Monitoring and analysis of surface subsidence of Chinese Jinan Metro line based on SBAS-InSAR and WT-LSTM

Qi Liu; Kanglin Cong; Xuan Wang; Junhao Zhang

doi:10.1117/12.3020878

19 January 2024 Monitoring and analysis of surface subsidence of Chinese Jinan Metro line based on SBAS-InSAR and WT-LSTM

Qi Liu, Kanglin Cong, Xuan Wang, Junhao Zhang

Author Affiliations +

Proceedings Volume 12980, Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023); 129801Y (2024) https://doi.org/10.1117/12.3020878
Event: Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023), 2023, Lianyungang, China

Abstract

Metro is an important component of modern urban rail transit. Monitoring deformations of urban metro lines is vital for protecting people and property. Sentinel-1A data with 36 scenes covering Jinan Metro Line 1 area are used for small baseline set differential interferometry to obtain the deformation rate and cumulative deformation of the area along the line. The Long-Short-Term Memory neural network model based on wavelet noise reduction is used to train and predict the deformation of the subsidence area. The results show that there is an uneven surface subsidence in the monitoring area, with a maximum subsidence rate of -24.96 mm/y and a maximum cumulative subsidence of -54.31mm. Subsidence phenomena is found in the Yanma Station to Jinan West Station section and the Wangfuzhuang Station to Zhaoying Station section. The Long-Short-Term Memory neural network model based on wavelet denoising can reasonably predict the development trend of surface subsidence and provide reference for the construction, operation, and maintenance of metros.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qi Liu, Kanglin Cong, Xuan Wang, and Junhao Zhang "Monitoring and analysis of surface subsidence of Chinese Jinan Metro line based on SBAS-InSAR and WT-LSTM", Proc. SPIE 12980, Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023), 129801Y (19 January 2024); https://doi.org/10.1117/12.3020878

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