Zi-jun Zhuo; Dun-yu Lv; Shu-ran Meng; Jian-yu Zhang; Song-bo Liu; Cui-ling Wang

doi:10.26599/JGSE.2023.9280028

doi: 10.26599/JGSE.2023.9280028

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出版历程
- 收稿日期: 2023-01-30
- 录用日期: 2023-05-10
- 网络出版日期: 2023-12-10
- 刊出日期: 2023-12-31

Factors driving surface deformations in plain area of eastern Zhengzhou City, China

Zi-jun Zhuo^{1, 2},
Dun-yu Lv^{1, 3
, ,},
Shu-ran Meng^{1, 3},
Jian-yu Zhang^{1, 3},
Song-bo Liu^{1, 3},
Cui-ling Wang^{1, 3}

1.
Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang 050061, China
2.
China University of Geosciences (Beijing), Beijing 100083, China
3.
Key Laboratory of Quaternary Chronology and Hydro-Environmental Evolution, China Geological Survey (CGS) & Hebei Province, Shijiazhuang 050061, China

More Information

Corresponding author: lvdunyu@foxmail.com

摘要

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Abstract: With the rapid socio-economic development and urban expansion, land subsidence has emerged as a major environmental issue, impeding the high-quality development of the plain area in eastern Zhengzhou City, Henan Province, China. However, effective prevention and control of land subsidence in this region have been challenging due to the lack of comprehensive surface deformations monitoring and the quantitative analysis of the factors driving these deformations. In order to accurately identify the dominant factor driving surface deformations in the study area, this study utilized the Persistent Scattered Interferometric Synthetic Aperture Radar (PS-InSAR) technique to acquire the spatio-temporal distribution of surface deformations from January 2018 to March 2020. The acquired data was verified using leveling data. Subsequently, GIS spatial analysis was employed to investigate the responses of surface deformations to the driving factors. The findings are as follows: Finally, the geographical detector model was utilized to quantify the contributions of the driving factors and reveal the mechanisms of their interactions. The findings are as follows: (1) Surface deformations in the study area are dominated by land subsidence, concentrated mainly in Zhongmu County, with a deformation rate of −12.5–−37.1 mm/a. In contrast, areas experiencing surface uplift are primarily located downtown, with deformation rates ranging from 0 mm to 8 mm; (2) Groundwater level, lithology, and urban construction exhibit strong spatial correlations with cumulative deformation amplitude; (3) Groundwater level of the second aquifer group is the primary driver of spatially stratified heterogeneity in surface deformations, with a contributive degree of 0.5328. The contributive degrees of driving factors are significantly enhanced through interactions. Groundwater level and the cohesive soil thickness in the second aquifer group show the strongest interactions in the study area. Their total contributive degree increases to 0.5722 after interactions, establishing them as the primary factors influencing surface deformation patterns in the study area. The results of this study can provide a theoretical basis and scientific support for precise prevention and control measures against land subsidence in the study area, as well as contributing to research on the underlying mechanisms.
- PS-InSAR /
- GIS spatial analysis /
- Geographical detector model /
- Degree of contribution of a driving factor /
- Spatially stratified heterogeneity

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Figure 1. Geographic location of the study area and the scope of Sentinel-1A images

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Figure 2. Hydrogeological cross sections

Notes：(Q_h^a1: Holocene alluvium; Q_p^3a1: Upper Pleistocene alluvium; Q_p^2a1: Middle Pleistocene alluvium; Q_p^1a1+1: Lower Pleistocene alluvium; N¹: Upper section of the Neogene)

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Figure 3. Spatial and temporal baselines of Sentinel-1A images

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Figure 4. Distribution of leveling measurement points, PS points, and benchmark of the leveling measurement

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Figure 5. Verification results of InSAR monitoring

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Figure 6. Spatial distribution of surface deformations rate in the study area

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Figure 7. Groundwater exploitation in the study area

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Figure 8. Elevation contours of groundwater level and cumulative deformation amplitude in the study area in 2019

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Figure 9. Spatial distribution of cohesive soil thickness and cumulative deformation amplitude

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Figure 10. Land use types in the study area during 2010‒2019 (1: Construction land; 2: Cultivated land; 3: Water body; 4: Grassland; 5: Forest land)

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Figure 11. Evolutionary relationships between construction land and zones with land subsidence based on equal-fan analysis

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Figure 12. Spatial distribution of driving factors

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Figure 13. Detection results of driving factor interaction

Note: ^# indicates the two-factor enhancement type, ^* indicates the non-linear enhancement type

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Table 1. Data types, description, and sources

Data type	Data description	Data resources
Satellite images	Sentinel-1A satellite images (January 2018‒March 2020)	ESA European Space Agency (https://viewer.esaworldcover.org/worldcover)
Geographical information data	Land use data (resolution: 30 m, date: 2011‒2019)	GeoData Institute (Yang and Huang, 2021)(https://essd.copernicus.org/articles/13/3907/2021/)
	Digital elevation model (resolution: 30 m)	Resource and Environment Science and Data Center of CAS (https://www.resdc.cn)
	Vector data of urban road network and buildings (resolution: 30 m)	Open Street Map website (https://www.openstreetmap.org)
Geological environment data	Cohesive soil thickness	Urban geological survey program Geological safety inspection and risk assessment sampling of Zhengzhou
	Groundwater level monitoring data (date: January 2018‒March 2020）
	Leveling data (date: January 2018-March 2020)

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Table 2. The interaction types and corresponding criteria

Criterion	Interaction type
q(X₁∩X₂)<Min(q(X₁),q(X₂))	Nonlinear weakening
Min(q(X₁),q(X₂))<q(X₁∩X₂)<Max(q(X₁),q(X₂))	Nonlinear weakening of a single factor
q(X₁∩X₂)> Max(q(X₁),q(X₂))	Enhancement of two factors
q(X₁∩X₂)=q(X₁)+q(X₂)	Independent
q(X₁∩X₂)>q(X₁)+q(X₂)	Nonlinear enhancement

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Table 3. Criteria for developmental degrees of surface deformations

Surface deformation grading	Severe	Moderate	Weak	Slight
Deformation rate/mm·a⁻¹	＜−18	−8 to −18	0 to −8	＞0
Cumulative deformation amplitude/mm	＜−30	−30 to −20	−10 to 0	＞0
*Note: for surface deformation rates and cumulative deformation amplitude, “+” denotes land uplift and “−” denotes land subsidence.

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Table 4. Statistics of surface deformation rates in the study area

Statistics	Deformation rate/mm·a⁻¹	Cumulative deformation amplitude/mm
Statistics	Deformation rate/mm·a⁻¹	Stage I: January 2018 to February 2019	Stage II: January 2019 to March 2020
Minimum	−37.1	−33.82	−40.36
Maximum	+8	+7.29	+8.71
Average	−5.66	−4.84	−5.01

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Table 5. Statistics of changes in the area of zones with different developmental degrees of surface deformations from January 2018 to March 2020

Developmental degree of surface deformations /mm·a⁻¹	Stage Ⅰ: January 2018 to February 2019		Stage Ⅱ: February 2019 to March 2020		January 2018 to March 2020
Developmental degree of surface deformations /mm·a⁻¹	Area /km²	Proportion /%	Area /km²	Proportion /%	Area /km²	Proportion /%
Severe (＜ −18）	16.24	0.74	26.34	1.20	21.24	0.97
Moderate (−8 to −18）	337.59	15.38	432.42	19.70	385.07	17.54
Weak (0 to −8)	1,225.25	55.82	1,086.53	49.50	1,155.81	52.66
Slight (＞0 )	615.92	28.06	649.72	29.60	632.88	28.83

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Table 6. Statistics of the increased area of construction land and the area of zones with different cumulative deformation amplitudes from January 2018 to March 2020

Direction	Increased area of construction land /km²	Area of a zone with different cumulative deformation amplitudes/km²				Direction	Increased area of construction land /km²	Area of zones with a cumulative deformation amplitude /km²
Direction	Increased area of construction land /km²	> −30	−20 to −30	0 to −10	Total	Direction	Increased area of construction land /km²	> 30	20 to 30	0 to 10	Total
N	23.4	4.098	12.294	24.588	40.98	S	34.8	3.884	11.652	23.304	38.84
NNE	29.75	5.565	16.695	33.39	55.65	SSW	34.53	4.463	13.389	26.778	44.63
NE	50.67	28.753	31.255	22.502	82.51	SW	28.26	1.101	3.303	6.606	11.01
NEE	115.46	263.1	131.55	131.55	438.5	SWW	28.85	0.793	2.379	4.758	7.93
E	123.91	114.10	190.17	76.068	380.3	W	27.96	2.756	8.268	16.536	27.56
SEE	98.88	33.527	100.581	201.162	335.2	NWW	22.4	4.451	13.353	26.706	44.51
SE	66.5	7.674	23.022	46.044	76.74	NW	45.56	28.648	55.944	51.888	136.48
SSE	37.51	1.361	4.083	8.166	13.61	NNW	25.72	5.454	16.362	32.724	54.54

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Table 7. Driving factors selected for the geographical detector model

Type	Driving factor	Indicator introduction
Urban contribution	Building density (X₁)	Reflecting building loads and the intensity of engineering activity
Urban contribution	Road density (X₂)
Groundwater exploitation	Groundwater level in the first aquifer group (X₃)	Reflecting the intensity of the groundwater exploitation in the study area
Groundwater exploitation	Groundwater level in the second aquifer group (X₄)
Lithology	Cohesive soil thickness of the first aquifer group (X₅)	Reflecting the elastic and plastic deformation capacities of soil mass
Lithology	Cohesive soil thickness of the second aquifer group (X₆)

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Table 8. Detection results of driving factors

Driving factor	X₁	X₂	X₃	X₄	X₅	X₆
Value q	0.0067^**	0.0983^**	0.2674^**	0.5382^**	0.0396^**	0.0713
Value p	0.00	0.00	0.00	0.00	0.00	0.00
Note: ^** denotes the significant enhancement of the interpretation degree of a driving factor.

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