Anterior|Siguiente

Analysis of ground subsidence along Zhengzhou metro based on time series InSAR

  • role： First author第一作者
  • Affiliation：

    School of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, China

  • Email：769420720@qq.com
  • Introduction：叶勇超，研究方向为地理空间分析。E-mail： 769420720@qq.com
YE Yongchao1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, China

    Yellow River Institute for Ecological Protection & Regional Coordination Development, Zhengzhou University, Zhengzhou 450001, China

  • Email：ycd@zzu.edu.cn
  • Introduction：闫超德，研究方向为地理空间分析。E-mail： ycd@zzu.edu.cn
YAN Chaode12*,  
  • Affiliation：

    Zhengzhou Urban Planning Design & Survey Research Institute, Zhengzhou 450052, China

LUO Xianxue3,  
  • Affiliation：

    School of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, China

ZHANG Ruifeng1,  
  • Affiliation：

    School of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, China

YUAN Guanjie1

resumen

As a mega city in central China, Zhengzhou is in a period of large-scale metro construction. The problem of ground subsidence along metro lines occurs during metro construction and operation. Thus, monitoring and analysis of ground subsidence along metro lines are important to ensure the safety of metro operation. However, the researches on long-time-series ground subsidence in the Zhengzhou metro network are lacking. Permanent scatterers–interferometric synthetic aperture radar (PS-InSAR) technology overcomes many shortcomings of traditional ground subsidence monitoring methods, such as high cost, limited monitoring range and points, and difficulties in long-term monitoring. Therefore, PS-InSAR technology is used to monitor the subsidence of the Zhengzhou metro network in this study.35 Envisat ASAR images and 44 Sentinel-1 images are employed to obtain the surface deformation information of Zhengzhou City from February 2005 to October 2010 and from July 2015 to May 2019 via PS-InSAR technology. By extracting PS points in a certain range on both sides of the metro lines, the temporal and spatial characteristics of ground subsidence along Zhengzhou metro are subjected to statistical, profile, and overlay analyses. To address the unequal time interval of ground subsidence time series data caused by SAR image discontinuity, an equidistant processing method based on inverse distance interpolation is proposed, and the subsidence of a typical metro station is predicted and analyzed using the Long Short-Term Memory (LSTM) model.Results show that the subsidence sections are mainly concentrated in the east of Lines 1 and 5, the maximum subsidence rate is more than 20 mm/a, and the maximum cumulative subsidence is about 80 mm. The overall deformation trend of Line 1 is similar to a parabola, and the uneven deformation is prominent. The changes in PS points in the time series differ in various regions. The subsidence trough near the Henan Orthopedic Hospital Station of Line 5 is basically symmetrical in space, and the subsidence at the center is expanding yearly. Experiments show that the LSTM model has high prediction accuracy, and the prediction results reveal that the north of the New Archives of Henan Province located in the south of Zhengzhou Sports Center Station will continue to settle at a rate of about 0.5 mm/month in the next two years. Hence, the station and its vicinity must be continuously monitored. This study confirms that PS-InSAR technology can meet the application needs of large-scale urban ground subsidence monitoring, and the results provide a scientific basis for the continuous dynamic monitoring of ground subsidence along Zhengzhou metro network and metro maintenance.

palabra clave

Zhengzhou metro;ground subsidence;PS-InSAR;LSTM;prediction analysis

References

  1. 1.
    Bai S J. 2017. Application of InSAR Technology in Deformation Monitoring Along Subway. Beijing: China University of Geosciences (Beijing): 1-2
  2. 2.
    Duan G Y, Liu H H, Gong H L and Chen B B. 2017. Evolution characteristics of uneven land subsidence along Beijing-Tianjin inter-city railway. Geomatics and Information Science of Wuhan University, 42(12): 1847-1853
  3. 3.
    Fan Z L and Zhang Y H. 2019. Research progress on intelligent algorithms based ground subsidence prediction. Geomatics and Spatial Information Technology, 42(5): 183-188
  4. 4.
    Ferretti A, Prati C and Rocca F. 2000. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 38(5): 2202-2212
  5. 5.
    Ferretti A, Prati C and Rocca F. 2001. Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1): 8-20
  6. 6.
    Ge D Q, Zhang L, Wang Y, Li M and Liu B. 2014. Monitoring subsidence on Shanghai metro line 10 during construction and operation using high-resolution InSAR. Shanghai Land and Resources, 35(4): 62-67
  7. 7.
    Guan L, Tang W, Dai H Y and Jia Z H. 2019. Monitoring ground subsidence in Zhengzhou city based on SBAS-InSAR technology. Beijing Surveying and Mapping, 33(4): 462-467
  8. 8.
    Hochreiter S and Schmidhuber J. 1997. Long short-term memory. Neural Computation, 9(8): 1735-1780
  9. 9.
    Jia X, Gong H L, Chen B B and Duan G Y. 2014. Impact of uneven land subsidence on operation of Beijing subway line 15. Remote Sensing Information, 29(6): 58-63
  10. 10.
    Jiang D C, Zhang Y H, Zhang J X, Wu H A and Kang Y H. 2017. Uneven land subsidence along Tianjin subway lines monitored by InSAR technology. Remote Sensing Information, 32(6): 27-32
  11. 11.
    Li H X, Zhao X H, Chi H Y and Zhang J J. 2009. Prediction and analysis of land subsidence based on improved BP neural network model. Journal of Tianjin University, 42(1): 60-64
  12. 12.
    Lin H, Ma P F and Wang W X. 2017. Urban infrastructure health monitoring with spaceborne multi-temporal synthetic aperture radar interferometry. Acta Geodaetica et Cartographica Sinica, 46(10): 1421-1433
  13. 13.
    Liu H R. 2016. Study on the Influence Applied to Surrounding Environment Induced by the Shield Construction of Zhengzhou Metro. Zhengzhou: Henan University of Technology: 28-45
  14. 14.
    Liu M K. 2014. The Research of Urban Village Reconstruction in Zhengzhou City. Wuhan: Central China Normal University: 11-28
  15. 15.
    Liu Q, Yue G S, Ding X B, Yang K, Feng G C and Xiong Z Q. 2019. Temporal and spatial characteristics analysis of deformation along Foshan subway using time series InSAR. Geomatics and Information Science of Wuhan University, 44(7): 1099-1106
  16. 16.
    Perissin D, Wang Z Y and Lin H. 2012. Shanghai subway tunnels and highways monitoring through Cosmo-SkyMed Persistent Scatterers. ISPRS Journal of Photogrammetry and Remote Sensing, 73: 58-67
  17. 17.
    Qin X Q, Yang M S, Wang H M, Yang T L, Lin J X and Liao M S. 2016. Application of high-resolution PS-InSAR in deformation characteristics probe of urban rail transit. Acta Geodaetica et Cartographica Sinica, 45(6): 713-721
  18. 18.
    Wang B C, Zhu L, Pan D, Guo L F and Peng P. 2020. Research on temporal and spatial evolution law of land subsidence in Zhengzhou. Remote Sensing for Land and Resources, 32(3): 143-148
  19. 19.
    Wang H Q, Feng G C, Xu B, Yu Y P, Li Z W, Du Y N and Zhu J J. 2017. Deriving spatio-temporal development of ground subsidence due to subway construction and operation in delta regions with PS-InSAR data: a case study in Guangzhou, China. Remote Sensing, 9(10): 1004
  20. 20.
    Wang H Q and Li S M. 2013. Estimating of sunshine percentage using the cloud classification data from FY-2C. Journal of Remote Sensing, 17(5): 1295-1310
  21. 21.
    Wang S H and Zhu B Q. 2021. Time series prediction for ground settlement in portal section of mountain tunnels. Chinese Journal of Geotechnical Engineering, 43(5): 813-821
  22. 22.
    Wang Y M, Luo X J, Yu B, Hao R C and Zhang S J. 2019. Monitoring ground subsidence in Zhengzhou with InSAR. Science of Surveying and Mapping, 44(9): 100-106
  23. 23.
    Wu S B, Le Y Y, Zhang L and Ding X L. 2020. Multi-temporal InSAR for urban deformation monitoring: progress and challenges. Journal of Radars, 9(2): 277-294
  24. 24.
    Xi L T. 2020. Research on Monitoring Data Forecasting Method of Mining-Induced Overburden Deformation Based on LSTM. Xi’an: Xi’an University of Science and Technology: 26-52
  25. 25.
    Zhang J S and Liu K. 2021. Land subsidence monitoring in Zhengzhou based on sentinel-1 A data. Shanxi Architecture, 47(6): 72-75
  26. 26.
    Zhu X X, Chen M, Gong H L, Li X J, Yu J, Zhu L, Zhou Y Y and Li Y. 2018. The subsidence monitoring along Beijing subway network based on MT-InSAR. Journal of Geo-Information Science, 20(12): 1810-1819

Leer el texto completo

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website