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1.
????????????????????????????????????????????????????????????????????????????????????????иΧ??????????????鹹???????????????????????????????????????????桢?′?????????′?桢????桢?????????????????“???????”??“?????”??“??????”??“??????”????????ò????????“???????????”??“????????”??“?????????”??“??????????”????????????????????????????Σ??????????????λ??????????????????????????????????????????????????????????????????? 相似文献
2.
?????????????????????GPS??????????????“5.12”??Ms8.0?????“4.20”??7.0??????????????????????????α????????????????1??????????????????????????????“?????”???Σ?2????????Ms8.0???????????????????ε?????????????????????ε??????????????????????????????Ms7.0??????λ??????????“?????”???伷???????????????30 nanostrain/a??????????????????????????????????????60 nanostrain/a???????????????????????????????????????????????????????????????????????λ?????????????????????????α?????????????????????????? 相似文献
3.
????????????????????????????1998-01-10???????????o????Ч?????????????????????????λ?????????Ч?????????仯???????о?????????????????Ч?????????仯????“???λ??”????????????????????仯?????????????Ч????????仯??????????λ???????????????????? 相似文献
4.
????????????????????????????????????????????????????????????????????????????????????????????????????Χ?????????????λ?仯??????????????λ??????С????????????????????????Blewitt?????????????????????????????????????????
???òв???С?????????????COMPASS??MEO?????????????????飬??????????????Ч???????????????????????? 相似文献
5.
�ؿǷֲ�͵ؿǺ�ȶ��봨����ͬ��ЧӦ��Ӱ�� 总被引：3，自引：1，他引：2
???????????λ???????USGS???????????????????о?????????????????????????????????????????PSGRN/PSCMP?????????????????????????????α???????仯??????????????????????????????Ч?????????????????????????λ???й??????????????????????????Ч????????????????????????????Ч?????????????????????????????????????10%~20%????????????????????30%?????г????????????????????????λ???γ??λ??????λ????????仯???????????18.4????18.0%??15.8%??16.2??????????40 km?????????????λ???γ??λ??????λ????????仯???????????4.6%??5.3%??3.8%??3.8%???????70 km??????????????3.5%??4.6%??3.0%??2.5%?? 相似文献
6.
??GPS???μ???У???????????·??Ч????????????????ν??????λ??????г????д??????????ν????????????λ??????????λ?????????????????λ????????????????????????GPS?????λ?е?????λ???ξ????????????????????????Kalman?????????к????????????y????????????RPDOP??????Kalman??????и???????Ч???? 相似文献
7.
����ԳƷ��ڵ��㶨λ�е�Ӧ�� 总被引：1，自引：0，他引：1
???GPSα????λ????????????????????μ?????????????λ??????????????ù????????????????????????????????棨???棩?????????????????????????????????α?????????????????????α???????????????????????λ??????????·??????????????????λ???????????????????????????????ν????????????????????????Ч????????λ????????????????? 相似文献
8.
���ڻ������ӵĻ�ɫģ���ڵؿ��α�Ԥ���е�Ӧ�� 总被引：2，自引：0，他引：2
?????????е???α????????????????λ?????????????????????????????????λ?仯???ɡ?????????????????ж?????????????????????????????α?λ?????н??е??????????GM(1,1)?????????????????????÷??????????????????????÷??????????????GM(1,1)?????????????Ч??????????????? 相似文献
9.
??OpenMP??????????Intel Math Kernel Library10.2??????????????????????????????????????????Ч??????????????1?????????????????γ??????????????????OpenMP????????????洢???????????????????????С??2???????????????????????????????γ?????????MKL?????????????Ч?????3??MKL??????Ч???????OpenMP????4?????????OpenMP??MKL??????????????????????????????????Ч??? 相似文献
10.
��̬Ӧ��������Ӱ�����Ӧ���仯�IJ��������Է��� 总被引：6，自引：0，他引：6
???????λ????????????????????????ж?????Ч???????????????????????????????С???????????????????????仯????????????????????????????о??У???Щ????????????????仯????????????????繹?????????????????????????????????????????棬??????????????????????????仯??????????????????????????仯???С?????????????????仯??????????????????? 相似文献
11.
淮河流域上消化道肿瘤与环境污染的模型分析 总被引：1，自引：0，他引：1
自20世纪70年代后期以来,淮河流域不断遭受工业点源污染和其他面源污染,媒体也陆续报道了淮河流域"癌症村"的出现。本文探讨了淮河流域14个监测县5810个行政村的消化道肿瘤与环境因子之间的空间分布规律。作者从流域和行政区划等多维空间角度出发,通过全局的最小二乘法线性回归和稳健回归对环境因子进行筛选分析,以局部地理加权回归方法探测各类环境因子,在不同地区对贝叶斯调整的上消化道肿瘤死亡率的影响程度,建立了消化道肿瘤死亡的风险评估模型,其中,包括地表水水质等级、浅层地下水质量分级、河网密度、土壤多环芳烃含量分级、化肥施用量和经济密度等6类环境危险因素。根据局部回归模型中各监测点环境因子的回归系数和统计学检验结果,提取出当地主要的环境影响因素。从14个监测县区总体上看,地表水水质等级和GDP与肿瘤呈负相关,其他环境因子均与肿瘤死亡存在正相关。但从局部角度看,不同地区环境影响因子种类和影响强度有较大差别。其中淮河流域江苏段以化肥施用量、土壤多环芳烃含量、GDP和河网密度为主要影响因子,安徽段以土壤多环芳烃含量和化肥为主,河南段主要是以地下水质量分级、河网密度和化肥为主,同时河南沈丘县地表水水质等级对当地影响较大。山东段虽然也探测出来部分环境危险因子的存在,但没有发现其与肿瘤死亡的关联关系,尚需进一步深化研究。 相似文献
12.
???й?????????IGS????,??????FES2004????????NAO99b?????????????????????????????????λ?????????????????????????????????λ????????cm??????????????U????????????????????????3~4????????????????????????????????mm????????????????????????С????????????????GPS????????????????10-8??????????????????????????????????С??mm?????????????????????????????1 cm?? 相似文献
13.
顾及降雨及库水位因素的滑坡时滞分析与预测——以三峡库区新铺滑坡为例 总被引：1，自引：0，他引：1
降雨及库水位涨落是引起库岸滑坡形变失稳的主要诱发因素,但滑坡位移速率对此类诱发因素的响应具有一定的滞后性,影响人类对滑坡所处运动状态的判断与预测。针对常规预测模型中未考虑时滞效应的问题,利用三峡库区新铺滑坡的GNSS位移监测数据、奉节气象站降雨数据以及三峡库区库水位涨落数据,通过对监测区内9个GNSS监测点的位移速率序列与降雨量、库水位高程序列进行时滞互相关分析,确定时滞参数,进而应用多变量灰色系统理论方法,建立了时滞GM(1,3)预测模型,并对滑坡位移速率进行预测验证。结果表明:三峡库区新铺滑坡位移速率与降雨量显著相关,对降雨量的响应滞后时间约为5 d,滑体中后部受降雨影响比前缘更明显;位移速率与库水位高程高度相关,对三峡库区库水位涨落的响应滞后时间约为31 d,滑坡前缘受库水位涨落影响更明显,且离长江越近,滞后时间越短;利用加入时滞参数的时滞GM(1,3)模型进行预测,模型拟合优度达到0.702,相比GM(1,1)模型和未顾及时滞因素的GM(1,3)模型,预测精度分别提升了53.8%和58.3%,平均绝对误差百分比分别降低了7.19%和7.47%,在滑坡位移速率预测及库岸滑坡防灾减灾领域具有一定的工程应用价值。 相似文献
14.
采用香港11个GPS测站的观测资料进行1 h、2 h、3 h和4h静态PPP解算，获得4组PPP坐标序列，利用调和分析求取11个测站处8个主要分潮的负荷位移参数（振幅和相位），将其与海潮模型计算的负荷位移参数进行对比，并比较分析PPP反演值与海潮模型值改正海潮负荷信号的效果。结果表明，垂直和水平方向上，不同PPP结果反演8个分潮的负荷位移分别具有约5 mm和7 mm的差异；PPP反演8个分潮垂向负荷位移优于全球海潮模型，但水平方向上的反演效果稍弱。 相似文献
15.
Newmark位移模型是研究地震滑坡易发性的经典模型,机器学习方法支持向量机模型也越来越多的应用到滑坡易发性评估研究。本文将Newmark位移模型与支持向量机模型相结合,建立基于物理机理的地震滑坡易发性评估模型并应用于2008年汶川地震重灾区汶川县。从震后遥感影像目视解译出汶川县1900处地震诱发滑坡,并将其随机划分为70%的训练数据集和30%的验证数据集。选择地形起伏度、坡度、地形曲率、与构造断裂带距离、与水系距离、与道路距离6个因子与Newmark位移值共同作为地震滑坡易发性影响因素。利用ROC曲线和模型不确定性等指标对模型结果进行评估,并与二元统计模型频率比和多元统计模型Logistic回归的结果进行对比。结果表明：与频率比和Logistic回归模型相比,支持向量机模型的正确率最高,训练集和验证集ROC曲线下的面积分别为0.876和0.851。将模型应用于绘制汶川县地震滑坡易发性图,结果显示滑坡易发性图与实际的滑坡点位分布一致性较高,有80.4%的滑坡位于极高和高易发区。这说明支持向量机与Newmark位移方法结合建立的地震滑坡易发性评估模型有较高的预测价值,可以为滑坡风险评估和管理提供依据。 相似文献
16.
海潮模型和格林函数对海潮位移改正的影响 总被引：1，自引：1，他引：0
根据海洋负荷潮理论，海潮位移改正的计算取决于海潮模型和格林函数的选取，因此，针对不同的海潮模型和不同的格林函数分别计算了海潮位移改正，并且比较和分析了它们对海潮位移改正所带来的影响。结果表明，不同海潮模型和不同格林函数对海潮改正的计算值有一定的影响，而且相对来说，不同海潮模型所引起的差别较大，但是这种差别对GPS数据处理的最终结果影响不大。 相似文献
17.
Existing quantitative migration studies are mainly at the inter-region or inter-province level for lacking of detailed geo-referenced migration data.Meanwhile,few of them integrate explorative spatial data analysis and spatial regression model into migration analysis.Based on aggregated registered floating population data from 2005 to 2008,the phenomena that population floating to Yiwu City in Zhejiang Province is analyzed at the provincial and county levels.The spatial layout of Yiwu’s pull forces is proved as a V-shaped pattern excluding Sichuan Province based on map visualization method.Using the migration ratio in 2007 as an explanatory variable,two models are compared using ordinary least square,spatial error model and spatial lag model methods for county-level data in Jiangxi and Anhui provinces.The model with migration stock provides an improved fitting over the model without migration stock according to the model fitting results.The floating population flocking into Yiwu City from Jiangxi is determined mostly by migration stock while the determinant factors are migration stock and distance to Yiwu City for Anhui.The distance-decay effect is true for migration flow from Anhui to Yiwu City while the distance rule is not confirmed in Jiangxi with the best fitting model.The correlation between per capita net income of rural labor forces and migration ratio is not significant in Jiangxi and significant but at the 0.1 level only in Anhui.Further analysis shows that the distance,income and man-land ratio are important factors to explain population floating at earlier stage.However,as the dynamic population floating process evolves,the determinant factor would be migration stock. 相似文献
18.
Responses of river runoff to climate change based on nonlinear mixed regression model in Chaohe River Basin of Hebei Province,China 总被引：1，自引：0，他引：1
Taking the nonlinear nature of runoff system into account,and combining auto-regression method and multi-regression method,a Nonlinear Mixed Regression Model (NMR) was established to analyze the impact of temperature and precipitation changes on annual river runoff process. The model was calibrated and verified by using BP neural network with observed meteorological and runoff data from Daiying Hydrological Station in the Chaohe River of Hebei Province in 1956–2000. Compared with auto-regression model,linear multi-regression model and linear mixed regression model,NMR can improve forecasting precision remarkably. Therefore,the simulation of climate change scenarios was carried out by NMR. The results show that the nonlinear mixed regression model can simulate annual river runoff well. 相似文献
19.
Foreign direct investments (or FDIs) have been employed since the early 1980s and they have become more and more important
in Chinese economic development. However, the roles of FDIs are very different between regions, partly due to the different
locational preference of various source countries. Some facts show that FDIs from Hongkong - Macao indicate a strong locational
preference. Therefore, this paper attempts to make an empirical research on the locational preference of Hongkong - Macao’s
FDIs and their spatial diffusion under the support of statistical data with regression analysis. In this paper, three statistical
models, including the special location model, the general location model and the spatial diffusion model, are created. The
results show that this kind of analysis is successful. The major conclusions are as follows. (1) The optimum location for
FDIs from Hongkong - Macao lies in the coastal area, especially Guangdong, Hainan, Jiangsu, Shandong, Fujian provinces. Besides,
Hubei Province is also an important region. (2) The FDIs from Hongkong - Macao in China have diffused gradually from the coastal
provinces to the inland regions, the northern and the metropolis and from the locations that had attracted a large number
of investments to their vicinities since the 1990s. (3) The special location factors, such as the border effect, the unique
social and kinship ties are the key factors determining the special locational distribution. (4) The general location and
spatial diffusion of Hongkong - Macao’s FDIs are the results of interplay of several economic factors. They are the economic
scale and advantage, the growth rate, the labor force and economic extrovert etc. 相似文献
20.
GUAN Zhi ZHAO Kai SONG Dong-sheng 《中国地理科学(英文版)》2006,16(1):83-86
1INTRODUCTIONSoil is the basis of human's living. Soil moisture plays asignificant role in studying the matter and energy ex-changes in global hydrology sphere. The evaporation ofsoil moisture has an influence on the water vapor cycle.Meanwhile soil moisture is also one of the firsthandmeasurable parameters in crop yield estimation and wa-ter resources management (JACKSON et al., 1993). Theinfluence of the interaction of land and atmosphere onsoil moisture can bring about anomalous cli… 相似文献