Deformation Monitoring of the Baihetan Ultra-High Arch Dam Surface Using Short Temporal Baseline Subset GB-InSAR

WANG Peng; SHI Bo; ZHANG Bin; DUAN Hang; KE Chuanfang; XU Fubin; WANG Yujia; JIN Xiao

doi:10.13203/j.whugis20250043

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Deformation Monitoring of the Baihetan Ultra-High Arch Dam Surface Using Short Temporal Baseline Subset GB-InSAR

Remote Sensing and Geological Disasters | 更新时间：2026-11-05

- Deformation Monitoring of the Baihetan Ultra-High Arch Dam Surface Using Short Temporal Baseline Subset GB-InSAR
- Geomatics and Information Science of Wuhan University Vol. 51, Issue 7, Pages: 1440-1451(2026)
- 作者机构：
  
  1.苏州科技大学地理科学与测绘工程学院，江苏苏州，215009
  2.长江空间信息技术工程有限公司（武汉），湖北武汉，430010
  3.中国三峡建工（集团）有限公司，四川成都，610000
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20250043
  CLC： P237
- Received：15 May 2025，
  
  Published：05 July 2026
- 稿件说明：
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王鹏,史波,张斌，等.短时间基线集GB-InSAR白鹤滩特高拱坝坝面变形监测应用[J].武汉大学学报（信息科学版）,2026,51(7):1440-1451.

WANG Peng,SHI Bo,ZHANG Bin,et al.Deformation Monitoring of the Baihetan Ultra-High Arch Dam Surface Using Short Temporal Baseline Subset GB-InSAR[J].Geomatics and Information Science of Wuhan University,2026,51(7):1440-1451.
王鹏,史波,张斌，等.短时间基线集GB-InSAR白鹤滩特高拱坝坝面变形监测应用[J].武汉大学学报（信息科学版）,2026,51(7):1440-1451. DOI：10.13203/j.whugis20250043

WANG Peng,SHI Bo,ZHANG Bin,et al.Deformation Monitoring of the Baihetan Ultra-High Arch Dam Surface Using Short Temporal Baseline Subset GB-InSAR[J].Geomatics and Information Science of Wuhan University,2026,51(7):1440-1451. DOI：10.13203/j.whugis20250043

摘要

常规大坝变形监测方法能够反映典型坝段和高程的变形情况，但测点密度不足，容易遗漏重要部位。地基合成孔径雷达干涉测量（ground-based synthetic aperture radar interferometry，GB-InSAR）技术为坝面区域性变形监测提供了有效手段。但由于受到大气扰动的严重制约，单主影像时序分析方法在长时序监测应用中难以获得理想的解算结果。针对这一问题，对时序GB-InSAR技术进行了改进，选取气象变化平稳时段的连续影像计算平均影像序列，据此构建短时间基线干涉对，利用稳定区域高质量像元（high quality pixel，HQP）的解缠相位进行干涉图大气建模和改正，进而反演变形序列。将所提方法应用于白鹤滩特高双曲拱坝的长时序变形监测，在准确识别坝面区域HQP的基础上，对时序GB-InSAR和传统垂线+水准的监测结果进行了对比分析。结果表明，除受深孔旁瓣效应影响区域外，两种方法在多个垂线测点处符合程度较高，均方根误差达到2 mm左右。短时间基线时序GB-InSAR能够克服近水区剧烈的大气环境变化干扰，解决日波变形同大气相位混杂的问题，可实现长期坝面变形的高精度测量，为高拱坝安全分析和结构变形解译提供重要技术支撑。

Abstract

Objectives

The conventional dam deformation monitoring method has been demonstrated to be capable of reflecting the displacement of typical dam sections and elevations. However

the density of measuring points is often inadequate

with important parts consequently being missed. Ground-based synthetic aperture radar interferometry (GB-InSAR) has emerged as an effective means of monitoring regional deformation of dam surfaces. Nevertheless

the precision of long-term sequential monitoring is frequently compromised by atmospheric disturbances

which significantly impede the ability to obtain reliable deformation measurements through single-master time series InSAR analysis.

Methods

In order to address this issue

improvements were made to the GB-InSAR time series technology. The optimized approach used continuous imagery acquired during periods of stable meteorological conditions to compute average image sequences

which were then used to construct short temporal baseline interferometric pairs. Atmospheric phase correction was then applied to the interferograms using high-quality pixel (HQP) identified in stable regions

enabling precise deformation inversion. The proposed method was applied to the long-term deformation monitoring of the Baihetan ultra-high hyperbolic arch dam. A comparative analysis was conducted between the time-series GB-InSAR results and the traditional monitoring method

based on the accurate identification of HQP across the dam surface.

Results

Except for localized regions affected by the sidelobe effect of deep outlets

the deformation sequences derived from both methods at multiple plumbline observation sites exhibit a high degree of consistency

with root mean square error values of approximately 2 mm. Furthermore

the deformation center of the dam

as identified by the GB-InSAR technique

is located at the top of the 16th dam section

which is consistent with the results obtained from numerical simulations.

Conclusions

The proposed method has the capacity to overcome the interference of severe atmospheric variations in near-water regions and address the issue of diurnal deformation signals being mixed with atmospheric phases in GB-InSAR

which facilitates high-precision measurements of long-term dam surface deformation. The approach provides critical technical support for safety analysis and structural deformation interpretation of high arch dams.

关键词

Keywords

references

WANG P ， XING C ， PAN X D ， et al . Microdeformation Monitoring by Permanent Scatterer GB-SAR Interferometry Based on Image Subset Series with Short Temporal Baselines： The Geheyan Dam Case Study ［J］. Measurement ， 2021 ， 184 ： 109944 .

WANG X ， WANG M N ， JIANG R L ， et al . Structural Deformation Monitoring During Tunnel Construction： A Review ［J］. Journal of Civil Structural Health Monitoring ， 2024 ， 14 （ 3 ）： 591 - 613 .

OMIDALIZARANDI M ， KARGOLL B ， PAFFENHOLZ J A ， et al . Robust External Calibration of Terrestrial Laser Scanner and Digital Camera for Structural Monitoring ［J］. Journal of Applied Geodesy ， 2019 ， 13 （ 2 ）： 105 - 134 .

吴星辉，马海涛，张杰 . 地基合成孔径雷达的发展现状及应用［J］. 武汉大学学报（信息科学版）， 2019 ， 44 （ 7 ）： 1073 - 1081 .

WU Xinghui ， MA Haitao ， ZHANG Jie . Development Status and Application of Ground-Based Synthetic Aperture Radar ［J］. Geomatics and Information Science of Wuhan University ， 2019 ， 44 （ 7 ）： 1073 - 1081 .

INTRIERI E ， DI TRAGLIA F ， DEL VENTISETTE C ， et al . Flank Instability of Stromboli Volcano （Aeolian Islands， Southern Italy）： Integration of GB-InSAR and Geomorphological Observations ［J］. Geomorphology ， 2013 ， 201 ： 60 - 69 .

NURZAMAN F ， IZUMI Y ， SATO M ， et al . A Robust APS Trend Estimation Technique for Ground-Based InSAR Measurements of Urban Landslide ［J］. IEEE Geoscience and Remote Sensing Letters ， 2024 ， 21 ： 4016905 .

GUNDERSEN R ， NORLAND R ， ROLSTAD DENBY C . Ground-Based Differential Interferometric Radar Monitoring of Unstable Mountain Blocks in a Coastal Environment ［J］. Remote Sensing ， 2018 ， 10 （ 6 ）： 914 .

MARTINEZ-VAZQUEZ A ， FORTUNY-GUASCH J . A GB-SAR Processor for Snow Avalanche Identification ［J］. IEEE Transactions on Geoscience and Remote Sensing ， 2008 ， 46 （ 11 ）： 3948 - 3956 .

LUZI G ， PIERACCINI M ， MECATTI D ， et al . Monitoring of an Alpine Glacier by Means of Ground-Based SAR Interferometry ［J］. IEEE Geoscience and Remote Sensing Letters ， 2007 ， 4 （ 3 ）： 495 - 499 .

DI PASQUALE A ， NICO G ， PITULLO A ， et al . Monitoring Strategies of Earth Dams by Ground-Based Radar Interferometry： How to Extract Useful Information for Seismic Risk Assessment ［J］. Sensors ， 2018 ， 18 （ 1 ）： 244 .

WANG P ， XING C ， PAN X D . Reservoir Dam Surface Deformation Monitoring by Differential GB-InSAR Based on Image Subsets ［J］. Sensors ， 2020 ， 20 （ 2 ）： 396 .

NICO G ， CIFARELLI G ， MICCOLI G ， et al . Measurement of Pier Deformation Patterns by Ground-Based SAR Interferometry： Application to a Bollard Pull Trial ［J］. IEEE Journal of Oceanic Engineering ， 2018 ， 43 （ 4 ）： 822 - 829 .

徐甫，李振洪，宋闯，等 . 一种优化的地基SAR时序反演非局部相干点选取方法［J］. 武汉大学学报（信息科学版）， 2023 ， 48 （ 11 ）： 1884 - 1896 .

XU Fu ， LI Zhenhong ， SONG Chuang ， et al . An Enhanced Nonlocal Coherent Pixel Selection Method for Time Series Inversion of GBSAR ［J］. Geomatics and Information Science of Wuhan University ， 2023 ， 48 （ 11 ）： 1884 - 1896 .

WANG Z ， LI Z H ， LIU Y X ， et al . A New Processing Chain for Real-Time Ground-Based SAR （RT-GBSAR） Deformation Monitoring ［J］. Remote Sensing ， 2019 ， 11 （ 20 ）： 2437 .

NOFERINI L ， TAKAYAMA T ， PIERACCINI M ， et al . Analysis of Ground-Based SAR Data with Diverse Temporal Baselines ［J］. IEEE Transactions on Geoscience and Remote Sensing ， 2008 ， 46 （ 6 ）： 1614 - 1623 .

PAN X D ， XU Y M ， XING C ， et al . Study of a GB-SAR Rail Error Correction Method Based on an Incident Angle Model ［J］. IEEE Transactions on Geoscience and Remote Sensing ， 2020 ， 58 （ 1 ）： 510 - 518 .

MO Y H ， LAI T ， WANG Q S ， et al . A Novel Methodology for D-GBSAR Repositioning Error Compensation Based on Maximum Likelihood Estimation ［J］. IEEE Transactions on Geoscience and Remote Sensing ， 2024 ， 62 ： 5204813 .

NOFERINI L ， PIERACCINI M ， MECATTI D ， et al . Permanent Scatterers Analysis for Atmospheric Correction in Ground-Based SAR Interferometry ［J］. IEEE Transactions on Geoscience and Remote Sensing ， 2005 ， 43 （ 7 ）： 1459 - 1471 .

TARCHI D ， CASAGLI N ， FANTI R ， et al . Landslide Monitoring by Using Ground-Based SAR Interferometry： An Example of Application to the Tessina Landslide in Italy ［J］. Engineering Geology ， 2003 ， 68 （ 1/2 ）： 15 - 30 .

BERARDINO P ， FORNARO G ， LANARI R ， et al . A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms ［J］. IEEE Transactions on Geoscience and Remote Sensing ， 2002 ， 40 （ 11 ）： 2375 - 2383 .

WANG Z ， LI Z H ， MILLS J . A New Approach to Selecting Coherent Pixels for Ground-Based SAR Deformation Monitoring ［J］. ISPRS Journal of Photogrammetry and Remote Sensing ， 2018 ， 144 ： 412 - 422 .

WANG P ， GE L C ， ZHANG B ， et al . GB-SAR Geocoding Considering the Radar Attitude Inclination Angles for Building Facade Deformation Extraction ［J］. International Journal of Remote Sensing ， 2024 ， 45 （ 22 ）： 8455 - 8470 .

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