绝对重力与相对重力混合平差的基准及质量控制

绝对重力与相对重力测量混合平差是综合利用各种重力测量资料确定重力网基准，提高重力网综合质量的有效途径，文中首先从重力观测的系统误差入手，讨论重力网平差的函数模型；进而根据两类重力观测信息对重力基准的贡献，分析了不同平差方法所对应的各类基准及基统计意义与可靠性；最后对重力网平差的质量控制方法进行了讨论。     

Somigliana–Pizzetti gravity: the international gravity formula accurate to the sub-nanoGal level

 The Somigliana–Pizzetti gravity field (the International gravity formula), namely the gravity field of the level ellipsoid (the International Reference Ellipsoid), is derived to the sub-nanoGal accuracy level in order to fulfil the demands of modern gravimetry (absolute gravimeters, super conducting gravimeters, atomic gravimeters). Equations (53), (54) and (59) summarise Somigliana–Pizzetti gravity Γ(φ,u) as a function of Jacobi spheroidal latitude φ and height u to the order ?(10⁻¹⁰ Gal), and Γ(B,H) as a function of Gauss (surface normal) ellipsoidal latitude B and height H to the order ?(10⁻¹⁰ Gal) as determined by GPS (`global problem solver'). Within the test area of the state of Baden-Württemberg, Somigliana–Pizzetti gravity disturbances of an average of 25.452 mGal were produced. Computer programs for an operational application of the new international gravity formula with (L,B,H) or (λ,φ,u) coordinate inputs to a sub-nanoGal level of accuracy are available on the Internet. Received: 23 June 2000 / Accepted: 2 January 2001     

Local geoid determination combining gravity disturbances and GPS/levelling: a case study in the Lake Nasser area, Aswan, Egypt

 The use of GPS for height control in an area with existing levelling data requires the determination of a local geoid and the bias between the local levelling datum and the one implicitly defined when computing the local geoid. If only scarse gravity data are available, the heights of new data may be collected rapidly by determining the ellipsoidal height by GPS and not using orthometric heights. Hence the geoid determination has to be based on gravity disturbances contingently combined with gravity anomalies. Furthermore, existing GPS/levelling data may also be used in the geoid determination if a suitable general gravity field modelling method (such as least-squares collocation, LSC) is applied. A comparison has been made in the Aswan Dam area between geoids determined using fast Fourier transform (FFT) with gravity disturbances exclusively and LSC using only the gravity disturbances and the disturbances combined with GPS/levelling data. The EGM96 spherical harmonic model was in all cases used in a remove–restore mode. A total of 198 gravity disturbances spaced approximately 3 km apart were used, as well as 35 GPS/levelling points in the vicinity and on the Aswan Dam. No data on the Nasser Lake were available. This gave difficulties when using FFT, which requires the use of gridded data. When using exclusively the gravity disturbances, the agreement between the GPS/levelling data were 0.71 ± 0.17 m for FFT and 0.63 ± 0.15 for LSC. When combining gravity disturbances and GPS/levelling, the LSC error estimate was ±0.10 m. In the latter case two bias parameters had to be introduced to account for a possible levelling datum difference between the levelling on the dam and that on the adjacent roads. Received: 14 August 2000 / Accepted: 28 February 2001     

中巴地球资源卫星应用及其发展

介绍了中巴地球资源卫星应用系统中 7个分系统的基本概况和数据处理系统的基本特点 ,还对星上三种传感器和高密度磁记录器的特点以及产品的生产能力和类型等进行了简单的描述。重点探索了中巴地球资源卫星遥感数据在资源、环境、灾害监测、各种数据库的建立与更新、遥感定量化等方面的应用前景展望。     

卫星跟踪卫星技术的进展及应用前景

卫星跟踪卫星技术被认为是 2 1世纪初最有价值和应用前景的高效重力探测技术 ,旨在测定中长波重力场的精细结构及时间相依变化。本文首先简要阐述卫星跟踪卫星技术的发展背景及概况 ,其次介绍目前已经实施和将要实施的卫星跟踪卫星计划 CHAMP和 GRACE的进展情况 ,最后讨论该技术在精化地球重力场和研究相关地学问题中的应用前景。     

基于FY-4A卫星数据的短时强降水监测预警指标

选取2018—2021年汛期短时强降水天气过程,利用相关性分析、箱线图法和极值统计法,尝试研究FY-4A卫星产品在短时强降水天气过程中的监测预警指标。研究表明：(1)FY-4A卫星多通道数据可以作为短时强降水监测预警的定量化指标予以应用。(2)筛选出相关性较好的13项产品统计出短时强降水的监测预警指标,其中赋值类指标4项,数值判别类指标9项(含辅助指标3项);初步设定13项指标中有9项达标时,短时强降水会发生。(3)在评估基础上完善了指标,监测预警效果有所提高,TS评分提高5.4%,空报率降低2.7%,漏报率降低1.9%。     

The gravitational perturbation spectrum in linear satellite theory

A new method for calculating the perturbation spectrum in the framework of Kaula's linear satellite theory (LST) is introduced. The novelty of this approach consists in using recent results on the spectral decomposition of the perturbation frequencies in LST to provide a closed formulation for the amplitude and the phase of each line in the perturbation spectrum. The theory presented here can be applied to perturbations in the elements or in the radial and transverse directions due to the geopotential or to the tides. Separate algorithms are developed for application to orbits with circulating or frozen perigee.     

基于深度学习的2D-3D地下密度异常体反演方法

地下密度异常体反演需要由二维数据反演三维结果,为了高效、高精度地反演地下异常体的位置及密度信息,本文提出2D-3D InvNet深度学习反演方法.方法的编-解码器结构,在编码阶段利用二维卷积网络结构提取地表重力异常及重力梯度异常数据信息,解码阶段利用三维卷积网络结构恢复异常体地下形态,实现了二维和三维网络结构的结合.为了精确地反演异常体密度,提出利用加权均方误差（WtdMSE）作为损失函数,同时为了更好地评估反演结果,引入核密度函数作为评价手段.与均方误差（MSE）相比,利用WtdMSE作为损失函数对异常体密度的反演结果更为准确,异常体所在区域的密度误差减少50%以上.对理论样本的反演结果表明,2D-3D InvNet在准确反演异常体地下位置的同时,也能给出准确的密度信息.应用此方法对西澳大利亚Kauring地区实测数据进行反演,我们成功获得了此区域地下异常体的密度分布.理论与实际应用结果表明,2D-3D InvNet深度学习方法稳定且具有较强的泛化性能,能在无需规定限制条件的情况下快速获得准确的反演结果.

     

Improved ocean-geoid resolution from retracked ERS-1 satellite altimeter waveforms

The ocean geoid can be inferred from the topography of the mean sea surface. Satellite altimeters transmit radar pulses and determine the return traveltime to measure sea-surface height. The ERS-1 altimeter stacks 51 consecutive radar reflections on board the satellite to a single waveform. Tracking the time shift of the waveform gives an estimate of the distance to the sea surface. We retrack the ERS-1 radar traveltimes using a model that is focused on the leading edge of the waveforms. While earlier methods regarded adjacent waveforms as independent statistical events, we invert a whole sequence of waveforms simultaneously for a spline geoid solution. Smoothness is controlled by spectral constraints on the spline coefficients. Our geoid solutions have an average spectral density equal to the expected power spectrum of the true geoid. The coherence of repeat track solutions indicates a spatial resolution of 31  km, as compared to 41  km resolution for the ERS-1 Ocean Product. While the resolution of the latter deteriorates to 47  km for wave heights above 2  m, our geoid solution maintains its resolution of 31  km for rough sea. Retracking altimeter waveform data and constraining the solution by a spectral model leads to a realistic geoid solution with significantly improved along-track resolution.     

融合多源海洋大地测量数据的南海海底地形多层感知机反演

本文融合SIO(Scripps Institution of Oceanography)发布的垂线偏差、重力异常和垂直重力梯度数据及NCEI(National Centers for Environmental Information)发布的船载测深数据, 利用多层感知机神经网络(Multi-Layer Perceptron, MLP)建立南海海域(108°E—121°E, 6°N—23°N)分辨率为1'×1'的海底地形模型(MLP_Depth).首先, 将642716个船载测深控制点的位置信息与周围4'×4'格网点处的地球重力信息(垂线偏差、重力异常、垂直重力梯度)作为输入数据, 将船载测深控制点处实测水深值作为输出数据, 训练MLP神经网络模型, 训练结束时决定系数R²为99%, 平均绝对误差MAE为39.33 m.然后, 将研究区域内1'×1'格网正中心点处的输入数据输入于MLP模型中, 可得格网正中心点处的预测海深值.最后, 根据预测海深值建立研究区域范围内分辨率为1'×1'的MLP_Depth模型.将MLP_Depth模型预测水深与160679个检核点处实测水深对比, 其差值的标准差STD(75.38 m)、平均绝对百分比误差MAPE(5.89%)与平均绝对误差MAE(42.91 m)皆优于GEBCO_2021模型、topo_23.1模型、ETOPO1模型与检核点实测水深差值的STD(108.88 m、113.41 m、229.67 m)、MAPE(6.11%、6.94%、18.37%)与MAE(47.33 m、52.24 m、130.08 m).同时, 为了研究不同区域内利用该方法建立的海底地形模型的精度, 本文在研究区域内分别建立了A、B区域的海底地形模型(MLP_Depth_A、MLP_Depth_B).经过验证得: MLP_Depth_A、MLP_Depth_B相比于MLP_Depth模型具有更高的精度, 更能反应海底地形的变化趋势.