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海洋中的声速剖面是影响水声设备效能的重要环境因素之一,声速剖面的自动分类和区划对海洋环境的应用意义重大.依据浅海30分方区按月统计声速剖面,通过归一化处理和Akima差值采样,建立了各方区按月归化后声速剖面的分层梯度样本集,并应用多种系统聚类算法分别对分层梯度样本集进行分析;计算各种算法在不同聚类数水平下聚类结果的总的类内离差和,依据总的类内离差和变化曲线拐点对应的聚类数目,结合分类结果确定浅海声速剖面最优聚类数目.对大量历史统计声速剖面数据的分析结果表明,该方法得到的聚类结果与人工经验分类结果吻合较好. 相似文献
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以海区30'网格方区多年月平均统计的声速剖面作为原始数据集,提取声速剖面的表层、主跃层和深海等温层分层结构特征,把我国近海及其邻近海域预分为Ⅰ,Ⅱ和Ⅲ类区。对Ⅱ,Ⅲ类区声速剖面,应用有序样本聚类算法分别进行表层分离。根据各类区的表层声速剖面数据,通过归一化处理和Akima差值采样得到梯度剖面,建立起按月归一化后的声速剖面分层梯度样本集,并应用系统聚类法和SOFM神经网络方法分别进行聚类分析,再根据分类结果并结合各类型海区的声学特点,得到各类型海区声速剖面的典型类型。通过对大量历史数据的分析结果表明,该方法为自动分类海洋声速剖面提供了一条有效路径,弥补了长期以来海洋声速剖面主要依靠人工分类的不足。 相似文献
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应用分层声速剖面模型(LSSPM)和BELLHOP高斯束声场计算模型,对深海声速剖面结构变化引起的会聚区偏移特性进行了分析.结果表明,声速值的整体变化对会聚区影响很小,而混合层、主跃层、深海等温层及声道轴的变化都会使会聚区位置出现不同程度的偏移.主跃层是上层海洋变化的主要体现,混合层变化对会聚区的影响也是通过改变主跃层的形态结构实现的,跃层强度的增大使会聚区向远离声源方向偏移.深海等温层的声速变化体现了深海水团的结构差异,与主跃层引起的会聚区偏移呈反相变化.声道轴附近的声速变化体现了不同类型中层水团侵入和混合的影响,所引起的会聚区偏移反映了声道轴上层与下层梯度变化的综合效应,声速最小值的增加使会聚区向远离声源方向偏移. 相似文献
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基于2006年Argo资料,计算两北太平洋海域0-1 500 m声速剖面.针对声速结构分类中分类数目难以客观选取和分类结果易陷入局部最优等问题,采用聚类中心动态调整的遗传编码方案和操作算子,充分利用模糊聚类与遗传算法的优势,运用改进的遗传聚类算法对西北太平洋声速剖面进行分类区划基于声场环境区划结果,运用Kraken简正波传播模型(Kraken Normal Wave Propagation Model)模拟典型声速结构的声传播损失场,借助表征声呐效能的优质因数(FO)M),分析典型声速结构的传播损失特征及其对声呐探测、水下潜器等活动的影响 相似文献
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中国近海声速剖面的模态特征 总被引：1，自引：0，他引：1
利用WOA05数据集提供的气候态声速场数据,通过模糊C-均值聚类分析,得到了中国近海声速剖面模态特征的区域性分布和季节性变化。结果表明,中国近海的声速剖面结构可分为深海型(D型)、浅海型(S型)和过渡型(T型)三个基本类型。深海型剖面为"季节性跃层/正梯度+主跃层+深海声道+深海正梯度"结构,南海和菲律宾海因所属水系不同呈现出明显差异;浅海型剖面季节性变化强烈,冬季为正梯度或均匀型结构,其它季节为"混合层+季节性跃层+下均匀层"结构,负梯度强度与季节性跃层的变化有关,在夏季达到最强;过渡型剖面形态与邻近的深海型上层结构类似,但因受地形制约产生与深海型不同的声传播特征。海面太阳辐射、海洋环流、混合层以及水团配置的季节性变化导致的温盐场空间分布差异是造成不同海区、不同季节声场速剖面结构差异的根本原因。 相似文献
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三种常用声速算法的比较 总被引：3，自引：0，他引：3
在近几年的西太平洋调查中使用了SV Plus声速测量仪,共获取了46个站点的声速剖面,并基于同步观测的CTD数据,利用3种常用的声速算法计算了这些站点的声速剖面。所有这些站点的测深度均超过1500m,而且调查时间为3个不同的季节。CTD数据计算得到的声速剖面与声速测量仪器观测的声速剖面的比较表明,在三种算法中,Chen和Millero算法在积分平均意义上是最好的。当定点比较时,在水深大于800m或者小于200m的范围内,Wilson算法较好;在其他水深范围内,Chen和Millero的算法的计算结果和实际测量结果较为一致。 相似文献
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提出一种基于温度剖面相似系数的水系划分方法。基本思想是:将各温度剖面视为独立样本,各深度数据为样本变量,先基于划分区域水文特征选取合理数量的水系中心剖面,再利用各样本与各中心剖面相似系数大小进行聚类,得到各水系划分数据集合;利用几何平均求得各水系集合新的中心剖面,重复剖面相似系数聚类过程,直至中心剖面不再变化为止。最后利用国家海洋信息中心发布的中国近海CTD温、盐产品对该方法进行试验,并对聚类得到的各类温度剖面展开讨论。结果较好的反应出各区域温度剖面特征,综合体现出东海各区域温度大小、海流、水团和水深特性。 相似文献
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Enrico De Marinis Paola Picco Alessandro Crise Otello Gasparini Stefano Salon 《Marine Ecology》2002,23(S1):122-137
Abstract. This paper describes a new Ocean Acoustic Tomography (OAT) methodology - a passive tomography - presently in an advanced development phase. This technique has been developed for long-term, extensive, remote monitoring of the seawater temperature spatial distribution, which is estimated from the received noise emitted from ships of opportunity. To test the passive tomographic processor under controlled conditions, the components of the naval noise from different kinds of vessels was analysed and realistic naval noise was simulated. The feasibility of the proposed methodology was confirmed by test-runs on semi-synthetic data; its capability to resolve temperature profiles will be better assessed with the use of real acoustic and environmental data collected during the INTIMATE00 experiment performed in October 2000 in the Atlantic Ocean off the Portuguese coast. An analysis of the space and time variability of the Empirical Orthogonal Function (EOF) decomposition of the sound speed (SSP) in the Mediterranean Sea has been carried out to identify areas where acoustic tomography can be successfully applied. Results from simulations in the South Adriatic Sea, which was identified as a region with a high sound speed variability associated with the seasonal cycle and with the main oceanographic processes, are reported. 相似文献
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Complex perturbations in the profile and the sparsity of samples often limit the validity of rapid environmental assessment (REA) in the South China Sea (SCS). In this paper, the remote sensing data were used to estimate sound speed profile (SSP) with the self-organizing map (SOM) method in the SCS. First, the consistency of the empirical orthogonal functions was examined by using k-means clustering. The clustering results indicated that SSPs in the SCS have a similar perturbation nature, which means the inverted grid could be expanded to the entire SCS to deal with the problem of sparsity of the samples without statistical improbability. Second, a machine learning method was proposed that took advantage of the topological structure of SOM to significantly improve their accuracy. Validation revealed promising results, with a mean reconstruction error of 1.26 m/s, which is 1.16 m/s smaller than the traditional single empirical orthogonal function regression (sEOF-r) method. By violating the constraints of linear inversion, the topological structure of the SOM method showed a smaller error and better robustness in the SSP estimation. The improvements to enhance the accuracy and robustness of REA in the SCS were offered. These results suggested a potential utilization of REA in the SCS based on satellite data and provided a new approach for SSP estimation derived from sea surface data. 相似文献