多波束海底声像图的形成及应用研究

在探讨多波束海底声像图形成原理基础上,重点研究多个扇面、多个条带的反向散射强度数据拼接、镶嵌方法,将海底反向散射强度值向图像灰度值转换,最后形成海底声像图,为海底地貌解译、海底目标物探测以及海底底质类型划分提供判读依据。     

混浊水域多波束反向散射强度传播损失改正

为了改善现有的反向散射强度传播损失改正方法在混浊水域适应性较弱的问题,提出了混浊水域声传播损失计算模型。首先利用不同深度的海洋环境参数构建声波吸收系数剖面,然后基于声速,沿波束传播路径,对每个波束分层计算传播损失。实验分析表明,本方法传播损失改正效果最佳,其Spearman等级相关系数绝对值仅为0.04,远小于传统模型和TVG改正,大大减弱了混浊水域情况下多波束回波强度与传播距离的相关性,有效改善了多波束声呐图像的质量。     

两种多波束反向散射强度数据归一化方法的建立与分析

多波束反向散射强度数据应用广泛,但由于受到角度响应的影响,导致生成的多波束声呐图像质量偏低,且现有角度响应改正方法在复杂海底底质环境下适应性较差。为此本文对散射强度进行分析,给出了两种多波束反向散射强度数据归一化方法,分别为基于高斯拟合以及角度响应的散射强度改正方法,前者主要是基于散射强度的变化规律进行改正,而后者则是基于声波的散射机理进行改正。实验结果表明两种方法较传统改正方法精度均有约30%的提升,并且角度响应方法较高斯拟合方法改正精度更高,但计算效率有所下降。以上实验验证了两种方法的有效性,实现了散射强度数据的归一化,提升了多波束声呐图像的质量。     

基于多维度声学特征优选的多波束海底底质分类

海底表层底质分布信息的准确获取在构建海洋基础地理数据库中发挥着重要作用。目前,多波束是实现大范围海底底质分类的有效手段之一,基于多波束测深和反向散射强度数据所派生的声学特征被广泛应用于底质分类建模。然而,随着特征维度的增加,特征空间中存在的无关和冗余特征严重影响底质分类精度。为了定量评估声学特征对底质类别的表征能力,并消除无效特征对分类结果的干扰,本文提出了基于多维度声学特征优选的海底底质分类方法。首先,结合实际底质样本的物理属性对多维特征进行排序和优选,排除冗余和无关特征。其次,分别应用支持向量机、随机森林和深度信念网络构建海底底质监督分类模型。通过利用爱尔兰海南部多波束调查数据和实地取样信息进行试验,结果表明提出方法对海底底质的总体分类精度和Kappa系数分别最高达到了86.20%和0.834,相较于主成分分析和熵指标特征选择方法有明显提高,突出了该方法在海底底质探测及制图的应用潜力。     

多波束回声数据的统计与底质分类应用

利用声反向散射数据作海底沉积物分类,是海洋地质学家感兴趣的话题,也是目前多波束声纳应用的一个研究热点。结合胶州湾实际调查数据,探讨了贝叶斯分类方法在该领域的应用。研究结果表明,该方法可以对不同的底质类型进行分类,可以识别未知的底质类型以及对混合在一起的两种不同类型的目标进行分类。     

介绍几种典型的海底底质分类技术

本文主要介绍了四家海洋仪器公司的最新海底底质分类技术。它们分别利用多波束的反向散射强度数据、单波束的回声波形结构数据和旁侧声纳数据。采用了多参数统计分析、波形结构分析和影像属性分析等方法，实现了快速、高效、大面积地对海底底质进行间接的分类。尽管它们的技术各不相同，但都可以分为监督分类方式和非监督分类方式。     

一种Kongsberg EM多波束后向散射强度波束指向性残余改正方法

Kongsberg EM多波束声呐的波束指向性与出厂标定存在偏差,导致后向散射强度呈现与扇区耦合的辐射畸变,针对这个问题提出了一种基于波束指向性模型的多扇区指向性残余改正方法。该方法首先在分析波束收发过程的基础上,构建EM声呐多扇区指向性辐射模型;然后选取特定测线,采用经验性声学模型估计后向散射强度-角度曲线,分离各扇区指向性残余信号;最后对回波强度进行指向性残余改正,得到与采集扇区无关的后向散射强度。实例计算结果表明：该方法能有效降低多扇区指向性残余对后向散射强度测量的干扰,提高EM系列多波束声呐在海底底质分析中的使用价值。     

JFR 海底热液区多波束声纳数据处理与分析

基于收集到的东太平洋海隆北段Juan de Fuca Ridge热液活动区的高精度多波束声纳数据,应用加权移动平均算法, 生成典型的高精度海底DTM;应用声纳图像处理技术,生成高分辨率海底声纳镶嵌图,并对其海底地形及海底声学图像进行 处理和分析。通过处理与分析,对JFR热液区海底地形地貌特征有了初步认识,对于我国大洋调查和海底热液区探测具有一 定的借鉴作用。     

多波束海底底质分类软件Simrad Triton的应用

研究了利用声学信号对海底底质自动分类的技术。介绍了挪威Simrad公司海底底质分类软件Triton的分类原理，它所应用的分类方法及其软件的体系结构。最后，用实例来说明Simrad Triton在海底底质分类中的应用。     

Processing Multibeam Backscatter Data

A new highly precise source of data has recently become available using multibeam sonar systems in hydrography. Multibeam sonar systems can provide hydrographic quality depth data as well as high-resolution seafloor sonar images. We utilize the seafloor backscatter strength data of each beam from multibeam sonar and the automatic classification technology so that we can get the seafloor type identification maps. In this article, analyzing all kinds of error effects in backscatter strength, data are based on the relationship between backscatter strength and seafloor types. We emphasize particularly analyzing the influences of local bottom slope and near nadir reflection in backscatter strength data. We also give the correction algorithms and results of these two influent factors. After processing the raw backscatter strength data and correcting error effects, we can get processed backscatter strength data which reflect the features of seafloor types only. Applying the processed backscatter strength data and mosaicked seafloor sonar images, we engage in seafloor classification and geomorphy interpretation in future research.     

Processing Multibeam Backscatter Data

A new highly precise source of data has recently become available using multibeam sonar systems in hydrography. Multibeam sonar systems can provide hydrographic quality depth data as well as high-resolution seafloor sonar images. We utilize the seafloor backscatter strength data of each beam from multibeam sonar and the automatic classification technology so that we can get the seafloor type identification maps. In this article, analyzing all kinds of error effects in backscatter strength, data are based on the relationship between backscatter strength and seafloor types. We emphasize particularly analyzing the influences of local bottom slope and near nadir reflection in backscatter strength data. We also give the correction algorithms and results of these two influent factors. After processing the raw backscatter strength data and correcting error effects, we can get processed backscatter strength data which reflect the features of seafloor types only. Applying the processed backscatter strength data and mosaicked seafloor sonar images, we engage in seafloor classification and geomorphy interpretation in future research.     

Detection of deep water benthic macroalgae using image-based classification techniques on multibeam backscatter at Cashes Ledge,Gulf of Maine,USA

Benthic macroalgae form an important part of temperate marine ecosystems, exhibiting a complex three-dimensional character which represents a vital foraging and spawning ground for many juvenile fish species. In this research, image-based techniques for classification of multibeam backscatter are explored for the detection of benthic macroalgae at Cashes Ledge in the Gulf of Maine, USA. Two classifications were performed using QTC-Multiview, differentiated by application of a threshold filter, and macroalgal signatures were independently extracted from the raw sonar datagrams in Matlab. All classifications were validated by comparison with video ground-truth data. The unfiltered classification shows a high degree of complexity in the shallowest areas within the study site; the filtered demonstrates markedly less variation by depth. The unfiltered classification shows a positive agreement with the video ground-truth data; 82.6% of observations recording Laminaria sp., 39.1% of Agarum cribrosum and 100.0% (n = 3) of mixed macroalgae occur within the same acoustically distinct group of classes. These are discrete from the 8.1% recorded agreement with absences and nulls (>40 m) of macrophytes (n = 32) from a total of 86 ground-truth locations. The results of the water column data extraction (WCDE) show similar success, accurately predicting 78.3% of Laminaria sp. and 30.4% of A. cribrosum observations.     

Seafloor acoustic remote sensing with multibeam echo-sounders and bathymetric sidescan sonar systems

This paper examines the potential for remote classification of seafloor terrains using a combination of quantitative acoustic backscatter measurements and high resolution bathymetry derived from two classes of sonar systems currently used by the marine research community: multibeam echo-sounders and bathymetric sidescans sonar systems. The high-resolution bathymetry is important, not only to determine the topography of the area surveyed, but to provide accurate bottom slope corrections needed to convert the arrival angles of the seafloor echoes received by the sonars into true angles of incidence. An angular dependence of seafloor acoustic backscatter can then be derived for each region surveyed, making it possible to construct maps of acoustic backscattering strength in geographic coordinates over the areas of interest. Such maps, when combined with the high-resolution bathymetric maps normally compiled from the data output by the above sonar systems, could be very effective tools to quantify bottom types on a regional basis, and to develop automatic seafloor classification routines.     

多波束声呐图像条带中央和边缘残差处理方法

多波束声呐图像是进行海底底质分类的主要数据源之一,由于受海洋噪声、声波散射和混响、仪器设备等因素影响,其经各项常规改正后仍存在明显残差,突出表现在中央波束区和条带重叠区,难以形成高质量的声呐图像。文中分析了多波束声呐图像残差的成因及影响,提出了一种基于多条带最小二乘拟合的多波束声呐图像残差处理方法。首先,得到相邻声脉冲(ping)信号中央区域、重叠区域以及整体趋势的拟合函数;然后,通过拟合函数计算得到中央和重叠区域的残差改正系数;最后,通过改正系数进行残差改正。实验分析表明,该方法在保留原始细节的基础上,有效削弱了残差对声呐图像的影响,对多波束声呐图像处理具有参考和应用价值。     

基于回波时间的多波束测深与声纳数据匹配方法

以多波束精确的水深数据为参照源,采用原始回波时间对多波束测深数据与其同源声纳数据进行匹配,从而获得高精度和高分辨率的海底影像数据,并避免了传统声纳图像处理过程中斜距改正所带来的几何形变。匹配结果采用光照图输出,并与三维水深图、原始声纳图像和CARIS处理后的声纳图像进行比较分析。该方法有效地提高了多波束数据的利用率,增强了对海底地形的探测分辨率。     

基于多波束背向散射强度信号的海底表层沉积物粒度分类研究——以澳洲Joseph Bonaparte湾为例

近海海底地形探测与沉积物精确分类对涉海工程建设、生物栖息地反演以及海底资源勘查与开发具有重要的现实意义。以澳洲Joseph Bonaparte湾为例,利用多波束测深技术获取了该海湾约880 km2水域的水深数据与背向散射强度信号,结合同步采集的54个海底表层沉积物样品,通过随机决策树模型对该海域海底表层沉积物进行了分类研究。结果表明:（1）利用随机决策树模型分析该海域沉积物类型与背向散射强度的关系时,当模型内部参数设置:树的总数为200,最小分裂节点为2,每棵树的最大分裂级数为5时,可提高预测准确率;（2）该参数设置下,利用13°和37°入射角的背向散射强度预测该海域沉积物类型时,准确率最高,其值为83.3%,且在研究海域,砂质砾和砾质砂分布在背向散射强度较强的深槽或海沟等地区,而砾质泥质砂和含砾泥质砂主要分布在背向散射强度较弱的浅水海域。分析还发现,当水深数据作为预测海底表层沉积物类型的特征变量时,有可能降低最终预测结果的准确率。