Matched-field inversion for geoacoustic model parameters in shallowwater

Matched-field inversion is used to, estimate geoacoustic properties from data obtained in an experiment with a vertical line array (VLA). The experiment was carried out using broad-band sources (shots) in water depths of about 200 m on the continental shelf off Vancouver Island. The data were processed to obtain spectral components of the field for frequencies near the bubble frequency for the shot. The ocean bottom in this region consists of a layer of mainly sandy sediments (about 100 m thick) overlying older consolidated material. Consequently, the inversion was designed to estimate the parameters of a two-layer elastic sediment model. In the inversion, an adaptive global search algorithm was used to investigate the multidimensional space of geoacoustic models in order to determine the set of values corresponding to the best replica field. Convergence is driven by adaptively guiding the search to regions of the parameter space associated with above-average values of the matched field correlation between the measured and replica fields. The geoacoustic profile estimated by the inversion consisted of a 125-m layer with compressional speed ~1700 m/s and shear speed ~400 m/s, overlying a layer with compressional speed ~1900 m/s. This model is consistent with the results from conventional seismic experiments carried out in the same region     

Matched field inversion for geoacoustic model parameters usingadaptive simulated annealing

A method is described for the estimation of geoacoustic model parameters by the inversion of acoustic field data using a nonlinear optimization procedure based on simulated annealing. The cost function used by the algorithm is the Bartlett matched-field processor (MFP), which related the measured acoustic field with replica fields calculated by the SAFARI fast field program. Model parameters are perturbed randomly, and the algorithm searches the multidimensional parameter space of geoacoustic models to determine the parameter set that optimizes the output of the MFP. Convergence is driven by adaptively guiding the search to regions of the parameter space associated with above-average values of the MFP. The performance of the algorithm is demonstrated for a vertical line array in a shallow water enviornment where the bottom consists of homogeneous elastic solid layers. Simulated data are used to determine the limits on estimation performance due to error in experimental geometry and to noise contamination. The results indicate that reasonable estimates are obtained for moderate conditions of noise and uncertainty in experimental geometry     

Efficiency of reduced-order, time-dependent adjoint data assimilation approaches

Applications of adjoint data assimilation, which is designed to bring an ocean circulation model into consistency with ocean observations, are computationally demanding. To improve the convergence rate of an optimization, reduced-order optimization methods that reduce the size of the control vector by projecting it onto a limited number of basis functions were suggested. In this paper, we show that such order reduction can indeed speed up the initial convergence rate of an assimilation effort in the eastern subtropical North Atlantic using in situ and satellite data as constraints. However, an improved performance of the optimization was only obtained with a hybrid approach where the optimization is started in a reduced subspace but is continued subsequently using the full control space. In such an experiment about 50% of the computational cost can be saved as compared to the optimization in the full control space. Although several order-reduction approaches seem feasible, the best result was obtained by projecting the control vector onto Empirical Orthogonal Functions (EOFs) computed from a set of adjusted control vectors estimated previously from an optimization using the same model configuration.     

Estimation of ocean-bottom properties by matched-field inversion ofacoustic field data

A method for estimating properties of the ocean bottom such as bathymetry and geoacoustic parameters such as sound speed, density and attenuation, using matched-field inversion is considered. The inversion can be formulated as an optimization problem by assuming a discrete model of unknown parameters and a bounded search space for each parameter. The optimization then involves finding the set of parameter values which minimizes the mismatch between the measured acoustic field and modeled replica fields. Since the number of possible models can be extremely large, the method of simulated annealing, which provides an efficient optimization that avoids becoming trapped in suboptimal solutions, has been used. The matching fields are computed using a normal mode model. In inversions for range-dependent parameters, the adiabatic approximation is employed. This allows mode values to be precomputed for a grid of parameter values and stored in look-up tables for fast reference, which greatly improves computational efficiency. Synthetic inversion examples are presented for realistic range-independent and range-dependent environments     

基于特征选取及广义傅立叶分形的SAR图像海洋溢油检测算法

针对海上溢油SAR图像中油膜与类油膜的识别问题,提出了一种结合傅立叶分形与特征提取的检测算法。由于分形特征可以具有无穷多的细节,并在不同的研究尺度存在自仿射特性。这与油膜及类油膜表面的几何形貌特征非常吻合。该算法通过计算样本的傅立叶分形特征,组成油膜与类油膜的特征空间。然后,应用基于差分进化的特征选取方法将利于分类的重要特征值筛选出来。再利用重要特征值对原有样本进行分类。实验表明,经特征选取的分形特征向量能够以100%的准确率将两类样本区分开。该算法在选取重要特征的同时实现了对高维特征空间降维的目的,该思想可以应用于其他的基于高维特征的识别系统中,具有普遍的适用性。     

Simulations of Matched-Field Processing in a Deep-Water Pacific Environment

Conventional bearing estimation procedures employ planewave steering vectors as replicas of the true field and seek to resolve in angle by maximizing a power function representing the agreement between actual and replica fields. For vertical arrays in oceanic waveguides the received field depends on range and depth, and it is natural to replace the "look-direction" (theta) by a "look-position" (r, z). Thus an environmental model is constructed by specifying ocean depth, sound speed profile, bottom properties, etc., and a propagation model is employed to construct a replica of the field that would be received on the array for a particular source position. The usual estimators (e.g., Bartlett or maximum likelihood) are then used to gauge the agreement between actual and replica fields and the true source position is identified as that position where the agreement is best. The performance of this kind of matched-field processing is strongly affected by the environment. In particular, we demonstrate through simulations that for a deep-water Pacific environment dominated by waterborne paths, ambiguities or sidelobes are associated with convergence zones. In the absence of mismatch between replica and actual fields we find that a 16-element array performs extremely well in low-frequency regimes. Mismatch caused by uncertainties in phone positions, bottom parameters, ocean sound speed, surface and bottom roughness, etc., causes degradation in localization performance. The impact of some of these effects on conventional and maximum likelihood estimators is examined through simulation.     

Real time time-frequency active sonar processing: a SIMD approach

A paradigm for massively parallel processing of matched filters, replica correlators, ambiguity functions, and time-frequency distributions is presented, using a SIMD (single instruction stream, multiple data stream) programming methodology. It is shown that active sonar detection algorithms, as implemented by frequency domain processing, can be a natural match to a SIMD methodology, meeting the extensive computational needs of enhanced active sonar systems. The decomposition process is presented, and examples are given of the output of the computer program CMASP (Connection Machine Ambiguity Surface Processor). CMASP can provide real-time simultaneous multiple-beam, Doppler, and waveform replica correlations. Synthetic data are processed, and the corresponding CMASP outputs are displayed as three-dimensional ambiguity surfaces on networked graphic workstations. Because of efficient problem decomposition, other time-frequency processing can be exploited. Specifically, real-time instantaneous-like time-frequency distributions have been realized in which the data set is presented and processed as time-varying spectral representations     

A comparison of sequential assimilation schemes for ocean prediction with the HYbrid Coordinate Ocean Model (HYCOM): Twin experiments with static forecast error covariances

We assess and compare four sequential data assimilation methods developed for HYCOM in an identical twin experiment framework. The methods considered are Multi-variate Optimal Interpolation (MVOI), Ensemble Optimal Interpolation (EnOI), the fixed basis version of the Singular Evolutive Extended Kalman Filter (SEEK) and the Ensemble Reduced Order Information Filter (EnROIF). All methods can be classified as statistical interpolation but differ mainly in how the forecast error covariances are modeled. Surface elevation and temperature data sampled from an 1/12° Gulf of Mexico HYCOM simulation designated as the truth are assimilated into an identical model starting from an erroneous initial state, and convergence of assimilative runs towards the truth is tracked. Sensitivity experiments are first performed to evaluate the impact of practical implementation choices such as the state vector structure, initialization procedures, correlation scales, covariance rank and details of handling multivariate datasets, and to identify an effective configuration for each assimilation method. The performance of the methods are then compared by examining the relative convergence of the assimilative runs towards the truth. All four methods show good skill and are able to enhance consistency between the assimilative and truth runs in both observed and unobserved model variables. Prediction errors in observed variables are typically less than the errors specified for the observations, and the differences between the assimilated products are small compared to the observation errors. For unobserved variables, RMS errors are reduced by 50% relative to a non-assimilative run and differ between schemes on average by about 5%. Dynamical consistency between the updated state space variables in the data assimilation algorithm, and the data adequately sampling significant dynamical features are the two crucial components for reliable predictions. The experiments presented here suggest that practical implementation details can have at least as much an impact on the accuracy of the assimilated product as the choice of assimilation technique itself. We also present a discussion of the numerical implementation and the computational requirements for the use of these methods in large scale applications.     

A fast inversion method for ocean parameters based on dispersion curves with a single hydrophone

The dispersion characteristics of shallow water can be described by the dispersion curves, which contain substantial ocean parameter information. A fast ocean parameter inversion method based on dispersion curves with a single hydrophone is presented in this paper. The method is achieved through Bayesian theory. Several sets of dispersion curves extracted from measured data are used as the input function. The inversion is performed by matching a replica calculated with a dispersion formula. The bottom characteristics can be described by the bottom reflection phase shift parameter P. The propagation range and the depth can be inverted quickly when the seabed parameters are represented by on parameter P. The inversion results improve the inversion efficiency of the seabed parameters. Consequently, the inversion efficiency and accuracy are improved while the number of inversion parameters is decreased and the computational speed of replica is increased. The inversion results have lower error than the reference values, and the dispersion curves calculated with inversion parameters are also in good agreement with extracted curves from measured data; thus, the effectiveness of the inversion method is demonstrated.     

The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems: Part I - System overview and formulation

The Regional Ocean Modeling System (ROMS) is one of the few community ocean general circulation models for which a 4-dimensional variational data assimilation (4D-Var) capability has been developed. The ROMS 4D-Var capability is unique in that three variants of 4D-Var are supported: a primal formulation of incremental strong constraint 4D-Var (I4D-Var), a dual formulation based on a physical-space statistical analysis system (4D-PSAS), and a dual formulation representer-based variant of 4D-Var (R4D-Var). In each case, ROMS is used in conjunction with available observations to identify a best estimate of the ocean circulation based on a set of a priori hypotheses about errors in the initial conditions, boundary conditions, surface forcing, and errors in the model in the case of 4D-PSAS and R4D-Var. In the primal formulation of I4D-Var the search for the best circulation estimate is performed in the full space of the model control vector, while for the dual formulations of 4D-PSAS and R4D-Var only the sub-space of linear functions of the model state vector spanned by the observations (i.e. the dual space) is searched. In oceanographic applications, the number of observations is typically much less than the dimension of the model control vector, so there are clear advantages to limiting the search to the space spanned by the observations. In the case of 4D-PSAS and R4D-Var, the strong constraint assumption (i.e. that the model is error free) can be relaxed leading to the so-called weak constraint formulation. This paper describes the three aforementioned variants of 4D-Var as they are implemented in ROMS. Critical components that are common to each approach are conjugate gradient descent, preconditioning, and error covariance models, which are also described. Finally, several powerful 4D-Var diagnostic tools are discussed, namely computation of posterior errors, eigenvector analysis of the posterior error covariance, observation impact, and observation sensitivity.     

Seafloor spreading anomalies and crustal ages of the Clarion-Clipperton Zone

The Clarion-Clipperton Zone (CCZ) of the central Pacific is one of the few regions in the world’s oceans that are still lacking full coverage of reliable identifications of seafloor spreading anomalies. This is mainly due to the geometry of the magnetic lineations’ strike direction sub-parallel to the Earth’s magnetic field vector near the equator resulting in low amplitude magnetic anomalies, and the remoteness of the region which has hindered systematic surveying in the past. Following recently granted research licenses for manganese nodules in the CCZ by the International Seabed Authority, new magnetic data acquired with modern instrumentation became available which combined with older underway data make the identification of seafloor spreading anomalies possible for large parts of the CCZ and adjacent areas. The spreading rates deduced from the seafloor spreading patterns show a sharp increase at the end of Chron 21 (47.5 Ma) which corresponds to the age of the bend in the Hawaii-Emperor seamount chain and an associated plate tectonic reorganisation in the Central Pacific. An accurate map of crustal ages for the central-eastern Pacific based on our anomaly picks may provide a basis for improved plate tectonic reconstructions of the region.     

High-resolution array processing using implicit eigenvectorweighting techniques

Many high-resolution bearing estimators require the explicit calculation of the eigenvectors and eigenvalues of the cross-spectral matrix of the sensor outputs. Once the eigenvectors have been calculated, various estimators can be derived by altering the eigenvalues to give a reweighting of the eigenvectors. These weighting functions are reminiscent of ideal filter responses in analog filter theory, where practical filters are designed by using polynomial approximations to the ideal desired response. The approximation theory developed for filter design is used to derive high-resolution bearing estimators that do not require explicit calculation of the eigenvectors     

A severity index to assess and monitor the incidence of pollution-related pathological conditions in marine organisms

A normalised matrix of the occurrences and co-occurrences of n-pathological conditions in a hypothetical population is termed a severity matrix and was used to define a volume in an n-dimensional vector space representing the severity or extent of the spread of the conditions in the population. The changes in this volume resulting from varying the numbers of occurrences and co-occurrences in the severity matrix are described by the varying determinant of the matrix. The natural logarithm of this determinant was shown to be a consistent measure or index of severity for comparing the spread of the same pathological conditions among different populations or among chronological samples from the same population. While the eigenvalues of the severity matrix are used to determine the relative contribution of each pathological condition to the index of severity, the eigenvectors serve to investigate possible associations among the conditions. Actual data from a population of the soft-shell clam Mya arenaria were used to demonstrate the application of this methodology to characterise the incidence of five pathological conditions assumed to be indicators of pollution related stress on the clam population.     

Automatic Sounding Generalization in Nautical Chart Considering Bathymetry Complexity Variations

Sounding acts as the main feature in a digital nautical chart as it describes the concerned marine topography for the safety of navigation. Unlike the geometry-oriented selection of point feature, the generalization of soundings for chart compiling is expected to be context-oriented, which means bathymetry complexity variations across the study region should be preserved in the sounding selection process. However, such variations are not explicitly accessible to automated systems. This paper proposes an approach that effectively analyzes and measures bathymetry complexity from sounding data, with a focus on topography variations among different regions. The presented approach first divides the exploring region into several subregions, by adopting techniques of computational geometry and graph theory. Then, the approach quantitatively measures the bathymetry complexity of the subregions from grid-based digital terrain model. Finally, a composite bathymetry complexity index integrating aspects of steepness and depth variation is developed to guide the operation of sounding selection in different subregions. Generally, when seafloor is rugged with steep slopes, the number of soundings is high. While in flatter areas, a smaller amount of soundings is retained. The potential of our approach is demonstrated by an application to a real data set.     

A hybrid efficient method to downscale wave climate to coastal areas

Long-term time series of sea state parameters are required in different coastal engineering applications. In order to obtain wave data at shallow water and due to the scarcity of instrumental data, ocean wave reanalysis databases ought to be downscaled to increase the spatial resolution and simulate the wave transformation process. In this paper, a hybrid downscaling methodology to transfer wave climate to coastal areas has been developed combining a numerical wave model (dynamical downscaling) with mathematical tools (statistical downscaling). A maximum dissimilarity selection algorithm (MDA) is applied in order to obtain a representative subset of sea states in deep water areas. The reduced number of selected cases spans the marine climate variability, guaranteeing that all possible sea states are represented and capturing even the extreme events. These sea states are propagated using a state-of-the-art wave propagation model. The time series of the propagated sea state parameters at a particular location are reconstructed using a non-linear interpolation technique based on radial basis functions (RBFs), providing excellent results in a high dimensional space with scattered data as occurs in the cases selected with MDA. The numerical validation of the results confirms the ability of the developed methodology to reconstruct sea state time series in shallow water at a particular location and to estimate different spatial wave climate parameters with a considerable reduction in the computational effort.     

Extended-aperture underwater acoustic multisource azimuth/elevationdirection-finding using uniformly but sparsely spaced vector hydrophones

Aperture extension is achieved in this novel ESPRIT-based two-dimensional angle estimation scheme using a uniform rectangular array of vector hydrophones spaced much farther apart than a half-wavelength. A vector hydrophone comprises two or three spatially co-located, orthogonally oriented identical velocity hydrophones (each of which measures one Cartesian component of the underwater acoustical particle velocity vector-field) plus an optional pressure hydrophone. Each incident source's directions-of-arrival are determined from the source's acoustical particle velocity components, which are extracted by decoupling the data covariance matrix's signal-subspace eigenvectors using the lower dimensional eigenvectors obtainable by ESPRIT. These direction-cosine estimates are unambiguous but have high variance; they are used as coarse references to disambiguate the cyclic phase ambiguities in ESPRIT's eigenvalues when the intervector-hydrophone spacing exceeds a half-wavelength. In one simulation scenario, the estimation standard deviation decreases with increasing intervector-hydrophone spacing up to 12 wavelengths, effecting a 97% reduction in the estimation standard deviation relative to the half-wavelength case. This proposed scheme and the attendant vector-hydrophone array outperform a uniform half-wavelength spaced pressure-hydrophone array with the same aperture and slightly greater number of component hydrophones by an order of magnitude in estimation standard deviation. Other simulations demonstrate how this proposed method improves underwater acoustic communications link performance. The virtual array interpolation technique would allow this proposed algorithm to be used with irregular array geometries     

基于Argo浮标数据的星载微波辐射计Aquarius数据产品质量评估

Aquarius是专门用于海洋盐度监测的L波段辐射计,于2011年6月发射入轨,目前已进入业务化运行阶段。本文以太平洋为研究区域,利用Argo盐度现场数据对星载微波辐射计Aquarius的2012年2级数据产品质量进行了分析与讨论,结果表明:与Argo数据比较,Aquarius数据盐度存在0.1的负偏差,标准差约为0.7,升轨和降轨数据差异不明显;受亮温陆地污染和无线电射频干扰的影响,近岸海域反演误差较大;海面温度较高的低纬海域反演结果优于中纬度海域;受亮温敏感性及粗糙海面发射率模型的影响,Aquarius在低温水域以及高风速条件下盐度反演误差较大,标准差可达1以上。     

多尺度边缘检测中尺度因子的选择

基于二种典型边缘的尺度空间边缘检测结果,分析实际计算过程中数字信号的最小分辨率对尺度因子进化的约束,导出了尺度因子对给定边缘和最小分辨率的关系不等式。该结果从实际计算的角度解释了尺度定理的产生原因,同时为多尺度边缘检测中尺度因子进化步长的选择提供了依据。     

Compensation for source heave by use of a Kalman filter

This paper presents a procedure for data filtering to compensate for the effects of the towed body dynamics (heave), in shallow marine seismic reflection records. A method to extract an approximate record of the heave contribution to data collected is outlined. The method utilizes the time to water-sediment interface on each acoustic return record to construct the required approximate heave motion record. The frequency response of the heave component record provides the basis for a proposed linear model for the heave motion. A formulation of the heave compensation requirement as a Kalman filtering problem in optimal linear estimation theory is given. A discussion of the computational aspects and practical results are discussed to conclude the paper.     

Trajectory-cell based method for the unmanned surface vehicle motion planning

A trajectory-cell based method was proposed for unmanned surface vehicle (USV) motion planning to combine the expression of the dynamic constraints and the discretization of the search space. The dynamic constraints were expressed by the USV trajectories produced by the mathematical model. The search space was performed by the discretization rules with the consideration of the path continuity, the search convenience and the maneuvering simplification. Therefore, the trajectory-cells were the discretized trajectories, which made the search space meet the USV dynamic constraints, and guaranteed the final spliced path continuous. After abstracting the characteristics of those cells, the available waypoints and headings were represented as the search indexes. Finally, a trajectory-cell based path searching strategy was proposed by determining the cost function of the A* algorithm. The results showed that the proposed algorithm can plan a practical motion path for the USV.