The Boussinesq and anelastic liquid approximations for convection in the Earth’s core

Convection in the Earth’s core is usually studied in the Boussinesq approximation in which the compressibility of the liquid is ignored. The density of the Earth’s core varies from ICB to CMB by approximately 20%. The question of whether we need to take this variation into account in core convection and dynamo models is examined. We show that it is in the thermodynamic equations that differences between compressible and Boussinesq models become most apparent. The heat flux conducted down the adiabat is much smaller near the inner core boundary than it is near the core-mantle boundary. In consequence, the heat flux carried by convection is much larger nearer the inner core boundary than it is near the core-mantle boundary. This effect will have an important influence on dynamo models. Boussinesq models also assume implicitly that the rate of working of the gravitational and buoyancy forces, as well as the Ohmic and viscous dissipation, are small compared to the heat flux through the core. These terms are not negligible in the Earth’s core heat budget, and neglecting them makes it difficult to get a thermodynamically consistent picture of core convection. We show that the usual anelastic equations simplify considerably if the anelastic liquid approximation, valid if αT?1, where α is the coefficient of expansion and T a typical core temperature, is used. The resulting set of equations are not significantly more difficult to solve numerically than the usual Boussinesq equations. The relationship of our anelastic liquid equations to the Boussinesq equations is also examined.     

Time-lapse traveltime change of singly scattered acoustic waves

We present a technique based on the single-scattering approximation that relates time-lapse localized changes in the propagation velocity to changes in the traveltime of singly scattered waves. We describe wave propagation in a random medium with homogeneous statistical properties as a single-scattering process where the fluctuations of the velocity with respect to the background velocity are assumed to be weak. This corresponds to one of two end-member regimes of wave propagation in a random medium, the first being single scattering, and the second multiple scattering. We present a formulation that relates the change in the traveltime of the scattered waves to a localized change in the propagation velocity by means of the Born approximation for the scattered wavefield. We validate the methodology with synthetic seismograms calculated with finite differences for 2-D acoustic waves. Potential applications of this technique include non-destructive evaluation of heterogeneous materials and time-lapse monitoring of heterogeneous reservoirs.     

一维波动方程的近似解及其在浅海声场计算中的应用

本文给出一维波动方程的一组近似解,它具有WKB解的简洁性,但又克服了WKB解在反转点附近发散的困难。文中用其结果计算了具有粗糙界面的负声速梯度浅海中的简正波声场。     

基于空间加权协同稀疏的高光谱解混算法研究

针对传统稀疏解混算法因空间信息利用不足带来的丰度图像空间分布连续性差的问题,本文提出了一种基于空间加权协同稀疏的解混方法.该方法利用协同稀疏正则项刻画丰度系数的行稀疏性;同时,在协同稀疏框架下,引入空间加权因子挖掘高光谱图像邻域像元间的空间相关性.本模型采用交替方向乘子法求解,通过交替迭代,对空间权重和丰度系数进行优化.模拟和真实高光谱数据实验结果表明本文方法能够比现有同类方法获得更精确的解混结果.     

虚拟球谐方法逼近市级大地水准面计算分析

研究了构建市级大地水准面的虚拟球谐方法。首先,选择构建区域的中心点和区域半径对观测值进行数据预处理,并统计分析数值范围和位置信息;然后,将观测值由地理坐标系转换到球冠坐标系,再转换到虚拟球坐标系中;最后,根据虚拟球谐方法构建市级大地水准面。以南昌市为例构建采样点,采用多项式拟合和神经网络方法与本文虚拟球谐方法进行比较分析,结果表明,虚拟球谐方法的RMS值为0.308 cm,拟合效果最好。     

基于压缩感知的气象雷达回波压缩采样与重建

由于Nyquist采样定理的限制,高分辨率气象雷达面临采样率过高、数据存储量过大等问题。压缩感知理论可以实现气象雷达信号的压缩采样,解决采样率过高等问题。基于压缩感知理论,分析了气象雷达回波信号的稀疏性,建立了气象雷达回波信号的压缩采样和重建的过程,并结合气象雷达实测的回波数据进行仿真。仿真结果表明,0.3倍采样率下的重建回波与原始回波存在较大误差,0.5倍采样率时误差明显降低,0.7倍采样率时则可高概率重建出原始回波。因此,将压缩感知理论应用于高分辨率气象雷达的信号处理中,可以实现在较低采样率下高概率重建原始回波信号。     

Efficient,simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding

Automatic detection of geospatial targets in cluttered scenes is a profound challenge in the field of aerial and satellite image analysis. In this paper, we propose a novel practical framework enabling efficient and simultaneous detection of multi-class geospatial targets in remote sensing images (RSI) by the integration of visual saliency modeling and the discriminative learning of sparse coding. At first, a computational saliency prediction model is built via learning a direct mapping from a variety of visual features to a ground truth set of salient objects in geospatial images manually annotated by experts. The output of this model can predict a small set of target candidate areas. Afterwards, in contrast with typical models that are trained independently for each class of targets, we train a multi-class object detector that can simultaneously localize multiple targets from multiple classes by using discriminative sparse coding. The Fisher discrimination criterion is incorporated into the learning of a dictionary, which leads to a set of discriminative sparse coding coefficients having small within-class scatter and big between-class scatter. Multi-class classification can be therefore achieved by the reconstruction error and discriminative coding coefficients. Finally, the trained multi-class object detector is applied to those target candidate areas instead of the entire image in order to classify them into various categories of target, which can significantly reduce the cost of traditional exhaustive search. Comprehensive evaluations on a satellite RSI database and comparisons with a number of state-of-the-art approaches demonstrate the effectiveness and efficiency of the proposed work.     

基于PCI_GXL系统解决弱交会条件下卫星影像纠正方法的研究

介绍了在弱交会条件下利用PCI_GXL系统实现稀少控制点区域卫星影像的几何纠正方法。阐述了基本操作流程以及每个步骤的注意事项,突显出PCI_GXL系统的高效等特点,并对纠正后影像做出精度检测并给出分析评价。对多源数据的处理能力给影像数据生产带来了新的动力。     

An Economical Approach to Flow-Adaptive Moderation of Spurious Ensemble Correlations and Its Application in the Proper Orthogonal Decomposition-Based Ensemble Four Dimensional Variational Assimilation Method

The purpose of this study is to describe an economical approach to an existing adaptive localization technique and its implementation in the proper orthogonal decomposition-based ensemble four-dimensional variational assimilation method(PODEn4DVar). Owing to the applications of the sparse processing and EOF decomposition techniques, the computational costs of this proposed sparse flow-adaptive moderation(SFAM) localization scheme are significantly reduced. The effectiveness of PODEn4 DVar with SFAM localization is demonstrated by using the Lorenz-96 model in comparison with the Smoothed ENsemble Correlations Raised to a Power(SENCORP) and static localization schemes, separately. The performance of PODEn4 DVar with SFAM localization shows a moderate improvement over the schemes with SENCORP and static localization, with low computational costs under the imperfect model.