低轨道人造卫星(CHAMP、GRACE、GOCE)与高精度地球重力场——卫星重力大地测量的最新发展及其对地球科学的重大影响

评述了卫星重力大地测量的最新发展及其对地球科学的重大影响。为了更好地理解地球内部物理构造与海洋动力学，以及大陆，冰川和海洋的相互作用，改善现有地球重力场模型（包括精度和空间解析度）是非常重要的。IUGG等国际组织对此已经强调了很多年。最近，由德国的GFZ（GeoForschungsZentrum)，美国的NASA（National Aeronautics and Space Adminitration)以及欧洲宇航局ESA（European Space Agency)开发研制了最先进的地球监测技术－SST（Satellite-to-Sateilite Tracking)。其主要特点是利用现有的GPS连续追踪新发射低轨道卫星，并由低轨道卫星对地球重力场作精密观测。已经发射和即将发射的卫星有3颗：GHAMP（Challenging Mini-Satellite Payload for Geophysical Research an Application)已经于2000年发射；GRACE（Gravity Recovery and Climate Experimert)定于2002年发射；GOCE（Gravity Field and Steady-state Ocean Cirulation Explorer)计划2004年发射，它们可以统称为重力卫星。载有SST技术的人造卫星的主要目的是获得具有前所未有的高精度和高空间解析度的全球重力场和大地水准面模型，加强人们对地球内部构造的理解并为海洋和气象研究提供更好地参考。上述3个重力卫星工作在有明显区别的不同波谱内，它们有不同的科学应用，仅有一小部分重合。所以，就应用而言它们是完全互补的。它们在地球科学中的应用将是广泛的，特别对于固体地球物理学，海洋学以及大地测量学等领域，它们将会带来革命性的变化，其意义不亚于GPS。     

对地方电视台转播CCTV时延的测定

讲述了同步广播卫星电视时间信号的测量方法和测量结果，获得了CCTV1、CCTV2通过同一颗卫星（亚太－1A）转播的时延差值为16333μs，测量精度在10ns以内；CCTV2、CCTV4分别经两颗卫星（亚太－1A、亚洲－Ⅱ）转播，在陕西天文台卫星地面接收站时延差值为1644．20μs，精度为50μs，并分析了影响时延差值和精度的原因。同时测量了地方电视台转播亚太－1A的CCTV信号与直接接收亚太－1A的CCTV信号的时延差值，其测量精度为0．1μs。这些结果为利用同步广播卫星的电视信号进行高精度的时间服务提供了参考依据。     

Flexure of Europa's lithosphere due to ridge-loading

Using photoclinometry, topographic profiles across europan ridges have been produced. These profiles allow the identification of bulges in the terrain adjacent to the ridges. The bulges are assumed to have been produced by flexure of the elastic lithosphere due to the load of the ridges, which lie along cracks in the crust. The distance from the crack to these “fore-bulges” depends on the thickness of the elastic plate being flexed. Based on a survey of ridges in Galileo images with resolution <300 m/pixel, the thickness of the elastic lithosphere has been determined by this method at a wide variety of sites along the leading and trailing hemispheres of Europa. The average thickness is about 200 m. The elastic lithosphere underneath smooth dilational bands tends to be thicker than plains morphology, an effect that is pronounced at Thynia Linea and Astypalaea Linea. Among the ridges investigated here, more recent loading correlates with a thicker elastic lithosphere, which may either reflect an intrinsically thicker layer, or less viscous relaxation over the shorter time period.     

卫星激光测距的新进展

扼要综述了近几年国际上卫星激光测距的进展，介绍了国内激光测距网的现状，展望了未来卫星激光测距的发展。     

Analysis of mapping coverage obtained from spacecraft

An exact analysis of the coverage obtained by spacecraft using cross-track scanning and nadir-centered conical imaging, under imposed viewing obliqueness and resolution requirements, is presented. In addition to exact expressions for the area acquired and the area acquisition rate, envelope theory is introduced to obtain the boundary of the imaged area. These expressions are relatively compact, allowing rapid machine computation. The effects of the sun phase angle, and of imaging system limitations are also examined. The Galileo mission encounter with Callisto is used as a numerical example, from which certain general conclusions are drawn regarding optimal imaging trajectories.     

企鹅珍珠贝人工苗生长的初步观察

为了解企鹅珍珠贝生长规律，观察了壳高2.5～5.0mm的出池幼苗，按月测量其生长参数和成活率，以及环境因子。结果表明，企鹅珍珠贝生长最快的月份为7～11月和次年4～6月，壳长，壳高和壳宽月均增加量分别为3.8～13.0mm、4.7～11.2mm、2.3～3.8mm，月成活率97.8%～98.6%。企鹅珍珠贝生长最慢月份是11月至次年3月。     

The spectral variability of Triton from 1997-2000

We present the results of a three-year observational program of long-slit spectroscopy and UBVRI photometry of Triton. We find evidence for short-lived albedo variations at wavelengths less than 0.5 μm with the most notable reddening event measured in October 1997 and “normal” colors returning by May 1998. We report a possible variation in the relative 0.73/0.89 μm methane absorption band strengths, suggesting that the transport or denudation of this species may play a major role in the reddening events.     

卫星电视授时系统及精度估计

我国已建成短波、长波授时台，进行毫秒级、微秒级的时间服务．随着现代科技和经济的发展，上述方法已不能满足国家及国际交流的需要．本文提出卫星电视授时系统，同步卫星定位精度小于１１０ｍ，卫星授时精度为亚微秒。两原子钟利用卫星进行时刻同步，其精度与两原子钟的距离有关，相距３千多公里的两钟同步精度小于３５ｎｓ，相距数百公里的两原子钟同步精度小于２０ｎｓ     

Neural network prediction of nitrate in groundwater of Harran Plain, Turkey

Monitoring groundwater quality by cost-effective techniques is important as the aquifers are vulnerable to contamination from the uncontrolled discharge of sewage, agricultural and industrial activities. Faulty planning and mismanagement of irrigation schemes are the principle reasons of groundwater quality deterioration. This study presents an artificial neural network (ANN) model predicting concentration of nitrate, the most common pollutant in shallow aquifers, in groundwater of Harran Plain. The samples from 24 observation wells were monthly analysed for 1 year. Nitrate was found in almost all groundwater samples to be significantly above the maximum allowable concentration of 50 mg/L, probably due to the excessive use of artificial fertilizers in intensive agricultural activities. Easily measurable parameters such as temperature, electrical conductivity, groundwater level and pH were used as input parameters in the ANN-based nitrate prediction. The best back-propagation (BP) algorithm and neuron numbers were determined for optimization of the model architecture. The Levenberg–Marquardt algorithm was selected as the best of 12 BP algorithms and optimal neuron number was determined as 25. The model tracked the experimental data very closely (R = 0.93). Hence, it is possible to manage groundwater resources in a more cost-effective and easier way with the proposed model application.     

Spatial Variability of Rock Depth in Bangalore Using Geostatistical, Neural Network and Support Vector Machine Models

Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 km². In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore, where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced level of rock has been presented and discussed.