基于雷达遥感对地观测技术的软土地区公路沉降监测方法

邢学敏; 杨东; 张锐; 熊旭平; 朱珺; 黄丽; 张济航

doi:10.11779/CJGE20220813

基于雷达遥感对地观测技术的软土地区公路沉降监测方法 English Version

邢学敏^{1, 2, 3,},
杨东¹,
张锐^{1, 2, ,},
熊旭平¹,
朱珺^{1, 3},
黄丽^{1, 3},
张济航^{1, 3}

1.
长沙理工大学交通运输工程学院, 湖南长沙 410114
2.
公路养护技术国家工程研究中心, 湖南长沙 410114
3.
洞庭湖生态环境遥感监测湖南省重点实验室, 湖南长沙 410114

基金项目:

国家自然科学基金项目 42074033

湖南省自然科学基金项目 2022JJ30589

中国水利水电第八工程局有限公司科研项目 2023060

湖南省自然资源科研项目 20230118CH

湖南省交通运输厅科技创新项目 202211

长沙市杰出创新青年培养计划项目 kq2209011

洞庭湖生态环境遥感监测湖南省重点实验室开放课题项目

详细信息

作者简介:
邢学敏(1983—)，女，博士，副教授，主要从事时序InSAR技术在地表形变监测中的应用方面的科研工作。E-mail: xuemin.xing@csust.edu.cn

通讯作者:
张锐, E-mail: zr@csust.edu.cn

中图分类号: TU447;P237
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出版历程
- 收稿日期: 2022-06-28
- 网络出版日期: 2023-03-05
- 刊出日期: 2023-09-30

Monitoring method for subsidence of highways in soft soil areas based on radar remote sensing earth observation technique

XING Xuemin^{1, 2, 3,},
YANG Dong¹,
ZHANG Rui^{1, 2, ,},
XIONG Xuping¹,
ZHU Jun^{1, 3},
HUANG Li^{1, 3},
ZHANG Jihang^{1, 3}

1.
School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China
2.
National Engineering Research Center of Highway Maintenance Technology, Changsha University of Science & Technology, Changsha 410114, China
3.
Hunan Key Laboratory of Remote Sensing of Ecological Environment in Dongting Lake Area, Changsha University of Science & Technology, Changsha 410114, China

摘要

摘要: 为克服传统公路沉降监测方法需耗费大量人力物力且监测范围有限的问题，旨在提出一种基于雷达遥感对地观测技术（InSAR）的软土地区公路沉降自动化、大范围监测方法。考虑软土沉降特征，将非线性黏弹塑性四元件组合流变模型引入InSAR形变建模，提出软土地区公路沉降物理模型，并建立InSAR时序相位方程组，估计沉降未知参数，以计算获取公路大范围面状沉降结果。通过模拟数据和广东伦桂路水准实测数据验证了该方法的可行性和可靠性。结果表明：与软土InSAR线性模型计算方法相比，该方法精度提升24%；与传统地面水准实测方法相比，该方法获取的软土地区公路沉降均方根误差为±5.6 mm，相对精度为5%，且趋势与水准实测结果一致。将该方法应用于湖南岳阳湖区公路大范围沉降监测，获取了该区1.5 a的时序沉降结果；该区域公路沉降呈现先快后慢的趋势，累积最大沉降达46 mm，沿湖区沉降明显大于内陆区。可为软土地区公路沉降早期识别和养护管理提供依据。
- 道路工程 /
- 时序沉降 /
- 合成孔径雷达干涉测量 /
- 形变监测 /
- 软土
Abstract: A large-scale automatic surface monitoring method for highways built in soft soil areas based on the InSAR technique is proposed to overcome the deficiencies of labor-consuming and unavailability of large-scale deformation by the traditional monitoring method for ground subsidence of highways. Considering the deformation characteristics of soft soils, a nonlinear viscoelastic-plastic four-component combined rheological model is introduced into the InSAR deformation modeling. Then the physical soft soil highway subsidence model is proposed and the time-series InSAR functions are established. The parameters for subsidence are estimated to calculate the results of the large-scale surface deformation areas of highways. The simulated and field tests on a segment of Lungui Road in Guangdong Province are carried out. Compared to that of the traditional linear model, the modeling accuracy of the proposed method has an improvement of 24%. The RMSE for Lungui Road is estimated as ±5.6 mm, with a relative accuracy of 5% and a good consistency with its deformation tendency. A case study of a highway near Dongting Lake in Yueyang, Hunan Province is carried out to verify the capacity, and the 1.5-year time-series settlement results are obtained, with the subsidence rate following a fast-to-slow nonlinear capacity. The results show that the subsidence near Dongting Lake is significantly higher than that in the inland area, with the maximum subsidence accumulated to 46 mm. The proposed method may provide a reference for early subsidence detection and maintenance management of soft soil highways.
- road engineering /
- time-series subsidence /
- InSAR /
- deformation monitoring /
- soft soil

HTML全文

图 1 时序InSAR沉降监测方法技术流程图

Figure 1. Flow chart of time-series InSAR subsidence monitoring method

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图 2 参数估计值偏差与真实值对比(噪声水平为0.5 rad)

Figure 2. Comparison between deviation of parameter estimation and the real data (noise level 0.5 rad)

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图 3 伦桂路测区位置

Figure 3. Location of Lungui Road

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图 4 伦桂路测区时序沉降

Figure 4. Time-series subsidences of Lungui Road (reference date: 1 January—December 2015)

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图 5 与水准点形变结果进行对比

Figure 5. Comparison of results by proposed method and levelling measurements on benchmark

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图 6 洞庭湖测区位置

Figure 6. Location of Dongting Lake

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图 7 洞庭湖测区时序沉降（起始时间：2011年12月28日）

Figure 7. Time-series subsidences in Dongting Lake area (reference date: 28 December 2011)

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图 8 特征点时序沉降结果对比

Figure 8. Comparison of subsidence results at feature points

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图 9 模型残余相位对比结果

Figure 9. Comparison of results of residual phase of models

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