Subsidence Research Articles

Integration Sentinel-1 SAR data and machine learning for land subsidence in-depth analysis in the North Coast of Central Java, Indonesia

Integration Sentinel-1 SAR data and machine learning for land subsidence in-depth analysis in the North Coast of Central Java, Indonesia

Understanding land subsidence in the Pearl River Delta region of China based on InSAR observations

Land subsidence occurs in river delta areas. A comprehensive understanding of the factors contributing to land subsidence is crucial for effective geohazard mitigation and land planning. This study investigates the distribution characteristics and spatiotemporal evolution pattern of the land subsidence in the Pearl River Delta based on the InSAR-derived ground deformation data spanning 2016 to 2021; and, the effects of the influencing factors, including the Quaternary sediment thickness, land use classification, land reclamation, and subway construction and operation, on the subsidence are analyzed. The results unveil the presence of four major subsidence zones and several minor coastal subsidence bowls in the research area, with a total area of about 1100 km, all of which exhibit prolonged and intense subsidence. In the remaining regions, land subsidence predominantly exhibits weak and short-term occurrences. The subsidence in the study area follows a distinct linear evolutionary pattern with a slight rebound, potentially linked to the rise of groundwater levels. Regions with Quaternary sediment thickness surpassing 20 m, engaging in aquaculture or agricultural activities, and featuring reclaimed land are predisposed to prolonged and intense ground subsidence. In addition, the construction and operation of subway lines may induce ground subsidence, especially in geologically vulnerable areas. This study provides valuable insights into land subsidence research in delta regions.

Detecting soil freeze-thaw dynamics with C-band SAR over permafrost in Northern Sweden and seasonally frozen grounds in the Tibetan Plateau, China

ABSTRACT The soil freeze-thaw (FT) state plays a significant role in cold ecosystems by influencing hydrology, geomorphology, ecology, thermodynamics, and soil chemistry. As climate change alters the frequency and timing of FT events, regions, such as the Tibetan Plateau and other subarctic permafrost zones, face significant environmental shifts, including land subsidence, altered hydrological processes, and carbon balance disturbances. This study aimed to detect frozen, thawed, and transition periods, with a particular emphasis on the identification of critical transition periods. A new approach called Percentile-based Freeze-Thaw Identification (PFTI) was applied to identify the three FT periodsusing C-band Synthetic Aperture Radar (SAR) data, specifically using Sentinel-1 VV and VH polarizations in both ascending and descending orbits. The PFTI approach is based on the Seasonal Threshold Approach (STA) with some modifications. We assessed the performance of PFTI against STA and validated it using the in-situ soil FT index. PFTI slightly improved the STA by 4% to 6% in detecting FT cycles over the Nagqu area of the Tibetan Plateau and Stordalen Mire in northern Sweden from 2017 to 2022. Moreover, PFTI demonstrated an accuracy of 94% without and 70% with transition periods in the Stordalen Mire, and 77% without and 50% with transition periods in the Nagqu area, as validated by in situ measurements. This study demonstrates the potential of PFTI on a monthly scale for detecting frozen, thawed, and transition periods on a regional scale to better monitor shifts due to the impacts of climate change in the most vulnerable regions.

The Influence of Land Subsidence on Risk of Constructing 150 kV Transmission Tower for South Sumatra – Bangka Belitung Interconnector

Transmission system is one of the national essential objects and should be reliable when functioning. Moreover, disturbances in transmission system can lead to disruption of the service function. A common risk to the reliability of electric distribution is land subsidence on transmission tower. Furthermore, the 150 KV interconnector of South Sumatra-Bangka Belitung (Sumsel-Babel) is located in the coastal area of Banyuasin Dua sub-district, South Sumatra. This area is a river delta with peat and clay soil structures and is prone to land subsidence. Therefore, this research aimed to identify environmental factors that affect land subsidence and cause risk to transmission tower. In the context of this research, Copras (Complex Proportional Assessment) and MCSA (Multi Criteria Spatial Analysis) were used for parameter weighting and risk assessment of tower location, respectively. The results of an expert survey to assess risk of potential land subsidence at tower location showed five parameters, namely, land use, distance to waters or potential flooding, land slope factor, soil type, and conus value. Individual parameters were weighted quantitatively and qualitatively based on the opinions of construction experts. Specifically, the qualitative method was performed by weighting based on criteria and sub-criteria, which were analyzed using Analytic Hierarchy Process (AHP). On the other hand, spatial analysis of locations at risk of land subsidence was conducted using GIS software. Based on the assessment results obtained, land use criteria had the largest weighting influence of 0.340 and the smallest weighting on the cone value was 0.08. Finally, tower location that are most at risk of land subsidence are on peatland, near water, and clay soil.

Analysis of Urbanization-Induced Land Subsidence in the City of Recife (Brazil) Using Persistent Scatterer SAR Interferometry

The article addresses anthropogenic and geological conditions related to the development of soil subsidence in the western zone of Recife (Brazil). Over the past 50 years, human activity has intensified in areas previously affected by soft soils (clay, silt, and sandstone) resulting in subsidence due to additional loads (landfills and constructions). The duration of the settlement process can be significantly influenced by the specific characteristics of the soil composition and geological conditions of the location. This work presents, for the first time, accurate InSAR time series maps that describe the spatial pattern and temporal evolution of the settlement, as well as the correlation with the geological profile, and validation with Global Navigation Satellite System (GNSS) data. Persistent Scatterer Interferometry (PS-InSAR) was employed in the analysis of Single Look Complex (SLC) images generated by 100 ascending COSMO-SkyMed (CSK) and 65 PAZ (32 ascending, and 33 descending) from the X-band, along with 135 descending Sentinel-1 (S1) acquisitions from the C-band. These data were acquired over the period from 2011 to 2023. The occurrence of subsidence was identified in several locations within the western region, with the most significant displacement rates observed in the northern, central, and southern areas. In the northern region, the displacement rates were estimated to be approximately −20 mm/year, with the Várzea and Caxangá neighborhoods exhibiting the highest rates. In the central region, the displacement rates were estimated to be approximately −15 mm/year, with the Engenho do Meio, Cordeiro, Torrões, and San Martin neighborhoods exhibiting the highest rates. Finally, in the southern region, the displacement rates were estimated to be up to −25 mm/year, with the Caçote, Ibura, and Ipsep neighborhoods exhibiting the highest rates. Additionally, east–west movements were observed, with velocities reaching up to −7 mm/year toward the west. These movements are related to the lowering of the land. The study highlights that anthropogenic effects in the western zone of Recife contribute to the region’s vulnerability to soil subsidence.

An assessment of ground subsidence from rock salt mining in Maceió (Northeast Brazil) from 2019 to 2023 using remotely sensed data

This study investigates ground subsidence in Maceió, the capital of Alagoas, Brazil, utilizing Sentinel-1 Synthetic Aperture Radar (SAR) data from November 2019 to December 2023. Ground subsidence poses significant risks to urban infrastructure and requires comprehensive monitoring and mitigation strategies. We employed the Interferometric Wide Swath (IW) mode of Sentinel-1A, acquiring high-resolution data in descending orbit with VV polarization. Through interferometric processing, including co-registration, phase unwrapping, and coherence estimation, we produced detailed subsidence maps for key neighborhoods such as Bebedouro, Farol, Mutange, Pinheiro, and Bom Parto. Our results reveal that subsidence in Maceió is highly variable across different neighborhoods, with cumulative subsidence reaching up to 3.83 meters in the most affected areas. The Bebedouro neighborhood, for instance, experienced subsidence up to 0.33 meters over an area of 41.85 hectares, while Farol saw significant ground movement impacting 59.49 hectares. The analysis indicates that high-density urban areas are particularly susceptible to subsidence, correlating with increased structural damage and flood vulnerability. The study underscores the importance of integrating geotechnical, urban planning, and environmental management strategies to address subsidence. We recommend the implementation of adaptive urban planning measures and enhanced monitoring techniques using satellite data to mitigate the impact of subsidence on infrastructure and communities. These findings provide a critical foundation for developing resilient urban environments in coastal cities prone to geological and anthropogenic subsidence.

Mapping of Land Surface Deformation Using Ps-Insar for Disaster Risk Management in the Future

DKI Jakarta is experiencing land subsidence due to overexploitation of its use and the increasing population. It is feared that this decline or deformation will occur in the location of the new national capital. The research objective is "Mapping of Land Surface Deformation using PS-InSAR for Disaster Risk Management in the Future." Quantitative and qualitative research and data collection methods use secondary and primary data. Secondary data in the form of Permanent Scatterers Interferometry Synthetic Aperture Radar (PS-InSAR) Sentinel-1A images to determine soil deformation. Primary data uses a questionnaire to assess disaster risk management. Data analysis uses spatial and statistical analysis. Spatial analysis for land deformation mapping and statistical analysis for risk management. The results showed that the pattern of land deformation before the determination of the location of the capital city of Indonesia was random. On the other hand, after decision-making, it appears to be more systematic and homogeneous in adjacent areas with a decreasing range of about 5 cm per year. Other findings show that disaster risk management carried out by several agencies, especially the problem of land deformation in East Kalimantan, is still far from expectations and very minimal. The findings can be used for future disaster risk management to minimize negative impacts and reduce disaster risk.Keywords: PS-INSAR; Land Deformation; Capital City; Disaster Risk Management

Analisis Bencana Longsor akibat Gempa bumi Cianjur 2022 dengan DInSAR

Indonesia is a country with a high potential for disasters such as earthquakes, which usually cause changes in the shape/position of the earth's surface (deformation). The Cianjur area has a seismic vulnerability level value based on maximum ground acceleration, maximum intensity and earthquake return period. On Monday, November 21, 2022 at 13.21 WIB there was an earthquake with a magnitude of 5.6 M in the Cianjur area of West Java based on BMKG data, until November 22, 2022 there were 140 aftershocks with magnitudes of 1.2-4.2 and an average depth of approximately 10 km. As a result, causing subsidence, the purpose of this study is to determine the magnitude of subsidence in landslides due to the 2022 Cianjur earthquake using the DInSAR method. Vertical land subsidence (displacement data) due to the earthquake from DInSAR observations on displacement data, which ranged up to -101mm vertically with two soil types andosol and cambisol Overlay analysis of soil type and displacement data showed the highest area of displacement, found a match based on information circulating that Cugenang Sub-district is the most severely affected area (the existence of a new fault "Cugenang").

3D deformation velocity field analysis and TEM method to detect the tectonic influence on the land subsidence in Zamora, Mexico

The city of Zamora is situated in a tectonic basin and, according to previous interferometric studies, has been experiencing sinking up to 13 cm/yr (2007-2011). Although the reported subsidence pattern (WNW-ESE) is similar to the orientation of the regional fault system, there is a lack of detailed studies to establish the connection between this phenomenon, the thickness of sediments, and the basement geometry. Therefore, this paper presents a 3D deformation velocity field analysis for the period of 2014–2021, using 130 images from the Sentinel-2 satellite to calculate the vertical and east-west components of the subsidence. Likewise, a campaign of 28 Transient Electromagnetic soundings was carried out around the city to determine the thickness of sediments and bedrock geometry. The results of the interferometric analysis reveal a WNW-ESE sinking pattern with the maximum vertical velocities observed near the Zamora Museum, amounting to 108 mm/yr (2014–2017) and 102 mm/yr (2018–2021). Meanwhile, the horizontal component has a heterogeneous behavior in both periods with rates reaching up to 1 cm/year. The Transient Electromagnetic survey results show the presence of two grabens, a horst, and a series of half-grabens. The thickness of sediments varies between 94 and 314 m, with the latter corresponding to the main graben located beneath the city. The axis of this structure and the largest sediment deposits are linked to the area of maximum sinking and the spatial pattern of subsidence.

Surface deformation monitoring of Raniganj coalfield, India, using advanced InSAR and DGPS

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.

Detection of land subsidence using hybrid and ensemble deep learning models

Detection of land subsidence using hybrid and ensemble deep learning models

Spatiotemporal characterization of the subsidence and change detection in Tehran plain (Iran) using InSAR observations and Landsat 8 satellite imagery

Urban areas worldwide are increasingly facing challenges related to land subsidence, a phenomenon exacerbated by uncontrolled groundwater extraction and urban expansion. This research focuses on the Tehran plain, Iran's capital city, where significant subsidence has been observed due to uncontrolled migrations influenced by various economic and political factors. This expansion has increased demand for energy, notably water, leading to irregular water withdrawals from underground sources and, consequently, land subsidence. Monitoring this subsidence, particularly its effects on urban infrastructure, has become a critical challenge. This research first reviewed the existing body of knowledge related to subsidence measurement in the Tehran plain with an emphasis on their findings and limitations and then used radar images to study the subsidence patterns in the Tehran plain from 2016 to the end of 2020. Finally, the results collaborated by optical imagery analysis to find the relationship between surface change detection and spatiotemporal distribution of subsidence. As a result, through processing Sentinel-1A SAR images, consistent vertical displacements (subsidence) were observed, especially in areas heavily reliant on groundwater from wells, with some areas experiencing a rate of more than −20 mm/year. Horizontal displacement, however, was approximately about ±8 mm/year. Also, our results show that the subsidence rate in this plain has decreased in recent years. Therefore, the study integrated multispectral satellite data to clarify this issue and compensate for missing groundwater level data, specifically the Normalized-Difference Vegetation Index (NDVI) and Normalized-Difference Moisture Index (NDMI). These datasets were used to monitor changes in vegetation cover distribution and moisture in response to the variations of groundwater depth over time. The results of this research can be beneficial in adequately managing groundwater resource utilization to reduce the potential damage to infrastructure and the environment.

DACLnet: A Dual-Attention-Mechanism CNN-LSTM Network for the Accurate Prediction of Nonlinear InSAR Deformation

Nonlinear deformation is a dynamically changing pattern of multiple surface deformations caused by groundwater overexploitation, underground coal mining, landslides, urban construction, etc., which are often accompanied by severe damage to surface structures or lead to major geological disasters; therefore, the high-precision monitoring and prediction of nonlinear surface deformation is significant. Traditional deep learning methods encounter challenges such as long-term dependencies or difficulty capturing complex spatiotemporal patterns when predicting nonlinear deformations. In this study, we developed a dual-attention-mechanism CNN-LSTM network model (DACLnet) to monitor and accurately predict nonlinear surface deformations precisely. Using advanced time series InSAR results as input, the DACLnet integrates the spatial feature extraction capability of a convolutional neural network (CNN), the advantages of the time series learning of a long short-term memory (LSTM) network, and the enhanced focusing effect of the dual-attention mechanism on crucial information, significantly improving the prediction accuracy of nonlinear surface deformations. The groundwater overexploitation area of the Turpan Basin, China, is selected to test the nonlinear deformation prediction effect of the proposed DACLnet. The results demonstrate that the DACLnet accurately captures developmental trends in historical surface deformations and effectively predicts surface deformations for the next two months in the study area. Compared to traditional LSTM and CNN-LSTM methods, the root mean square error (RMSE) of the DACLnet improved by 85.09% and 68.57%, respectively. These research results can provide crucial technical support for the early warning and prevention of geological disasters and can serve as an effective alternative tool for short-term ground subsidence prediction in areas lacking hydrogeological and other related data.

Evaluation of Land Subsidence Susceptibility in Kunming Basin Based on Remote Sensing Interpretation and Convolutional Neural Network

Evaluation of Land Subsidence Susceptibility in Kunming Basin Based on Remote Sensing Interpretation and Convolutional Neural Network

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.

Identifying Factors Influencing Surface Deformations from Underground Mining Using SAR Data, Machine Learning, and the SHAP Method

The article presents the results of significance analyses of selected mining and geological variables for an area of underground mining activity. The study area was a region of an underground copper ore mine located in southwest Poland. The input data consisted of satellite radar data from the Sentinel 1 mission as well as mining and geological data. The line-of-sight subsidence, calculated with the use of the small baseline subset method and arranged in time series, was decomposed to extract the vertical component. The significance analysis of individual variables for the observed surface subsidence was performed using the SHapley Additive exPlanations method for the XGBoost machine learning model. The results of the analysis showed that the observed ground surface subsidence velocities were most influenced by the thickness of the PZ3 layer, which is located approximately 200 m above the roof of the mined seam, the thickness of the seam, and the timing of mining. It was also found that the proposed model was able to detect a nonlinear relationship between the analyzed excavations. The most significant influence on ground subsidence over mine excavations are mining parameters such as the spatially averaged thickness of the deposit and the time since liquidation of the deposit. The proposed approach can be successfully employed in planning both mining operations and mine closure in such a manner that the environmental impact is minimized.

Assessments of Land Subsidence in the Tehran Metropolitan Using Satellite Radar Interferometry Technique

Background and objective Many cities in the world have land subsidence problems because of environmental issues related to urban development. The arid and semi-arid climatic conditions prevailing in most of the interior regions of Iran and the need for the increasing industrial, agricultural, and drinking water exploitation from underground water resources, as well as urban development, have increased the occurrence of land subsidence in Iran, especially in the metropolis of Tehran. This study aims to assess land subsidence in Tehran, Iran, using the satellite radar interferometry (InSAR) technique. Method In this research, 31 images of Sentinel-1 were processed using the time series of persistent scatter interferometry (PSI) in 2022, and a map of the average annual rate of land subsidence in Tehran metropolis was prepared. Finally, after validating the results of interferometry with GPS station observations, the relationship between land subsidence and exploitation of underground water resources was examined as the most important factor in ground surface changes in the study area. Results The findings showed a decreasing trend in land subsidence from plain to urban areas. The highest land subsidence, with a rate of -43 mm per year occurred in the southern and southwestern parts of Tehran. Districts 10, 11, 12, 16, 17, 18, 19, and 20, which comprise about 26% of Tehran’s population, were experiencing land subsidence. The drop in the water level of observation wells was considerable in the areas where the highest rate of land subsidence occurred. Another finding of the research was the decrease of the underground water level from the north to the south of Tehran, indicating that the water depth in the southern areas of Tehran decreased due to human activities such as water pumping. Conclusion There is an increasing pressure in the aquifer through pumping water from wells in the study area for various industrial, agricultural and drinking water uses which has led to the increase in the occurrence of land subsidence.

Monitoring and Cause Analysis of Land Subsidence along the Yangtze River Utilizing Time-Series InSAR

Time-series monitoring of the land subsidence in the Yangtze River coastal area is crucial for maintaining river stability and early warning of disasters. This study employed PS-InSAR and SBAS-InSAR techniques to monitor the land subsidence along the Yangtze River in Nanjing, using a total of 42 Sentinel-1A images obtained between April 2015 and November 2021. The accuracy of both methods was compared and validated, while a comprehensive analysis was conducted to ascertain the spatial distribution characteristics and underlying causes of land subsidence. The maximum deviation between the two methods and six leveling point data did not exceed ±5 mm. Within the 5 km buffer zone on either side of the Yangtze River in Nanjing, four subsidence funnels were identified. Analysis of the factors contributing to land subsidence in this area indicates that underground engineering construction and operation, increasing ground building area, and geological condition all have certain correlations to the land subsidence. The results obtained through PS-InSAR and SBAS-InSAR technologies revealed a high degree of consistency in monitoring outcomes, and the latter method exhibited superior monitoring accuracy than the former one in this area. This study holds significant implications for guiding the scientific management of urban geohazards along the Yangtze River.

The effects of land subsidence and its mitigating measures on shallow groundwater salinization in the low-lying coastal plain of East Japan

Land subsidence in low-lying coastal regions results from geological and human factors, causing inundation during high tides. Mitigation measures, like pumping stations and ditch systems, aim to address this challenge. However, their impact on groundwater salinity near tidal rivers is understudied. Using a coupled surface-subsurface model, we investigate this issue in the lower Nabaki River region, Shirako Town, Japan. The simulation reveals adverse effects of pumping stations that induce intrusion of saline water from the tidal river into surrounding groundwater. While they are designed to prevent floods, these stations and ditches may inadvertently raise groundwater vulnerability to saltwater contamination. Despite 2D model limitations, it offers valuable insights into coastal groundwater dynamics and salinization. This study provides important information for policymakers and land managers to better understand the consequences of flood mitigation strategies on groundwater quality in vulnerable coastal areas.

Utilising InSAR for monitoring infrastructure at national scales

Interferometric SAR (InSAR) is now a widely used tool for the monitoring of infrastructure, in particular mines, dams, and oil and gas facilities. The technology is also widely used for the monitoring of construction activities such as tunnelling. With the growth of globally available and continuously updated SAR datasets, the monitoring of entire regional and national infrastructure networks is now feasible. Here we show how continuously updated, network-wide, InSAR datasets can be used to proactively manage large infrastructure networks such as roads, railways, and water pipelines. In the UK continuously updated InSAR data (derived from Sentinel-1 SAR data) is being effectively used across the entire railway network to proactively monitor potential embankment failures, large-scale landslides, and potential hazards outside of the boundary fence. A challenge of using InSAR data in this way is the huge volume of data that is available, as such custom risk metrics and machine learning algorithms are applied to highlight some of the areas at heightened risk. InSAR is also being applied to tackle the issue of water pipe leakage caused by natural ground movements, such as soil shrink-swell. In this case, regularly updated InSAR data has been used in the UK and Australia to detect areas at elevated risk of pipeline failures based on ground movement patterns, and by combining historical movement data and known failure events it is possible to refine such models. With expanding global SAR constellations proactive, nationwide, monitoring of critical assets is now possible, with the challenge being how we derive insight from these very large datasets.