无/缺水下地形数据的高原堰塞湖水量遥感估算

水量遥感动态监测对于高原堰塞湖风险评估、预报预警和处置决策等具有重要意义。针对高原无资料或缺资料区,充分利用空天遥感技术,文章提出了一种无/缺水下地形数据的高原堰塞湖水量遥感定量估算方法。该方法首先通过遥感水域面积提取,获取堰塞湖淹没空间范围;进而采用不规则复杂多边形中线定位算法,确定堰塞湖中心线位置;然后基于河道中心特定点高程信息,结合局部河道比降估算,生成堰塞湖水下地形河道中线约束因子;再根据河道边坡高程信息和水下地形约束因子自适应拟合出局部堰塞河道的水下未知地形;最后通过三维曲面离散积分实现堰塞湖水量遥感动态定量估算。实验以东帕米尔高原的萨雷兹堰塞湖为研究区,展开遥感水量调查与局部验证研究,结果表明:萨雷兹堰塞湖当前水域面积约为89.09 km²,水量约为162.49亿m³;这一结果与专家预估的水资源量155—165亿m³基本吻合。经局部模拟实验精度对比验证,模拟结果与实际数据动态误差总体控制在10%以内,相关系数达到0.95(P<0.01,双尾),进一步证明了算法的鲁棒性和估算结果的可信度。为无/缺水下地形数据的高原堰塞湖水量遥感估算提供了一种有效的方法,实现了水下地形未知的高原堰塞湖水量遥感快速反演与定量测算。     

基于DEM的洪涝灾害信息提取与损失估算

数字高程模型(DEM)作为洪涝灾害遥感研究的辅助信息源可以提供两类信息,一是地面点的高程和所处地形部位以及与邻域的相互关系等方面的背景数据,二是研究区高程-面积关系、高程-容积关系及面积-容积关系等基本关系信息。利用这些背景数据和基本关系信息,结合洪水期遥感图像的解译,可以获取洪水淹没范围、淹没区积水量、水深、淹没历时以及作物受灾程度等灾情信息。本文阐述了这种研究的原理和方法。     

基于CLG光学流和波场分解的逆时偏移角度道集提取方法研究

角度域共成像点道集是衔接叠前地震数据与储层特征的重要桥梁,对地震偏移成像与储层描述具有重要意义.与克希霍夫偏移和单向波动方程偏移相比,逆时偏移是复杂地区最精确的成像方法.高效稳健地生成逆时偏移角度道集目前仍然是一个挑战.本文主要讨论如何采用光学流方法高效、高质量地提取角度道集.在逆时偏移波场外推过程中,光学流方法可以估计波场传播方向.其中Lucas-Kanade(LK)和Horn-Schunck(HS)方法是光学流方法中两种典型的方法.LK光学流方法是一种局部方法,该方法依赖于局部点的梯度值,但是容易出现奇异现象,HS光学流方法属于全局方法,波场方向估计依赖于整个波场,易受噪声影响,对异常值比较敏感,导致整体波场方向计算精度不高.本文提出采用局部和整体结合(Combining Local and Global,CLG)的光学流方法估计波场传播方向.该方法可以有效地提高波场方向的精度,并且简单高效,便于并行处理.对比HS光学流方法,CLG光学流方法几乎不增加额外的计算量.另外,为了弱化光学流方法无法处理波前重叠问题,本文利用解析波场和方向滤波对波场进行方向分解,仅需波场的空间傅里叶变换即可实现任意波场方向分解,将分解后的波场分别估计波场反向,提取成像结果.进一步地,在估计反射张角和方位角时,本文提出有效的归一化方法和改进的最小二乘除法,提高角度估计的精度和稳定性.最后,理论和实际资料例证了本文提出方法的有效性.     

利用常熟地震台同场地形变仪记录检测对比地球自由振荡信号

利用常熟地震台同场地观测的洞体应变仪、体应变仪、水管倾斜仪、垂直摆倾斜仪记录数据,采用小波变换与功率谱密度估计方法,检测2011年3月11日日本9.0级大地震激发的地球自由振荡信号,其中检测到47个球型基频振型（₀S₃—₀S₄₉）、15个环型基频振型（₀T₅—0T₂₅）以及部分球型谐频振型,与地球初步参考模型（PREM）理论频率值基本符合,表明检测结果较好。对比结果显示：水管倾斜仪检测球型振荡振型能力最强,垂直摆倾斜仪检测环型振荡振型能力最强;体应变仪可检测到清晰的环型基频振型,且信噪比较高;倾斜仪对自由振荡信号的检测能力优于应变仪。     

冰川区不同气温估算方法评估 ——以藏东南帕隆4号冰川为例

冰川作为地表特殊的下垫面,冰川区内气温明显低于同高度非冰川区大气温度。如何利用低海拔非冰川区观测资料精确估算高海拔冰川区气温,直接关系着青藏高原冰川消融估算及其水文效应的评估。利用架设在藏东南帕隆藏布4号冰川不同高度带的四台自动气象站资料,分析了冰川区与非冰川区气温的波动特征,评估了迄今为止通用的线性递推模型（DT模型）、分段拟合模型（SM模型）和简化热力学模型（GB模型）三种方法在藏东南冰川区气温估算方面的应用效果。对比研究发现：SM模型在帕隆4号冰川上的模拟效果最为理想且操作相对简单;传统DT模型在消融区存在严重的高估,帕隆4号冰川表面夏季（6-8月）正积温的高估比例接近39%;GB模型由于受到诸如冰川风边界层厚度等不确定性的影响,降低了大范围温度估算的可操作性。     

A multiscaling‐based intensity–duration–frequency model for extreme precipitation

Rainfall intensity–duration–frequency (IDF) curves are a standard tool in urban water resources engineering and management. They express how return levels of extreme rainfall intensity vary with duration. The simple scaling property of extreme rainfall intensity, with respect to duration, determines the form of IDF relationships. It is supposed that the annual maximum intensity follows the generalized extreme value (GEV) distribution. As well known, for simple scaling processes, the location parameter and scale parameter of the GEV distribution obey a power law with the same exponent. Although, the simple scaling hypothesis is commonly used as a suitable working assumption, the multiscaling approach provides a more general framework. We present a new IDF relationship that has been formulated on the basis of the multiscaling property. It turns out that the GEV parameters (location and scale) have a different scaling exponent. Next, we apply a Bayesian framework to estimate the multiscaling GEV model and to choose the most appropriate model. It is shown that the model performance increases when using the multiscaling approach. The new model for IDF curves reproduces the data very well and has a reasonable degree of complexity without overfitting on the data.     

Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: Instrumental bias and variability induced by observers

Manually collected snow data are often considered as ground truth for many applications such as climatological or hydrological studies. However, there are many sources of uncertainty that are not quantified in detail. For the determination of water equivalent of snow cover (SWE), different snow core samplers and scales are used, but they are all based on the same measurement principle. We conducted two field campaigns with 9 samplers commonly used in observational measurements and research in Europe and northern America to better quantify uncertainties when measuring depth, density and SWE with core samplers. During the first campaign, as a first approach to distinguish snow variability measured at the plot and at the point scale, repeated measurements were taken along two 20 m long snow pits. The results revealed a much higher variability of SWE at the plot scale (resulting from both natural variability and instrumental bias) compared to repeated measurements at the same spot (resulting mostly from error induced by observers or very small scale variability of snow depth). The exceptionally homogeneous snowpack found in the second campaign permitted to almost neglect the natural variability of the snowpack properties and focus on the separation between instrumental bias and error induced by observers. Reported uncertainties refer to a shallow, homogeneous tundra-taiga snowpack less than 1 m deep (loose, mostly recrystallised snow and no wind impact). Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even in the case when observers were not familiar with a given snow core sampler.     

潜艇重力梯度极值和深度的关系研究

研究潜艇重力梯度各分量的极值点坐标与潜艇到观测点垂直距离的关系。数值分析发现,除V_zz外,梯度张量其余分量均有多个极值点;V_xx和V_xz所有极值点的Y坐标、V_yy和V_zz中心极值点的Y坐标不随高度的变化而变化;V_yy、V_yz和V_zz所有极值点的X坐标、V_xx中心极值点的X坐标随高度的变化为非线性关系且变化较小;V_xx两侧极值点的X坐标、V_xz极值点的X坐标、V_xy极值点的X和Y坐标、V_yy两侧极值点的Y坐标和V_yz极值点的Y坐标随高度的变化为线性关系。根据极值点与潜艇的深度关系,验证了利用极值点坐标反演潜艇深度的可行性。     

三维坐标转换的稳健加权总体最小二乘算法

针对当前加权对称相似变换中仅考虑观测值含有随机误差,而不考虑观测值含有粗差的情况,基于加权对称相似变换进一步验证其不具有抗差性,并在此基础上采用选权迭代法进行稳健加权对称相似变换。由中位数法求解具有抗差性的单位权中误差,构造标准化残差,求解权因子,进而求得可靠的参数解。实验结果表明,当观测值含有4~6个粗差时,采用该方法能够探测出更多可能存在粗差的数据,给出的权因子更合理,所得参数解精度更高、稳定性更强。     

基于总体最小二乘的直线拟合方法探究

在直线拟合问题中,经典的最小二乘拟合方法在自变量选取不同时,拟合的参数值和中误差存在较大差别,故本文利用模拟数据对经典最小二乘和总体最小二乘拟合结果进行对比分析,得出结论认为:经典最小二乘自变量选取不同结算参数的原因是在进行拟合计算时忽略了自变量的误差,使拟合结果只能在一个方向上保持最佳;利用总体最小二乘参数拟合的方法进行直线拟合时拟合结果不受自变量变化的影响,并能够提高拟合精度。