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超高阶次勒让德函数递推计算中的压缩因子和Horner求和技术
引用本文:赵丽华,杨元喜,王庆良.超高阶次勒让德函数递推计算中的压缩因子和Horner求和技术[J].测绘学报,2011,40(4):0-441.
作者姓名:赵丽华  杨元喜  王庆良
作者单位:1. 长安大学地质工程与测绘学院,陕西西安,710054
2. 西安测绘研究所,陕西西安,710054
3. 中国地震局第二监测中心,陕西西安,710054
基金项目:国家自然科学基金(40774001;40841021;40902081); 国家863计划(2007AA12Z331)
摘    要:摘要:超高阶次(如2000阶次)缔合勒让德函数值的递推计算,在接近两极时达到极大的数量级(超过10的数千次方),这导致现有递推方法在计算缔合勒让德函数值及其导数值时失效。我们通过插入压缩因子技术,提出一个修改的递推算法,并结合使用Horner求和技术计算球谐级数的部分和。试验表明,该算法至少可以计算到3600完全阶次的球谐级数式,优于现有结果。

关 键 词:拟合推估  地壳形变分析  区域构造特征  高崩溃污染率  抗差估计
收稿时间:2010-05-27

Collocation Model Based on Regional Tectonic Features in Crustal Deformation Analysis
ZHAO Lihua,YANG Yuanxi,WANG Qingliang.School of Geology Engineering , Geomatics,Chang'an University,Xi'an ,China,.Xi'an Research Institute of Surveying , Mapping,.Second Monitoring Center,China Earthquake Administration.Collocation Model Based on Regional Tectonic Features in Crustal Deformation Analysis[J].Acta Geodaetica et Cartographica Sinica,2011,40(4):0-441.
Authors:ZHAO Lihua  YANG Yuanxi  WANG QingliangSchool of Geology Engineering  Geomatics  Chang'an University  Xi'an  China  Xi'an Research Institute of Surveying  Mapping  Second Monitoring Center  China Earthquake Administration
Institution:ZHAO Lihua1,YANG Yuanxi2,WANG Qingliang31.School of Geology Engineering and Geomatics,Chang'an University,Xi'an 710054,China,2.Xi'an Research Institute of Surveying and Mapping,3.Second Monitoring Center,China Earthquake Administration
Abstract:Collocation method,when applied to crustal deformation analysis,can estimate the deformation trend reliably not only of the observed points but also of the unobserved points.But if there are faults or hidden faults in the researched area,accustomed collocation is not proper to deformation analysis because of the discontinuous deformation.So collocation based on regional tectonic deformation background is proposed,which divides the area roughly according to geology information and then establishes collocatio...
Keywords:collocation  crustal deformation analysis  regional tectonic features  high breakdown point  robust estimation  
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