黄浦江航道多波束测深数据处理研究

针对上海世博会对黄浦江航道核心水域高精度海底地形的需求,采用SeaBat8125多波束测深系统对该区域进行了全覆盖测量.通过潮汐基准面归算、潮汐改正精度内符合检验、原始数据各项改正、除噪处理及测深误差分析等几个步骤,得到基于吴淞零点的最终水深成果.各项检验表明,此次多波束测深成果质量满足实际需求.     

多波束测深残余折射处理技术的对比研究

针对多波束条幅存在的残余声线折射影响,加拿大Caris HIPS多波束资料处理软件除了支持声速剖面改正外,还提供了一个折射改正工具。该折射改正工具简单易用,效果所见即所得,能够快速消除声线折射产生的假地形。通过实测数据对这两种改正方法的结果进行了比较。     

多波束测深数据处理及成图

针对多波束测深系统测量的特点,分别分析声速改正技术和潮位改正技术。从声速在海水中的传播出发,阐述海水中声速的测量,对声速的较正方法进行探讨,随后针对潮汐效应的影响,对多波束测深数据进行潮位改正,并利用海上试验实测的多波束测深数据,将处理后的数据绘制成海底地形数字地图。     

多波束测深及影响精度的主要因素

通过多波束测深的基本原理、参数校正和数据改正方法的讨论,阐述了保证多波束测深精度的主要校改正方法,并在模型分析的基础上,探讨了声速剖面的结构及其时空变化对多波束测深精度的影响,指出了三个特征海区声速结构的分布特点,并提出了抑制三海区声速改正误差的可能方法及控制多波束测量中声速改正精度的措施     

浅水多波束测深声纳关键技术剖析

基于多波束测深声纳工作原理,结合国内外主流浅水多波束测深声纳产品与技术发展现状,分析了浅水多波束测深声纳具备的5种典型能力,提出了从能力表征的角度理解浅水多波束测深声纳性能指标;总结了保障这些能力所采用的主要关键技术,讨论了技术实现的具体思路,列举了仿真或试验数据处理结果;展望了多波束测深声纳的发展趋势。     

多波束与单波束测深数据融合处理方法

受声线弯曲的影响,多波束测深的边缘波束的数据质量较低,而单波束测深受声线弯曲的影响比较小。结合多波束覆盖面大和声速剖面误差对单波束影响相对较小的特点,研究了多波束和单波束的测深数据融合方法,利用同一位置单波束和多波束测深数据的差值,拟合一个与坐标位置相关的误差模型,并利用该误差曲面对多波束测深数据进行综合改正,从而提高多波束测深的数据质量。     

多波束测深波束脚印位置归算模型研究

推导了多波束测深脚印位置归算模型,通过与CARIS软件进行实例比对,验证了该模型的有效性。在数据处理过程中,采用时标序列检索的方式处理导航时间延迟,取得了良好的效果;采用条带姿态进行姿态改正避免了"杠杆"旋转对边缘波束深度的影响。实例比对表明,该模型结果与CARIS软件输出结果一致。     

深水多波束测深系统测深性能综合评估

多波束测深系统越来越被广泛应用于深海调查,并逐渐成为远海综合调查船的标配仪器.利用其开展水深测量作业前对其测深性能进行有效而全面地评估是极为关键的一步.利用2套SeaBeam3012深水多波束测深系统、1套HY1690深水单波束测深系统,在不同条件海域开展一系列系统性试验,并对测深数据分别开展"线与线"、"线与面"及"...     

多波束测深中声速剖面的横向加密方法

针对声速剖面在横向空间上的低密度、分布不均匀导致水深测量误差的问题,提出了一种容易实现且有效的思路,采用分层取样、空间插值的方法,在后处理过程中人为地增加声速剖面的数量,使其密度足够大、分布更均匀,达到改善声速改正的效果和提高多波束测深精度的目的。     

基于中值滤波加权修正的多波束声呐测深数据趋势面滤波方法

针对传统趋势面滤波方法中多项式拟合曲面系数向量的求取和作为阈值的均方根误差的求取都受到异常数据的影响,使该方法在异常测深数据较多的情况下滤波效果不佳的问题,提出了一种中值滤波加权修正的改进方法。在构造趋势面之前,对水深数据进行加权修正,以前后两次修正后数据的拟合优度的变化量作为是否进行下一步水深修正的依据,利用最终修正后的水深数据求取多项式拟合曲面系数向量和均方根误差,大幅降低了异常数据的影响,具有很强的抗差性。经仿真模拟数据和多波束实测数据滤波试验,该方法在异常数据较多的情况下依然良好,能够保持良好的滤波效果,明显优于传统趋势面滤波;同时,该方法能够保持较高的运算效率,适用于海量多波束测深数据的自动滤波。     

多波束反向散射强度数据处理研究

在探讨多波束测深系统反向散射强度与海底底质类型的关系基础上,研究影响反向散射强度的各种因素,主要分析了海底地形起伏、中央波束区反射信号对反向散射强度的影响,并给出了消除这些影响的方法;将处理后的“纯”反向散射强度数据镶嵌生成海底声像图,为海底底质类型划分以及地貌解译提供了基础数据和辅助判读依据.     

近岸多波束测量中的GPS-RTK差分技术及其受影响的因素

分析了近岸多波束测量中应用GPS-RTK差分技术对船只导航与定位,对在受波浪影响的多波束测量中,船只所受潮汐、换能器吃水的动态变化量等诸影响因素的改正以及船只的运动姿态纠正等问题进行了讨论。使用GPS-RTK系统可以有效地解决潮汐(水位)、波浪影响和换能器吃水的动态变化量对多波束测量精度综合影响的问题,如果使用"一加三"的GPS-RTK接收机,还可以实现船只姿态的高精度综合修正。同时,对4个影响GPS-RTK差分技术系统能力的主要因素应引起重视,采取措施加以解决,使近岸的多波束测量工作更加富有成效。     

Correction for depth biases to shallow water multibeam bathymetric data

Vertical errors often present in multibeam swath bathymetric data. They are mainly sourced by sound refraction, internal wave disturbance, imperfect tide correction, transducer mounting, long period heave, static draft change, dynamic squat and dynamic motion residuals, etc. Although they can be partly removed or reduced by specific algorithms, the synthesized depth biases are unavoidable and sometimes have an important influence on high precise utilization of the final bathymetric data. In order to confidently identify the decimeter-level changes in seabed morphology by MBES, we must remove or weaken depth biases and improve the precision of multibeam bathymetry further. The fixed-interval profiles that are perpendicular to the vessel track are generated to adjust depth biases between swaths. We present a kind of postprocessing method to minimize the depth biases by the histogram of cumulative depth biases. The datum line in each profile can be obtained by the maximum value of histogram. The corrections of depth biases can be calculated according to the datum line. And then the quality of final bathymetry can be improved by the corrections. The method is verified by a field test.     

基于CUBE算法的多波束测深数据自动处理研究

对CUBE算法自动处理多波束测深数据的模型建立、格网节点的多重估计和最优估值选取准则进行了详细介绍,深入分析了多重估计的实用性,并通过实测数据对该算法进行实现.利用了抗差Kalman滤波改进CUBE算法.通过模拟数据对改进的CUBE算法进行实验,验证了算法改进的必要性.     

Improved processing of Hydrosweep DS multibeam data on the R/V Maurice Ewing

We have developed a new software package, called MB-System, for processing and display of Hydrosweep DS multibeam data on the R/V Maurice Ewing. The new software includes tools for modeling water sound velocity profiles, calculating multibeam bathymetry from travel time values by raytracing through a water sound velocity profile, interactive and automatic editing of multibeam bathymetry, as well as a variety of tools for the manipulation and display of multibeam data. A modular input/output library allows MB-System programs to access and manipulate data in any of a number of supported swath-mapping sonar data formats, including data collected on Hydrosweep DS, Sea-Beam Classic, SeaBeam 2000, SeaBeam 2100, H-MR1, Simrad EM12, and other sonars. Examples are presented of the software's application to Hydrosweep data recently collected on the R/V Maurice Ewing.     

Automatic calibration for wobble errors in shallow water multibeam bathymetries

The wobble errors caused by the imperfect integration of motion sensors and transducers in multibeam echo-sounder systems(MBES) manifest as high-frequency wobbles in swaths and hinder the accurate expression of high-resolution seabed micro-topography under a dynamic marine environment.There are many types of wobble errors with certain coupling among them.However,those current calibration methods ignore the coupling and are mainly manual adjustments.Therefore,we proposed an automatic calibration ...     

A new method for weakening the combined effect of residual errors on multibeam bathymetric data

Multibeam bathymetric system (MBS) has been widely applied in the marine surveying for providing high-resolution seabed topography. However, some factors degrade the precision of bathymetry, including the sound velocity, the vessel attitude, the misalignment angle of the transducer and so on. Although these factors have been corrected strictly in bathymetric data processing, the final bathymetric result is still affected by their residual errors. In deep water, the result usually cannot meet the requirements of high-precision seabed topography. The combined effect of these residual errors is systematic, and it’s difficult to separate and weaken the effect using traditional single-error correction methods. Therefore, the paper puts forward a new method for weakening the effect of residual errors based on the frequency-spectrum characteristics of seabed topography and multibeam bathymetric data. Four steps, namely the separation of the low-frequency and the high-frequency part of bathymetric data, the reconstruction of the trend of actual seabed topography, the merging of the actual trend and the extracted microtopography, and the accuracy evaluation, are involved in the method. Experiment results prove that the proposed method could weaken the combined effect of residual errors on multibeam bathymetric data and efficiently improve the accuracy of the final post-processing results. We suggest that the method should be widely applied to MBS data processing in deep water.     

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。     

The filtering and compressing of outer beams to multibeam bathymetric data

Some errors and noises are often present in multibeam swath bathymetric data. Echo detection error (EDE) is one of the main errors. It causes the depth error to become bigger in outer beams and looks like sound refraction. But depth errors due to EDEs have a trumpet-shaped appearance, instead of a curved appearance that is caused by the sound refraction errors. EDEs, including systematic acoustic signal detection errors and internal noises, cannot be removed during the correction of sound refraction. It causes depth inconsistencies between adjacent swaths and degrades precision of outer beams. Sometimes, the bathymetric errors caused by EDEs do not even meet the requirements of IHO (International Hydrographic Organization). Therefore, a post-processing method is presented to minimize the EDEs by filtering outliers and compressing outer beams of multibeam bathymetric data. The outliers caused by internal noises are removed by an automatic filter algorithm first. Then the outer beams are compressed to reduce systematic acoustic signal detection errors according to their depths, the calculated depth line and standard deviations (SDs). The automatic filter process is important for calculating the depth line. The selection of inner beams to calculate the average SD of beam depths is crucial to achieving compressing goals. The quality of final bathymetric data in outer beams can be improved by these steps. The method is verified by a field test.