Cause Of Subsidence Research Articles

Land Subsidence Velocity and High-Speed Railway Risks in the Coastal Cities of Beijing–Tianjin–Hebei, China, with 2015–2021 ALOS PALSAR-2 Multi-Temporal InSAR Analysis

Sea-level rise has important implications for the economic and infrastructure security of coastal cities. Land subsidence further exacerbates relative sea-level rise. The Beijing–Tianjin–Hebei region (BTHR) along the Bohai Bay is one of the areas most severely affected by ground subsidence in the world. This study applies the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS InSAR) method to analyze 47 ALOS PALSAR-2 images with five frames, mapping subsidence across 21,677.7 km2 and revealing spatial patterns and trends over time from 2015 to 2021. This is one of the few published research studies for large-scale and long-term analysis of its kind using ALOS-2 data in this region. The results reveal the existence of six major areas affected by severe subsidence in the study area, with the most pronounced in Jinzhan Town, Beijing, with the maximum subsiding velocity of −94.42 mm/y. Except for the two subsidence areas located in Chaoyang District of Beijing and Guangyang District of Langfang City, the other areas with serious subsidence detected are all located in suburban areas; this means that the strict regulations of controlling urban subsidence for downtown areas in the BTHR have worked. The accumulated subsidence is highly correlated with the time in the time series. Moreover, the subsidence of 161.4 km of the Beijing–Tianjin Inter-City High-Speed Railway (HSR) and 194.5 km of the Beijing–Shanghai HSR (out of a total length of 1318 km) were analyzed. It is the first time that PALSAR-2 data have been used to simultaneously investigate the subsidence along two important HSR lines in China and to analyze relatively long sections of the routes. The above two railways intersect five and seven subsiding areas, respectively. Within the range of the monitored railway line, the percentage of the section with subsidence velocity below −10 mm/y in the monitoring length range is 11.2% and 27.9%; this indicates that the Beijing–Shanghai HSR has suffered more serious subsidence than the Beijing–Tianjin Inter-City HSR within the monitoring period. This research is also beneficial for assessing the subsidence risk associated with different railways. In addition, this study further analyzed the potential reasons for the serious land subsidence of the identified areas. The results of the geological interpretation still indicate that the main cause of subsidence in the area is due to hydrogeological characteristics and underground water withdrawal.

Land Subsidence Detection Using SBAS- and Stacking-InSAR with Zonal Statistics and Topographic Correlations in Lakhra Coal Mines, Pakistan

The adverse combination of excessive mining practices and the resulting land subsidence is a significant obstacle to the sustainable growth and stability of regions associated with mining activities. The Lakhra coal mines, which contain some of Pakistan’s largest coal deposits, have been overlooked in land subsidence monitoring, indicating a considerable oversight in the region. Subsidence in mining areas can be spotted early when using Interferometric Synthetic Aperture Radar (InSAR), which can precisely monitor ground changes over time. This study is the first to employ the Small Baseline Subset (SBAS)-InSAR and stacking-InSAR techniques to identify land subsidence at the Lakhra coal mines. This research offers critical insights into subsidence mechanisms in the study area, which has never been previously investigated for ground deformation monitoring, by utilizing 150 Sentinel-1A (ascending) images obtained between January 2018 and September 2023. A total of 102 deformation spots were identified using SBAS-InSAR, while stacking-InSAR detected 73 deformation locations. The most extensive cumulative subsidence in the Lakhra coal mine was −114 mm, according to SBAS-InSAR, with a standard deviation of 6.63 mm. In comparison, a subsidence rate of −19 mm/year was reported using stacking-InSAR with a standard deviation of 1.17 mm/year. The rangeland covered 88.8% of the total area and exhibited the most significant deformation values, as determined by stacking and SBAS-InSAR techniques. Linear regression showed that there was not a strong correlation between subsidence and topographic factors. As detected by optical remote sensing data, the subsidence locations were near or above the mines in the research area, indicating that widespread mining in Lakhra coal mines was the cause of subsidence. Our findings suggest that SAR interferometric time series analysis is helpful for proactively identifying and controlling subsidence difficulties in mining regions by closely monitoring activities, hence reducing negative consequences on operations and the environment.

Impact of the Urbanization Process on the Subsidence Phenomena Using Remote Sensing in the Inner Districts of Ho Chi Minh City, Vietnam

Abstract Subsidence in Ho Chi Minh city (HCM city) occurs in many places, the current reason no longer comes from natural disasters but mainly from human activities (groundwater exploitation, ground improvement,…). Especially in innercity districts, during high tide or heavy rain, many areas are locally flooded, concentrated in places with high construction density. To monitor the subsidence phenomenon in urban areas, the advent of remote sensing technology is an effective tool, helping researchers and managers capture information quickly, accurately and continuously. In this study, the DInSAR method was used to determine the rate of subsidence in urban districts of HCM city. DInSAR is a popular method for estimating land displacement based on the principles of Interferometric Synthetic Aperture Radar (InSAR) using different time series data. We used C-band SAR Sentinel 1 data in the period 2015 - 2023 to analyze subsidence, the results showed that subsidence hot spots were concentrated in District 7, Binh Thanh District and part of Go Vap District at high speeds from 2 - 3 cm/year. Over time, the phenomenon of subsidence with a value of > 2cm/year was distributed in Binh Tan and Tan Phu districts following the trend of new urban expansion to the West of HCM city. Additionally, Sentinel 2 imaging data was also used in this study to demonstrate that areas with high construction density have high subsidence rates. From there, it can be seen that the urbanization process is one of the causes of subsidence in HCM city.

Evaluation of groundwater-caused deformation patterns in a metropolitan area using time series InSAR and retrieval of vertical and east-west displacement: a case study in Taipei City

This study explores the influences of groundwater fluctuations in an urban area which lead to long-term ground surface subsidence using a time-series synthetic aperture radar interferometry (InSAR) technique. Unlike most prior studies that only focus on line-of-sight (LOS) deformation, assume negligible horizontal movements, and quantify InSAR-groundwater relationship on a sparse temporal scale, this study utilized the Persistent Scatterers InSAR (PS-InSAR) technique to extract east-west and vertical displacement by decomposing Sentinel-1 ascending and descending datasets spanning from 2018 to 2022. Additionally, an approach was proposed to effectively quantify the relationship between changes in groundwater levels and vertical displacement on a daily basis. The results underwent robust validation against ground-based leveling surveys, revealing a satisfactory NRMSE of 1.06 mm/yr. Furthermore, founded on the estimated vertical displacement, three districts within Taipei City (e.g. Wugu, Xinzhuang, and Taishan) were identified as subsidence hotspots, with maximum vertical deformation rates reaching −11.77 mm/yr. Eventually, the correlation analysis and quantification between vertical displacement and daily groundwater data underscore long-term groundwater fluctuations as the primary cause of subsidence. These findings, derived from InSAR-based 2D displacements, have proven valuable in understanding groundwater-caused ground subsidence hazards in urban areas and empowering relevant stakeholders to set out corresponding adaptation and mitigation measures.

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.

LAND SUBSIDENCE IN WUHAN REVEALED USING A MULTI-SENSOR INSAR TIME SERIES FUSION APPROACH

Abstract. Satellite Interferometric Synthetic Aperture Radar (InSAR) is widely utilized for topographic, geological, and natural resource investigations. However, many existing InSAR studies on ground deformation are limited to relatively short observation periods and single sensors. This paper introduces a novel method for fusing multi-sensor InSAR time series data, specifically designed to address scenarios involving partial overlaps and temporal gaps. The method employs a new Power Exponential Knothe Model (PEKM) to fit and fuse overlaps in the deformation curves, while a Long Short-Term Memory (LSTM) neural network predicts and fuses data during temporal gaps in the series. In this study, the city of Wuhan in China was selected as the experimental area. SAR datasets from COSMO-SkyMed (2011–2015), TerraSAR-X (2015–2019), and Sentinel-1 (2019–2021) were fused to map long-term surface deformation over the past decade. An independent InSAR time series analysis from 2011 to 2020, based on 230 COSMO-SkyMed scenes, was used as a reference for comparison. The correlation coefficient between the fusion algorithm’s results and the reference data is 0.87 in the time overlapping region and 0.97 in the time-interval dataset. The overall correlation coefficient of 0.78 demonstrates that the proposed algorithm achieves a similar trend as the reference deformation curve. Based on the long time series settlement results obtained through fusion, a detailed analysis of the causes of settlement was conducted for several subsidence zones. The subsidence in the Houhu area is primarily attributed to the consolidation and compression of soft soil. Soil mechanics were employed to estimate the expected completion time of subsidence and calculate the degree of consolidation for each year. The COSMO-SkyMed PSInSAR results indicate that the area has entered the late stage of consolidation and compression, gradually stabilizing over time. Furthermore, the subsidence curve observed in the area around Xinrong reveals that the construction of an underground section of subway Line 21 has caused significant settlement in that particular region. The high temporal granularity of the PSInSAR time series also enables precise detection of a rebound phase following a major flooding event in 2016. The experimental results demonstrate the accuracy of the proposed fusion method in providing robust time series for analyzing long-term land subsidence mechanisms. Additionally, these findings unveil previously unknown characteristics of land subsidence in Wuhan, thus clarifying the relationship with urban causative factors.

Cumulative Land Subsidence in Populated Asian Coastal Cities

In contrast to sea-level rise, which is often associated with climatic and environmental changes, land subsidence has largely been overlooked in discussions regarding future adaptation to higher sea water levels. In order to articulate the critical contribution of this phenomenon, this short paper provides a reliable chart that details land subsidence in densely populated East and Southeast Asian coastal cities. The causes of subsidence at these locations are also discussed based on existing literature. Land subsidence was observed to be one or two orders of magnitude faster than sea-level rise caused by an anthropogenic global warming, and can continue unabated unless its root causes are addressed through clear policies at the municipal level. Subsidence is clearly a localized problem, and is thus easily overlooked by regional or national governments. Nevertheless, once subsidence takes place it cannot be reversed, and hence it is crucial to rapidly formulate appropriate countermeasures when it is identified. The examples of these Asian cities demonstrate that, if subsidence is recognized and adequate policies are put in place and implemented, it can be brought under control in a fairly short period of time.

Using InSAR and GPR Techniques to Detect Subsidence: Application to the Coastal Area of “A Xunqueira” (NW Spain)

Climate change represents an important cause of subsidence, especially in coastal cities affected by changes in surface water level and water table. This paper presents a complementary study of Interferometric Synthetic Aperture Radar (InSAR) and Ground Penetrating Radar (GPR) for the early detection of subsidence and sinkhole phenomena. The methodology was applied to a coastal urban area in Galicia, northwest Spain (humid region), showing apparent signs of subsidence and building settlement during the last two years. Two different InSAR methods are compared for the period from June 2021 to March 2022: PSI (Persistent Scatterer Interferometry) and SBAS (Small Baseline Subsets), and the average deformation velocities obtained resulted in −3.0 mm/yr and −4.1 mm/yr, respectively. Additional GPR data were collected in January 2022 to validate the InSAR results, which detected subsidence in agreement with the persistent scatters obtained from the PSI method. This is crucial information to plan preventive maintenance.

Disentangling Shallow Subsidence Sources by Data Assimilation in a Reclaimed Urbanized Coastal Plain, South Flevoland Polder, the Netherlands

AbstractThis research targets disentangling shallow causes of anthropogenically induced subsidence in a reclaimed and urbanized coastal plain. The study area is the city of Almere, in the South Flevoland polder, the Netherlands, which is among the countries' fastest subsiding areas. The procedure consists of integrating Interferometric Synthetic Aperture Radar (InSAR) data with high‐resolution phreatic groundwater and lithoclass models, and a database containing construction details. The InSAR data were derived from Sentinel‐1, one ascending and one descending track, over a period from March 2015 until June 2020. The two main parts of the workflow are isolation of the InSAR points of structures without a pile foundation and a data assimilation procedure. The shrinkage of surficial clay beds by phreatic groundwater level lowering is identified as the main cause of subsidence in the area, with an average contribution of 6 mm per year. The history‐matched physics‐based model predicts that 1 m drop in phreatic groundwater level now translates into 10 mm of subsidence in the next 5 years. Additionally, a groundwater deficiency due to severe dry periods should be considered as an accelerator of subsidence. To ensure a robust network to estimate subsidence, we recommend a consistent monitoring strategy of the phreatic groundwater level.

The Weight of New York City: Possible Contributions to Subsidence From Anthropogenic Sources

AbstractNew York City faces accelerating inundation risk from sea level rise, subsidence, and increasing storm intensity from natural and anthropogenic causes. Here we calculate a previously unquantified contribution to subsidence from the cumulative mass and downward pressure exerted by the built environment of the city. We enforce that load distribution in a multiphysics finite element model to calculate expected subsidence. Complex surface geology requires multiple rheological soil models to be applied; clay rich soils and artificial fill are calculated to have the highest post‐construction subsidence as compared with more elastic soils. Minimum and maximum calculated building subsidence ranges from 0 to 600 mm depending on soil/rock physical parameters and foundation modes. We compare modeled subsidence and surface geology to observed subsidence rates from satellite data (Interferometric Synthetic Aperture Radar and Global Positioning System). The comparison is complicated because the urban load has accumulated across a much longer period than measured subsidence rates, and there are multiple causes of subsidence. Geodetic measurements show a mean subsidence rate of 1–2 mm/year across the city that is consistent with regional post‐glacial deformation, though we find some areas of significantly greater subsidence rates. Some of this deformation is consistent with internal consolidation of artificial fill and other soft sediment that may be exacerbated by recent building loads, though there are many possible causes. New York is emblematic of growing coastal cities all over the world that are observed to be subsiding (Wu et al., 2022, https://doi.org/10.1029/2022GL098477), meaning there is a shared global challenge of mitigation against a growing inundation hazard.

ПРОГНОЗИРОВАНИЕ ПРОСАДКИ ДНЕВНОЙ ПОВЕРХНОСТИ ПРИ ПРОХОДКЕ ПОДЗЕМНЫХ ВЫРАБОТОК

The article discusses the causes of subsidence of the daytime surface as a result of underground workings, as well as various methods of their assessment. In particular, among the empirical methods, a method based on the use of the Gaussian error function was singled out. Calculations of the stability of the face and estimates of the pressure of the soil in it when tunneling using tunnel-tunneling mechanized complexes are also presented.

Predictors of Vertebral Endplate Fractures after Oblique Lumbar Interbody Fusion.

Cage subsidence after oblique lumbar interbody fusion (OLIF) induces restenosis and adversely affects patient outcomes. Many studies have investigated the causes of subsidence, one of which is endplate fracture (EF). This study aimed to identify predictors of EF after OLIF. This retrospective study reviewed consecutive patients who underwent OLIF at a single institute between August 2019 and February 2022. A total of 104 patients were enrolled. The patients' demographic data and surgical details were collected through chart reviews. Radiographic variables were measured. Related variables were also analyzed using binomial logistic regression, dividing each group into those with versus without EF. EF occurred at 30 of 164 levels (18.3%), and the binary logistic analysis revealed that sex (odds ratio [OR], 11.07), inferior endplate concave depth (OR, 1.95), disc wedge angle (OR, 1.22), lumbar lordosis (OR, 1.09), pelvic incidence (OR, 1.07), sagittal vertical axis (OR, 1.02), sacral slope (OR, 0.9), L3-4 level (OR, 0.005), and L4-5 level (OR, 0.004) were significantly related to EF. OLIF in older Asian patients should be performed carefully after recognizing the high possibility of EF and confirming the factors that should be considered preoperatively.

Integrated analysis of Hashtgerd plain deformation, using Sentinel-1 SAR, geological and hydrological data

Due to its proximity to Tehran, the Hashtgerd catchment in Iran is an important region that has experienced alarming subsidence rates in recent years. This study estimated the ground surface deformation in the Hashtgerd plain between 2015 and 2020 using Sentinel-1 SAR data and InSAR technique. The average LOS displacement of the ascending and descending tracks was − 23 cm/year and − 22 cm/year, respectively. The central area of the plain experienced the greatest vertical subsidence, with a more than − 100 cm cumulative displacement. The Karaj-Qazvin railway and highway that pass through this area have been damaged by subsidence, according to an analysis of profiles drawn along the transportation lines. The southern sections of Hashtgerd city have experienced a total displacement of − 30 cm/year over the course of about 6 years. The relationship between changes in groundwater level and subsidence rate in this region was examined using piezometer and precipitation data. Geoelectric sections and piezometric well logs were also utilized to investigate the geological characteristics of the Hashtgerd aquifer. According to the findings, the leading causes of subsidence were uncontrolled groundwater abstraction. This research highlights the need to comprehend the spatial distribution of confined aquifers and their effect on subsidence, which can aid in the development of a suitable management strategy to restore these aquifers.

The Monitoring and Analysis of Land Subsidence in Kunming (China) Supported by Time Series InSAR

As urban construction has been leaping forward recently, large-scale land subsidence has been caused in Kunming due to the special hydrogeological conditions of the city; the subsidence scope has stretched out, and the subsidence rate has been rising year by year. As a consequence, Kunming’s sustainable development has seriously hindered. The PS-InSAR (Persistent Scatterer Interferometric Synthetic Aperture Radar) and the SBAS-InSAR (Small Baseline Subsets Interferometric Synthetic Aperture Radar) technologies were adopted to process the descending Sentinel-1A data stacks from July 2018 to November 2020 to monitor the land subsidence of Kunming, so as to ensure the sustainable development of the city. Moreover, the causes were analyzed. As revealed by the results, (1) the overall subsidence trend of Kunming was large in the south (Dian lakeside), whereas it was relatively small in the north. The significant subsidence areas showed major distributions in Xishan, Guandu and Jining district. The maximal average subsidence rates of PS-InSAR and SBAS-InSAR were −78 mm/a and −88 mm/a, respectively. (2) The ground Subsidence field of Kunming was analyzed, and the correlation coefficient R2 of the two methods was reported as 0.997. In comparison with the leveling data of the identical period, the root mean square error (RMSE) is 6.5 mm/a and 8.5 mm/a, respectively. (3) Based on the urban subway construction data, geological structure, groundwater extraction data and precipitation, the causes of subsidence were examined. As revealed by the results, under considerable urban subways construction, special geological structures and excessive groundwater extraction, the consolidation and compression of the ground surface could cause the regional large-area subsidence. Accordingly, the monthly average precipitation in Kunming in the identical period was collected for time series analysis, thereby indicating that the land subsidence showed obvious seasonal variations with the precipitation. The results of this study can provide data support and facilitate the decision-making for land subsidence assessment, forecasting and construction planning in Kunming.

PS-InSAR Based Monitoring of Land Subsidence by Groundwater Extraction for Lahore Metropolitan City, Pakistan

Groundwater dynamics caused by extraction and recharge are one of the primary causes of subsidence in the urban environment. Lahore is the second largest metropolitan city in Pakistan. The rapid expansion of this urban area due to high population density has increased the demand for groundwater to meet commercial and household needs. Land subsidence due to inadequate groundwater extraction has long been a concern in Lahore. This paper aims to present the persistent scatterer interferometry synthetic aperture radar (PS-InSAR) technique for monitoring the recent land subsidence in Lahore, based on the Sentinel-1 data obtained from January 2020 to December 2021. PS-InSAR techniques are very efficient and cost-effective, determining land subsidence and providing useful results. Areas of high groundwater discharge are prone to high subsidence of −110 mm, while the surroundings show an uplifting of +21 mm during the study period. The PS-InSAR study exposes the subsidence area in detail, particularly when the subsoil is characterized by alluvial and clay deposits and large building structures. This type of observation is quite satisfactory and similar to ground-based surface deformation pertinent to a high subsidence rate. Results will enable more effective urban planning, land infrastructure building, and risk assessment related to subsidence.

The Risk of Subsidence in Standalone Oblique Lumbar Interbody Fusion: A 12-Month Follow-Up Prospective Study

Background Data: The incidence of interbody cage subsidence in OLIF is 8–9.5%. It occurs mainly secondary to osteoporosis or endplate damage during disc space clearance. An anatomical study correlated the surface area contact and position between the cage and the disc space to the incidence of subsidence. Studies have concentrated on the optimal place in the disc space to place the cage to obtain less incidence of subsidence, as it was reported that the central part of the disc space, called the epiphyseal ring, is the toughest part. Subsidence is usually noted in the superior vertebral endplate. Until now, there has been a lack of data regarding the main cause of subsidence.

Application of an improved multi-temporal InSAR method and forward geophysical model to document subsidence and rebound of the Chinese Loess Plateau following land reclamation in the Yan'an New District

Land reclamation in the Yan'an New District (YND) on the Chinese Loess Plateau is one of the largest earthworks projects in the world, involving the excavation of loess from ridges and the deposition of the material in adjacent valleys, flattening an area of more than 78 km2. It can take multiple years for the landscape to adjust to the new topography after the earthworks are completed, with subsidence in the fill areas and uplift in the areas of excavation. Understanding the pattern and extent of this differential vertical movement has great importance for ensuring the structural integrity of any infrastructure built on the site. We therefore studied the spatial deformation field of the YND from January 2015 to December 2018 using satellite synthetic aperture radar interferometry (InSAR) technology. Persistent scatterers (PS) and distributed scatterers (DS) were combined in a two-tier network to overcome problems with temporal decorrelation of the InSAR phase signals. The derived deformation field was validated with in-situ ground leveling and GNSS measurements, and interpreted using a forward geophysical model. The results indicate that our approach provides a more detailed understanding of the deformation than conventional PSI and SBAS methods. Subsidence of up to 87 mm/yr occurred over about 4.4 km2 of the fill region, and uplift of up to 26 mm/yr in the excavated areas. There is a strong linear relationship between these displacements and prior topographic elevation change. The primary cause of subsidence in fill areas is compaction of remoulded loess. Instantaneous elastic and gradual poroelastic deformation are the main causes of uplift in excavated areas.

Monitoring of construction-induced urban ground deformations using Sentinel-1 PS-InSAR: The case study of tunneling in Dangjin, Korea

The construction of underground tunnels can prime or trigger ground subsidence, and thus surface deformation monitoring is required during and post-construction. Freely available satellite data allow significant possibilities for the use of remote sensing techniques, particularly multi-temporal interferometric synthetic aperture radar (Mt-InSAR), for surface deformation monitoring. However, track records and field demonstrations of InSAR-based tunneling-related deformation monitoring are limited, particularly in identifying the spatiotemporal evolution of ground deformation and causal factors thereof from construction activities. Therefore, this study explores the feasibility of using satellite SAR data to monitor the surface deformation associated with different tunnel construction phases. Tunneling-related surface deformation was analyzed using the persistent scatterer InSAR (PS-InSAR) technique and Sentinel-1B SAR data; the PS-InSAR results were compared with those from the conventional leveling method. The velocity map of the study site revealed a localized subsidence trough associated with the construction of vertical shafts and shield tunneling. The cumulative time-series displacement map successfully tracked the spatial and temporal progression of ground deformations related to the phases of tunnel construction activities. The maximum subsidence rate in the radar line-of-sight direction was estimated to exceed 40 mm/yr, with maximum cumulative subsidence of ∼ 200 mm. The results clearly showed that dewatering during tunneling aggravated the subsidence phenomenon. The consequent compaction and consolidation of the compressible soil layers due to the lowered groundwater table were the main causes of subsidence. Our results demonstrate the viability of the PS-InSAR technique based on the C-band Sentinel-1 product as a monitoring method to examine the spatiotemporal evolution of ground deformation patterns during tunneling activities and as a possible preliminary surveying tool to identify ongoing abnormal settlement prior to tunnel construction.

Prediction of Subsidence during TBM Operation in Mixed-Face Ground Conditions from Realtime Monitoring Data

The prediction of settlement during tunneling presents multiple challenges, as such settlement is governed by not only the local geology but also construction methods and practices, such as tunnel boring machine (TBM). To avoid undesirable settlement, engineers must predict the settlement under given conditions. The widely used methods are analytical solutions, empirical solutions, and numerical solutions. Analytical or empirical solutions, however, have limitations, which cannot incorporate the major causes of subsidence, such as unexpected geological conditions and TBM operational issues, among which cutterhead pressure and thrust force-related factors are the most influential. In settlement prediction, to utilize the machine data of TBM, two phases of long short-term memory (LSTM) models are devised. The first LSTM model is designed to capture the features affecting surface settlement. The second model is for the prediction of subsidence against the extracted features. One thing to note is that predicted subsidence is the evolution of settlement along TBM drive rather than its maximum value. The proposed deep-learning models are capable of predicting the subsidence of training and test sets with excellent accuracy, anticipating that it could be an effective tool for real-world tunneling and other underground construction projects.