利用SBAS-InSAR技术分析西宁市地面沉降监测及驱动因素

doi:10.13474/j.cnki.11-2246.2022.0186.

摘要/Abstract

摘要： 地面沉降具有时间持续性与空间扩张性的特点,获取长时间序列、覆盖范围广及精度较高的地面沉降时空演化特征可以预防地面沉降造成的潜在危害。本文采用SBAS-InSAR技术,结合2017年4月—2021年2月的Sentinel-1A影像对西宁市城市地面进行沉降监测。研究结果表明,监测期间西宁市地表形变具有城区形变稳定、局部区域沉降明显及存在缓慢隆升区域的趋势;3处明显快速沉降区域(城西区的沉降区Ⅰ、城东区的沉降区Ⅱ和城北区的沉降区Ⅲ)的沉降速率约为20～35 mm/a;沉降的驱动因素为沉降区域的湿陷性黄土地层,其具有土层结构性脆弱承重特点,在覆盖土层的自重应力及建筑物附加应力的综合作用下,土质受水浸湿后,土壤的结构性能被迅速破坏,土层会发生显著的附加下沉,其强度也迅速降低,从而引起建筑物的不均匀沉降。

关键词: SBAS-InSAR, 时序分析, 沉降监测, 驱动因素

Abstract: Surface subsidence has the characteristics of time persistence and spatial expansion. The spatio-temporal characteristics of surface subsidence with time series, wide coverage and high accuracy prevent the potential harm caused by surface subsidence. This paper utilizes SBAS-InSAR and Sentinel-1A images (from April 2017 to February 2021) to monitor surface subsidence in Xining city. The results suggest that the surface deformation in Xining city has the trend of stable urban deformation, obvious local regional subsidence and slow uplift. The subsidence rate of three obvious subsidence areas (subsidence area Ⅰ in the east of the city, subsidence area Ⅱ in the west of the city and subsidence area Ⅲ in the north of the city) is approximately 20~35 mm/a. The driving factors of the subsidence are attributed to the collapsible loess stratum in the subsidence area, which has the bearing characteristics of fragile soil structure. Under the combined action of the self-weight stress of the covering soil layer and the additional stress of buildings. After the soil is soaked by water, the structural performance of the soil is rapidly damaged. The soil layer will have significant additional subsidence, and its strength will be rapidly reduced, so as to cause uneven subsidence of buildings.

Key words: SBAS-InSAR, time-series analysis, monitoring subsidence, driving factors

中图分类号:

P237

曾学宏, 赵义花. 利用SBAS-InSAR技术分析西宁市地面沉降监测及驱动因素[J]. 测绘通报, 2022, 0(6): 137-142.

ZENG Xuehong, ZHAO Yihua. Monitoring surface subsidence and driving factors in Xining city using SBAS-InSAR[J]. Bulletin of Surveying and Mapping, 2022, 0(6): 137-142.

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