多个超高阶重力场模型精度分析

分析了国际和国内公布的EGM2008、GECO、Eigen6c4、UGM2005、DQM2000d和DQM2006等超高阶重力场模型的内符合精度;利用中国境内的实测GPS/水准数据对各类模型进行了外符合精度检验;结合不同的方法对系统差进行了处理;最后基于试验区数据讨论了不同模型之间频谱组合方法对高程异常计算结果的改善情况。实验表明,Eigen-6C4模型具有更高的内符合精度,国内公布的UGM2005和DQM2006两种模型精度相当;本文提出的十参数法相比于传统四参数方法精度获得了4.6%~22.20%的提升;GECO模型和Eigen-6C4模型在实验区内外符合精度较好;不同模型之间的频谱组合可以在一定程度上提高试验区内模型确定高程异常的精度。     

GIS支持下的油气多因素综合预测方法——以苏里格气田苏120区为例

苏里格气田为一致密沙岩气田,油气富集区的预测优选为气藏开发奠定基础。常规的油气富集区预测方法存在着多学科成果综合分析困难、预测过程复杂、效率低下等弊端,本文针对苏里格120区块开展了基于GIS的油气富集区多因素综合预测方法的研究。首先借助GIS的空间数据库,实现多学科成果图件的集成管理;然后基于空间定位,从多学科成果图件中提取预测多因子,构建油气多因素综合预测模型;再次,运用GIS强大的叠加分析方法进行多因素综合预测,自动生成油气富集区预测平面图。本文的研究,为油气富集区的预测提供了新的技术方法,有效地提高了预测效率。     

视觉显著性与知觉组织相结合的高分影像居民地提取方法

受人类视觉认知机制的启发,提出了一种利用视觉显著性与知觉组织相结合的高分辨率遥感影像居民地提取方法。首先利用认知物理学中的数据场构建居民地的视觉显著性模型,并通过自适应阈值法实现候选居民地的自动提取,然后利用多尺度小波变换的高频特征实现居民地的知觉组织,最后通过集合交运算提取同时满足这两种视觉机制的居民地。通过ZY-3和Quickbird两种高分传感器的影像数据集进行居民地提取试验,验证了该方法的有效性。     

利用参考重力场模型基于能量法确定GRACE加速度计校准参数

利用多个参考重力场模型分别对GRACE一个月的实测加速度计观测数据进行检校。数值计算结果的比较分析表明了利用参考重力场模型确定加速度计校准参数是有效的。     

随机Dirichlet问题解的调和展开

针对重力学随机Dirichlet问题,通过适当地对边界检验函数的分解,并在随机边界样本空间中提取确定性部分的对偶基,本文将随机Dirichlet问题的一般解展开为一随机系数的调和级数形式。     

LAS格式解析及其扩展域的应用

激光雷达是近年来兴起的一种快速获取地物目标三维信息的主动遥感技术。其采集的离散激光点具有数据量大,信息丰富等特点。目前常用的LIDAR数据存储格式,在存储LIDAR数据时存在种种不足,且数据交换和共享困难。LAS格式是ASPRS制定的标准LIDAR数据交换格式,能较好地解决多属性离散激光点数据的存储问题,具有结构严谨,便于扩展等优点。本文在对LAS格式进行解析的基础上,针对LAS变长记录的扩展域,介绍了一些实际的应用。     

点质模型求定月球重力场及其特征分析

利用Lunar-Prospector扩展任务的视线加速度数据,根据点质模型恢复了月球近区的重力场,将其与LP165月球重力场模型进行了比较和分析,并利用恢复的重力场联合月球地形数据对Mas-con进行分析,总结了月球重力场的主要特征及其研究方向发展趋势,将重力场恢复技术与我国的探月计划-"嫦娥"工程结合起来,为建立高精度的月球重力场和地形模型提供了一种有效而实用的方法。     

The GeoForschungsZentrum Potsdam/Groupe de Recherche de Gèodésie Spatiale satellite-only and combined gravity field models: EIGEN-GL04S1 and EIGEN-GL04C

The recent improvements in the Gravity Recovery And Climate Experiment (GRACE) tracking data processing at GeoForschungsZentrum Potsdam (GFZ) and Groupe de Recherche de Géodésie Spatiale (GRGS) Toulouse, the availability of newer surface gravity data sets in the Arctic, Antarctica and North-America, and the availability of a new mean sea surface height model from altimetry processing at GFZ gave rise to the generation of two new global gravity field models. The first, EIGEN-GL04S1, a satellite-only model complete to degree and order 150 in terms of spherical harmonics, was derived by combination of the latest GFZ Potsdam GRACE-only (EIGEN-GRACE04S) and GRGS Toulouse GRACE/LAGEOS (EIGEN-GL04S) mean field solutions. The second, EIGEN-GL04S1 was combined with surface gravity data from altimetry over the oceans and gravimetry over the continents to derive a new high-resolution global gravity field model called EIGEN-GL04C. This model is complete to degree and order 360 and thus resolves geoid and gravity anomalies at half- wavelengths of 55 km at the equator. A degree-dependent combination method has been applied in order to preserve the high accuracy from the GRACE satellite data in the lower frequency band of the geopotential and to form a smooth transition to the high-frequency information coming from the surface data. Compared to pre-CHAMP global high-resolution models, the accuracy was improved at a spatial resolution of 200 km (half-wavelength) by one order of magnitude to 3 cm in terms of geoid heights. The accuracy of this model (i.e. the commission error) at its full spatial resolution is estimated to be 15 cm. The model shows a reduced artificial meridional striping and an increased correlation of EIGEN-GL04C-derived geostrophic meridional currents with World Ocean Atlas 2001 (WOA01) data. These improvements have led to select EIGEN-GL04C for JASON-1 satellite altimeter data reprocessing. Electronic Supplementary Material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Methodology and use of tensor invariants for satellite gravity gradiometry

Although its use is widespread in several other scientific disciplines, the theory of tensor invariants is only marginally adopted in gravity field modeling. We aim to close this gap by developing and applying the invariants approach for geopotential recovery. Gravitational tensor invariants are deduced from products of second-order derivatives of the gravitational potential. The benefit of the method presented arises from its independence of the gradiometer instrument’s orientation in space. Thus, we refrain from the classical methods for satellite gravity gradiometry analysis, i.e., in terms of individual gravity gradients, in favor of the alternative invariants approach. The invariants approach requires a tailored processing strategy. Firstly, the non-linear functionals with regard to the potential series expansion in spherical harmonics necessitates the linearization and iterative solution of the resulting least-squares problem. From the computational point of view, efficient linearization by means of perturbation theory has been adopted. It only requires the computation of reference gravity gradients. Secondly, the deduced pseudo-observations are composed of all the gravitational tensor elements, all of which require a comparable level of accuracy. Additionally, implementation of the invariants method for large data sets is a challenging task. We show the fundamentals of tensor invariants theory adapted to satellite gradiometry. With regard to the GOCE (Gravity field and steady-state Ocean Circulation Explorer) satellite gradiometry mission, we demonstrate that the iterative parameter estimation process converges within only two iterations. Additionally, for the GOCE configuration, we show the invariants approach to be insensitive to the synthesis of unobserved gravity gradients.     

Efficient GOCE satellite gravity field recovery based on least-squares using QR decomposition

We develop and apply an efficient strategy for Earth gravity field recovery from satellite gravity gradiometry data. Our approach is based upon the Paige-Saunders iterative least-squares method using QR decomposition (LSQR). We modify the original algorithm for space-geodetic applications: firstly, we investigate how convergence can be accelerated by means of both subspace and block-diagonal preconditioning. The efficiency of the latter dominates if the design matrix exhibits block-dominant structure. Secondly, we address Tikhonov-Phillips regularization in general. Thirdly, we demonstrate an effective implementation of the algorithm in a high-performance computing environment. In this context, an important issue is to avoid the twofold computation of the design matrix in each iteration. The computational platform is a 64-processor shared-memory supercomputer. The runtime results prove the successful parallelization of the LSQR solver. The numerical examples are chosen in view of the forthcoming satellite mission GOCE (Gravity field and steady-state Ocean Circulation Explorer). The closed-loop scenario covers 1 month of simulated data with 5 s sampling. We focus exclusively on the analysis of radial components of satellite accelerations and gravity gradients. Our extensions to the basic algorithm enable the method to be competitive with well-established inversion strategies in satellite geodesy, such as conjugate gradient methods or the brute-force approach. In its current development stage, the LSQR method appears ready to deal with real-data applications.