利用数据场模型评价北京地面沉降交通载荷程度

doi:10.13278/j.cnki.jjuese.201705205

吉林大学学报(地球科学版) ›› 2017, Vol. 47 ›› Issue (5): 1511-1520.doi: 10.13278/j.cnki.jjuese.201705205

利用数据场模型评价北京地面沉降交通载荷程度

周超凡^1,2,3, 宫辉力^1,2,3, 陈蓓蓓^1,2,3, 贾煦^1,2,3, 朱锋⁴, 郭琳^1,2,3

1. 首都师范大学三维信息获取与应用教育部重点实验室, 北京 100048;
2. 北京市城市环境过程与数字模拟国家重点实验室培育基地, 北京 100048;
3. 首都师范大学资源环境与旅游学院, 北京 100048;
4. 北京市育英学校, 北京 100072

收稿日期:2016-12-26 出版日期:2017-09-26 发布日期:2017-09-26
通讯作者: 宫辉力(195),男,教授,博士生导师,主要从事区域地面沉降理论与应用方面的研究,E-mail:gonghl_1956@126.com E-mail:gonghl_1956@126.com
作者简介:周超凡(1990),女,博士研究生,主要从事区域地沉降方面的研究,E-mail:chaofan0322@126.com
基金资助:
国家自然科学基金项目（4140010982，41130744，41171335）；北京市教委专项（20130419）

Assessment to Ground Subsidence Traffic Load in Beijing Area Using Data Field Mode

Zhou Chaofan^1,2,3, Gong Huili^1,2,3, Chen Beibei^1,2,3, Jia Xu^1,2,3, Zhu Feng⁴, Guo Lin^1,2,3

1. Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China;
2. State Key Laboratory Breeding Base of Process of Urban Environment and Digital Simulation, Beijing 100048, China;
3. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China;
4. Beijing Yuying School, Beijing 100072, China

Received:2016-12-26 Online:2017-09-26 Published:2017-09-26
Supported by:
Supported by National Natural Science Foundation of China (4140010982, 41130744, 41171335) and Beijing Municipal Education Commission Special (20130419)

摘要/Abstract

摘要： 地面沉降是指地面标高降低的一种缓变地质灾害现象，严重时对城市建设和人民生活会构成威胁。为评价地面沉降交通载荷程度，利用永久散射体干涉测量（PS-InSAR）技术提取北京东部地区沉降信息，利用数据场模型，以地铁站信息和道路节点信息为指标，采用因子贡献权重方法，获取北京地面沉降交通载荷程度分区图。研究结果表明：PS-InSAR方法可以获取区域地面沉降的空间分布趋势，研究区内最大沉降速率达到77.69 mm/a；地面沉降交通高载荷主要分布在北京市朝阳区北部与中部区域，而交通低载荷区域主要位于远离城市重要交通道路和地铁线路不发达乡镇。

关键词: 地面沉降, PS-InSAR, 数据场, 交通载荷程度评价

Abstract: Land subsidence is a kind of slowly changing geological disaster caused by the reduction of ground elevation. In order to evaluate the degree of land subsidence and traffic load, we used PS-InSAR technique to obtain the spatial character of the land subsidence in the east of Beijing area, and evaluated the traffic load level of the ground subsidence based on the information of the subway station and the road node by using data field model. Further, we used the factor contribution weight method to achieve the land subsidence traffic load degree zoning map of Beijing. The results show that the land subsidence information can be extracted by PS-InSAR, and the maximum subsidence reaches 77.69 mm/a. The high level of regional land subsidence traffic load is mainly distributed in the northern and central regions of Chaoyang; while the area of low level regional land subsidence traffic load is mainly located far away from the city's main traffic roads and subway lines.

Key words: land subsidence, PS-InSAR, data field, evaluation of traffic load level

中图分类号:

P642.26

周超凡, 宫辉力, 陈蓓蓓, 贾煦, 朱锋, 郭琳. 利用数据场模型评价北京地面沉降交通载荷程度[J]. 吉林大学学报(地球科学版), 2017, 47(5): 1511-1520.

Zhou Chaofan, Gong Huili, Chen Beibei, Jia Xu, Zhu Feng, Guo Lin. Assessment to Ground Subsidence Traffic Load in Beijing Area Using Data Field Mode[J]. Journal of Jilin University(Earth Science Edition), 2017, 47(5): 1511-1520.

参考文献

[1] Deng Zeng, Ke Yinghai, Gong Huili, et al. Land Subsidence Prediction in Beijing Based on PS-InSAR Technique and Improved Grey-Markov Model[J]. GIScience & Remote Sensing,2017:1-22.
[2] 陈蓓蓓,宫辉力,李小娟,等.北京地下水系统演化与地面沉降过程[J].吉林大学学报(地球科学版),2012,42(1):373-379. Chen Beibei, Gong Huili, Li Xiaojuan, et al. Groundwater System Evolution and Land Subsidence Process in Beijing[J]. Journal of Jilin University (Earth Science Edition),2012,42(1):373-379.
[3] Lesniak A, Porzycka S. Environment Monitoring Using Satellite Radar Interferometry Technique (PSInSAR)[J]. Polish Journal of Environmental Studies, 2008, 17(3A):382-387.
[4] Kim S W, Wdowinski S, Dixon T H, et al. Measurements and Predictions of Subsidence Induced by Soil Consolidation Using Persistent Sscatterer InSAR and a Hyperbolic Model[J]. Geophysical Research Letters, 2010, 37(5):87-105.
[5] Ciampalini A, Raspini F, Lagomarsino D, et al. Landslide Susceptibility Map Refinement Using PSInSAR Data[J]. Remote Sensing of Environment, 2016, 184:302-315.
[6] Budhu M, Adiyaman I B. Mechanics of Land Subsi-dence Due to Groundwater Pumping[J]. International Journal for Numerical & Analytical Methods in Geomechanics, 2010, 34(14):1459-1478.
[7] Bakr M. Influence of Groundwater Management on Land Subsidence in Deltas[J]. Water Resources Management, 2015, 29(5):1541-1555.
[8] 贾三满,王海刚,赵守生,等. 北京地面沉降机理研究初探[J]. 城市地质,2007,2(1):20-26. Jia Sanman, Wang Haigang, Zhao Shousheng, et al. A Tentative Study of the Mechanism of Land Subsidence in Beijing[J].City Geology,2007,2(1):20-26.
[9] 付延玲,骆祖江,廖翔,等.高层建筑引发地面沉降模拟预测三维流固全耦合模型[J]. 吉林大学学报(地球科学版),2016,46(6):1781-1789. Fu Yanling, Luo Zujiang, Liao Xiang, et al. A Three-Dimensional Full Coupling Model to Simulate and Predict Land Subsidence Caused by High-Rise Building[J].Journal of Jilin University (Earth Science Edition),2016,46 (6):1781-1789.
[10] 骆祖江,刘金宝,李朗. 第四纪松散沉积层地下水疏降与地面沉降三维全耦合数值模拟[J]. 岩土工程学报,2008,30(2):193-198. Luo Zujiang, Liu Jinbao, Li Lang. Three-Dimensional Full Coupling Numerical Simulation of Groundwater Dewatering and Land-Subsidence in Quaternary Loose Sediments[J]. Chinese Journal of Geotechnical Engineering,2008,30(2):193-198.
[11] Kim K, Lee S, Oh H, et al. Assessment of Ground Subsidence Hazard near an Abandoned Underground Coal Mine Using GIS[J]. Environmental Geology, 2006, 50(8):1183-1191.
[12] 宫辉力,张有全,李小娟,等.基于永久散射体雷达干涉测量技术的北京市地面沉降研究[J].自然科学进展,2009,19(11):1261-1266. Gong Huili, Zhang Youquan, Li Xiaojuan, et al. The Research of Land Subsidence in Beijing Based on Permanent Scatterers Interferometric Synthetic Aperture Radar(PS-InSAR) Technique[J].Progress in Natural Science,2009,19(11):1261-1266.
[13] 胡蓓蓓,姜衍祥,周俊,等. 天津市滨海地区地面沉降灾害风险评估与区划[J]. 地理科学, 2008,28(5):693-697. Hu Beibei, Jiang Yanxiang, Zhou Jun, et al. Assessment and Zonation of Land Subsidence Disaster Risk of Tianjin Binhai Area[J]. Scientia Geographica Sinica,2008,28(5):693-697.
[14] 万小琴,胡波,马清林. 基于PS-InSAR技术的澳门地表沉降研究[J]. 测绘工程,2012, 21(3):39-43. Wan Xiaoqin, Hu Bo, Ma Qinglin. Monitoring Ground Subsidence in Macau with Persistent Scatters Interferometry[J]. Engineering of Surveying and Mapping,2012,21(3):39-43.
[15] Colesanti C, Ferretti A, Prati C, et al. Monitoring Landslides and Tectonic Motions with the Permanent Scatterers Technique[J]. Engineering Geology, 2003, 68(1):3-14.
[16] Teatini P, Tosi L, Strozzi T, et al. Mapping Regional Land Displacements in the Venice Coastland by an Integrated Monitoring System[J]. Remote Sensing of Environment, 2005, 98(4):403-413.
[17] 雷坤超,贾三满,陈蓓蓓,等. 基于PS-InSAR技术的廊坊市地面沉降监测研究[J]. 遥感技术与应用,2013(6):1114-1119. Lei Kunchao, Jia Sanman, Chen Beibei. Land Subsidence Detection Based on PS-InSAR in Langfang[J]. Remote Sensing Technology and Application,2013(6):1114-1119.
[18] 李德毅,刘常昱,杜鹢,等. 不确定性人工智能[J]. 软件学报,2004,15(11):1583-1594. Li Deyi, Liu Changyu, Du Yi, et al. Artificial Intelligence with Uncertainty[J]. Journal of Software,2004,15(11):1583-1594.
[19] 王树良,邹珊珊,操保华,等. 利用数据场的表情脸识别方法[J]. 武汉大学学报(信息科学版),2010,35(6):738-742. Wang Shuliang, Zou Shanshan, Cao Baohua, et al. Facial Expression Recognition Based on Data Field[J]. Geomatics and Information Science of Wuhan University,2010,35(6):738-742.
[20] 蒋新宇,范久波,张继权,等. 基GIS的松花江干流暴雨洪涝灾害风险评估[J]. 灾害学,2009,24(3):51-56. Jiang Xinyu, Fan Jiubo, Zhang Jiquan, et al. GIS-Based Risk Assessment on Rain and Flood Disasters of Songhua River[J]. Journal of Catastrophology,2009,24(3):51-56.
[21] 鲍新华,张宇,李野,等. 松辽盆地增强型地热系统开发选区评价[J]. 吉林大学学报(地球科学版),2017,47(2):564-572. Bao Xinhua, Zhang Yu, Li Ye, et al. Evaluation of Development Selection for Enhanced Geothermal System in Songliao Basin[J]. Journal of Jilin University (Earth Science Edition),2017,47(2):564-572.

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利用数据场模型评价北京地面沉降交通载荷程度

Assessment to Ground Subsidence Traffic Load in Beijing Area Using Data Field Mode

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