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遥感技术与应用  2022, Vol. 37 Issue (2): 460-473    DOI: 10.11873/j.issn.1004-0323.2022.2.0460
遥感应用     
融合PS、SBAS、DS InSAR技术的昆明地面沉降研究
郭世鹏1(),张王菲2(),康伟1,张庭苇2,李云2
1.西南林业大学 地理与生态旅游学院,云南 昆明 650224
2.西南林业大学 林学院,云南 昆明 650224
The Study on Land Subsidence in Kunming by Integrating PS, SBAS and DS InSAR
Shipeng Guo1(),Wangfei Zhang2(),Wei Kang1,Tingwei Zhang2,Yun Li2
1.College of Geography and Ecotourism,Southwest Forestry University,Kunming 650224,China
2.College of Forestry,Southwest Forestry University,Kunming 650224,China
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摘要:

时序InSAR技术为城市地面沉降监测和防治提供了有效方法,然而PS-InSAR和SBAS-InSAR技术自身的缺陷限制了InSAR技术的监测精度,特别是复杂地形产生的低相干性引起的PS点稀疏问题。在PS和SBAS算法基础上,通过引入Stacking技术联合PS-InSAR进行相干目标点选取,提出融合PS、SBAS、DS-InSAR的技术,并对比提出的方法与常规PS+SBAS-InSAR的监测结果。研究结果表明:提出的方法与PS+SBAS-InSAR反演的昆明沉降速率结果存在较好的一致性,其中本实验提出的方法能增强观测区域点目标空间分布的密度,进而得到更多有效的地表形变信息。从整个研究区来看,昆明市城区地表整体存在-22~8 mm/a的沉降速率,严重沉降区集中在官渡区、西山区和五华区,并已经形成多个沉降漏斗。自1989年以来,小板桥和河尾村依然是最严重的两个沉降漏斗中心,而东北方向的蒋家营则是本次研究中发现的新沉降点。结合历史资料的验证分析表明:昆明地面沉降主要受地下水抽汲、建筑荷载与工程施工以及断陷盆地的构造运动的影响。

关键词: 地表形变融合DS昆明沉降漏斗    
Abstract:

Time-series InSAR technology provides an effective method for monitoring and controlling land subsidence in Kunming city. However, the defects of PS-InSAR and SBAS-InSAR technology limit the monitoring accuracy, especially the low coherence of PS points caused by complex terrain. In this paper, we proposed InSAR technique combining PS, SBAS and DS to monitor the subsidence in Kunming urban area, and the results of the proposed method are compared with PS+SBAS-InSAR. The results show that the proposed method is in good agreement with the results of the Kunming subsidence rate inversion by PS+SBAS-InSAR, and the proposed method can enhance the spatial distribution density of the points in the observation area and obtain more effective surface deformation information. From the perspective of the whole study area, the subsidence rate of the urban surface of Kunming city is -22~8 mm/a, and the serious subsidence areas are concentrated in Guandu District, Xishan District and Wuhua District, and several subsidence funnels have been formed. Since 1989, Xiaobanqiao and Hewei Village are still the two most serious subsidence funnel centers, while Jiangjiaying in the northeast is the new subsidence point found in this study. Combined with the analysis of historical data, it is shown that the ground subsidence in Kunming is mainly affected by groundwater pumping, building load, engineering construction and tectonic movement of faulted basin.

Key words: Surface deformation    Combining    DS    Kunming    The subsidence funnel
收稿日期: 2021-02-24 出版日期: 2022-06-17
ZTFLH:  P237  
基金资助: 国家地区自然科学基金项目(31860240);云南省教育厅科学研究基金(2019J0182)
通讯作者: 张王菲     E-mail: gsp18360576907@163.com;mewhff@163.com
作者简介: 郭世鹏(1995-),男,江苏泰州人,硕士研究生,主要从事InSAR地形形变研究。E?mail:gsp18360576907@163.com
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引用本文:

郭世鹏,张王菲,康伟,张庭苇,李云. 融合PS、SBAS、DS InSAR技术的昆明地面沉降研究[J]. 遥感技术与应用, 2022, 37(2): 460-473.

Shipeng Guo,Wangfei Zhang,Wei Kang,Tingwei Zhang,Yun Li. The Study on Land Subsidence in Kunming by Integrating PS, SBAS and DS InSAR. Remote Sensing Technology and Application, 2022, 37(2): 460-473.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.2.0460        http://www.rsta.ac.cn/CN/Y2022/V37/I2/460

图1  研究区域
序列数据时间T/dBp/m序列数据时间T/dBp/m
12017-03-15-576-19.381 7192018-09-1824-42.419 2
22017-04-08-5520.927 720*2018-10-1200
32017-05-14-5163.264 7212018-11-1736-43.542 4
42017-06-19-480-31.757 5222018-12-1160-75.667 5
52017-07-25-444-71.943 7232019-01-1696-51.437 7
62017-08-18-420-46.929242019-02-09122-61.734
72017-09-23-384-41.817 4252019-03-29168-30.488 8
82017-10-05-372-32.062 4262019-04-2219230.519 3
92017-11-22-324-3.944272019-05-0420434.326 8
102017-12-16-300-13.059 7282019-06-09240-143.678 2
112018-01-09-276-58.746 8292019-07-03264-31.153 2
122018-02-14-24022.946 9302019-08-08300-19.360 4
132018-03-22-204-167.78312019-09-13336-78.614 4
142018-04-27168-22.75322019-10-07360-47.098 5
152018-05-21-144-69.516332019-11-24408-8.560 3
162018-06-26-108-14.748 9342019-12-06420-80.196 4
172018-07-20-84-67.381 1352020-01-1145640.489 1
182018-08-13-60-60.850 1362020-02-16492-83.256 0
表1  Sentinel-1A数据列表
图2  SAR时空基线分布
图3  时序InSAR流程图
主影像辅影像时间基线/d空间基线/m
第一组PS-InSAR方法2018101220171005-420-46.93
本文方法201810122018111736-43.54
第二组PS-InSAR方法2018101220190913336-78.61
本文方法201810122018121160-75.67
表2  两组数据时、空基线列表
图4  不同方法相干性比较
图5  PS点相位标准差
PS+SBASPS+SBAS+DS
PS点sigma值平均值标准差PS点sigma值平均值标准差
第一次迭代4761480~1.2350.0410.16811333490~1.2670.1460.303
第二次迭代4776770~1.2400.0390.16011143240~1.2540.1400.283
第三次迭代4804090~1.2200.0340.14311284840~1.2410.1000.218
表3  两种MT-InSAR技术的PS点相位结果
图6  两种MT-InSAR方法高程改正量对比
图7  两种时序InSAR形变速率结果
图8  小板桥和河尾村沉降量以及形变点分布
图9  PS点位移随时间变化
图10  河尾村和小板桥平均年沉降速率变化

时间

沉降区

1987~19941994~19982007~20082008~20102014-2017本文(2017~2020)

速率

mm/a

速率

mm/a

速率

mm/a

速率

mm/a

速率

mm/a

速率

mm/a

广卫村-20-20.3----21.4
渔户村-6.4-12.6----27.8
小板桥-16.0-31.1-21.6-20.2-54.2-48.9
河尾村-2.5-25.1-27.5-26.1-23.7-25.6
罗家村-----29.3-25.1
曹家村-----27.1-23
东菊新村---24.6-25.5-25.4-17.3
小渔村---26.4-23.3-17.4-22.3
表4  不同时期昆明村落地表沉降变化 (mm/a)
图11  村落沉降速率分布图
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