Multiple Parameter Regularization: Numerical Solutions and Applications to the Determination of Geopotential from Precise Satellite Orbits

Kaula’s rule of thumb has been used in producing geopotential models from space geodetic measurements, including the most recent models from satellite gravity missions CHAMP. Although Xu and Rummel (Manuscr Geod 20 8–20, 1994b) suggested an alternative regularization method by introducing a number of regularization parameters, no numerical tests have ever been conducted. We have compared four methods of regularization for the determination of geopotential from precise orbits of COSMIC satellites through simulations, which include Kaula’s rule of thumb, one parameter regularization and its iterative version, and multiple parameter regularization. The simulation results show that the four methods can indeed produce good gravitational models from the precise orbits of centimetre level. The three regularization methods perform much better than Kaula’s rule of thumb by a factor of 6.4 on average beyond spherical harmonic degree 5 and by a factor of 10.2 for the spherical harmonic degrees from 8 to 14 in terms of degree variations of root mean squared errors. The maximum componentwise improvement in the root mean squared error can be up to a factor of 60. The simplest version of regularization by multiplying a positive scalar with a unit matrix is sufficient to better determine the geopotential model. Although multiple parameter regularization is theoretically attractive and can indeed eliminate unnecessary regularization for some of the harmonic coefficients, we found that it only improved its one parameter version marginally in this COSMIC example in terms of the mean squared error.     

我国土地资源遥感调查与监测技术体系建设构想

本文在全面分析总结我国土地资源遥感调查与监测历程的基础上,提出了我国土地资源遥感调查与监测的技术框架与体系以及主要问题和关键技术。研究结果表明我国土地资源遥感调查与监测体系建立已经极为迫切,其目标在于建立土地资源遥感调查与监测本底库和动态库,形成多系统组成的技术体系框架。     

Chinese Surveying and Control Network for Earth-Orbit Satellites and Deep Space Detection

Introduction Astorealizetheprojectofspatialgeodesyby geodeticsatellites,itisnecessarytoestablisha satellitecontrollingandsurveyingnetwork(SC SN)totrack,tosurvey,toremotelycontroland tocommunicatewithearth orbitsatellite,forre alizingtheprojectofdeepspaced…     

2-level r-tree index based on spatial grids and Hilbert R-tree

IntroductionSpatial index is the data structure between spatialoperation algorithms and spatial data objects, and itis usually used to improve the efficiency of spatialdata operations. Spatial index is one of the funda-ment techniques to manage the spatia…     

Constructing geo-information sharing architecture

IntroductionAlong with the development of computer andInternet technology , GIS applications are be-coming broad increasingly ,geo-information sha-ring are of increasingi mportance ,and geo-infor-mationinteroperabilityis the trend in the futureGIS applica…     

Observation on height changes of chinese tide gauges by GPS

IntroductionBecause of global temperature rising and glacierthawing,the sea level has risen about 10-25 cmin the past century[1]. The sea level change isone of the main ai ms in global change monito-ring. Presently the basic tool used in sea levelmonitori…     

一种Delaunay三角网的快速生成算法

本文以Lawson提出的逐点插入法为基础，借鉴方向搜索的思想，在确定插入点的影响凸包时，采用递归的局部搜索策略，形成了一种Delaunay三角网的快速生成算法。实验证明，新算法构网时间与点数基本成线性增长关系，具有较高的效率。     

1：5万地形图框架数据质量影响因素分析

数据质量成因分析是地图数据质量控制的一项基础性的工作，只有找出产生误差的原因才能提出控制质量方法。本文介绍了1：5万框架图数据生产的基本情况，详细分析了产生框架图数据误差的诸多原因，认为地图数据质量与生产模式、生产条件、技术准备、作业环境、生产资料、技术培训、软件系统、作业人员等多种因素有关。     

Palaeovariations in the East-Asian Monsoon Regime Geochemically Recorded in Varved Sediments of Lake Sihailongwan (Northeast China, Jilin Province). Part 2: a 200-Year Record of Atmospheric Lead-210 Flux Variations and its Palaeoclimatic Implications

Palaeovariations of the atmospheric ²¹⁰Pb flux in Northeast China (Long Gang area, Jilin province), quantified by high resolution ²¹⁰Pb measurements on seasonally laminated sediments of Lake Sihailongwan are presented on decadal scale. The mean flux of unsupported ²¹⁰Pb between 1790 and 1970 for the centre of the lake basin is 517 Bq m⁻² yr⁻¹ with maximum deviations between −23% and +27% of this value. Flux rates above this average were found between 1783 and 1813, around 1836, and between 1860 and 1901. The mean ²¹⁰Pb^exc flux rates derived were clearly lower around 1821, 1908, 1930, and 1953. The ²¹⁰Pb^exc flux reached its minimum of 344 Bq m⁻² yr⁻¹ during the period 1977–1982. The atmospheric flux of unsupported ²¹⁰Pb is correlated with the precipitation frequency during the summer monsoon and shows coinciding variability with geochemical proxies that document the groundwater inflow into the lake. Al₂O₃-rich dust of remote provenance scavenged by wet-deposition in the rainy season is the major carrier of the atmospheric ²¹⁰Pb^exc flux.     

Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data

When travelling, people are accustomed to taking and uploading photos on social media websites, which has led to the accumulation of huge numbers of geotagged photos. Combined with multisource information (e.g. weather, transportation, or textual information), these geotagged photos could help us in constructing user preference profiles at a high level of detail. Therefore, using these geotagged photos, we built a personalised recommendation system to provide attraction recommendations that match a user's preferences. Specifically, we retrieved a geotagged photo collection from the public API for Flickr (Flickr.com) and fetched a large amount of other contextual information to rebuild a user's travel history. We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation (the matching process) and candidate ranking. In the matching process, we used a support vector machine model that was modified for multiclass classification to generate the candidate list. In addition, we used a gradient boosting regression tree to score each candidate and rerank the list. Finally, we evaluated our recommendation results with respect to accuracy and ranking ability. Compared with widely used memory-based methods, our proposed method performs significantly better in the cold-start situation and when mining ‘long-tail’ data.