星载SAR海面风场遥感研究进展

海面风场是研究海洋环境的重要参数,是物理海洋和海洋气象学中最基本、最重要的要素之一.传统的海面风场测量方法测量范围小、测点少,且易受到天气条件的限制.随着遥感技术的发展,人们越来越注重利用机载或星载传感器对海面风场进行遥感探测.星载微波散射计是迄今为止最主要的海面风场传感器,但难以满足某些高分辨率风场数据的应用需求,尤其是不能满足近岸海区的观测要求.星载SAR具有较高的空间分辨率,能够提供大范围、精细化的海面风场信息.目前,星载SAR已成为海面风场研究的重要技术手段之一.本文首先从SAR海洋遥感基本原理出发,阐述了SAR海面风场的遥感探测机理,展示了利用SAR遥感图像遥感探测海面风场的可行性.其次,通过风条纹形成机理与风条纹与海面风向之间的关系分析,给出了国内外利用SAR遥感图像风条纹开展海面风向遥感探测的研究进展.同时,重点针对C波段VV极化、HH极化、交叉极化,X波段VV极化、HH极化和L波段SAR海面风场遥感反演地球物理模式函数,以及基于理论模型、入射角差异、方位向波数截断和神经网络等的其他多种SAR海面风速反演模型进行了系统梳理分析与对比;并对最优化方法、M-NRCS方法、双极...     

一个三维变分海洋资料同化系统的设计和初步应用

介绍了新完成的一个基于三维变分方法的通用海洋资料同化系统OVALS(Ocean Variational Analysis System)的设计方案和在热带太平洋海洋资料同化中的初步应用. OVALS可以同化常规的现场海洋温盐观测资料和卫星高度计资料, 其中在海面高度资料同化中引入了一个新的基于三维变分的同化方案, 该方案考虑了背景场误差的垂直相关性和非线性的温-盐关系. 非线性的温-盐关系是通过将三维变分方法中的线性平衡约束方案推广到非线性的情况来实现的, 从而通过同化高度计资料来直接调整模式的温度和盐度场. 另外, OVALS还可以同化近年来投入运行的国际ARGO漂流浮标的温、盐廓线资料以及其他海温和盐度资料, 如船报海温资料和一些浮标阵列资料等. 利用OVALS和大洋环流模式在热带太平洋进行了21 a的同化试验. 试验结果表明, 同化系统通过对各种海洋资料的同化, 可以有效改进对海洋温度和盐度的估计, 其在420 m深度以浅的上层海洋的月平均温、盐度估计误差分别为0.63℃和0.34 psu.     

三维变分资料同化中的非线性平衡约束

在三维变分资料同化的许多应用中, 平衡约束都是通过背景误差协方差矩阵B来考虑的: 首先, 要定义模式变量非平衡部分的背景误差协方差矩阵B(u)(一个块对角矩阵); 其次, 要定义一个把非平衡部分变换到模式变量的平衡算子, 再用B =KB(u)K ^T来构造B . 这种形式只适用于那些线性或线性化的平衡约束. 许多实际应用中需要考虑非线性平衡约束, 比如海洋资料同化中的温盐关系. 我们在线性形式的基础上, 导出了几种可以推广到非线性的形式, 提出了在三维变分同化中考虑非线性平衡约束的方案. 利用提出的方案, 进行了从海面动力高度来估计温、盐的资料同化数值试验. 结果表明, 考虑非线性平衡约束的方案比不考虑非线性平衡约束的方案对结果有很大改善.     

结合反问题技巧对一维海温模式变分资料同化的理论分析及数值试验

以一维海温模式为例, 采用变分资料同化技术及处理数学物理反问题的技巧, 对初始条件、边界条件和模式参数等进行最优估计和确定. 为了克服反问题中不适定性带来的困难, 例如对于依赖于时间和空间的模式参数的估计, 引进了反问题中的正则化思想, 在目标泛函中增加了稳定泛函, 其目的在于克服不适定和计算不稳定. 数值试验结果表明, 与采用通常的变分同化技术相比, 引进正则化思想以后的变分同化技术, 无论目标泛函的下降速度、迭代收敛速度, 还是解的精度都有较明显的改进.     

用变分玻恩迭代方法重建二维非均匀介质结构

提出了用于二维轴对称非均匀介质结构的反演和成像的一种新的反演迭代方法──变分玻恩迭代方法（ＶＢＩＭ）．首先利用玻恩近似将非线性积分方程线性化,然后应用变分方法导出用于反演的电场积分方程．正演数据则利用高效的数值模式匹配方法获得．数值结果表明,ＶＢＩＭ与ＢＩＭ相比,其收敛速度、成像质量等均得到较大的改善。     

用变分玻恩迭代方法重建二维非均匀介质结构

提出了用于二维轴对称非均匀介质结构的反演和成像的一种新的反演迭代方法──变分玻恩迭代方法（ＶＢＩＭ）．首先利用玻恩近似将非线性积分方程线性化,然后应用变分方法导出用于反演的电场积分方程．正演数据则利用高效的数值模式匹配方法获得．数值结果表明,ＶＢＩＭ与ＢＩＭ相比,其收敛速度、成像质量等均得到较大的改善。     

基于分灾模式的钢筋混凝土梁抗弯加固性能评估方法

针对当前钢筋混凝土梁抗弯加固性能评估法未考虑不同建筑结构的抗灾差异,所求得的各项承载力参数不够客观,从而造成评估结果与实际检测结果差异大,评估精度低的问题,提出基于分灾模式的钢筋混凝土梁抗弯加固性能评估方法。计算梁底初始拉应变、梁正截面极限抗弯承载力和钢筋混凝土梁正截面荷载挠度,然后设计分灾模式的钢筋混凝土梁抗弯加固,通过分析载荷与压应变、载荷与挠度以及剪力与位移变化,得到钢筋混凝土梁抗弯加固后各项承载力参数,输入各项梁承载力参数到ANSYS通用程序,利用该程序结合钢筋混凝土梁抗弯加固性能进行评估。通过进行仿真实验,结果表明加入分灾元件的钢筋混凝土梁抗弯加固性能有明显提高。将本文方法评估结果与实验实际评估结果对比可知,提出的评估方法与实际检测结果基本一致,评估精度准确性较高。     

修正数值天气预报的非系统性误差的变分方法

变分方法被用于根据分析场来估计数值天气预报的非系统性误差,从而对预报作出订正．这一方法中假设预报误差与预报场的某种组合线性相关,奇异值分解（SVD）技术被用于由一系列预报和分析场样本产生的相关矩阵得到预报场和误差场之间的映射关系,同时也降低了问题的维数,并使目标函数中的背景误差协方差矩阵成为简单的对角矩阵．利用两种有不同误差的浅水方程模式和模式资料对连续20次的6h,24h和48h预报进行了误差订正试验．试验采用了3种预报场组合方案：第一种方案是末时刻的预报场;第二种方案是初始场和末时刻预报场的组合;第三种方案是末时刻的预报场以及预报的倾向场的组合．结果表明当预报误差明显超过分析误差时,订正能够明显改进预报,其中第三种方案对预报误差的订正效果最好,而其他两种方案不能保证订正都能改进预报．     

基于本征正交分解的显式四维变分同化方法：理论与验证

将本征正交分解（POD）方法用于四维空间预报集合提取标准正交基,该标准正交基在最小二乘意义下是最优的,与奇异值分解（SVD）技术相比,它能捕捉到预报集合空间更多的能量,能够更好地表征四维变量的空间结构以及时间演变特征．将分析向量依截断的POD基展开后,控制变量会显式地出现在代价函数中,避免了传统的四维变分方法所必需的伴随模式的运用,使得同化过程简单．用土壤湿度预报方程和人造资料进行一系列的数值试验对该基于本征正交分解的显式变分方法与基于SVD基的方法以及集合Kalman滤波进行比较,结果表明：POD／SVD方法从同化精度和同化时效上都要远远优于一般的集合Kalman滤波方法;由于POD基在最小二乘意义下的最优性,基于POD分解的同化方法要优于基于SVD分解的方法,尤其在模式存在误差的情况下表现得更为明显．     

一种显式四维变分资料同化方法

提出一种新的资料同化方法——显式变分四维同化方法, 该方法将奇异值分解(SVD)技术用于四维空间的预报集合提取正交基向量, 这些基向量不但能够表现分析变量的空间结构, 也能反映它的时间演变特征. 将分析变量依截断的基向量展开后, 控制变量会显式地出现在代价函数中, 避免了传统的变分四维同化方法所必需的伴随模式的运用, 使同化过程变得简单. 用浅水方程模式和人造资料进行的一系列数值试验对所提方法的有效性作了检验并和传统的变分四维同化方法进行比较. 结果表明, 在观测点很密集, 观测和模式都没有误差的情况下, 它不如传统的变分四维同化方法好. 但是当观测点稀疏时显式方法会好于传统的方法, 它对模式误差及观测误差的敏感性也远远小于传统的方法.     

Ocean wind fields retrieved from the advanced synthetic aperture radar aboard ENVISAT

In this paper an algorithm is presented which enables high-resolution ocean surface wind fields to be retrieved from the advanced synthetic aperture radar (ASAR) data acquired by the European remote sensing satellite ENVISAT. Wind directions are extracted from wind-induced streaks that are visible in ASAR images at scales above 200 m and that are approximately in line with the mean surface wind direction. Wind speeds are derived from the normalized radar cross section (NRCS) and image geometry of the calibrated ASAR images, together with the local ASAR-retrieved wind direction. Therefore the empirical C-band model CMOD4, which describes the dependency of the NRCS on wind and image geometry, is used. CMOD4 is a semi-empirical model, which was originally developed for the scatterometer of the European remote sensing satellites ERS-1 and 2 operating at C-band with vertical polarization. Consequently, CMOD4 requires modification when applied to ASAR images that were acquired with horizontal polarization in transmitting and receiving. This is performed by considering the polarization ratio of the NRCS. To demonstrate the applicability of the algorithm, wind fields were computed from several ENVISAT ASAR images of the North Sea and compared to atmospheric model results of the German weather service.Acknowledgements The authors were supported by the German Bundesministerium für Bildung und Forschung (BMBF) in the framework of the project. A new perspective of the Ocean ENVISAT Oceanography (ENVOC). The ENVISAT ASAR data were kindly made available by the European Space Agency in the framework of the ENVISAT Project AO-ID 220, Biological and geophysical parameters from synthetic aperture radar over the ocean (BIGPASO).     

基于Landsat与Sentinel-3A卫星数据的当惹雍错1988-2018年湖泊水位-水量变化及归因

湖泊变化是气候变化的指示器.为探索利用单一短时间尺度的卫星水位数据源估算长时间序列的湖泊水量变化的可行性,本文利用短时间尺度（2016—2018年） Sentinel-3A合成孔径雷达高度计（SRAL）作为唯一卫星水位数据源,以藏北高原内陆湖泊当惹雍错为例,结合基于Landsat光学遥感数据提取的1988—2018年的湖泊面积,综合分析2016—2018年间的非结冰期遥感湖泊面积与遥感湖泊水位变化,基于该时段范围的水位变化-面积变化关系和水量估算公式,估算1988—2018年湖泊水位水量变化与2001—2018年的年内变化,并结合GLDAS产品数据与雪线变化情况初步探讨湖泊变化的可能原因.结果表明：当惹雍错近30年湖泊面积扩张明显,湖泊水位、水量增加显著,相比1988年,2018年的湖泊面积、水位、水量分别增加21.1 km²、5.29 m、44.75亿m³.其中1988—1998年湖泊面积-水位-水量有所减少,2000—2018年间湖泊变化总体呈增加趋势.2001—2018年内湖泊面积、水位、水量变化呈现干湿季特征.1996—2014/2015年湖泊水量变化为38.3亿/35.5亿m³,水量变化趋势、变化量与以往对应时间段的研究结果具有较强的一致性.湖泊面积扩张主要发生在水下地形平缓的东南部和中西部区域.结合气候因素与雪线变化的分析表明,湖泊水量变化受降雨、气温影响复杂,长时间年际尺度上的湖泊水量增长与气温的一致性较降水量强,湖泊湿季受降水量与气温的影响都较大,其中2008—2018年的湿季降水量、气温与水量变化散点拟合的确定性系数R²分别为0.613、0.845.该研究表明Sentinel-3A合成孔径雷达数据在湖泊水量变化估算上的潜力,为利用单一且只具有短时段数据的卫星雷达数据估算长时间序列湖泊水量变化提供依据.     

Application of synthetic aperture radar (SAR) imagery to volcano mapping in the humid tropics: a case study in East Java, Indonesia

 Volcanoes in humid tropical environments are frequently cloud covered, typically densely vegetated and rapidly eroded. These factors complicate field and laboratory studies and even the basic identification of potentially active volcanoes. Numerous previous studies have highlighted the potential value of radar remote sensing for volcanology in equatorial regions. Here, cloud- and vegetation-penetrating L_HH-band (λ≈24 cm) synthetic aperture radar (SAR) data from the Japanese Earth Resources Satellite (JERS-1) are used to investigate persistently active volcanoes and prehistoric calderas in East Java, Indonesia. The L_HH-band JERS-1 SAR produces high-spatial-resolution (18 m) imagery with relatively high incidence angle that highlights structures and topographic variations at or greater than the wavelength scale while minimising geometrical distortions such as layover and foreshortening. These images, along with Internet browse data derived from the Canadian RADARSAT mission, provide new evidence relating regional tectonics to volcanism throughout East Java. Volcanic events, such as caldera collapse at the Tengger caldera, appear to have been partly controlled by northwest-aligned faults related to intra-arc sedimentary basins. Similar regional controls appear important at historically active Lamongan volcano, which is encircled by numerous flank maars and cinder cones. A previously undocumented pyroclastic sheet and debris avalanche deposit from the Jambangan caldera complex is also manifested in the synoptic radar images. At the currently active Semeru volcano these data permit identification of recent pyroclastic flow and lahar deposits. Radar data therefore offer a valuable tool for mapping and hazard assessment at late Quaternary volcanoes. The criteria developed in the analysis here could be applied to other regions in the humid tropics. Received: 25 June 1998 / Accepted: 20 January 1999     

Operational performance of current synthetic aperture radar sensors in mapping soil surface characteristics in agricultural environments: application to hydrological and erosion modelling

Synthetic aperture radar (SAR) sensors are often used to characterize the surface of bare soils in agricultural environments. They enable the soil moisture and roughness to be estimated with constraints linked to the configurations of the sensors (polarization, incidence angle and radar wavelength). These key soil characteristics are necessary for different applications, such as hydrology and risk prediction. This article reviews the potential of currently operational SAR sensors and those planned for the near future to characterize soil surface as a function of users' needs. It details what it is possible to achieve in terms of mapping soil moisture and roughness by specifying optimal radar configurations and the precision associated with the estimation of soil surface characteristics. The summary carried out for the present article shows that mapping soil moisture is optimal with SAR sensors at low incidence angles (<35 ). This configuration, which enables an estimated moisture accuracy greater than 6% is possible several times a month taking into account all the current and future sensors. Concerning soil roughness, it is best mapped using three classes (smooth, moderately rough, and rough). Such mapping requires high‐incidence data, which is possible with certain current sensors (RADARSAT‐1 and ASAR both in band C). When L‐band sensors (ALOS) become available, this mapping accuracy should improve because the sensitivity of the radar signal to Soil Surface Characteristics (SSC) increases with wavelength. Finally, the polarimetric mode of certain imminent sensors (ALOS, RADARSAT‐2, TerraSAR‐X, etc.), and the possibility of acquiring data at very high spatial resolution (metre scale), offer great potential in terms of improving the quality of SSC mapping. Copyright © 2007 John Wiley & Sons, Ltd.     

Integrating gravimetric and interferometric synthetic aperture radar data for enhancing reservoir history matching of carbonate gas and volatile oil reservoirs

Reservoir history matching is assuming a critical role in understanding reservoir characteristics, tracking water fronts, and forecasting production. While production data have been incorporated for matching reservoir production levels and estimating critical reservoir parameters, the sparse spatial nature of this dataset limits the efficiency of the history matching process. Recently, gravimetry techniques have significantly advanced to the point of providing measurement accuracy in the microgal range and consequently can be used for the tracking of gas displacement caused by water influx. While gravity measurements provide information on subsurface density changes, i.e., the composition of the reservoir, these data do only yield marginal information about temporal displacements of oil and inflowing water. We propose to complement gravimetric data with interferometric synthetic aperture radar surface deformation data to exploit the strong pressure deformation relationship for enhancing fluid flow direction forecasts. We have developed an ensemble Kalman‐filter‐based history matching framework for gas, gas condensate, and volatile oil reservoirs, which synergizes time‐lapse gravity and interferometric synthetic aperture radar data for improved reservoir management and reservoir forecasts. Based on a dual state–parameter estimation algorithm separating the estimation of static reservoir parameters from the dynamic reservoir parameters, our numerical experiments demonstrate that history matching gravity measurements allow monitoring the density changes caused by oil–gas phase transition and water influx to determine the saturation levels, whereas the interferometric synthetic aperture radar measurements help to improve the forecasts of hydrocarbon production and water displacement directions. The reservoir estimates resulting from the dual filtering scheme are on average 20%–40% better than those from the joint estimation scheme, but require about a 30% increase in computational cost.     

基于合成孔径声学深拖调查的海底浅表层流体活动研究——以SAMS DT6000深拖在琼东南海域调查为例

声学深拖作为一个声学设备搭载平台,主要功能是获取高分辨率的声学数据,精细刻画海底地形地貌特征以及浅层剖面结构,对于研究海底浅表层流体活动系统的类型、形成机制和演化模式有着重要作用.本文介绍的合成孔径声学深拖（Synthetic Aperture Sonar Deep-tow）搭载了合成孔径声呐、浅地层剖面仪以及多波束系统等声学设备,相比于传统的侧扫声呐,合成孔径声呐采用小物理孔径基阵通过信号处理虚拟合成大孔径基阵来获得方位向高分辨率,大大提高了测绘速率,同时结合高分辨率的浅地层剖面和多波束背散射数据,可实现海底浅表层特征的三维立体显示.为查明调查区海底浅层流体活动的声学特征,分析天然气水合物相关的流体渗漏活动性与浅层构造之间的关系,我们利用声学深拖对研究区进行了全覆盖的扫测,获得了高分辨率的合成孔径声呐图像、浅地层剖面资料以及多波束背散射数据,平面上识别出多个呈条带状的海底丘状体,火焰状的流体渗漏,新月形的麻坑构造等流体活动地质构造;浅层剖面上可见气体聚集的声学空白段落,凸起的活跃喷口,以及反射杂乱的柱状浑浊带.通过识别流体活动的特征,我们总结了浅层流体活动演化模式具有周期性：游离气体通过高渗透运移通道上升至海底,首先扩散聚集造成局部沉积物体积膨胀形成丘状体;然后受其各种外界因素影响丘状体崩塌而引起气体渗漏;最后流体逸散剥蚀海底松散沉积物而形成麻坑构造;随着流体排出,喷口重新闭合,流体在地层中再次聚集,聚集的气体又将沉积地层上拱,在麻坑底部又可能生成含气丘状体.海底浅表层蕴藏着丰富的地质信息,这对于研究海底复杂的流体活动有着重要意义.     

矿井瞬变电磁法超前探测合成孔径成像研究

在矿井瞬变电磁超前探测数据处理和解释中,为了更好地识别巷道前方含水体电性分界面,利用扩散场与波动场间的函数关系,将矿井瞬变电磁法超前探测中瞬变电磁波场转换为拟地震波场,实现波场转换.同时,为了解决波场转换后信号幅值偏弱,以增加信噪比利于解释,利用雷达的合成孔径方法对波场转换后的拟地震信号进行相关叠加处理.将矿井瞬变电磁法巷道超前探测的同一测点不同探测方向的数据作为孔径数据进行合成孔径成像,以突出弱异常而提高信噪比,结合应用实例验证了方法的效果.研究表明,矿井瞬变电磁法合成孔径成像方法能够提高电性界面信息的信噪比和界面几何分辨率,对矿井瞬变电磁法精细超前探测含水构造具有重要的理论意义和应用价值.