Subsidence Features Research Articles

Monitoring of Surface Deformation in the Xi'an Coal Mine, Liaoyuan, Based on Time-Series InSAR

In this study, we employed SBAS-InSAR and Stacking-InSAR methods to monitor and analyze the surface deformation of coal mines in the Xi'an District of Liaoyuan. The Pearson correlation coefficient of the deformation results obtained from the two methods is 0.9. Moreover, the deformation regions monitored by the two techniques exhibit a high degree of consistency in their spatial distribution, validating the accuracy of our monitoring results. Using the monitoring data derived from InSAR and field investigations, we revealed the spatial distribution characteristics of surface subsidence and its temporal evolution between 2018 and 2021. Our findings indicate that subsidence predominantly occurred in five regions, with subsidence velocity slowing after October 2020, suggesting a gradual weakening of surface deformation activities. Additionally, we analyzed surface deformation indicators, such as tilt and curvature, revealing the complexity of the deformation areas characterized by multiple tilt and curvature deformation centers. These results provide a crucial scientific basis for geological disaster prevention and ecological restoration in the mining area, aiding the development of more effective management and restoration strategies.

Research on the surface subsidence characteristics and prediction models caused by coal mining under the reverse fault

Predicting and understanding the phenomenon of surface subsidence caused by coal mining in working faces with faults are important issues for safe coal mining and efficient production. In numerical simulation experiments, it was found that the phenomenon of surface subsidence manifests when faults exist, and the degree of influence of faults with different dip angles on surface subsidence varies. This phenomenon is attributed to fault activation. According to the experimental results, the impact of faults with different dip angles on surface subsidence falls into three levels: level I for 35° faults, level II for 45° and 55° faults, and level III for 65° and 75° faults. Similarly, the relationship between the difficulty of fault activation and the dip angle of faults can be categorized as 35° faults prone to activation, 45° and 55° faults difficult to activate, and 65° and 75° faults not prone to activation. The probability integral correction model for fault mining, which integrates the surface subsidence values caused by fault-induced attenuation and the subsidence arising from separation spaces, was introduced, thereby constructing a surface subsidence prediction model. This proposed prediction model can accurately predict surface subsidence, with a root mean square error of 10.74 mm between the predicted and measured values, as validated using DInSAR results from the III 6301 working face in the Jincheng mining area.

Study on the Bearing Structure of Key Strata and the Linkage Evolution Mechanism of Surface Subsidence in Shallow Coal Seam Mining

Shallow coal seam mining results in the formation of various bearing structures in key strata, leading to varying degrees of surface subsidence and severe disruption to the surface ecological environment. To investigate the coupled evolution characteristics of key strata fracture-bearing structures and surface subsidence in shallow coal seam mining, with a focus on the 1–2 coal seam mining at Longhua Coal Mine in northern Shaanxi as the research background, this study employed physical similarity simulation to establish the correlation between key strata fracture-bearing structures and surface subsidence. The study also utilized theoretical calculations to develop models for the trapezoidal hinged arch structure and the coupling between key strata-bearing structures and surface subsidence. Mechanical properties of bearing structures and the coupled evolution characteristics of surface subsidence were examined, and the scientific validity of the models was verified through field monitoring. The research reveals that the inclined section of the working face in shallow coal seam mining forms a trapezoidal hinged arch structure, where stress transmission actually resembles an arch shape. Based on the fracture characteristics of rock strata, this structure can be categorized into three types: a full-trapezoidal hinged arch structure, a semi-trapezoidal hinged arch structure, and a trapezoidal-like hinged arch structure. A mechanical calculation model for the trapezoidal hinged arch structure was constructed, and the mechanical calculation formula for this structure was derived based on mechanical equilibrium conditions. Using a masonry beam mechanical model, the formula for calculating the subsidence of key blocks in the key strata fracture was obtained. Based on the “masonry beam” mechanical model, a formula was derived to calculate the subsidence of key blocks in fractured key strata. The relationship between key strata-bearing structures and surface subsidence curves was analyzed, leading to the development of a calculation model for both. This model reveals the coupled evolution between rock movement and surface subsidence. Field measurements indicate a maximum surface subsidence of 1.93 m, with a subsidence coefficient of 0.65, showing that the surface helps suppress and reduce the overall subsidence.

DEVELOPMENT OF METHODS FOR DESIGNING PILE FOUNDATIONS FOR MULTI-STOREY CIVIL BUILDINGS CONSTRUCTED ON MACROPOROUS SUBSIDENCE SOILS

The study considers the methods (separate stages) of designing pile foundations made of driven reinforced concrete piles on macroporous subsidence soils of the second type of subsidence using multi-storey buildings as an example. The paper provides a brief description of the buildings under consideration, analyzes the soil conditions of construction, including data on physical, deformation, strength, subsidence characteristics of soils and methods of their determination. Using the calculation, it establishes the type of soil conditions in terms of subsidence, describes the method of selecting the type of piles and their length in macroporous subsidence soils. The data of experimental studies of bearing capacity of driven piles in macroporous subsidence soils, justification of the method of numerical studies of pile performance and theoretical evaluation of their bearing capacity are given.

Surface damage reduction effect of ultra-high working face in Shangwan coal mine

Green mining is the basic background of high-quality mining, and source reduction is the most important part, among which the optimization of working face height and length is the most active. How to control the working face parameters, reasonably evaluate the surface subsidence characteristics of the super-long working face, and identify the limit working face length and the surface discontinuous deformation threshold is particularly important. In order to solve the problem of irreversible damage to the surface caused by the mining process of the working face, this paper discusses the whole process of surface movement in the 8.8 m super-large mining height working face of Shangwan Coal Mine through field investigation, the maximum subsidence is 6.20 m, with a subsidence coefficient of 0.72. Combined with the inflection point trajectory, advancing degree and subsidence coefficient, the 0.9 coefficient of the working face subsidence basin boundary and the loss reduction strategy far from the limit working face threshold are proposed. The results show that the subsidence basin of 12,401 working face accounts for 34%, the continuous deformation area of surface accounts for 64%, and the influence area of discontinuous deformation area is within 10 times of mining height. With the help of borehole detection verification, the caving zone height within the coal seam overburden to be between 33.2 and 33.25 m and the fracture zone height between 118.08 and 132.83 m. Therefore, the caving ratio is about 3.8, and the fracture ratio is about 13–15, which provides a strong basis for the optimization design of working face and the timing of surface ecological management.

Simulation and prediction of land subsidence in Decheng District under the constraint of InSAR deformation information

Land subsidence, marked by a decline in surface elevation, poses a significant threat to urban infrastructure and safety. Accurate subsidence information and a reliable prediction model are crucial for prevention and control. In this study, we used persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technology to obtain long-term land subsidence data and analyzed subsidence characteristics in Decheng District. By integrating hydrogeological and groundwater data, we developed a three-dimensional groundwater flow and one-dimensional compaction model through numerical simulation. Furthermore, the subsidence data monitored by PS-InSAR were used to further constrain and validate the model. The evolution trend of land subsidence under different groundwater exploitation scenarios was predicted and analyzed. The results showed that from May 2017 to December 2021, the cumulative maximum subsidence in Decheng District reached −173 mm. The subsidence area is mainly concentrated in the northern area, and its subsidence center is near Qiaoyuan Town. According to the Land Subsidence Prevention and Control Plan of Dezhou City, Shandong Province (2018–2025), we set up different groundwater mining scenarios with the goal that the rate of land subsidence in the key prevention and control area is less than 35 mm/yr in 2025.The Fluid-solid coupled model prediction analysis results indicated that a 30% reduction in groundwater exploitation is reasonable.

Research on prediction method of coal mining surface subsidence based on MMF optimization model

Coal seam mining causes fracture and movement of overlying strata in goaf, and endangers the safety of surface structures and underground pipelines. Based on the engineering geological conditions of 22,122 working face in Cuncaota No.2 Coal Mine of China Shenhua Shendong Coal Group Co., Ltd. a similar material model test of mining overburden rock was carried out. The subsidence of overburden rock was obtained through the full-section strain data of distributed optical fiber technology, and the characteristics of mining surface subsidence were studied. The Weibull model was used to adjust the mathematical form of the first half of the surface subsidence curve via the MMF function. On this basis, the prediction model of coal seam mining surface subsidence was established, and the parameters of the prediction model of surface subsidence were determined. The test results show that with the advancement of coal seam mining, the fit goodness of the surface subsidence prediction curve based on the MMF optimization model reaches 0.987. Compared with the measured values, the relative error of the surface subsidence prediction model is reduced to less than 10%. The model displays good prediction accuracy. The time required for settlement stability in the prediction model is positively correlated with parameter a and negatively correlated with parameter b. The research results can be further extended to the prediction of overburden “three zones” subsidence, and provide a scientific basis for the evaluation of surface subsidence compression potential in coal mine goaf.

Subtle Land Subsidence Elevates Future Storm Surge Risks Along the Gulf Coast of the United States

AbstractWe developed a robust InSAR processing strategy that can effectively mitigate severe decorrelation noise in a large volume of InSAR data. We mapped the average land subsidence rate (2017–2020) over the 131,572 km2 Upper Texas and Louisiana coasts from Sentinel‐1 data, with ∼2 mm/yr accuracy based on independent GPS and tide gauge validation at 189 locations. The improved InSAR observations reveal widespread subsidence that was previously undetected in coastal wetlands and rural areas with small communities. Our InSAR surface deformation map is at the spatial scale that overlaps with the scale of hydrodynamic model grids. This allows us to integrate InSAR observations into operational storm surge models to analyze future flooding risks due to relative sea level change. We found that these subtle millimeter‐to‐centimeter subsidence features can substantially increase hurricane‐induced inundation, and passive flood mapping (known as the “bathtub” approach) can lead to inaccurate flood risk predictions.

Subsidence Characteristics of Hydrate-Bearing Sediments during Depressurization: Insights from Experimental and Discrete Element Method Simulations

Subsidence Characteristics of Hydrate-Bearing Sediments during Depressurization: Insights from Experimental and Discrete Element Method Simulations

Monitoring nonlinear large gradient subsidence in mining areas through SBAS-InSAR with PUNet and Weibull model fusion.

The subsidence of the earth's surface in mining areas is characterized by fast speed and large gradients. Conventional small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) monitoring can significantly underestimate results, making it challenging to capture the surface's temporal subsidence features. In this context, this paper proposes a method for monitoring subsidence in mining areas. It utilizes a phase unwrapping network (PUNet) and a fused Weibull model within the SBAS-InSAR framework to address nonlinear and large-gradient subsidence. The basic principle of this method is to first process the SAR images using the small baseline method to obtain the differential interferogram, utilizing the PUNet to obtain reliable large-gradient unwrapped phases. Next, the Weibull model parameters of each pixel are calculated based on the unwrapped phase, and the temporal subsidence of each point on the surface is determined using the calculated parameters. This method introduces a nonlinear model into the SBAS-InSAR solution, which is more consistent with the subsidence characteristics of mining areas. Through experimentation in a backfilled mining working face, the proposed method in this paper yields superior monitoring results compared to conventional approaches.

Impact of surface temperature changes on subgrade settlement of permafrost regions railway: a case study of the Qinghai-Tibet railway, China

ABSTRACT Parts of sections in permafrost regions of the Qinghai-Tibet Railway (QTR) have been deformed due to permafrost degradation, the global warming and human activities. Therefore, monitoring the subgrade settlement of the QTR in permafrost regions has been a major working in QTR maintenance. For this reason, this paper investigates the subsidence characteristics and derivative mechanisms of railway subgrades in the permafrost regions of the QTR. Additionally, it analyses the impact mechanisms of surface temperature changes on the subsidence of permafrost subgrades. Building on this investigation, a C-I-GWO-BP neural network prediction model was developed to forecast subgrade settlement in permafrost regions. The findings reveal a substantial correlation (R2 = 0.834) between the deformation of railway subgrades in permafrost regions and changes in surface temperature. Meanwhile，with the intensification of global warming and human activities, the subsidence of permafrost subgrade has shown an increasing trend year by year. The primary areas of deformation are predominantly concentrated in ice-rich permafrost regions, including rivers, lakes, and snowmelt runoff areas. Additionally, when compared to traditional BP neural network models, the C-I-GWO-BP neural network prediction model proposed in this article showcases diminished prediction errors and heightened accuracy in identifying optimal weights and thresholds. This model offers technical support for the efficient, accurate, and automated prediction of railway subgrade subsidence in permafrost regions.

Asymmetric analogous hyperbola model of overburden movement and its verification

The extraction of underground coal resources induces the fracture and movement of overlying strata, leading to geological hazards such as surface deformation, cracks, and even subsidence. Utilizing the analogous hyperbola model of overlying strata movement, we conducted a mechanical analysis to examine the asymmetric fracture mechanism resulting from coal seam mining in thick loose strata. An asymmetric analogous hyperbola model was established by introducing distinct virtual half-axis lengths (b). The thickness impact of thick loose layers (H) and bedrock layer (h) on the asymmetric movement of overlying rock during mining was also discussed. Similarity model tests were conducted to research the migration characteristics and surface subsidence patterns of overburdened rock and thick loose layers at different mining stages and validate the hypothesis of asymmetric overburdened rock migration. Additionally, the discrete element numerical model for thick and loose layers mining was established by using UDEC and discussed the asymmetric analogous hyperbola behaviour of overburden movement and surface subsidence. The comparison results show that the established asymmetric hyperbolic model can effectively predict the movement law of overlying strata and surface subsidence characteristics. Therefore, the proposed model can provide valuable theoretical support for predicting the movement patterns of overburden under thick loose layers and mitigating surface subsidence disasters.

Surface Subsidence Characteristics and Causes Analysis in Ningbo Plain by Sentinel-1A TS-InSAR

In recent years, the Ningbo Plain has experienced significant surface subsidence due to urbanization and industrialization, combined with the area’s unique geological and hydrological conditions. To study the surface subsidence and its causes in the Ningbo Plain, this study analyzed 166 scenes of Sentinel-1A SAR images between January 2018 and June 2023. The time series interferometric synthetic aperture radar (TS-InSAR) technique was used to acquire surface subsidence information in the area. The causes of subsidence were analyzed. The results show that: (1) the annual deformation rate of the Ningbo Plain ranges from −44 mm/yr to 12 mm/yr between 2018 and 2023. A total of 15 major subsidence zones were identified by using both the subsidence rate map and optical imagery. The most severe subsidence occurred in the northern industrial park of Cixi City, with a maximum subsidence rate of −37 mm/yr. The study reveals that the subsidence issue in the main urban area has been significantly improved compared to the 2017 subsidence data from the Ningbo Bureau of Natural Resources and Planning. However, three new subsidence areas have emerged in the main urban area, located, respectively, in Gaoqiao Town, Lishe Town, and Qiuyi Village, with maximum rates of −29 mm/year, −24 mm/year, and −23 mm/year, respectively. (2) The causes of subsidence were analyzed using various data, including land use data, geological data, groundwater-monitoring data, and transportation network data. It is found that a strong link exists between changes in groundwater levels, compressible layer thickness, and surface subsidence. The groundwater levels changes and the soft soil layer thickness are the main natural factors causing subsidence in the Ningbo Plain. Additionally, the interaction between static loads from large-scale industrial production and urban construction, along with the dynamic loads from transportation networks, contribute significantly to surface subsidence in the Ningbo Plain. The results from this study enhance the understanding of the driving factors of subsidence in the Ningbo Plain, which can provide necessary guidance for the economic development and decision-making in the region, helping to manage and potentially mitigate future subsidence issues.

Urban land subsidence monitoring and risk assessment using the point target based SBAS-InSAR method: a case study of Changsha City

ABSTRACT Changsha City is the political, economic and cultural centre of Hunan Province. In recent years, because of large-scale urban construction and special geological structure, land subsidence has become one of the main geological disasters in Changsha City, which seriously threatens the health of buildings and infrastructure. To solve this problem, this paper monitored the land subsidence in Changsha City from 2015 to 2021 using the Point Target (PT) based Small Baseline Subset InSAR (SBAS-InSAR), revealed the spatial distribution characteristics of land subsidence in the study area, studied the causes of subsidence, and carried out geological disaster risk assessment. The results show that the overall stability of Changsha City is dominated by uneven subsidence. The subsidence areas are mainly distributed in the north of Kaifu District, Moon Island, Malan Mountain Chuangzhi Park and the periphery of the main urban area. The maximum settlement rate is about 87 mm/year, and the cumulative settlement reaches 250 mm. The settlement monitoring results are basically consistent with the actual situation at the same time, which is mainly caused by engineering construction.

Research on the Mechanical Characteristics of Thick Alluvium on the Surface Subsidence Features of Thin Bedrock Deposits at Depth

Research on the Mechanical Characteristics of Thick Alluvium on the Surface Subsidence Features of Thin Bedrock Deposits at Depth

Spatio-Temporal Characteristics of Land Subsidence and Driving Factors Analysis in Shenzhen

Analyzing land subsidence using Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology holds significant importance for the secure development of urban areas. Shenzhen, being a crucial component of the Pearl River Delta, faces the threat of land subsidence, similar to most deltaic cities. Numerous studies have already indicated the presence of severe land subsidence in certain localities of Shenzhen. However, due to limitations in data scope and research methodologies, the comprehensive spatial-temporal distribution of land subsidence across the entire city of Shenzhen remains unclear. This study initially employed MT-InSAR technology to process a total of 534 Sentinel-1A SAR images from three different frames (P11F71, P113F71, P11F65), covering the entire city of Shenzhen. This processing resulted in the generation of subsidence rate maps and subsidence time series. Subsequently, the temporal evolution patterns of the subsidence were analyzed while significant subsidence regions were identified. By integrating information from optical images reflecting human activities on the Earth’s surface, the study deduced the subsidence mechanisms in various significant subsidence areas. Research findings indicate that land subsidence in Shenzhen is primarily caused by construction activities, with a concentration in the western coastal areas of Shenzhen, reaching a maximum rate of 80 mm/yr, located at the estuary of Dongbao River (113.770385, 22.745305). The cumulative subsidence from March 2017 to June 2023 amounts to 500 mm. The expansion of the Qinglinjing Reservoir has led to an increased demand for water, resulting in a significant rise in formation pressure and subsequent land subsidence. InSAR land subsidence monitoring and analysis in urban areas can address the spatial and temporal resolution limitations of traditional subsidence monitoring methods, providing effective recommendations for widespread subsidence prevention and control.

Analysis and Prediction of Urban Surface Transformation Based on Small Baseline Subset Interferometric Synthetic Aperture Radar and Sparrow Search Algorithm-Convolutional Neural Network-Long Short-Term Memory Model.

With the acceleration of urbanisation, urban areas are subject to the combined effects of the accumulation of various natural factors, such as changes in temperature leading to the thermal expansion or contraction of surface materials (rock, soil, etc.) and changes in precipitation and humidity leading to an increase in the self-weight of soil due to the infiltration of water along the cracks or pores in the ground. Therefore, the subsidence of urban areas has now become a serious geological disaster phenomenon. However, the use of traditional neural network prediction models has limitations when examining the causal relationships between time series surface deformation data and multiple influencing factors and when applying multiple influencing factors for predictive analyses. To this end, Sentinel-1A data from March 2017 to February 2023 were used as the data source in this paper, based on time series deformation data acquired using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique. A sparrow search algorithm-convolutional neural network-long short-term memory (SSA-CNN-LSTM) neural network prediction model was built. The six factors of temperature, humidity, precipitation, and ground temperature at three different depths below the surface (5 cm, 10 cm, and 15 cm) were taken as the input of the model, and the surface deformation data were taken as the output of the neural network model. The correlation between the spatial and temporal evolution characteristics of the ground subsidence in urban areas and various influencing factors was analysed using grey correlation analysis, which proved that these six factors contribute to some extent to the deformation of the urban surface. The main urban area of Hohhot City, Inner Mongolia Autonomous Region, was used as the study area. In order to verify the efficacy of this neural network prediction model, the prediction effects of the multilayer perceptron (MLP), backpropagation (BP), and SSA-CNN-LSTM models were compared and analysed, with the values of the correlation coefficients of the feature points of A1, B1, and C1 being in the range of 0.92, 0.83, and 0.93, respectively. The results show that compared with the traditional MLP and BP neural network models, the SSA-CNN-LSTM model achieves a higher performance in predicting time series surface deformation data in urban areas, which provides new ideas and methods for this area of research.

Prediction model for three-dimensional surface subsidence of salt cavern storage with different shapes

Previous prediction models have faced challenges in characterizing the three-dimensional (3D) surface subsidence of salt caverns with different shapes. In order to overcome these limitations, this study proposes a novel theoretical model based on stochastic medium theory. This model provides a theoretical formula for characterizing the 3D surface subsidence of salt caverns with different shapes (oblate spherical, spherical, and vertical ellipsoidal). By employing the Particle Swarm Optimization (PSO) algorithm to address the theoretical model, it can generate a three-dimensional settlement surface and subsequently compare it with the numerical results. The results demonstrate a strong consistency between the theoretical and numerical results. By conducting the parameter sensitivity analysis, it becomes evident that the shape of salt caverns holds paramount significance and should not be disregarded in the study of 3D surface subsidence. As the main influence angle increases and the burial depth decreases, the maximum settlement value of the salt cavern continues to increase while the maximum influence radius continues to decrease. The maximum settlement value and maximum influence radius of salt caverns gradually increase with the increase of the span. This suggests that the proposed theoretical model can effectively and intuitively describe the 3D surface subsidence characteristics of salt caverns.

Time-series InSAR reveals new insights into land subsidence in the Yellow River Delta, China

In the past decade, the synthetic aperture radar interferometry (InSAR) technique has been extensively employed in deltas, making it possible to obtain more subsidence signals. However, the spatiotemporal nonlinearity of land subsidence often results in inaccurate estimations. Based on 72 Sentinel-1A images captured from 2016 to 2021 and the time-series persistent scatterer-interferometric SAR (PS-InSAR) method, we comprehensively analyze the temporal and spatial dynamics of recent land subsidence in the Yellow River Delta (YRD). The findings suggest a notable spatial shift in the subsidence funnel area within this delta region over the past 6 years (2016–2021), with gradual westward movement from the salt fields in the northeast to those in the central north. Additionally, with 2020 as the demarcation point, there was a notable change in magnitude, initially exhibiting acceleration at −333 mm/yr, followed by deceleration at −231 mm/yr and culminating in renewed acceleration at −413 mm/yr. The subsidence characteristics may be attributed to changes in the underground brine reserves, leading to consolidation of the confined aquifer. Furthermore, the ground subsidence trend suddenly slowed at the end of 2019, which we speculate is related to the significant reduction in human activities caused by COVID-19 prevention and control measures.

Monitoring Analysis of Urban Subsidence in Northern Henan Province Based on TS-InSAR Technology

The protracted and pervasive incidence of land subsidence emerges as a pivotal factor exerting a substantial impact on the sustainable development of urban landscapes. A nuanced comprehension of the spatiotemporal evolution characteristics of land subsidence within the Northern Henan Plain assumes paramount significance in the context of mitigating potential urban geological disasters. This study endeavors to redress the deficiency in information concerning the temporal and spatial evolution characteristics of enduring deformation in cities within the northern plain of Henan Province. To this end, the authors leveraged Sentinel-1A radar data processed through persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technology to elucidate the distribution patterns of ground deformation and temporal evolution characteristics within the expansive 24-scene coverage research area. Empirical findings illuminate conspicuous surface deformation in Anyang, Puyang, and Hebi throughout the monitoring period. Spatially, land subsidence in the study area predominantly clusters in the suburban peripheries of the cities, with Hebi and Puyang registering a maximum subsidence rate exceeding 25 mm per annum. Temporally, land subsidence manifests predominantly during autumn and winter, whereas spring and summer display relatively stable land subsidence interspersed with a slight ground uplift. In order to rectify the spatial disparities observed between leveling data and PS-InSAR monitoring data, this experiment employed an averaging procedure on the PS-InSAR monitoring data, subsequently subjecting it to comparative analysis with the leveling data. Additionally, through the integration of the singular spectrum analysis (SSA) method and the time series deformation model, this study aspires to attain a comprehensive understanding of the temporal dynamics manifested in the PS-InSAR monitoring outcomes, while concurrently elucidating the factors influencing the observed deformations. Ultimately, this analysis discloses that the monitoring outcomes derived via PS-InSAR technology exhibit a root mean square error of ±12.9 mm and a standard deviation of ±13.31 mm. These statistical metrics furnish valuable insights into the precision and consistency of the PS-InSAR monitoring data. Drawing upon a comparative scrutiny of on-site data and historical remote sensing imagery within the study area, it has been discerned that excessive groundwater extraction and expansive surface engineering initiatives stand as the principal instigators of land subsidence in the research domain. Consequently, this experiment assumes the role of a salient reference for the mitigation of urban ground subsidence within the study area.