Monitoring and Comparative Analysis of Hohhot Subway Subsidence Using StaMPS-PS Based on Two DEMS

Author:

Zhao Sihai1,Li Peixian1ORCID,Li Hairui2,Zhang Tao1,Wang Bing1

Affiliation:

1. School of Earth Science and Surveying and Mapping Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China

2. Hohhot Surveying and Mapping Geographic Information Center, Hohhot Land and Spatial Planning Institute, Hohhot 010010, China

Abstract

The subway alleviates the traffic pressure in the city but also brings the potential risk of land subsidence. The land subsidence caused by the subway is a global problem that seriously affects the safety of subway operations and surrounding buildings. Therefore, it is very important to carry out long-term deformation monitoring on the subway system. StaMPS-PS is a time-series Interferometric Synthetic Aperture Radar (InSAR) technique that serves as an effective means for monitoring urban ground subsidence. However, the accuracy of external (Digital Elevation Models) DEM will affect the accuracy of StaMPS-PS monitoring, and previous studies have mostly used SRTM-1 arc DEM (30 m) as the external DEM. In this study, to obtain a more precise measurement of surface deformation caused by the excavation of the Hohhot subway, a total of 85 scenes of Sentinel-1A data from July 2015 to October 2021, as well as two different resolution digital elevation models (DEMs) (ALOS PALSAR DEM and SRTM-1 arc DEM), were used to calculate and analyze the subsidence along the subway line in Hohhot city. The StaMPS-PS monitoring results showed the ALOS PALSAR DEM, as an external DEM, had higher accuracy, and there was regional subsidence in both the construction processes of Line 1 and Line 2 of the Hohhot subway, with a maximum subsidence rate of −21.1 mm/year. The dynamic changes in subway subsidence were fitted using the Peck formula and the long short-term memory (LSTM) model. The Peck formula results showed the width and maximum subsidence of the settlement troughs gradually expanded during the construction of the subway. The predicted values of the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of the LSTM model were less than 4 mm and 10%, respectively, consistent with the measured results. Furthermore, we discussed the factors that affect settlement along the subway line and the impact of two external DEMs on StaMPS-PS. The study results provide a scientific method for DEM selection and subsidence analysis calculations in the StaMPS-PS monitoring of urban subway subsidence.

Funder

Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Ecological-Smart Mines Joint Research Fund of the Natural Science Foundation of Hebei Province

State Key Laboratory of Coal Mining and Clean Utilization

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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