平均绝对百分比误差(Mean Absolute Percentage Error, MAPE)为0.99; 利用实测数据对NDCI模型的时间适用性进行检验, 表明NDCI模型能有效地估算出叶绿素a浓度的空间分布特征。2) 叶绿素a浓度呈现出从近岸向湾外逐步降低的趋势, 养殖区中叶绿素a浓度的总体趋势为池塘养殖区>滩涂插养区>网箱养殖区>浮筏养殖区; 受到水体交换、降雨及养殖活动的影响, 池塘养殖区中的叶绿素a浓度在投放幼苗期的2月最低, 其变化趋势为2月<4月<6月<12月。本文的研究结果可为相关部门对柘林湾养殖水体的环境监测提供参考。     

Quantifying uncertainty in extreme values of design parameters with resampling techniques

In this paper, a methodology for the selection of statistical models for describing the extreme wave heights on the basis of resampling techniques is presented. Two such techniques are evaluated: the jackknife and the bootstrap. The methods are applied to two high-quality datasets of wave measurements in the Mediterranean and one from the East Coast of the USA. The robustness of the estimates of the extreme values of wave heights at return periods important for coastal engineering design is explored further. In particular, we demonstrate how an ensemble error norm can be used to select the most appropriate extreme probability model from a choice of cumulative distribution functions (CDFs). This error norm is based on the mean error norm of the optimised CDF for each resampled (replicate) data series. The resampling approach is also used to present confidence intervals of the CDF parameters. We provide a brief discussion of the sensitivity of these parameters and the suitability of each model in terms of uncertainty with resampling techniques. The advantages of resampling are outlined, and the superiority of the bootstrap over the jackknife in quantifying the uncertainty of extreme quantiles is demonstrated for these records.     

黄海水温垂直剖面的遥感反演与数值预报模型

基于黄海水温垂直剖面分层结构统计分析,建立了一个卫星遥感SST反演水温垂直剖面的参数化模式,以该模式反演数据作为一维数值预报模式的初值,同时用大气预报场资料作为预报模式的过程资料,发展了一个以卫星遥感资料作为主要输入量的黄海水温垂直剖面的数值预报模型.模型实现了利用卫星遥感资料反演预报黄海水温垂直剖面的目的,5 d时效的预报效果明显优于已往强温跃层数值预报模式.