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This study addresses the issue of localized ground subsidence and its effect on buried pipelines. Timoshenko beam model, placed on a Pasternak foundation, is used to analyze the internal force response of buried pipelines under foundation subsidence. The load on the pipeline, resulting from localized ground subsidence, is assumed to be symmetric. The load distribution on the buried section of the pipeline is represented using a McLaurin series. Analytical solutions for the deflection and bending moment of the pipeline under arbitrary symmetrical loading are derived based on the theory of elastic foundation beams. Additionally, the accuracy of the analytical solutions is verified through comparisons with experimental studies, finite element analysis, and existing theories. In the analysis, the shear modulus of the Timoshenko beam is set to infinity, resulting in the degeneration of the model into the Euler-Bernoulli beam. The effect of the shear modulus and diameter-span ratio (D/l) of the Timoshenko beam is investigated in the parameter analysis, and the applicability for both beam models is determined. The results indicate that, for buried pipelines with a diameter-span ratio greater than 0.1, the Timoshenko beam model provides more accurate deflection calculations than the Euler-Bernoulli beam model. [ABSTRACT FROM AUTHOR]

As the extraction of mineral resources intensifies, ground subsidence in mining areas has escalated, posing substantial challenges to sustainable development and operational safety. This subsidence, resulting directly from mining activities, significantly compromises the safety of nearby residents by damaging residential structures and infrastructure. Thus, developing precise and dependable methods for predicting ground subsidence is crucial. This study introduces an innovative Cabs-Unet model, which enhances the U-Net architecture by integrating a Convolutional Block Attention Module (CBAM) and Depthwise Separable Convolutions (DSC). This model aims to predict the spatiotemporal dynamics of the Interferometric Synthetic Aperture Radar (InSAR) time series. Employing Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS InSAR) technology, we gathered and validated data on ground subsidence at the Pengzhuang coal mine from May 2017 to November 2021, covering 130 scenes, with its accuracy corroborated by levelling survey results. An empirical evaluation of the Cabs-Unet model in two distinct subsidence zones demonstrated superior performance over conventional methods like Convolutional Long Short-Term Memory (ConvLSTM) and Predictive Recurrent Neural Network (PredRNN), with Root Mean Square Error (RMSE) values of 1.44 and 1.70, respectively. These findings highlight the model’s efficacy in accurately predicting spatiotemporal InSAR ground subsidence. Further predictive analysis using InSAR data indicated an expected increase in subsidence, projecting cumulative declines of −457 mm in Area A and −1278 mm in Area B by 17 July 2022. Our model proves effective in assessing subsidence, promptly detecting potential risks and facilitating the rapid implementation of risk mitigation strategies. [ABSTRACT FROM AUTHOR]

The disturbance of shallow-buried tunnel excavation with large section is the main factor causing the ground subsidence, and the coupling effect between double-line tunnels makes the ground subsidence regularity more complicated. Based on the actual project, the law of ground subsidence and its influencing factors during the excavation of double-line tunnel under different compressive states are discussed by numerical simulation method. The results show that the ground subsidence curve of the double-line tunnel is asymmetrically W-shaped, and the maximum ground subsidence is inclined to the side with larger buried-depth; Under the non-biased state, the axial distance of the double-line tunnel has the most significant effect on the ground subsidence, while under the biased state, the influence factor is the transverse bias angle, and the maximum ground subsidence increases with the increase of the transverse bias angle. Based on the numerical results, three indexes are proposed to quantitatively describe the relationship respectively between the ground subsidence and the excavation section area, the axial distance of the double-line tunnel and the bias angle during the excavation of the double-line tunnel, and the indexes can be used to predict the ground subsidence triggered by the excavation of shallow-buried tunnel under the biased state. Furthermore, the study results can provide reference values for tunnel construction under similar conditions. [ABSTRACT FROM AUTHOR]

The process of land subsidence deals with the removal of excess pore water pressure and the compaction of soil mass under the effect of natural or human factors. The detrimental effects of land subsidence include changes in the morphology of the land surface and the formation of earth fissures, as well as structural and non-structural damage to surface and subsurface infrastructures. In Joshimath on 2nd January 2023, an incidence of ground subsidence occurred which damaged many buildings and infrastructures. This study addresses the exploratory work on rapid visual damage assessment of buildings based on method developed by National Disaster Management Authority (NDMA) and European Macroseismic Scale (EMS) − 98. The building vulnerability was assessed using building attributes like typology, number of storeys, area, construction materials, occupancy, configuration, construction practice etc. The damage attributes considered are based on siting issues, soil and foundation conditions, architectural features and elements, structural aspects and components, material & construction details, crack monitoring etc. In the critical buildings, cracks were monitored using crack meters. This study concludes out of total 2364 building surveyed, 37%, 42%, 20%, 1% buildings fall under "Usable", "Further Assessment", "Unusable", "to be demolished", grades respectively. [ABSTRACT FROM AUTHOR]

Objective The spatial distribution of ground subsidence in the coastal area of Tianjin was predicted using a random forest machine learning model, in which the performance and significance of the variables were evaluated. Methods The random forest model was trained and validated in this study using datasets of ground subsidence in 2020, aquifer lithology, water level differences in aquifers in 2020, and hydrogeological parameters. Results The results demonstrate the effectiveness of the random forest model for fitting and predicting ground subsidence (R2=0.98, RMSE=0.52 mm). Moreover, it is found that water level difference emerges as the most influential factor affecting ground subsidence, followed by lithology and hydrogeological parameters. Conclusion The present study introduces several novel contributions: ① utilization of spatial distribution data for training ground subsidence models; ② identification of significant controlling factors based on physical mechanisms; ③ assessment of the relative importance of these controlling factors. Additionally, this paper highlights the limitations and future directions in ground subsidence research, offering valuable insights for the rapid and accurate prediction of ground subsidence using the random forest model.

Abstract Ground subsidence caused by extraction of longwall panels has always been a great concern all over the world. Conventional longwall mining system (CLMS) gives rise to wavy subsidence causing great damage to surface structures. A coal mine in Shanxi, China, utilizes a split-level longwall layout (SLL) for a sub-horizontal No. 8 coal seam to improve the cavability of mudstone interlayer and top coal. This layout, however, also produced unexpectedly favorable surface subsidence. Subsidence of No. 6 and No. 8 longwall panels was monitored while mining was conducted. Field instrumentation and numerical simulation were carried out. It is demonstrated that an asymmetric subsidence profile with stepped subsidence and cracks occurred on the tailgate side but relatively mild and smooth deformation on the other. Due to elimination of conventional parallelepiped gate pillar, No. 6 and No. 8 gobs were connected. Extraction of two SLL panels acted as one supercritical panel. The maximum possible subsidence was reached which lowers the likelihood of potential future secondary subsidence as underground gob fractures and voids have closed. Therefore, SLL is more favorable for post-mining land reuse as gobs are more consolidated underground.

According to studies by relevant scholars, the ground subsidence rate in Sanhe City, Hebei, increased gradually from 2003 to 2016, with the most severe subsidence observed in the Yanjiao area , which has become contiguous with the subsidence area in Tongzhou, Beijing. However, the development trend of ground subsidence disasters in Sanhe City after 2016 remains unclear. With the implementation of national policies such as the integration of Beijing-Tianjin-Hebei region and the construction of Beijing’s sub-center, it is crucial to identify the development and evolution characteristics of land subsidence disasters in Sanhe City and analyze their causes to ensure the urban safety and sustainable development of Sanhe City. In this context, the authors used SBAS-InSAR technology to interpret the evolution characteristics of ground subsidence in Sanhe City from 2018 to 2020, and analyzed several inducing factors that led to land subsidence in Sanhe City, summarizing the main causes. Through this study, the spatial distribution and evolutionary characteristics of land subsidence disasters in Sanhe City were grasped: the western part of the city exhibits severe subsidence, while the eastern part is less affected, with a focus on the overall characteristics of key subsidence areas. Two main subsidence areas were identified, namely, the Yanjiao Town subsidence area with three subsidence funnels and the DuanJialing town subsidence area with one subsidence funnel, with the former being the most severely affected. From 2018 to 2020, the overall trend of ground subsidence disasters in Sanhe City showed a slowdown. Through comparative analysis, it was determined that severe overexploitation of groundwater, soil properties, urbanization development, and population growth are the main factors contributing to the occurrence and development of ground subsidence disasters in Sanhe City. The research results of this paper will provide reference for the prevention and control of ground subsidence disasters in the region.

Ground subsidence caused by extraction of longwall panels has always been a great concern all over the world. Conventional longwall mining system (CLMS) gives rise to wavy subsidence causing great damage to surface structures. A coal mine in Shanxi, China, utilizes a split-level longwall layout (SLL) for a sub-horizontal No. 8 coal seam to improve the cavability of mudstone interlayer and top coal. This layout, however, also produced unexpectedly favorable surface subsidence. Subsidence of No. 6 and No. 8 longwall panels was monitored while mining was conducted. Field instrumentation and numerical simulation were carried out. It is demonstrated that an asymmetric subsidence profile with stepped subsidence and cracks occurred on the tailgate side but relatively mild and smooth deformation on the other. Due to elimination of conventional parallelepiped gate pillar, No. 6 and No. 8 gobs were connected. Extraction of two SLL panels acted as one supercritical panel. The maximum possible subsidence was reached which lowers the likelihood of potential future secondary subsidence as underground gob fractures and voids have closed. Therefore, SLL is more favorable for post-mining land reuse as gobs are more consolidated underground. [ABSTRACT FROM AUTHOR]

Coniolo and Brusaschetto, are two small towns located in the Monferrato area of the Alessandria Province, northern Italy. These communities have similar histories related to development and subsequent abandonment of marl quarry activity that began more than a century ago and continued until recently. Quarrying occurred until soil conditions, water infiltration, and excessive depth made cost of extracting and7 lifting material prohibitive. Quarries consisted of tunnels located directly beneath the towns at about 150 m below ground surface. Collapse of the tunnels led to surface subsidence and destruction of overlying homes and much of the municipal infrastructure. In the early Twentieth Century, regulations pertaining to mine and quarry safety were typically deficient, entirely absent, or not followed. Extractive activities of non-energy mineral resources from quarries and mines were and continue to be widespread in Italy, which currently ranks fifth among what are now countries of the European Union (EU). Mining sites are present in all regions of Italy, particularly in the northern part of the country and along coasts, often in areas of geohydrogeological risk. Consequences of anthropogenic pressures that alter the natural environment, such as the physical size of aquifer drawdowns, are linked to issues for a number of extractive sites across the country. This report analyzes historical and technical documents, conducts a geomorphological analysis of hilly slopes surrounding these communities, and examines urban planning and geophysical surveys to determine the impact of subsurface quarrying activities on the overlying ground surface. The study highlights significant problems that are applicable to other localities globally. This research demonstrates: (a) the importance of geological considerations to development and abandonment of mining activity in inhabited areas; (b) the importance of establishing and following safety protocols; and (c) the manner in which economic interests can take precedence over the well-being and lives of those employed to extract resources. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Earth Science (1001-8166) is the property of Advances in Earth Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The issue of uneven ground settlement caused by the excavation of subway tunnels represents a significant challenge in the design and construction of subway projects. This paper examined the fractal characteristics of surface settlement caused by subway excavation, employing wavelet transform and fractal theory. Firstly, the noise reduction effects of different wavelet functions, decomposition levels, threshold functions, and threshold selection rules were evaluated using the SNR and RMSE. Subsequently, 291 data points were derived from 18 interpolation points through fractal interpolation, representing a utilization of only 18% of the original data, to enhance the original monitoring data information by a factor of 2.94. The average relative error between the fractal interpolation results and the monitoring data was approximately 13%, which was indicative of a high degree of accuracy. Finally, the fractal dimension of the monitoring curves under different monitoring frequencies was calculated using the box-counting method. The denoising effect of the dbN wavelet function was found to be superior to that of the symN wavelet function in the denoising process of subway construction surface deformation monitoring data. Furthermore, the hard threshold method was observed to be more effective than the soft threshold method. As the monitoring frequency decreased, the fractal dimension of the deformation curves showed an overall decreasing trend. This indicated that high-frequency monitoring could capture more details and complexity of the surface settlement, while low-frequency monitoring led to a gradual flattening of the curves and a decrease in details. [ABSTRACT FROM AUTHOR]

Within the framework of the elastoplastic model, the authors propose a theoretical description procedure for troughs of ground movements with high gradients of subsidence. The approach uses reduction of shear modulus in rock mass bordering a movement trough. The pre-requisite for the shear modulus reduction in such areas is the increased mining-induced pressure, which initiates damage and aggravates the defect structure of rocks through widening of micro-cracks, inter-grain contacts, etc. The estimation procedure of the induced reduction factor for the shear modulus uses the comparative analysis of ground slopes. This approach enables a reliable calculation of ground movements with high gradients of subsidence, and ensures the adequate stress–strain analysis of undermined rock mass, including location of plastic deformation zones. [ABSTRACT FROM AUTHOR]

According to studies by relevant scholars, the ground subsidence rate in Sanhe City, Hebei, increased gradually from 2003 to 2016, with the most severe subsidence observed in the Yanjiao area, which has become contiguous with the subsidence area in Tongzhou, Beijing. However, the development trend of ground subsidence disasters in Sanhe City after 2016 remains unclear. With the implementation of national policies such as the integration of Beijing-Tianjin-Hebei region and the construction of Beijing's sub-center, it is crucial to identify the development and evolution characteristics of land subsidence disasters in Sanhe City and analyze their causes to ensure the urban safety and sustainable development of Sanhe City. In this context, the authors used SBAS-InSAR technology to interpret the evolution characteristics of ground subsidence in Sanhe City from 2018 to 2020, and analyzed several inducing factors that led to land subsidence in Sanhe City, summarizing the main causes. Through this study, the spatial distribution and evolutionary characteristics of land subsidence disasters in Sanhe City were grasped: the western part of the city exhibits severe subsidence, while the eastern part is less affected, with a focus on the overall characteristics of key subsidence areas. Two main subsidence areas were identified, namely, the Yanjiao Town subsidence area with three subsidence funnels and the DuanJialing town subsidence area with one subsidence funnel, with the former being the most severely affected. From 2018 to 2020, the overall trend of ground subsidence disasters in Sanhe City showed a slowdown. Through comparative analysis, it was determined that severe overexploitation of groundwater, soil properties, urbanization development, and population growth are the main factors contributing to the occurrence and development of ground subsidence disasters in Sanhe City. The research results of this paper will provide reference for the prevention and control of ground subsidence disasters in the region. [ABSTRACT FROM AUTHOR]

Abstract The southeast Texas (SETX) coastal area, owing to its unique geographical location and geological attributes, is facing a spectrum of geological challenges, such as ground subsidence, flooding, and coastal erosion. This study endeavors to evaluate the recent instances of ground subsidence and their associated rates, focusing on comprehending their implications for flooding within SETX. Employing the Persistence Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique, this study employs Sentinel-1 SAR satellite data with descending orbit observations spanning from January 2020 to March 2023. Our findings indicate that both the northwestern and eastern regions of Houston have been settled with rates up to 2 cm/year. This settlement trend is consistent with data derived from GPS and groundwater level measurements. This investigation explicitly shows the substantial temporal and spatial variations in subsidence rates, predominantly influenced by localized groundwater extraction due to urbanization (e.g., population growth, land development, etc.). By advocating for the integration of InSAR, GPS, and groundwater measurements, this research aspires to make valuable contributions toward the mitigation of subsidence and flood-related hazards in the SETX area.

The Raniganj coalfield, which is the oldest coal mine in India, is susceptible to ground subsidence. For the purpose of detecting surface deformation, this work makes use of field surveys, interferometric synthetic aperture radar (InSAR), and Differential GPS (DGPS). The study utilised Sentinel-1 InSAR data spanning from 2017 to 2023. PS-InSAR was used for both ascending and descending datasets, while SBAS-InSAR was used for descending datasets alone. Records show a maximum subsidence rate of −21.18 mm/year. Three surface deformation maps were generated from time-series assessments of individual locations, revealing regions undergoing significant changes. Known mining collapse locations and continuing deformation zones were designated with four differential GPS stations. By analysing the DGPS data with the GAMIT/GLOBK software, we were able to measure surfaces that were undergoing rapid deformation. Using Google Earth Engine (GEE), we generated thermal maps to delve deeper into the coal fire activity. We found coal bed methane (CBM) mining causing substantial subsidence in Kataberia and the surrounding areas. Shyamsundarpur and New Kenda are impacted by mining voids and coal fire-induced subsidence, respectively. The results of this study give the Raniganj region’s decision-making procedures more credibility in terms of minimising and controlling geological dangers.

ABSTRACTGround subsidence is a common geological hazard in urban areas that endangers the safety of infrastructure, such as subways. In this study, the ground subsidence risk assessment method considering both ground subsidence intensity and susceptibility is proposed and applied to assess ground subsidence risk of the Shanghai Metro network. Initially, PS-InSAR is used for the ground subsidence survey in the Shanghai Metro area. Subsequently, ten subsidence causal factors are collected, and the LightGBM machine learning algorithm is employed to conduct the ground subsidence susceptibility analysis. Then, a risk matrix is introduced to define ground subsidence risk by combining subsidence intensity and susceptibility. Finally, the risk map is generated in ArcGIS and classified into five levels. The assessment results were used to identify ground subsidence risk at different scales. The results indicate that the risk is higher in the southwest part of the study area, and the ground subsidence risk of the metro network exhibits a regional-related characteristic. On-site investigations were conducted to verify the results. The method enables fast ground subsidence assessment over a large area at a low cost and the assessment results can provide data for the prevention and management of ground subsidence hazards in the city.

The southeast Texas (SETX) coastal area, owing to its unique geographical location and geological attributes, is facing a spectrum of geological challenges, such as ground subsidence, flooding, and coastal erosion. This study endeavors to evaluate the recent instances of ground subsidence and their associated rates, focusing on comprehending their implications for flooding within SETX. Employing the Persistence Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique, this study employs Sentinel-1 SAR satellite data with descending orbit observations spanning from January 2020 to March 2023. Our findings indicate that both the northwestern and eastern regions of Houston have been settled with rates up to 2 cm/year. This settlement trend is consistent with data derived from GPS and groundwater level measurements. This investigation explicitly shows the substantial temporal and spatial variations in subsidence rates, predominantly influenced by localized groundwater extraction due to urbanization (e.g., population growth, land development, etc.). By advocating for the integration of InSAR, GPS, and groundwater measurements, this research aspires to make valuable contributions toward the mitigation of subsidence and flood-related hazards in the SETX area. [ABSTRACT FROM AUTHOR]

Ground subsidence often occurs over a large area. Although traditional monitoring methods have high accuracy, they cannot perform wide-area ground deformation monitoring. Synthetic Aperture Radar (SAR) interferometry (InSAR) technology utilizes phase information in SAR images to extract surface deformation information in a low-cost, large-scale, high-precision, and high-efficiency manner. With the increasing availability of SAR satellite data and the rapid development of InSAR technology, it provides the possibility for wide-area ground deformation monitoring using InSAR technology. Traditional time-series InSAR methods have cumbersome processing procedures, have large computational requirements, and rely heavily on manual intervention, resulting in relatively low efficiency. This study proposes a strategy for wide-area InSAR multi-scale deformation monitoring to address this issue. The strategy first rapidly acquires ground deformation information using Stacking technology, then identifies the main subsidence areas by setting deformation rate thresholds and visual interpretation, and finally employs advanced TS-InSAR technology to obtain detailed deformation time series for the main subsidence areas. The Turpan Basin in Xinjiang, China, was selected as the study area (7474.50 km2) to validate the proposed method. The results are as follows: (1) The basin is generally stable, but there is ground subsidence in the southern plain area, mainly affecting farmland. (2) From 2016 to 2019, the maximum subsidence rate in the farmland area was approximately 0.13 m/yr, with a maximum cumulative subsidence of about 0.25 m, affecting a total area of approximately 952.49 km2. The subsidence mainly occurred from late spring to mid-autumn, while lifting or subsidence mitigation occurred from late autumn to early spring. The study also analyzed the impacts of rainfall, geographical environment, and human activities on subsidence and found that multiple factors, including water resource reduction, overexploitation, geological characteristics, and the expansion of human activities, contributed to the subsidence problem in the Turpan Basin. This method contributes to wide-area InSAR deformation monitoring and the application of InSAR technology in engineering. [ABSTRACT FROM AUTHOR]

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ground deformation associated with naturaland anthropogenic activities can be damaging for infrastructure and can cause enormous economic loss, particularly in developing countries which lack measuring instruments. Remote sensing techniques like interferometric synthetic aperture radar (InSAR) can thus play an important role in investigating deformation and mitigating geohazards. Rawalpindi and Islamabad are twin cities in Pakistan with a population of approximately 5.4 million, along with important government and private entities of national and international interest. In this study, we evaluate rapid paced subsidence in this area using a modified small baseline subset technique with Sentinel-IA imagery acquired between 2015 and 2022. Our results show that approximately 50 mm/year subsidence occurs in the older city of Rawalpindi, the most populated zone. We observed that subsidence in the area is controlled by the buried splays of the Main Boundary Thrust, one of the most destructive active faults in the recent past. We suggest that such rapid subsidence is most probably due to aggressive subsurface water extraction. It has been found that, despite provision of alternate water supplies by the district government, a very alarming number of tube wells are being operated in the area to extract ground water. Over 2017-2021, field data showed that near-surface aquifers up to 50-60 m deep are exhausted, and most of the tube wells are currently extracting water from depths of approximately 150-160 m. The dropping water level is proportional to the increasing number of tube wells. Lying downstream of tributaries originating from the Margalla and Murree hills, this area has a good monsoon season, and its topography supports recharge of the aquifers. However, rapid subsidence indicates a deficit between water extraction and recharge, partly due to the limitations inherent in shale and the low porosity near the surface lithology exposed in the area. Other factors amplifying the impacts are fast urbanization, uncontrolled population growth, and non-cultivation of precipitation in the area. [ABSTRACT FROM AUTHOR]

This article assesses the causes behind the ongoing subsidence-induced instabilities in Dar village, which is situated over deposits of palaeo-landslides in Kumaun Himalaya. A detailed field investigation has been carried out in and around the study area to identify the indications of subsidence there and to understand the factors responsible for the activation of the same. Subsequently, the geotechnical characterisations of overburden slope material and numerical slope stability assessment have been performed to substantiate the field observations. It has been found that the village is situated on a slope comprising weak material ~90% finer particles (sand, silt and clay) rich in disintegrated micaceous sediments having low cohesion and plasticity index. Including this, the physiographical characteristics of the slope make it vulnerable to mass movement. The slope stability assessment also indicated the critical state of the slope with a factor of safety 1.05, posing vulnerability to sliding failure. However, it is evident that modification of slope geometry with the construction of the road from Sobla to Tidang, which passes through the base of the village, and consequently, modification of the hydrological characteristics in the study area are the most important contributing factors to the ongoing subsidence in Dar. Since the village is at risk of a possible disaster, preliminary mitigation measures are also proposed, which will be helpful in minimising the risk to life and property. [ABSTRACT FROM AUTHOR]

Land subsidence, resulting from natural or human activities, is a global environmental geological disaster. The Interferometric Synthetic Aperture Radar (InSAR) time-series analysis technique offers high spatial and continuous temporal resolution, providing data and a foundation for investigating regional land subsidence and its evolution mechanism. Beijing Capital International Airport (BCIA) has experienced uneven land subsidence since 1935, together with severe fissures significantly affecting its normal operations. In this study, the time-series InSAR method was successfully applied to monitor the gradual increase in uneven local subsidence and ground fissures activity at BCIA from June 2003 to March 2023. Initially, ENVISAT-ASAR, Cosmo-SkyMed, and Sentinel-1 data were processed by time-series InSAR techniques to generate deformation rate maps and time series for the airport area. Subsequently, a comparison was made between the displacement time series from InSAR and ground leveling measurements to assess the accuracy of InSAR-derived measurements. Through a comprehensive analysis of the distribution characteristics of land subsidence at the airport, a long-standing ground fault was located within the airport was identified. A preliminary discussion on the development status of this ground fissure was carried out based on the visual interpretation of optical images. Lastly, the inducing factors and evolutionary conditions of land subsidence were discussed. This case demonstrates the applicability of InSAR technology in identifying and monitoring geological processes such as land subsidence and ground fissure activities. It provides a scientific approach to exploring and studying the causes and formation mechanisms of land subsidence and ground fissures in the Beijing Capital Airport area. [ABSTRACT FROM AUTHOR]

Underground coal-mining-induced ground subsidence deformation is a common geological disaster impacting buildings, transportation and water supplies. Models predicting ground subsidence dynamically with high precision are important for the prevention of damage derived from ground subsidence. In this paper, the Hook function is utilized to develop a model describing the velocity of ground subsidence due to underground coal mining. Based on the subsidence velocity model, a dynamic subsidence model is established by taking an integral of the velocity model. Coefficients of the model, which depend on maximum subsidence, maximum subsidence velocity and the time corresponding to the maximum subsidence velocity, are related to the geological and mining conditions of the coal seam being investigated. A Levenberg–Marquardt-algorithm-based method is also proposed to calculate the optimal model coefficients based on subsidence velocity observations. Four continuously operating Global Navigation Satellite System (GNSS) stations were constructed above a typical longwall coal mining working face in the Jining mining area, China. These GNSS stations collected subsidence observations over two years, which were used to validate the developed prediction model. The results show that the root-mean-square (RMS) of the model-predicted ground subsidence error is 56.1 mm, and the maximum relative error is 2.5% for all four GNSS stations, when the ground subsidence is less than 6000 mm. [ABSTRACT FROM AUTHOR]

This research evaluates the coastal vulnerability of Shanghai, evaluates the effectiveness of existing natural habitats in reducing that vulnerability, and, finally, provides recommendations to improve the resilience of the coastal areas. Shanghai is an important economic center that is home to a large population. However, the combination of ground subsidence, rising sea levels, and more frequent coastal flooding due to tropical cyclones poses escalating climate risks for Shanghai, demanding urgent mitigation measures. The InVEST Coastal Vulnerability Model was used in this study to assess Shanghai's coastal vulnerability under the current situation and various scenarios that simulated the absence of natural habitats. The assessment results were analyzed through a comparison between different scenarios and spatial aggregation analysis. This study pinpointed highly vulnerable areas, primarily located on the east coast of Chongming Island, the east and northeast coasts of Hengsha Island, and the east coast of the mainland of Shanghai. These areas need to be prioritized for intervention. Also, it demonstrated the effectiveness of existing natural habitats in reducing coastal vulnerability, with large green spaces and salt marshes playing a greater role compared to small green spaces. This is the first study applying the InVEST Coastal Vulnerability Model to Shanghai, demonstrating the model's potential in providing valuable information regarding coastal protection against the impacts of climate change in Shanghai. Insights from the findings of this study are useful in crafting sustainable land-use policies and plans for Shanghai. [ABSTRACT FROM AUTHOR]

We develop a moving-mesh permafrost model that contains a ground subsidence computation module to estimate the effects of ground subsidence on permafrost simulation under different warming scenarios. Including the ground subsidence process in the permafrost simulation produces only a relatively small improvement in the simulation performance of the ground temperature field, as validated by observations from two sites on the Qinghai–Tibetan Plateau (QTP). It is shown that ignoring ground subsidence tends to achieve a larger active layer thickness (ALT) but a smaller original thickness of permafrost that has thawed when simulating permafrost changes in a warming climate. The heat consumed by permafrost changes will be underestimated in simulations that do not consider ground subsidence. The effects that ground subsidence exerts within the permafrost simulation are clearly demonstrated under a strong warming scenario, which will influence the global energy budget. Projections indicate that the permafrost in the continuous permafrost area of the QTP may be close to the phase transition temperature to become zero thermal gradients in 2030–2040 under the SSP5-8.5 scenario, and there will be a great risk of ground subsidence by that stage. For permafrost regions with rich ground ice, the downward propagating temperature signals caused by ground subsidence are more attenuated. However, the heat calculation error will be larger in a simulation that does not consider ground subsidence there. This study quantifies the effects of ground subsidence, which can provide a better understanding of the permafrost thaw and energy budget of the QTP. [ABSTRACT FROM AUTHOR]

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.

Coniolo and Brusaschetto, are two small towns located in the Monferrato area of the Alessandria Province, northern Italy. These communities have similar histories related to development and subsequent abandonment of marl quarry activity that began more than a century ago and continued until recently. Quarrying occurred until soil conditions, water infiltration, and excessive depth made cost of extracting and7 lifting material prohibitive. Quarries consisted of tunnels located directly beneath the towns at about 150 m below ground surface. Collapse of the tunnels led to surface subsidence and destruction of overlying homes and much of the municipal infrastructure. In the early Twentieth Century, regulations pertaining to mine and quarry safety were typically deficient, entirely absent, or not followed. Extractive activities of non-energy mineral resources from quarries and mines were and continue to be widespread in Italy, which currently ranks fifth among what are now countries of the European Union (EU). Mining sites are present in all regions of Italy, particularly in the northern part of the country and along coasts, often in areas of geohydrogeological risk. Consequences of anthropogenic pressures that alter the natural environment, such as the physical size of aquifer drawdowns, are linked to issues for a number of extractive sites across the country. This report analyzes historical and technical documents, conducts a geomorphological analysis of hilly slopes surrounding these communities, and examines urban planning and geophysical surveys to determine the impact of subsurface quarrying activities on the overlying ground surface. The study highlights significant problems that are applicable to other localities globally. This research demonstrates: (a) the importance of geological considerations to development and abandonment of mining activity in inhabited areas; (b) the importance of establishing and following safety protocols; and (c) the manner in which economic interests can take precedence over the well-being and lives of those employed to extract resources.

The issue of uneven ground settlement caused by the excavation of subway tunnels represents a significant challenge in the design and construction of subway projects. This paper examined the fractal characteristics of surface settlement caused by subway excavation, employing wavelet transform and fractal theory. Firstly, the noise reduction effects of different wavelet functions, decomposition levels, threshold functions, and threshold selection rules were evaluated using the SNR and RMSE. Subsequently, 291 data points were derived from 18 interpolation points through fractal interpolation, representing a utilization of only 18% of the original data, to enhance the original monitoring data information by a factor of 2.94. The average relative error between the fractal interpolation results and the monitoring data was approximately 13%, which was indicative of a high degree of accuracy. Finally, the fractal dimension of the monitoring curves under different monitoring frequencies was calculated using the box-counting method. The denoising effect of the dbN wavelet function was found to be superior to that of the symN wavelet function in the denoising process of subway construction surface deformation monitoring data. Furthermore, the hard threshold method was observed to be more effective than the soft threshold method. As the monitoring frequency decreased, the fractal dimension of the deformation curves showed an overall decreasing trend. This indicated that high-frequency monitoring could capture more details and complexity of the surface settlement, while low-frequency monitoring led to a gradual flattening of the curves and a decrease in details.

Ground deformation associated with natural and anthropogenic activities can be damaging for infrastructure and can cause enormous economic loss, particularly in developing countries which lack measuring instruments. Remote sensing techniques like interferometric synthetic aperture radar (InSAR) can thus play an important role in investigating deformation and mitigating geohazards. Rawalpindi and Islamabad are twin cities in Pakistan with a population of approximately 5.4 million, along with important government and private entities of national and international interest. In this study, we evaluate rapid paced subsidence in this area using a modified small baseline subset technique with Sentinel-1A imagery acquired between 2015 and 2022. Our results show that approximately 50 mm/year subsidence occurs in the older city of Rawalpindi, the most populated zone. We observed that subsidence in the area is controlled by the buried splays of the Main Boundary Thrust, one of the most destructive active faults in the recent past. We suggest that such rapid subsidence is most probably due to aggressive subsurface water extraction. It has been found that, despite provision of alternate water supplies by the district government, a very alarming number of tube wells are being operated in the area to extract ground water. Over 2017–2021, field data showed that near-surface aquifers up to 50–60 m deep are exhausted, and most of the tube wells are currently extracting water from depths of approximately 150–160 m. The dropping water level is proportional to the increasing number of tube wells. Lying downstream of tributaries originating from the Margalla and Murree hills, this area has a good monsoon season, and its topography supports recharge of the aquifers. However, rapid subsidence indicates a deficit between water extraction and recharge, partly due to the limitations inherent in shale and the low porosity near the surface lithology exposed in the area. Other factors amplifying the impacts are fast urbanization, uncontrolled population growth, and non-cultivation of precipitation in the area.

To further accurately analyze the mechanical response of buried pipelines and pavement structures subjected to ground subsidence, this study established a dual-beam model consisting of an upper Euler–Bernoulli beam and a lower Euler–Bernoulli beam to simulate pavement structures and pipelines. The embankment fill layer and foundation soil were idealized as in the Winkler foundation model, which was divided into three zones. Coupling differential equations for the behavior of this dual-beam–foundation system were established according to the force equilibrium among the pavement structure, buried pipeline, embankment fill, and foundation soil with voids. The stress distribution and deformation of buried pipelines and pavement structures were solved according to the boundary and geometric conditions. The proposed mechanical model was evaluated on test data and corresponding finite element codes, concretely verifying the model's effectiveness, and a parametric study was proposed to investigate the behavior of this system, such as the bending stiffness of pipelines, traffic load, the strength of fill layers, and foundation soil. [ABSTRACT FROM AUTHOR]

Expansive clay soils are subject to considerable volume changes due to seasonal variations in soil–water content. Its shrink–swell characteristics can lead to differential movements, which can cause damage to lightly weighted structures and pavements. The damage can be severe with the presence of trees, as the root system would take a significant quantity of water from the soil, resulting in restricted shrinkage settlement. This paper presents a case study of tree-root-induced subsidence and cracks in the surface of a netball court located in an eastern suburb of Melbourne, Australia. A geotechnical investigation was conducted, and four boreholes were drilled close to and sufficiently away from a group of Australian native Eucalypts (Corymbia maculata). The daily water use of the selected tree was monitored using a sap flow instrument. The soil suction and water content profile were developed based on the measurement results, indicating that the severe soil drying developed at 1.5 m below the surface of the netball court, where the soil was the driest up to 1.0 m located approximately 7.6 m from the tree group. A contour map was delineated based on the level survey carried out around the cracking surface of the court, and the results revealed that the crack is most likely attributed to the soil settlement caused by the drying effect of tree roots. [ABSTRACT FROM AUTHOR]

Displacement prediction of transmission towers is essential for the early warning of transmission network deformation. However, there is still a lack of prediction on the ground subsidence of the tower foundation. In this study, we first used the multi-temporal interferometric synthetic aperture radar (MT-InSAR) approach to acquire time series deformation for the transmission lines in the Salt Lake area. Based on the K-shape clustering method and field investigation results, towers #95 and #151 with representative foundation deformation characteristics were selected for displacement prediction. Combined with field investigations and the characteristics of saline soil in the Salt Lake area, the trigger factors of transmission tower deformation were analyzed. Then, the displacement and trigger factors of the transmission tower were decomposed by variational mode decomposition (VMD), which could closely connect the characteristics of the foundation saline soil with the influence of the trigger factors. To analyze the contribution of each trigger factor, the maximum information coefficient (MIC) was quantified, and the best choice was made. Finally, the hyperparameters of the long short-term memory (LSTM) neural networks were optimized using a convolutional neural network (CNN) and the grey wolf optimizer (GWO). The findings reveal that the refined deep learning models outperform the initial model in generalization potential and prediction precision, with the CNN–LSTM model demonstrating the highest accuracy in predicting the total displacement of tower #151 (RMSE and R2 for the validation set are 0.485 and 0.972, respectively). Given the scant research on the multifactorial influence on the ground subsidence displacement of transmission towers, this study's methodology offers a novel perspective for monitoring and early warning of ground subsidence disasters in transmission networks. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Geological Hazard & Control is the property of China Institute of Geological Environmental Monitoring (CIGEM) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, we have witnessed an increase in road subsidence accidents in urban areas, threatening the safety of citizens. Various road facilities, such as water and sewage pipes, and telecommunication facilities are buried under roads, and the aging of these facilities is one of the factors causing road subsidence. In particular, old sewer pipes are a primary cause of road subsidence. However, most maintenance work on such facilities is carried out based on how long ago they were buried underground, without considering the risk of road subsidence caused by them. Therefore, this study aims to present a reliable method to assess road subsidence risk that considers various sewer pipe specifications and the environment surrounding them. To derive the factors influencing subsidence, sewer pipes near the target region, where road subsidence occurs the most, were extracted to analyze the correlation between road subsidence, pipe integrity, and the surrounding environment. An effective analysis method was selected by comparing logistic regression analysis and AHP (Analytic Hierarchy Process) analysis, and a weighted road subsidence risk assessment method was proposed by evaluating the importance of factors affecting ground subsidence. Its applicability was examined by comparing actual road subsidence data and analyzing risk in a pilot study area to validate the reliability of the proposed methodology. The results showed that it was possible to make reliable predictions of road subsidence risk areas. [ABSTRACT FROM AUTHOR]

The rupture of an unpressurized pipeline below the water table can lead to the leakage of groundwater along with soil particles into the pipeline. This not only causes blockages in the pipeline but, more critically, can result in ground subsidence. Understanding the factors influencing this phenomenon is a subject of great interest. To delve into this matter, this study employs the DEM-CFD methodology to synergistically encompass particle dynamics and interactions within the flow domain. It introduces an innovative framework for simulating water and soil erosion subsequent to the rupture of subaqueous unpressurized pipelines. This pioneering approach introduces a novel modeling and simulation paradigm catering to the analysis of intricate phenomena of this nature. Upon validating the flow field, our investigation specifically focused on three key factors: particle friction coefficient, groundwater level, and particle size distribution. We conducted a thorough examination of the process and mechanism of water and soil loss at the pipeline leakage point and the subsequent development of stratum subsidence. Our results indicate that particles with a friction coefficient of 0.6 had a reduced maximum displacement by 8.9%, compared to particles with a friction coefficient of 0.3. Similarly, a groundwater depth of 2 m resulted in a 29.6% decrease in maximum displacement compared to a 4 m depth, with a corresponding 160.9% increase in maximum force chain strength. Discontinuous particle gradation, in contrast to continuous gradation, yielded a notable 40.3% reduction in maximum displacement and a substantial 495.1% increase in maximum force chain strength. This underscores the noteworthy influence of particle friction coefficient, groundwater table elevation, and soil particle diameter on the stability of the overlying soil strata in the vicinity of a compromised unpressurized conduit. [ABSTRACT FROM AUTHOR]

Copyright of Industrial Minerals & Processing / Huagong Kuangwu yu Jiagong is the property of Industrial Minerals & Processing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to realize the monitoring of deformation in the urban area of Hohhot city, this paper uses the permanent scatterer synthetic aperture radar interferometry （PS-InSAR） technique to perform temporal interferometric processing on 25 scenes of Sentinel-1A data from March 2017 to April 2021 in the urban area of Hohhot city to extract the surface subsidence information in the urban area and analyze the causes of subsidence in the main subsidence centers. The results show that there are five obvious centres of subsidence in the urban area of Hohhot, and that the overall average annual surface subsidence rate is 3.65 mm/a, with an average cumulative subsidence of 15.50 mm. The subsidence is mainly due to over-exploitation of groundwater. Given that the free Sentinel-1A data has the advantages of short time baseline and sufficient number of images, it is conducive to InSAR time series analysis, which can meet similar urban surface deformation monitoring work and serve urban safety.

Ground subsidence often occurs over a large area. Although traditional monitoring methods have high accuracy, they cannot perform wide-area ground deformation monitoring. Synthetic Aperture Radar (SAR) interferometry (InSAR) technology utilizes phase information in SAR images to extract surface deformation information in a low-cost, large-scale, high-precision, and high-efficiency manner. With the increasing availability of SAR satellite data and the rapid development of InSAR technology, it provides the possibility for wide-area ground deformation monitoring using InSAR technology. Traditional time-series InSAR methods have cumbersome processing procedures, have large computational requirements, and rely heavily on manual intervention, resulting in relatively low efficiency. This study proposes a strategy for wide-area InSAR multi-scale deformation monitoring to address this issue. The strategy first rapidly acquires ground deformation information using Stacking technology, then identifies the main subsidence areas by setting deformation rate thresholds and visual interpretation, and finally employs advanced TS-InSAR technology to obtain detailed deformation time series for the main subsidence areas. The Turpan Basin in Xinjiang, China, was selected as the study area (7474.50 km2) to validate the proposed method. The results are as follows: (1) The basin is generally stable, but there is ground subsidence in the southern plain area, mainly affecting farmland. (2) From 2016 to 2019, the maximum subsidence rate in the farmland area was approximately 0.13 m/yr, with a maximum cumulative subsidence of about 0.25 m, affecting a total area of approximately 952.49 km2. The subsidence mainly occurred from late spring to mid-autumn, while lifting or subsidence mitigation occurred from late autumn to early spring. The study also analyzed the impacts of rainfall, geographical environment, and human activities on subsidence and found that multiple factors, including water resource reduction, overexploitation, geological characteristics, and the expansion of human activities, contributed to the subsidence problem in the Turpan Basin. This method contributes to wide-area InSAR deformation monitoring and the application of InSAR technology in engineering.

Featured Application: Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory. The main cause of ground subsidence accidents in urban areas is cavities formed by damage to underground utilities. For this reason, the attribute information of underground utilities should be used to prepare against ground subsidence accidents. In this study, attribute information (pipe age, diameter, burial depth, and density) of six types of underground utilities (water, sewer, gas, power, heating, and communication) and history information of ground subsidence were collected. A correlation analysis was conducted using the collected data, and a prediction model of vulnerability to ground subsidence was developed through the ordinary least squares (OLS) method and spatial regression analysis (spatial lag model (SLM) and spatial error model (SEM)). To do this, the target area was divided into a grid of 100 m × 100 m. Datasets were constructed using the attribute information of underground utilities included in the divided grid and the number of ground subsidence occurrences. To analyze the OLS of the constructed data, the variance inflation factor (VIF) of the attribute information of underground utilities was studied. An OLS analysis was conducted using the appropriate factors, and the results show that the spatial data were autocorrelated. Subsequently, SEM and SLM analyses, which were spatial regression analyses, were conducted. As a result, the model using SLM was selected as suitable for analyzing the vulnerability of ground subsidence, and the density of six types of underground utilities was found to be the highest influencing factor. In addition, a vulnerability map of ground subsidence in the target area was prepared using the model. The vulnerability map demonstrates that regions with frequent ground subsidence can be predicted to be highly vulnerable. [ABSTRACT FROM AUTHOR]

Long-term field monitoring finds that serious surface subsidence can still occur even if the high strength cemented fill method is adopted. Combining the results of numerical simulations with global position system (GPS) monitoring, we took a typical filling mining mine with a steeply inclined ore body as an example, and explored its ground subsidence mechanism. The results show that the ground subsidence caused by the mining of steep ore body is characterized by two settlement centers and a significantly uneven spatial distribution, which is visibly different from ground subsidence characteristic of the coal mine. The subsidence on the hanging wall is much larger than that on the footwall, and the settlement center tends to move to the hanging wall with the increase of mining depth. The backfill improves the strength and surrounding rock bearing capacity, which leads to a lag of about 3 years of the subsidence. However, under the actions of continuous and repeated mining disturbances, the supporting effect of the backfill can only reduce the amplitude of the deformation, but it cannot prevent the occurrence of settlement. [ABSTRACT FROM AUTHOR]

Ground subsidence is a significant safety concern in mining regions, making large-scale subsidence forecasting vital for mine site environmental management. This study proposes a deep learning-based prediction approach to address the challenges posed by the existing prediction methods, such as complicated model parameters or large data requirements. Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was utilized to collect spatiotemporal ground subsidence data at the Pingshuo mining area from 2019 to 2022, which was then analyzed using the long-short term memory (LSTM) neural network algorithm. Additionally, an attention mechanism was introduced to incorporate temporal dependencies and improve prediction accuracy, leading to the development of the AT-LSTM model. The results demonstrate that the Pingshuo mine area had subsidence rates ranging from −205.89 to −59.70 mm/yr from 2019 to 2022, with subsidence areas mainly located around Jinggong-1 (JG-1) and the three open-pit mines, strongly linked to mining activities, and the subsidence range continuously expanding. The spatial distribution of the AT-LSTM prediction results is basically consistent with the real situation, and the correlation coefficient is more than 0.97. Compared with the LSTM, the AT-LSTM method better captured the fluctuation changes of the time series for fitting, while the model was more sensitive to the mining method of the mine, and had different expressiveness in open-pit and shaft mines. Furthermore, in comparison to existing time-series forecasting methods, the AT-LSTM is effective and practical. [ABSTRACT FROM AUTHOR]

In this paper, the effect of tunnel excavation and Metro station has been investigated on adjacent bridge foundation with two objectives: investigation of the subsidence by tunneling and the effect of tunnel excavation on the behavior of adjacent bridge foundation by Plaxis 3D finite element software. Therefore, the tunnel of Line 3 of the Tehran Metro near Nobonyad Square, which passes through a pier of the Sadr Bridge to the Nobonyad Metro Station, was selected. To explore the effects of the pile length on settlement, the axial load and bending moments of three scenarios, including piles with a length of 37 m, piles with a length of 47 m, and no piles, were evaluated. It was found that settlement above the station and tunnel reduced near the bridge foundation, suggesting a stiff foundation. To study the effects of the tunnel on adjacent piles, the changes in the axial loads and bending moments of the piles under tunneling and station construction and then in two different models. The post-tunneling axial loads of the pipes showed significant differences from the pre-tunneling values. The results revealed that the axial pile load was slightly higher at a pile length of 47 m than at a pile length of 37 m. As with the axial load, the bending moment increased after tunneling. A comparison of the bending moment between the two models indicated that a 10-m reduction in the pile length would not change the behavior and bending moment of the piles. [ABSTRACT FROM AUTHOR]

In this study, a machine learning-based prediction model was developed using the attribute information of underground pipelines and the history information of ground subsidence in order to predict the risk level of ground subsidence in urban areas. The target area was divided into a grid with sizes of 100m $\times 100\text{m}$ , 300m $\times 300\text{m}$ , and 500m $\times 500\text{m}$ , and the attribute information of underground pipelines in the grid and ground subsidence data were utilized to build a dataset. For input data, the pipeline’s diameter, the number of years used, and density were selected based on the pipeline’s length as the basic unit. Additionally, the risk level of ground subsidence was determined as the output data using historical information. A total of 36 datasets were built according to the conditions, and factors with significant correlation were selected through a correlation analysis of the datasets. The developed datasets were divided into training data and evaluation data. The synthetic minority oversampling technique was used to resolve the data imbalance. The model performance evaluation indexes used in this study were F1-score and AUC(Area Under the Curve). The performance of each model was compared, and the comparison results showed that a model that applied a preprocessed dataset with 500m $\times 500\text{m}$ grid size, 10 years in use, 100mm pipeline diameter, and 1–2 ground sinks in Level 1 risk range to the LGBM(Light Gradient Boosting Model) classifier derived the best evaluation indexes(F1-Score:0.750, AUC:0.840). The map was found to be effective for predicting the risk level of ground subsidence in urban areas.

Due to the overexploitation of water resources, ground subsidence is becoming increasingly problematic in Beijing, China's political, economic, and cultural capital. This article aims to investigate the relationship between ground subsidence and changes in groundwater depth, and water supply from a long-term point of view. Multisource synthetic aperture radar (SAR) data using the interferometric SAR (InSAR) technique were adopted in this research, combined with a set of leveling and ground subsidence data in the Beijing Plain area from 2003 to 2020. The InSAR results demonstrate that ground subsidence in the plain area increased steadily from 2003 to 2015, expanding from sporadic to continuous laminar dispersion and producing five major subsidence centers. The South-to-North Water Diversion Project (SNWDP) that was completed in 2008 and 2015 considerably reduced the demand for groundwater supply in the Beijing Plain area. Since then, the groundwater level depth has continued to increase. However, since 2016, the ground subsidence rate has dramatically slowed down. The obtained results showed that, thanks to the SNWDP, which resulted in a decline in groundwater exploitation and an increase in renewable water recycling, the ground subsidence in Beijing's plain area has been effectively managed.

In recent years, the mined-out area resulting in more than 20 goaf collapse disasters in the north Pizhou, Jiangsu Province. The traditional monitoring method of surface soil deformation is difficult to meet the requirement of advance prediction of surface subsidence. In order to solve the lack of monitoring and early warning mechanism of mined-out subsidence in gypsum mining area, a monitoring and early warning system based on fiber sensing technology was developed in this area. According to the results of preliminary investigation, analysis and numerical simulation, the ground collapse mechanism of gypsum mine is deduced, dynamic monitoring of groundwater level, displacement of deep rock and soil mass and microseismic monitoring of bottom aquifer above loose layer were monitored by optical fiber sensor, in the course of monitoring, the alarm will be triggered when the fluctuation of water level and the displacement of rock and soil suddenly exceed the normal fluctuation value or when a strong vibration signal is received, and the effective early warning will be carried out in the early stage of the ground collapse, and can provide adequate lead time for disaster prevention and mitigation efforts. Through the implementation of monitoring and early-warning work, the validity of these monitoring and early-warning means has been verified, which can provide data basis and scientific basis for the monitoring, prediction and prevention of ground subsidence in mining areas, it can also provide reference for prevention, control, monitoring and early warning of ground collapse geological disaster in gypsum mine and other similar conditions.