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

Author:

Xu Yuanmao1,Wu Zhen1,Zhang Huiwen2,Liu Jie3,Jing Zhaohua4

Affiliation:

1. Lanzhou Institute of Seismology, China Earthquake Administration, Lanzhou 730000, China

2. State Key Laboratory Breeding Base of Desertification and Aeolian Sand Disaster Combating, Gansu Desert Control Research Institute, Lanzhou 730070, China

3. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

4. China State Construction Engineering Corporation, AECOM Consultants Co., Ltd., Lanzhou 730000, China

Abstract

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 km2, while the high-subsidence-risk area covers 54.47 km2. 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.

Funder

Project of Gansu Significant Natural Science Foundation

Project of Gansu Natural Science Foundation

Special Fund for Innovation Team, Gansu Earthquake Agency

Science for Earthquake Resilience of the China Earthquake Administration

Basic Scientific Research Foundation of the China Earthquake Administration

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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4. Wang, F. (2018). Experimental Study on Deformation Characteristics of Structural Loess under High Stress in Yan’an Area. [Master’s Thesis, Xi’an University of Science and Technology].

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