Analysis of spatial-temporal distribution characteristics and influencing factors of land subsidence in Bozhou City, Anhui Province based on SBAS-InSAR technology
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摘要:
近年来皖北平原地区地面沉降问题相对突出，区域地面沉降驱动力的量化研究尚且匮乏。为深入研究沉降灾害的发育特征，文章以亳州市为例，基于62景Sentinel-1数据，利用SBAS-InSAR技术获取2021年10月至2022年10月期间地面沉降的时空分布特征，并结合地理加权回归模型对亳州市地面沉降主要驱动力进行探讨。研究结果表明：（1）亳州市主体沉降速率为5～30 mm/a，平均沉降速率为5.7 mm/a。（2）最严重沉降区位于涡阳县公吉寺镇北侧，幅值为84.3 mm/a，沉降主要受煤矿开采所致；非采煤沉降区，最大沉降速率为25.8 mm/a，位于谯城区东北侧。（3）各驱动力因素对地面沉降的贡献度从大到小排序为深层水位变幅、中深层水位变幅、中深层地下水埋深、深层地下水埋深、单位面积GDP、松散层厚度、道路密度、人口密度。研究结果可为地质灾害防治提供基础数据支撑。
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关键词:
- 亳州市 /
- 地面沉降 /
- SBAS-InSAR /
- 地理加权回归模型 /
- 形变驱动力
Abstract:In recent years, land subsidence issues have become relatively prominent in the northern plain area of Anhui province, and there is lack of quantitative research on the driving forces of regional land subsidence. In order to further investigate the developmental characteristics of subsidence disasters and provide scientific, this paper takes Bozhou City as an example. Based on 62 scenes of Sentinel-1 data, SBAS-InSAR technology is employed to obtain the spatial-temporal distribution characteristics of land subsidence from October 2021 to October 2022. Additionally, a geographic weighted regression model is applied to explore the main driving factors of land subsidence in Bozhou city. The research results indicate: (1) The main subsidence rate in Bozhou City ranges from 5 to 30 mm/year,with an average subsidence rate of 5.7 mm /year. (2) The most serious subsidence area is located north of Gongji Temple Town in Woyang County, with an amplitude of 84.3 mm/year, mainly caused by coal mining. In non-coal mining subsidence areas, the maximum subsidence rate is 25.8 mm/year, located in the northeast of Qiaocheng District. (3) The contribution order of various driving factors to ground subsidence is as follows: fluctuation of deep water level, fluctuation of middle-deep water level, burial depth of middle-deep groundwater, burial depth of deep groundwater, GDP per unit area, thickness of loose layer, road density, and population density. The study results can provide basic data support for geological disaster prevention and control.
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表 1 Sentinel-1卫星数据参数表
Table 1. Parameters of Sentinel-1 satellite data
参数 数值 监测日期 轨道高度/km 700 2021-10-02、2021-10-14、2021-10-26、
2021-11-07、2021-11-19、2021-12-01、
2021-12-13、2022-01-06、2022-01-18、
2022-01-30、2022-02-11、2022-02-23、
2022-03-07、2022-03-19、2022-03-31、
2022-04-12、2022-04-24、2022-05-06、
2022-05-18、2022-05-30、2022-06-11、
2022-06-23、2022-07-05、2022-07-17、
2022-07-29、2022-08-10、2022-08-22、
2022-09-03、2022-09-15、2022-09-27、
2022-10-09重访周期/d 12 入射角/（°） 29～46 分辨率/m 5×20 幅宽/m 250 极化方式 VV 轨道号 142,101 / 142,106 
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表 2 模型多重共线性检验
Table 2. Model multicollinearity test
因子 VIF 因子 VIF 中深层地下水埋深 1.234526 松散层厚度 1.519002 中深层水位变幅 1.625721 人口密度 1.116396 深层水位变幅 1.681352 道路密度 1.053348 深层地下水埋深 2.087465 单位面积GDP 2.481104
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表 3 2022年地面沉降GWR回归模型参数
Table 3. Ground subsidence GWR regression model parameters for 2022
监测年份 带宽 赤池信息准则 可决系数 校正可决系数 2022年 824 11850.657545 0.394583 0.373125
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表 4 SBAS-InSAR监测结果与水准数据对比
Table 4. Comparison between SBAS-InSAR monitoring results and leveling data
点名 实测形变量/mm SBAS-InSAR监测的形变量/mm 差值/mm BJ01 3 3.83 0.83 BJ02 −1 −0.54 −0.46 BXJ08 −4 −3.78 −0.22
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表 5 模型运算结果叙述性统计
Table 5. Descriptive statistics of model calculation results
变量 最小值 中值 最大值 平均值 深层水位变幅 -1.487 0.938 7.769 3.141 中深层水位变幅 -1.482 0.602 2.674 0.596 中深层地下水埋深 -0.747 -0.311 0.065 -0.341 深层地下水埋深 -0.293 -0.050 0.085 -0.104 单位面积GDP -0.003 0.000 0.001 -0.001 松散层厚度 -0.014 0.000 0.013 -0.0005 道路密度 -0.000 0.000 0.001 0.0005 人口密度 -0.001 0.000 0.001 0.000
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