基于正则化的GPS/BDS单频单历元模糊度固定

针对单频单历元模糊度固定中的秩亏问题,给出一种基于双差载波系数阵奇异值分解的正则化方法。通过对坐标改正参数进行约束,改善法矩阵的病态性,并结合LAMBDA方法实现单历元模糊度固定。采用长度不同的两组GPS/BDS基线数据进行单频单历元模糊度解算,并与选权拟合法比较。结果表明,采用该方法,5.8 m基线BDS系统模糊度解算成功率提高17.61%,2.34 km基线GPS、BDS及GPS/BDS模糊度解算成功率分别提高4.67%、3.56%和3.63%。当截止高度角为10°时,E、N方向定位精度达到mm级,U方向达到mm至cm级。     

Data structures for three-dimensional spatial information systems in geology

Abstract

The accumulation of geological information in digital form, due to modern exploration methods, has introduced the possibility of applying geographical information system technology to the field of geology. To achieve the benefits in information management and in data analysis and interpretation, however, it will be necessary to develop spatial models and associated data structures which are specifically designed for working in three dimensions. Some progress in this direction has already been demonstrated, with the application of octree spatial subdivision techniques to the storage of uniform volume elements representing mineral properties. By imposing octree tessellations on more precisely-defined geometric data, such as triangulated surfaces and polygon line segments, it may now be possible to combine efficient spatial addressing with topologically-coded boundary representations of geological strata. The development of storage schemes capable of representing such geological boundary models at different scales poses a particular problem, a possible solution to which may be by means of hierarchical classification of the vertices of triangulated surfaces according to shape contribution.     

Comparative assessment of semantic-sensitive satellite image retrieval: simple and context-sensitive Bayesian networks

In recent years, Bayesian networks using unsupervised extracted image features have been applied in many remote sensing information mining systems to enable semantic-sensitive image retrieval. However, a simple Bayesian network insufficiently accounts for the spatial information, that is, the relations among image regions, for the semantic inference process. This drawback significantly impacts the retrieval performance, especially if the utilised features contain no or little spatial information. Therefore, this article proposes a context-sensitive Bayesian network, which infers semantic concepts of image regions based on the spectral and textural characteristics of the regions themselves as well as their contexts, that is, the adjacent regions. In order to compare the context-sensitive Bayesian network with the simple Bayesian network, comprehensive experiments were conducted based on high-resolution multispectral IKONOS imagery. The results show that the incorporation of the image regions' spatial relations not only significantly improves the accuracy of the semantic concepts inference, but also allows more flexibility in choosing the type of low-level features.     

Spatio-temporal analysis of tree height in a young cork oak plantation

Cork oak is one of the most valuable natural forest genera in the Mediterranean basin. Modelling cork oak growth has been a challenge for foresters in recent years because of strong site and genetic influences, below-ground competition, management regimes and age effects. Because cork productivity is related to forest height, which is, in turn, related directly with site characteristics, an increase in the accuracy of height prediction implies improved productivity estimation. A Bayesian maximum entropy (BME) geostatistical model was applied to characterize the space–time pattern of height of young cork oak in a forest stand from central Sardinia in the years 2000, 2002, 2003, 2006 and 2008. Cork oak height maps were produced for each of the 5 years. The main goals were to analyse and interpret through time (i) the changes in spatial correlation and (ii) the changes in spatial distribution of cork oak height. The plantation was characterized by an increasing spatial dependence through time, whereas the temporal range was 2 years. Cork oak height was significantly correlated with wind speed (reduced by a neighbouring forest) in all the years implying a single trend. The correlations were larger for 2006 and 2008 than for previous years. Three other environmental variables (shade, elevation and slope) were less significant and their influence restricted to 2 years only. This research has several implications for the management of cork oak in the young phase.     

From marginal to conditional probability functions of parameters in a conceptual rainfall–runoff model: an event-based approach

ABSTRACT

A parameter estimation strategy for a conceptual rainfall–runoff (CRR) model applied to a storm sewer system in an urban catchment (Chassieu, Lyon, France) is proposed on the basis of event-by-event Bayesian local calibrations. The marginal distribution formed by locally-estimated parameters is divided into conditional functions, clustering the event-based parameters based on their transferability to similar rainfall events. The conditional functions showed to be consistent with an observed bimodality in the marginal representation, reflecting two different hydrological conditions mainly related to the magnitude of the rainfall intensities (high or low). The improvements achieved by expressing the parameter probability functions into a conditional form are shown in terms of accuracy (Nash-Sutcliffe efficiency criterion), precision (average relative interval length) and reliability (percentage of coverage) for simulating flow rate in 255 and 110 calibration/verification events.     

贝叶斯算法在拟合自由核章动参数中的应用

用高精度重力技术检测地球自由核章动(FCN)参数(包括周期和Q值等)的难点是资料观测信噪比低,传统的方法是使用最小二乘拟合,但是获得的FCN共振参数精度不理想.本文利用武汉国际潮汐基准站高精度超导重力仪和全球超导重力仪观测的时变重力资料,根据贝叶斯算法拟合地球自由核章动(FCN)参数.我们将贝叶斯拟合方法与传统最小二乘法实施了对比分析,研究了不同台站资料差异.讨论了潮波选择和不同海潮模型等因素对FCN参数的影响.结果表明用贝叶斯算法获得的FCN品质因子与空间大地测量VLBI结果吻合的更好,这说明贝叶斯算法可靠性高,为研究地球深内部构造参数(核幔边界粘滞系数等)提供了有效依据.     

Determination of gravity anomaly at sea level from inversion of satellite gravity gradiometric data

Gravity gradients can be used to determine the local gravity field of the Earth. This paper investigates downward continuation of all elements of the disturbing gravitational tensor at satellite level using the second-order partial derivatives of the extended Stokes formula in the local-north oriented frame to determine the gravity anomaly at sea level. It considers the inversion of each gradient separately as well as their joint inversion. Numerical studies show that the gradients T_zz, T_xx, T_yy and T_xz have similar capability of being continued downward to sea level in the presence of white noise, while the gradient T_yz is considerably worse than the others. The bias-corrected joint inversion process shows the possibility of recovering the gravity anomaly with 1 mGal accuracy. Variance component estimation is also tested to update the observation weights in the joint inversion.     

Seismic characterization of reservoirs with multiple fracture sets using velocity and attenuation anisotropy data

Knowledge about the spatial distribution of the fracture density and the azimuthal fracture orientation can greatly help in optimizing production from fractured reservoirs. Frequency-dependent seismic velocity and attenuation anisotropy data contain information about the fractures present in the reservoir. In this study, we use the measurements of velocity and attenuation anisotropy data corresponding to different seismic frequencies and azimuths to infer information about the multiple fracture sets present in the reservoir. We consider a reservoir model with two sets of vertical fractures characterized by unknown azimuthal fracture orientations and fracture densities. Frequency-dependent seismic velocity and attenuation anisotropy data is computed using the effective viscoelastic stiffness tensor and solving the Christoffel equation. A Bayesian inversion method is then applied to measurements of velocity and attenuation anisotropy data corresponding to different seismic frequencies and azimuth to estimate the azimuthal fracture orientations and the fracture densities, as well as their uncertainties. Our numerical examples suggest that velocity anisotropy data alone cannot recover the unknown fracture parameters. However, an improved estimation of the unknown fracture parameters can be obtained by joint inversion of velocity and attenuation anisotropy data.     

基于MATLAB神经网络方法的多层砖房震害预测

提出利用MATLAB人工神经网络工具箱建立基于贝叶斯正则算法的BP神经网络模型,以地震区多层砖房震害调查数据为因子的震害预测方法.神经网络模型输入震害因子包括建筑的层数、施工质量、房屋整体性等,输出值为建筑物在地震作用下的破坏程度.结果表明,本方法可以对多层砖房的震害样本进行预测并达到较理想的效果.