SBAS-InSAR监测融合MGWR与地理探测器的黄河流域地面沉降驱动机制：以甘肃天水市秦州区为例

胡祥祥; 石亚亚; 安乐平; 刘宝康; 吴成永; 于志远; 庞栋栋

doi:10.19509/j.cnki.dzkq.tb20250192

SBAS-InSAR监测融合MGWR与地理探测器的黄河流域地面沉降驱动机制：以甘肃天水市秦州区为例

doi: 10.19509/j.cnki.dzkq.tb20250192

1.
天水师范大学资源与环境工程学院，甘肃天水 741001
2.
黄河水土保持天水治理监督局，甘肃天水 741001

基金项目: 国家自然科学基金项目（42361020；42461064）；2024年甘肃省高等学校人才培养质量提升项目（甘教高函〔2024〕18 号）；甘肃省科技厅青年科技基金项目（23JRRE727）；天水师范学院创新基金（CXJ2023-19）

详细信息

作者简介:
胡祥祥：E-mail：huxiangxiang@tsnu.edu.cn

通讯作者:
E-mail：shiyaya@lzb.ac.cn

计量
- 文章访问数: 276
- PDF下载量: 22
- 被引次数: 0
出版历程
- 收稿日期: 2025-04-25
- 录用日期: 2025-07-08
- 修回日期: 2025-07-07
- 网络出版日期: 2025-07-12

Driving mechanisms of land subsidence in the Yellow River basin based on SBAS-InSAR monitoring integrated with MGWR and GeoDetector: A case study of Qinzhou District, Tianshui City

1.
School of Resources and Environmental Engineering, Tianshui Normal University, Tianshui, Gansu 741001, China
2.
Tianshui Administration and Supervision Bureau of Soil and Water Conservation in the Yellow River Basin, Tianshui, Gansu 741001, China

More Information

Author Bio:
E-mail：huxiangxiang@tsnu.edu.cn

Corresponding author: E-mail：shiyaya@lzb.ac.cn

摘要

摘要:
黄河流域是我国重要的生态保护屏障和经济高质量发展区域，地面沉降问题在典型丘陵−山地−城市复合区如甘肃天水市秦州区表现尤为突出。以黄河上游节点城市秦州区为研究区，分析地面沉降的空间异质性特征与多因子协同驱动机制。基于2021年6月至2024年6月共50景Sentinel-1A影像，利用小基线集合成孔径雷达干涉测量（SBAS-InSAR）技术监测地面沉降动态，并结合多尺度地理加权回归（MGWR）和地理探测器方法开展深入分析。结果表明：（1）秦州区地面沉降呈现明显空间异质性，沉降区主要集中在城区东南部及南部地区，最大年均形变速率达−14.9 mm/a，最大累计形变量为−76.91 mm；主城区整体则呈抬升趋势，最大年均形变速率达12.3 mm/a，最大累计形变量36.81 mm。（2）MGWR模型揭示出人类足迹强度、夜间灯光等人类活动因子在城区沉降区作用显著；高程、降水等因子总体表现为负向效应，而NDVI和水源涵养量则呈现出较明显的空间异质性特征；地下水储量变化速率在南部和东南部地区与地面沉降关系更为密切。（3）地理探测器交互作用分析进一步发现，温度与地下水储量变化速率、人类足迹与蒸发量、高程与植被覆盖等关键因子组合表现出强烈的非线性交互增强效应。秦州区地面沉降是自然与人文因子多重协同作用的结果。本研究深化了对黄河流域典型丘陵−山地−城市复合区地面沉降机制的认识，为区域生态保护与高质量发展提供科学依据与实践指导。
- 小基线集合成孔径雷达干涉测量（SBAS-InSAR） /
- 多尺度地理加权回归（MGWR）模型 /
- 地面沉降 /
- 影响因子 /
- 地质灾害
Abstract:
Objective
The Yellow River basin is a critical ecological barrier and a strategic area for high-quality economic development in China. However, land subsidence issues are particularly pronounced in typical hilly-mountainous-urban transitional zones, such as Qinzhou District. This study aims to analyze the spatial heterogeneity and multi-factor driving mechanisms of land subsidence in Qinzhou District, an important urban node in the upper Yellow River basin.
Methods
Based on 50 Sentinel-1A SAR images acquired between June 2021 and June 2024, the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique was employed to monitor land subsidence dynamics. Multiscale Geographically Weighted Regression (MGWR) was then applied to quantitatively explore the spatial heterogeneity of multiple influencing factors. Additionally, the GeoDetector model was used to analyze the interaction effects among key factors to comprehensively identify their synergistic impacts on land subsidence.
Results
(1) Significant spatial heterogeneity in land subsidence was identified in Qinzhou District. The main subsidence areas were concentrated in the southeastern and southern urban zones, with a maximum annual subsidence rate of −14.9 mm/a and a cumulative displacement of −76.91 mm. In contrast, the central urban area exhibited an overall uplift trend, with the maximum annual uplift rate reaching 12.3 mm/a and a cumulative uplift of 36.81 mm. (2) The MGWR model revealed that human activity-related factors, represented by human footprint intensity and nighttime light, play significant roles in urban subsidence zones. By contrast, elevation and precipitation generally exhibit negative effects, whereas NDVI and water conservation show pronounced spatial heterogeneity. Moreover, groundwater storage changes are more closely linked to ground subsidence in the southern and southeastern parts of the study area. (3) GeoDetector interaction analysis further identified strong nonlinear interactive enhancement effects between critical factor pairs, such as temperature and groundwater storage variation, human footprint intensity and evapotranspiration, and elevation and vegetation cover, indicating that land subsidence in Qinzhou District is driven by multiple interacting natural and anthropogenic factors.
Conclusion
Land subsidence in Qinzhou District results from complex synergistic interactions between natural and anthropogenic factors. This study deepens the understanding of subsidence mechanisms in the typical hilly–mountainous–urban transitional areas of the Yellow River basin and provides scientific insights and practical guidance for regional ecological protection and high-quality sustainable development.
- SBAS-InSAR /
- Multiscale Geographically Weighted Regression (MGWR) model /
- Land subsidence /
- Influencing factors /
- Geological hazards

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图 1 研究区概况图

Figure 1. G Overview map of the study area

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图 2 本研究技术路线图

InSAR. 合成孔径雷达干涉测量；SBAS-InSAR. 小基线集合成孔径雷达干涉测量；OLS-GWR-MGWR. 普通最小二乘法-地理加权回归-多尺度地理加权回归；$ {y}_{m} $为第m个样本点的估计值；$ {\beta }_{0} $为截距；$ {\beta }_{\text{n}} $为根据样本点估计模型的第n 个影响因子拟合系数；$ {x}_{mn} $为第m个样本点的第n个影响因子；K为影响因子数；$ {\varepsilon }_{\text{m}} $为第m个样本点的模型回归残差；$ \left({u}_{m}， {v}_{m}\right) $为第m个样本点的空间地理坐标；$ {\beta }_{0}\left({u}_{m}， {v}_{m}\right) $为位置$ \left({u}_{m}， {v}_{m}\right) $处的常数项；$ {\beta }_{n}\left({u}_{m}， {v}_{m}\right) $为连续函数$ {\beta }_{n}\left({u}_{}， v\right) $在第m个样本点的值；$ {\beta }_{{{b}_{w}}， n} $为第n个影响因子的局部回归系数；$ {b}_{w} $为因子的带宽

Figure 2. Technical Framework of This Study

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图 3 SBAS-InSAR技术流程图

DEM. 数字高程模型；GCP. 地面控制点；SBAS. 小基线集

Figure 3. Flowchart of SBAS-InSAR Technology

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图 4 秦州区2021年6月至2024年6月年均形变速率

Figure 4. shows the average annual deformation rate of Qinzhou District from June 2021 to June 2024

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图 5 秦州区2021年6月至2024年6月累计形变量

Figure 5. Cumulative deformation of Qinzhou District from June 2021 to June 2024

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图 6 秦州区2021年6月至2024年6月特征点累计形变量

Figure 6. Cumulative displacement of feature points

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图 7 地面沉降影响因子（Z标准化）

Figure 7. Ground subsidence Influencing Factors (Z-standardized)

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图 8 多尺度地理加权回归模型回归系数空间分布

Figure 8. Spatial distribution of regression coefficients in the multi-scale geographically weighted regression model

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图 9 主要影响因子之间的交互作用强度与类型

Figure 9. Interaction Intensity and Types Between Major Influencing Factors

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图 10 秦州区沉降机制和影响模式

Figure 10. Settlement mechanism and influence Model of Qinzhou District

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表 1 交互作用判断依据

Table 1. Basis for judging interaction

判断依据	交互作用关系
q(X₁∩X₂)<Min(q(X₁),q(X₂))	非线性减弱
Min(q(X₁),q(X₂))<q(X₁∩X₂)<Max(q(X₁)),q(X₂))	单因子非线性减弱
q(X₁∩X₂)>Max(q(X₁)),q(X₂))	双因子增强
q(X₁∩X₂)=q(X₁)+q(X₂)	独立
q(X₁∩X₂)>q(X₁)+q(X₂)	非线性增强
注：X₁，X₂. 驱动因子；q(X₁)，q(X₂). 驱动因子X₁，X₂的单独解释力；q(X₁∩X₂). 驱动因子X₁，X₂组合后的交互解释力

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表 2 去掉水系距离和道路距离后影响因子多重共线性检验结果

Table 2. shows the multicollinearity test results of the influencing factors after removing the water system distance and road distance

影响因子	变量	容差	方差膨胀因子
气候条件	降水	0.24	4.21
	温度	0.15	6.74
	蒸散发	0.51	1.96
地形地貌	高程	0.28	3.56
	坡度	0.77	1.30
	侵蚀类型	0.63	1.60
地质水文	地下水储量变化速率	0.31	3.24
地质水文	距地震带距离	0.25	4.08
生态环境	NDVI	0.28	3.56
	水源涵养量	0.31	3.19
	人类足迹强度	0.22	4.56
基础数据	人口密度	0.43	2.34
基础数据	夜间灯光	0.52	1.93

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表 3 地理加权回归模型与多尺度地理加权回归模型指标对比

Table 3. Comparison of Indicators between the geographically weighted regression model and the multi-scale geographically weighted regression model

模型指标	最小二乘模型	地理加权回归模型	多尺度地理加权回归模型
拟合优度判定系数	0.20	0.39	0.54
改进的赤池信息量准则	9301.00	8937.00	3175.93
残差平方和	1128.32	691.47	643.05

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表 4 地理加权回归模型与多尺度地理加权回归模型的带宽计算结果

Table 4. Bandwidth calculation results of the geographically weighted regression model and the multi-scale geographically weighted regression model

影响因子	变量	GWR带宽	MGWR带宽
气候条件	降雨	294	496
	温度	294	1402
	蒸发量	294	1402
地形地貌	高程	294	704
	坡度	294	167
	侵蚀类型	294	441
地质水文	地下水储量变化速率	294	1402
地质水文	距地震带距离	294	1402
生态环境	NDVI	294	1402
	水源涵养量	294	1204
	人类足迹强度	294	1402
基础数据	夜间灯光	294	1402
基础数据	人口密度	294	1402

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表 5 多尺度地理加权回归模型回归系数统计描述

Table 5. Statistical Description of Regression Coefficients of the multi-scale geographically weighted regression model

变量	平均值	标准差	最小值	中位数	最大值
降雨	−0.229	0.01	−0.242	−0.23	−0.211
温度	0.404	0.005	0.394	0.404	0.413
蒸发量	0.009	0.008	−0.002	0.007	0.025
高程	−0.264	0.122	−0.462	−0.265	−0.067
坡度	−0.05	0.107	−0.539	−0.028	0.153
侵蚀类型	−0.117	0.103	−0.506	−0.089	0.033
地下水储量变化速率	−0.02	0.006	−0.031	−0.02	−0.01
距地震带距离	−0.115	0.008	−0.129	−0.114	−0.101
NDVI	0.284	0.074	0.139	0.282	0.488
水源涵养量	0.235	0.022	0.177	0.241	0.272
人类足迹强度	0.102	0.002	0.099	0.102	0.103
夜间灯光	0.108	0.003	0.104	0.108	0.114
人口密度	−0.046	0.001	−0.049	−0.047	−0.044

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