南海海底地形的卫星测高数据反演

深入讨论了重力异常与海深的关系 ,在考虑地壳均衡补偿效应影响的基础上 ,推导了由重力异常用FFT技术计算海深的模型。最后 ,利用推导的模型联合测高卫星数据和海洋重力资料反演了中国南海 2 5′× 2 5′海底地形     

用卫星测高资料反演大尺度海底地形

本文在分析卫星测高应用潜力的基础上,提出了利用卫星测高资料反演大尺度海底地形的新思路,研究了匹配滤波法、垂线偏差剖面法的原理及优缺点,提出了带约束的线性回归法和带约束的曲面拟合法,并且,根据我们在反演中的体会,指出了几点提高反演精度的途径。     

高精度测高重力场反演南海海底地形

随着测高技术的不断发展,测高海洋重力场的精度不断提高,由其反演计算的海底地形精度也相应提高。利用Sandwell 2014年最新发布主要由Jason-1末期大地测量任务和Cryosat-2观测资料反演的测高重力场V23.1,采用重力地质法(GGM)反演了中国南海海域海底地形模型,并对结果进行了精度评价。该过程中首先直接计算一系列密度差下的反演结果,并通过船测数据检核选定优化的密度差范围,然后利用向下延拓的方法确定了最优密度差异常数为7g/cm3。与船测水深数据相比,反演得到的GGM模型与检核点船测水深数据差值的标准差达到了±70.32m;此外,还计算了由测高重力异常V15.2反演的海底模型,比较这两个模型发现:测高重力场短波部分的改善对海底地形反演精度的提升作用有限,为得到更高精度的海底地形模型,需引入短波更为敏感的重力梯度资料。     

测高重力场反演海底地形方法比较

根据重力异常与海底地形的相关性,利用日本西之岛附近海域内的重力异常和船测水深,分别采用SandwellSmith方法(SS,1994)和重力地质法(GGM)反演了该海域内1'×1'海底地形模型,并将反演的模型与格网化船测水深模型和ETOPO1模型进行了对比分析。在GGM中,使用了迭代法向下延拓确定密度差常数为5.7g/cm~3。与SS模型相比,GGM模型与格网化船测水深模型的差值的标准差达到了37.10m。     

卫星测高在海洋测绘中的应用

研究了卫星测高数据在海山探测以及无图海域水深预测方面的应用。介绍了匹配滤波法检测海山的理论与方法，并研究了反演海底地形时的重力导纳理论以及滤波窗口和滤波函数的选择。实践表明，利用卫星测高数据反演海底地形的精度可优于水深的10％。     

基于频域法的留尼汪海域海底地形反演

海底地形是全球地形的重要组成部分,对地球物理科学研究、经济活动等具有重要作用。基于Parker公式,利用卫星测高重力异常和船测水深数据,采用频域的方法反演了疑似马航MH370失事区域的留尼汪海域的10°×10°的海底地形。最后将反演的水深和船测水深、国际通用的海深模型ETOPO1作比较进行精度评估,结果表明:本文反演结果与船测水深相比误差平均值为-26.038 m,标准差为176.588 m;与ETOPO1相比,差异平均值为-33.541 m,标准差为160.769 m。这表明采用重力异常数据,结合船测数据能较高精度地反演海底地形。     

浅论海面地形特征的形成机制

本文从地热学、海洋学、气象学、物理海洋学和海底构造学角度,探讨了海面地形特征的形成机制。研究表明,形成海面地形起伏差异的主要因素有以下三个:一是有特殊的热量来源,二是有保持热量不流失的外部条件——特有的地貌特征和海底地形,三是有特殊的海流影响。     

卫星测高在海洋学中的应用

讨论了卫星测高数据在确定大洋环流、大洋潮汐、海面风场和波浪中的应用。介绍了基本原理、研究人员普遍采用的方法和普遍为人们所接受的研究成果。     

两种测高重力异常反演海底地形方法比较

针对基于测高重力异常反演海底地形理论众多、选取标准无法确定的情况,利用中国南海海域内的测高重力异常和船测水深数据研究比较了重力地质法(GGM)和Smith&Sandwell (SAS)法两种精度高、计算速度相对较快的海底地形反演理论。其中,GGM方法的密度差异常数Δρ由向下延拓技术确定为2.15 g·cm^-3,SAS方法采用移去-恢复技术得到反演波段内重力异常和水深数据。结果表明:测线分布条件一定时,水深多在-1 000 m左右或反演区域岛礁、海山等复杂海底地形较多时选取SAS方法,水深主要在-3 000 m以深的区域或海底地形复杂程度不高时选取GGM方法则能获取更好的效果,其效果最优处与船测水深在检核点处的差值最优平均值能达-0.61 m,标准差可达14.67 m。     

利用卫星测高资料推求西北太平洋海域的海洋大地水准面

本文根据卫星测高数据和海面动力地形资料,绘制了西北太平洋海域局部大地水准面的精细结构图,对解决卫星测高技术中大地水准面和海面地形的可分性问题作了初步尝试。     

卫星测高在确定地球重力场中的应用

根据测高所得的几何量与地球重力场物理量之间的关系，探讨了卫星测高数据在确定地球重力场参数中的应用，主要包括利用卫星测高数据确定海洋大地水准面、确定海洋重力异常和改善地球重力场模型。     

Bathymetry Data Correction Using Global Optimization Method

The bathymetry data of marine bodies have been collected over a century, and the collected data have a wide range of resolution and accuracy. Acquisition of bathymetry data is very costly and time-consuming. One can use the old, low-quality bathymetry data to fill the gap in high-quality, recently acquired bathymetry data after correcting the old data to improve its quality so that it is comparable to the high-quality data. The old data correction can be treated as a nonlinear inverse problem. Simulated annealing (SA) global optimization method was used here in solving this problem. The two sets of data that were used are project survey (PS) and Vietnamese Navy Chart (VNC) data. The PS data were collected in 2000 in an offshore survey from the Vietnam coast in the South China Sea (SCS). The VNC data were obtained by digitizing VNC that was published in 1981. Inverse distance weighted (IDW) interpolation method was used for forward modeling. Weperformed the SA algorithm run starting at a high "temperature," then lowering the "temperature" gradually up to the "critical temperature" and then staying there for the rest of the run. The best model chosen by the algorithm showed an improvement of 63% from the original model. We then constructed a digital bathymetry model (DBM) of the study area with the combined corrected VNC and the PS data.     

Bathymetry Data Correction Using Global Optimization Method

The bathymetry data of marine bodies have been collected over a century, and the collected data have a wide range of resolution and accuracy. Acquisition of bathymetry data is very costly and time-consuming. One can use the old, low-quality bathymetry data to fill the gap in high-quality, recently acquired bathymetry data after correcting the old data to improve its quality so that it is comparable to the high-quality data. The old data correction can be treated as a nonlinear inverse problem. Simulated annealing (SA) global optimization method was used here in solving this problem. The two sets of data that were used are project survey (PS) and Vietnamese Navy Chart (VNC) data. The PS data were collected in 2000 in an offshore survey from the Vietnam coast in the South China Sea (SCS). The VNC data were obtained by digitizing VNC that was published in 1981. Inverse distance weighted (IDW) interpolation method was used for forward modeling. Weperformed the SA algorithm run starting at a high "temperature," then lowering the "temperature" gradually up to the "critical temperature" and then staying there for the rest of the run. The best model chosen by the algorithm showed an improvement of 63% from the original model. We then constructed a digital bathymetry model (DBM) of the study area with the combined corrected VNC and the PS data.     

多波束测深数据异常值检测算法比较

简要介绍了现行的部分国内外多波束测深数据异常值检测算法,比较和分析了算法的各自优缺点,包括中值滤波、基于趋势面的测深异常检测、抗差估计异常值检测。总结得出,通过综合利用以上各种算法,能有效提高测深数据的异常值检测效果。     

Large-Scale Errors in ERS Altimeter Data

A method is described for mapping time-uncorrelated large-scale errors in satellite altimeter sea surface heights. Standard deviations of differences between pairs of successive measurements at track crossovers are computed, and the functional dependence of these deviations on absolute time difference is used to estimate the errors of individual measurements. This is first applied to all of ERS-1,2 altimeter data in the Pacific Ocean, yielding average errors of 3.2 cm in the deep ocean (>1 km) and 4.7 cm in the shallow seas (<1 km). The procedure is repeated for variable latitude bands, each with a full range of possible time differences, yielding a meridional profile of computed errors, ranging from 2.6 cm near the Antarctic continent (67–60S) and South Subtropical regions (25–5S) to 3.5 cm in the Antarctic Circumpolar Current (60–45S) and the Northern Hemisphere Subtropical and Subpolar Gyres. Finally, coarse-resolution maps of these errors are produced by subdividing the Pacific Ocean into latitude-longitude bins, each large enough to contain a sufficient number of samples for the functional fits. The larger errors are in Northwest and Subtropical Pacific, especially in South China Sea (4.3 to 4.5 cm) and off northern Australia (5.4 cm), while the smaller errors (2.5 to 3 cm) are in Northeast Pacific, central Tropical Pacific and near Antarctica in Southeast Pacific Ocean. These are lower bounds on altimeter errors, as they do not include contributions from time-correlated errors. We find that the computed error fields are not correlated with sea level standard deviations, thus disproving the notion that altimeter error variance can be scaled with the variance of sea surface height data.     

Coastal Bathymetry from Hyperspectral Remote Sensing Data: Comparisons with High Resolution Multibeam Bathymetry

We present a large-scale quantitative test of a hyperspectral remote-sensing reflectance algorithm. We show that coastal bathymetry can be adequately derived through model inversions using data from the Airborne Visible-Infrared Imaging Spectrometer instrument. Data are analyzed from a shore-perpendicular transect 5 km offshore Sarasota, Florida at water depths ranging from 10 m to 15.5 m. Derived bottom depths are compared to a high-resolution multibeam bathymetry survey. Model-derived depths are biased 4.9% shallower than the mean of the multibeam depths with an RMS error of 7.83%. These results suggest that the model performs well for retrieving bottom depths from hyperspectral data in subtropical coastal areas in water depths ranging from 10 m to 15.5 m.     

高度计大气湿度校正的方法及其应用分析

大气中水汽造成的路径延迟的测量误差是目前卫星高度计存在误差的重要原因,这种误差对高度计数据的分析应用产生很大的影响.介绍了高度计大气湿度校正的2种方法,并对它们的结果做了比较.最后分析了大气湿度校正对高度计数据应用的影响.