Land Subsidence Monitoring and Building Risk Assessment Using InSAR and Machine Learning in a Loess Plateau City-A Case Study of Lanzhou, China

被引:6
作者
Xu, Yuanmao [1 ]
Wu, Zhen [1 ]
Zhang, Huiwen [2 ]
Liu, Jie [3 ]
Jing, Zhaohua [4 ]
机构
[1] China Earthquake Adm, Lanzhou Inst Seismol, Lanzhou 730000, Peoples R China
[2] Gansu Desert Control Res Inst, State Key Lab Breeding Base Desertificat & Aeolian, Lanzhou 730070, Peoples R China
[3] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
[4] AECOM Consultants Co Ltd, China State Construction Engn Corp, Lanzhou 730000, Peoples R China
基金
中国国家自然科学基金;
关键词
land subsidence; InSAR; machine learning; Lanzhou; risk assessment; PERMANENT SCATTERERS; BASIN;
D O I
10.3390/rs15112851
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
As a representative city located in the Loess Plateau region of China, Lanzhou is affected by various environmental and engineering factors, such as precipitation, earthquake subsidence, and building construction, which all lead to frequent geological disasters. Obtaining information on land subsidence over a long time series helps us grasp the patterns of change in various types of ground hazard. In this paper, we present the results of using Interferometric Synthetic Aperture Radar (InSAR) to monitor land subsidence in the main urban area of Lanzhou from 26 October 2014 to 12 December 2021. The main influential factors leading to subsidence were analyzed and combined via machine learning simulation to assess the land subsidence risk grade distribution of a building unit. The results show that the annual average deformation rate in Lanzhou ranged from -18.74 to 12.78 mm/yr. Linear subsidence dominated most subsidence areas in Lanzhou during the monitoring period. The subsidence areas were mainly distributed along the Yellow River, the railway, and villages and towns on the edges of urban areas. The main areas where subsidence occurred were the eastern part of Chengguan District, the railway line in Anning District, and the southern parts of Xigu District and Qilihe urban area, accounting for 38.8, 43.5, 32.5, and 51.8% of the area of their respective administrative districts, respectively. The random forest model analysis results show that the factors influencing surface subsidence in Lanzhou were, in order of importance, precipitation, the distribution of faults, the lithology of strata, high-rise buildings, and the distance to the river and railway. Lanzhou experienced excessive groundwater drainage in some areas from 2015 to 2017, with a 1 m drop in groundwater and 14.61 mm surface subsidence in the most critical areas. At the same time, extensive subsidence occurred in areas with highly compressible loess ground and most railway sections, reaching a maximum of -11.68 mm/yr. More than half of the super-tall building areas also showed settlement funnels. The area at a very high risk of future subsidence in Lanzhou covers 22.02 km(2), while the high-subsidence-risk area covers 54.47 km(2). The areas at greatest risk of future subsidence are Chengguan District and Qilihe District. The city contains a total of 51,163 buildings in the very high-risk area, including about 44.57% of brick-and-timber houses, 51.36% of old housing, and 52.78% of super-tall buildings, which are at especially high risk of subsidence, threatening the lives and properties of the population. The deformation results reveal poor building safety in Lanzhou, providing an essential basis for future urban development and construction.
引用
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页数:32
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共 92 条
[11]  
Casu F., 2006, P FRING 2005 WORKSH
[12]   Land subsidence due to groundwater drawdown in Shanghai [J].
Chai, JC ;
Shen, SL ;
Zhu, HH ;
Zhang, XL .
GEOTECHNIQUE, 2004, 54 (02) :143-147
[13]   Analytical forecasting of long-term railway track settlement [J].
Charoenwong, C. ;
Connolly, D. P. ;
Woodward, P. K. ;
Galvin, P. ;
Costa, P. Alves .
COMPUTERS AND GEOTECHNICS, 2022, 143
[14]   Land subsidence in central Mexico detected by ALOS InSAR time-series [J].
Chaussard, Estelle ;
Wdowinski, Shimon ;
Cabral-Cano, Enrique ;
Amelung, Falk .
REMOTE SENSING OF ENVIRONMENT, 2014, 140 :94-106
[15]   Detection of Land Subsidence Associated with Land Creation and Rapid Urbanization in the Chinese Loess Plateau Using Time Series InSAR: A Case Study of Lanzhou New District [J].
Chen, Guan ;
Zhang, Yi ;
Zeng, Runqiang ;
Yang, Zhongkang ;
Chen, Xi ;
Zhao, Fumeng ;
Meng, Xingmin .
REMOTE SENSING, 2018, 10 (02)
[16]   Detailed mining subsidence monitoring combined with InSAR and probability integral method [J].
Chen Yang ;
Tao QiuXiang ;
Liu GuoLin ;
Wang LuYao ;
Wang FengYun ;
Wang Ke .
CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2021, 64 (10) :3554-3566
[17]   Urban growth and land subsidence: Multi-decadal investigation using human settlement data and satellite InSAR in Morelia, Mexico [J].
Cigna, Francesca ;
Tapete, Deodato .
SCIENCE OF THE TOTAL ENVIRONMENT, 2022, 811
[18]   Present-day land subsidence rates, surface faulting hazard and risk in Mexico City with 2014-2020 Sentinel-1 IW InSAR [J].
Cigna, Francesca ;
Tapete, Deodato .
REMOTE SENSING OF ENVIRONMENT, 2021, 253
[19]   Geodetic Model of the March 2021 Thessaly Seismic Sequence Inferred from Seismological and InSAR Data [J].
De Novellis, Vincenzo ;
Reale, Diego ;
Adinolfi, Guido Maria ;
Sansosti, Eugenio ;
Convertito, Vincenzo .
REMOTE SENSING, 2021, 13 (17)
[20]   Normal faulting vs regional subsidence and sedimentation rate [J].
Doglioni, C ;
D'Agostino, N ;
Mariotti, G .
MARINE AND PETROLEUM GEOLOGY, 1998, 15 (08) :737-750