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.     

Prediction of UT1–UTC,LOD and AAM χ<Subscript>3</Subscript> by combination of least-squares and multivariate stochastic methods

This article presents the application of a multivariate prediction technique for predicting universal time (UT1–UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ ₃). The multivariate predictions of LOD and UT1–UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ ₃ time-series as the explanatory variable for the computation of LOD or UT1–UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ ₃ data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ ₃ predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1–UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1–UTC based on LS extrapolation or on LS + AR. In particular, the UT1–UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ ₃ time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ ₃ reveal the best accuracy for LS + AR.     

沿空掘巷合理煤柱宽度综合分析与确定

在总结分析经验法、理论计算法、数值模拟法、现场实测等确定煤柱宽度方法优缺点的基础上,提出了采用现场实测和数值模拟相结合的方法来确定保留煤柱宽度。根据石炭井二矿生产实际,分别对巷道开挖前后,不同煤柱宽度下围岩应力、变形进行了模拟,同时利用21053工作面异型煤柱的有利条件,对不同煤柱宽度下巷道围岩变形、支护结构受力等进行了实测。经过对监测数据的分析,得出了巷道围岩变形、支护结构受力与煤柱宽度之间的关系,结合数值模拟结果,确定了石炭井二矿煤柱宽度。     

吉林省海沟金矿床的地球化学异常模式

在总结海沟金矿床区域水系沉积物、矿区水系沉积物及矿体的岩石、土壤地球化学异常特征的基础上,建立了海沟金矿床的地球化学异常模式,并依据该模式对区域47个异常进行了研究对比,预测出一处寻找海沟式金矿床的有望异常。     

基于神经网络方法的芦苇叶面积指数遥感反演

提出了一种从TM图像上获取芦苇冠层叶面积指数的方法:首先对芦苇的生长背景进行分类;然后,对不同的背景光谱利用冠层反射率(FCR)模型计算得到查找表;最后,利用实测数据和查找表中的数据作为参数进行BP神经网络模型训练,从而得到芦苇冠层LAI。结果表明,人工神经网络方法有很强的非线性拟合能力,能够消除背景对反演结果的影响,有效提高LAI反演的精度。     

夏甸地区Ⅶ-支脉矿体控矿构造特征

构造复合现象的研究,不仅有助于解决构造运动时期先后的问题,而且在生产实践中有着重要的实际意义[1]。本文以山东夏甸地区Ⅶ—支脉矿体为例,通过区域构造、Ⅶ—支构造断裂特征、闪长玢岩构造特征、矿体构造特征的系统研究,结果表明:Ⅶ—支构造的新老构造体系、同一构造体系复合现象突出,多表现为构造形迹的归并。其中成矿断裂的多期活动、构造性质的多次变化、成矿作用的多次叠加是矿体形成的关键因素;在NEE向Ⅶ—支构造与近SN向脉岩截接部位形成矿体,低级序的NNE、NE、NEE向构造成为主要的容矿构造。     

基于小波变换的全色和多光谱遥感图像融合

基于多分辨率小波变换,针对高分辨率全色图像和多光谱图像的融合,提出了一种基于方向可调滤波器的区域能量测量的小波变换融合算法.将此方法与HIS和传统小波的融合方法比较,结果表明,该方法在保留多光谱图像光谱信息的同时,能够更好地保留高分辨率图像的空间信息,融合效果有较大改善.     

福鼎山后尖玄武岩筒地质特征及其形成机制探讨

福鼎山后尖玄武岩位于福鼎一福清北东向断裂带北东段,呈岩筒状产于石帽山群下组下段火山沉积岩中,属钠质碱性玄武岩系列,由玄武质火山角砾岩、碧玄岩、橄榄辉长一玄武岩组合,经历火山爆发、喷溢、次火山侵入3个阶段而形成。     

河南镇平秋树湾矿区铜、钼矿床地质特征及深部找矿潜力分析

秋树湾矿区是一座中型规模的铜、钼矿床。在水平方向上矿区具北铜南钼的分布特征,即北山铜矿床和南山钼矿床;从垂向上看,北山铜矿床深部赋存有规模较大的钼矿体,并具垂直分带现象,即上铜下钼。这主要是矿区成矿后,构造活动使南山赋矿区向上逆冲,使其上部的铜矿体被剥蚀,而北山赋矿区上部的矿体则保存了下来。因此,研究认为,在北山铜矿区的下部及南山钼矿区中深部寻找隐伏钼矿床具有较大潜力。     

现代地图语言

地图符号发展到今天,已经超越了其符号本身,应当把它当作一种语言来研究,即地图语言。地图语言使人们使用地图进行空间认识更加贴近现实,使地理空间信息的传输更加准确、生动和形象。文中主要对现代地图语言的形成,现代地图语言的主要表现形式即视觉符号、听觉符号和触觉符号,以及地图语言的本质即视觉变量、听觉变量和触觉变量进行了探讨。