|本期目录/Table of Contents|

[1]刘金昌,王慧妮*,徐 吟,等.地铁施工塌陷的SBAS-InSAR长时序监测与早期识别[J].武汉工程大学学报,2024,46(01):105-110.[doi:10.19843/j.cnki.CN42-1779/TQ.202209027]
 LIU Jinchang,WANG Huini *,XU Yin,et al.Long-term monitoring and early detection of subway construction-induced subsidence using SBAS-InSAR[J].Journal of Wuhan Institute of Technology,2024,46(01):105-110.[doi:10.19843/j.cnki.CN42-1779/TQ.202209027]
点击复制

地铁施工塌陷的SBAS-InSAR
长时序监测与早期识别
(/HTML)
分享到:

《武汉工程大学学报》[ISSN:1674-2869/CN:42-1779/TQ]

卷:
46
期数:
2024年01期
页码:
105-110
栏目:
资源与土木工程
出版日期:
2024-03-12

文章信息/Info

Title:
Long-term monitoring and early detection of subway construction-
induced subsidence using SBAS-InSAR
文章编号:
1674 - 2869(2024)01 - 0105 - 06
作者:
刘金昌12王慧妮*1徐 吟3李康伦1张华睿1刘天鹤1
1. 武汉工程大学土木工程与建筑学院,湖北 武汉 430074;
2. 中铁十一局集团有限公司,湖北 武汉 430061;
3. 武汉综合交通研究院有限公司,湖北 武汉 430015
Author(s):
LIU Jinchang12WANG Huini *1 XU Yin3 LI Kanglun1 ZHANG Huarui1 LIU Tianhe1
1. School of Civil Engineering and Architecture,Wuhan Institute of Technology,Wuhan 430074,China;
2. China Railway 11th Bureau Group Co.,Ltd,Wuhan 430061,China;
3. Wuhan Comprehensive Transportation Research Institute Co.,Ltd,Wuhan 430015,China
关键词:
SBAS-InSAR地面塌陷地铁施工形变监测
Keywords:
SBAS-InSAR land subsidence subway construction deformation monitoring
分类号:
TB34
DOI:
10.19843/j.cnki.CN42-1779/TQ.202209027
文献标志码:
A
摘要:
以2019年青岛市沙子口和胜利桥发生的2处地铁施工塌陷事故为研究对象,基于改进的SBAS-InSAR技术,利用60景Sentinel-1卫星影像数据,获取地面塌陷前后的青岛市的地表形变时序信息,分析了塌陷事故区地面形变过程与塌陷事故的相关性,提出了利用合成孔径雷达卫星遥感数据开展城市地铁施工地面塌陷监测预警的方法。通过对沙子口和胜利桥2处事故点的长时间序列地表形变监测数据进行统计分析比较,将累积形变量转化为每日平均沉降速率,提出了用于早期识别地表沉降的指标,即在1个周期(12 d)内,平均沉降速率超过0.02 mm/d,而施工预警阈值为连续观测3个周期,平均沉降速率超过0.02 mm/d。

Abstract:
This study focuses on the two subway construction-induced subsidence that occurred at Shazikou and Shengliqiao in Qingdao of China in 2019. Using an improved SBAS-InSAR technique and selecting data from 60 sets of Sentinel-1 satellite images,we captured the temporal information of surface deformation in Qingdao before and after the ground subsidence events. We investigated the correlation between the ground deformation process in the subsidence accident zones and the subsidence incidents. Furthermore,we established a method for utilizing synthetic aperture radar satellite remote sensing data to conduct monitoring and early warning of ground subsidence induced by urban subway construction. By conducting a statistical analysis and comparison of long-term sequential ground deformation monitoring data from the Shazikou and Shengliqiao incident sites,the cumulative deformation variables are transformed into daily average subsidence rates. The paper proposes indicators for the early identification of surface subsidence,where an average subsidence rate exceeds 0.02 mm/d within a single cycle (12 d),while the construction warning threshold is defined after a continuous observation of an average subsidence rate exceeding 0.02 mm/d for three cycles (36 d).

参考文献/References:

[1] 冯雪冬,周小龙,胡亚晴.岩溶地区隧道突涌水机理的研究进展[J].武汉工程大学学报,2022,44(3):250-259,354.

[2] 韦超,朱鸿鹄,高宇新,等. 地面塌陷分布式光纤感测模型试验研究[J]. 岩土力学,2022,43(9):2443-2456.
[3] CHANG L, HANSSEN R F. Detection of cavity migration and sinkhole risk using radar interferometric time series [J]. Remote Sensing of Environment,2014,147:56-64.
[4] MACCHIARULO V, MILILLO P, BLENKINSOPP C,et al. Monitoring deformations of infrastructure networks:a fully automated GIS integration and analysis of InSAR time-series [J]. Structural Health Monitoring,2022,21(4):1849-1878.
[5] 熊威,孙志杰,张必昌.升降轨时序InSAR技术监测天津市地面沉降[J].地理空间信息,2021,19(12):45-49.
[6] 徐燕春,李佳,吴立新,等.基于SBAS-InSAR技术的长沙城区2017—2020年地表沉降监测[J].海洋测绘,2021,41(5):37-42.
[7] 林广坤,吴志峰,曹峥,等.基于SBAS-InSAR技术的围填海区域地面沉降监测[J].遥感技术与应用,2021,36(6):1358-1367.
[8] PERNA S,ESPOSITO C,LANARI R,et al. An algorithm for phase-offset evaluation in InSAR DEM generation [C]// NOTARNICOLA C, PALOSCIA S, PIERDICCA N. Proceedings of SPIE 8891,SAR Image Analysis,Modeling,and Techniques XIII. Bellingham Washington:SPIE, 2013:88910A.
[9] TIZZANI P,BERARDINO P,CASU F,et al. Surface deformation of Long Valley caldera and Mono Basin,California,investigated with the SBAS-InSAR approach [J]. Remote Sensing of Environment,2007,108(3):277-289.
[10] 张铁勤,何祺胜,荆琛琳,等. 基于InSAR的北京市平原区地下水动态监测[J]. 科学技术与工程,2019,19(12):16-22.
[11] 张严. 基于多时域InSAR技术的城市地面塌陷监测研究 [D].西安:长安大学,2020.
[12] 卢旺,王承安,常帅. 基于哨兵1号的呼和浩特市沉降及成因分析[J].内蒙古师范大学学报(自然科学汉文版),2022,51(3):236-242.
[13] 宿宝忠. TS-InSAR技术在郑济高铁区域沉降时空动态分析中的应用[J].铁道勘察,2022,48(3):32-38.
[14] 王凤云,陶秋香,郭在洁,等. 基于SBAS-InSAR技术的矿区地面沉降监测与分析[J].中国科技论文,2022,17(5):571-580.
[15] 樊俊廷,董燕. 基于SBAS-InSAR和PS-InSAR监测阜阳市主城区地面沉降[J].城市勘测,2022(2):96-101.
[16] 杨帆,巩世彬,陈梓萌. 时序InSAR 技术对大连主城区沉降分析[J].测绘工程,2022,31(3):61-67.
[17] DOIN M P, LASSERRE C, PELTZER G, et al. Corrections of stratified tropospheric delays in SAR interferometry:validation with global atmospheric models [J]. Journal of Applied Geophysics,2009,69(1):35-50.

相似文献/References:

[1]周春梅,李沛,虞珏,等.金属矿山地下开采引起地面塌陷的规律[J].武汉工程大学学报,2010,(01):61.
 ZHOU Chun mei,LI Pei,Yu Jue,et al.Research on mechanism of surface subsidence area of underground metal mining[J].Journal of Wuhan Institute of Technology,2010,(01):61.

备注/Memo

备注/Memo:
收稿日期:2022-09-06
基金项目:国家自然科学基金(51778510);武汉工程大学第十六期大学生校长基金(XZJJ2021149)
作者简介:刘金昌,本科生。Email:2624226864@qq.com
*通信作者:王慧妮,博士,副教授。Email:wanghuini@wit.edu.cn
引文格式:刘金昌,王慧妮,徐吟,等. 地铁施工塌陷的SBAS-InSAR长时序监测与早期识别[J]. 武汉工程大学学报,2024,46(1):105-110.
更新日期/Last Update: 2024-03-01