InSAR填海区地铁沿线地表沉降反演分析Land subsidence inversion analysis along the subway in the reclamation area based on InSAR
施显健,任超,周吕,黄远林,梁月吉,朱子林
摘要(Abstract):
为了更好地监测和掌握深圳填海区地铁工程结束后地铁沿线的地面沉降情况,该文利用TS-InSAR技术和20景2017年8月15日—2019年3月14日的Sentinel-1A SAR数据,借助POD精密定轨星历和ASTER GDEM V2分别去除轨道误差和地形相位,反演了深圳填海区2017—2019年地表沉降时间序列,并在此基础上重点分析了填海区地铁沿线地面沉降的时空演变规律以及地面沉降成因。结果显示,填海区各地铁沿线的地面沉降特征较为明显,最大沉降速率为-17.52 mm/a。其中,宝安中心、前海湾、深圳湾区段地铁沿线的地面沉降趋势较为严重,其地面沉降呈现逐渐增强和扩散趋势。
关键词(KeyWords): 合成孔径雷达;填海地区;地铁网络;沉降反演;哨兵-1A
基金项目(Foundation): 国家自然科学基金项目(41461089);; 广西自然科学基金资助项目(2018GXNSFBA050006);; 广西科技计划项目(桂科AD19110107);; 武汉市科技计划项目(2019010702011314);; 武汉大学地球空间环境与大地测量教育部重点实验室开放基金资助项目(18-01-01);; 桂林理工大学科研启动基金资助项目(GUTQDJJ2018036);; 广西中青年教师基础能力提升项目(2018KY0247);; 广西空间信息与测绘重点实验室项目(16-380-25-22,15-140-07-34)
作者(Author): 施显健,任超,周吕,黄远林,梁月吉,朱子林
DOI: 10.16251/j.cnki.1009-2307.2021.02.021
参考文献(References):
- [1]XU B,FENG G,LI Z,et al.Coastal subsidence monitoring associated with land reclamation using the point target based SBAS-InSAR method:a case study of Shenzhen,China[J].Remote Sensing,2016,8(8):652-674.
- [2]DU Y,FENG G,LI Z,et al.Effects of external digital elevation model inaccuracy on StaMPS-PS processing:a case study in Shenzhen,China[J].Remote Sensing,2017,9(11):1115-1134.
- [3]GUO J,ZHOU L,YAO C,et al.Surface subsidence analysis by multi-temporal InSAR and grace:a case study in Beijing[J].Sensors,2016,16(9):1495-1513.
- [4]ZHOU L,GUO J,HU J,et al.Subsidence analysis of ELH bridge through ground-based interferometric radar during the crossing of a subway shield tunnel underneath the bridge[J].International Journal of Remote Sensing,2018,39(6):1911-1928.
- [5]ZHOU L,GUO J,HU J,et al.Wuhan surface subsidence analysis in 2015-2016based on Sentinel-1Adata by SBAS-InSAR[J].Remote Sensing,2017,9(10):982-1003.
- [6]CHEN G,ZHANG Y,ZENG R,et al.Detection of land subsidence associated with land creation and rapid urbanization in the Chinese Loess Plateau using time series InSAR:a case study of Lanzhou new district[J].Remote Sensing,2018,10(2):270-293.
- [7]ZHANG Z,WANG C,WANG M,et al.Surface deformation monitoring in Zhengzhou city from 2014to2016using time-series InSAR[J].Remote Sensing,2018,10(11):1731-1747.
- [8]TANG W,YUAN P,LIAO M,et al.Investigation of ground deformation in Taiyuan Basin,China from 2003to 2010,with atmosphere-corrected time series InSAR[J].Remote Sensing,2018,10(9):1499-1521.
- [9]WANG R,YANG T,YANG M,et al.A safety analysis of elevated highways in Shanghai linked to dynamic load using long-term time-series of InSAR stacks[J].Remote Sensing Letters,2019,10(12):1133-1142.
- [10]刘冰,张永红,吴宏安,等.时间序列InSAR技术辅助下的北京市高速公路网沉降监测应用[J].测绘通报,2018(2):120-125.(LIU Bing,ZHANG Yonghong,WU Hongan,et al.Subsidence monitoring for expressway network in Beijing based on time-series InSAR technique[J].Bulletin of Surveying and Mapping,2018(2):120-125.)
- [11]王茹,杨天亮,杨梦诗,等.PS-InSAR技术对上海高架路的沉降监测与归因分析[J].武汉大学学报(信息科学版),2018,43(12):2050-2057.(WANG Ru,YANGTianliang,YANG Mengshi,et al.Attribution analysis on deformation feature of the Shanghai elevated highway by persistent scatterer SAR interferometry[J].Geomatics and Information Science of Wuhan University,2018,43(12):2050-2057.)
- [12]CHEN W F,GONG H L,CHEN B B,et al.Spatiotemporal evolution of land subsidence around a subway using InSAR time-series and the entropy method[J].GIScience&Remote Sensing,2017,54(1):78-94.
- [13]祝秀星,陈蜜,宫辉力,等.采用时序InSAR技术监测北京地铁网络沿线地面沉降[J].地球信息科学学报,2018,20(12):1810-1819.(ZHU Xiuxing,CHEN Mi,GONG Huili,et al.The subsidence monitoring along Beijing subway network based on MT-InSAR[J].Journal of Geo-information Science,2018,20(12):1810-1819.)
- [14]刘琦,岳国森,丁孝兵,等.佛山地铁沿线时序InSAR形变时空特征分析[J].武汉大学学报(信息科学版)2019,44(7):1009-1106.(LIU Qi,YUE Guosen,DINGXiaobing,et al.Temporal and spatial characteristics analysis of deformation along Foshan subway using time series InSAR[J].Geomatics and Information Science of Wuhan University,2019,44(7):1009-1106.)
- [15]张荐铭,甘淑,袁希平,等.PS-InSAR技术的昆明地表沉降特征提取与分析[J].测绘科学,2019,44(1):53-59,89.(ZHANG Jianming,GAN Shu,YUAN Xiping,et al.The extraction and analysis of Kunming ground deformation characteristics based on PS-InSAR[J].Science of Surveying and Mapping,2019,44(1):53-59,89.)