Ground Deformation Monitoring Research Articles

On the Monitoring of Small Islands Belonging to the Aeolian Archipelago by MT-InSAR Data

AbstractThe aim of the present work is to evaluate the performance of several MT-InSAR techniques based on satellite SAR data in monitoring ground deformation phenomena affecting complex scenarios such as small islands of volcanic origin. To such purpose, PS, SBAS and IPTA approaches are applied in the study of Lipari, Salina and Vulcano islands belonging to the Aeolian archipelago, southern Tyrrhenian Sea, Italy. The outcomes retrieved from each technique are then discussed in terms of intrinsic features, spatial coverage, linear trend and coherence. Moreover, the accuracy of PS, SBAS and IPTA results are evaluated by comparison with in-situ measurements from the GNSS network managed by INGV-OE and private operators considering different metrics. Experimental results show that in this case there is no preferred MT-InSAR technique in an absolute way but each of them has strengths and drawbacks that have to be taken into account in the monitoring of complex scenarios.

Non-linear ground deformation detection and monitoring using time series InSAR along the coastal urban areas of Pakistan.

Conventional geodetic methods rely on point measurements, which have drawbacks for detecting and tracking geologic disasters at specific locations. In this study, the time series Interferometric Synthetic Aperture Radar (InSAR) approach was incorporated to estimate non-linear surface deformation caused by tectonic, shoreline reclamation, and other anthropogenic activities in economically important urban regions of Pakistan's southern coast, which possesses around 270km. The shoreline is extended from the low-populated area on the premises of the Hub River in the west to the highly populated Karachi City and Eastern Industrial Zone, where we collected the Sentinel-1A C-band data from 2017 to 2023 to address urban security and threats to human life and property. The main advantage of opting for the non-linear persistent scatterer interferometric SAR (PSInSAR) approach for this study is that it exposes minute movements without any prior consideration of conventional monitoring techniques, making it valid in continuously varying regions. An average vertical displacement range of - 170 to + 82mm per year was found, which was used to investigate the potential correlation with the most effective causative parameters of deformation. The densely populated areas of the study area experience an annual subsidence of 170mm, and the less populated western region experiences an uplift of 82mm annually. Land deformation varies along the coast of the study area, where the eastern region is highly reclaimed and is affected by erosion. Groundwater table-depleting regions experienced high levels of land subsidence, and tectonic activities controlled vertical displacement in the region. Major variation was detected after an earthquake occurred along fault lines. This study was designed because a non-linear approach is required to address ground movement activities acutely, and it will make it possible to plan surface infrastructure and handle issues brought on by subsidence more effectively.

An Evaluation of Tropospheric Correction Models for InSAR in Ground Deformation Monitoring: A Case Study in Zhejiang Province, China

Interferometric Synthetic Aperture Radar (InSAR) is a powerful and cost-effective technology to monitor ground deformation. Its accuracy is highly influenced by the atmospheric characteristic of the monitoring area. Separating the true ground deformation from atmospheric signals remains one of the major challenges in the application of InSAR. In this paper, the phase-based linear model, high-spatial resolution weather model (MERRA-2 and GACOS) and combination of the MERRA-2 and phase-based linear model are selected, and their performances in reducing the tropospheric delay are assessed based on the detrending standard deviation (DStd) of all Persistent Scattered (PS) points. A framework for the assessment is proposed and applied to a selected region of Shaoxing, Zhejiang Province, China. A total of 26 Sentinel-1A images are used and processed by the method of PS-InSAR. It is found that the phase-based linear model outperforms the other models by at least 6.6% if the whole monitoring time span of the SAR images in the study area is considered. The proper tropospheric correction model in different seasons is not the same. The phase-based linear model is robust against the variations in the atmospheric characteristics of the four seasons.

Geodetic Evidence for Cascading Landslide Motion Triggered by Extreme Rain Events at Joshimath, NW Himalaya

AbstractSlope instability due to tectonic, hydrological and anthropogenic activities cause severe landslides in Himalaya. Joshimath, a densely populated Himalayan town witnessed a catastrophic landslide event during December 2022 and January 2023 causing damages to ∼700 buildings. We use Interferometric synthetic aperture radar, Global Positioning System and rainfall measurements to probe the kinematics of the Joshimath landslide. We separate the seasonal and episodic deformation components using singular spectrum analysis. While the low amplitude annual landslide motions are modulated by seasonal precipitation, acceleration phases are triggered by extreme rain events. Our analysis revealed episodes of cascading motions triggered by extreme rain events resulting an overall increase in landslide velocity from −22 mm/yr during 2004–2010 to −325 mm/yr during 2022–2023. We estimate the landslide depth (∼30 m) and hydraulic diffusivity (∼3 × 10−5 m2/s) using a 1‐D pore‐water pressure diffusion model. Our study reveals the importance of systematic monitoring of ground deformation and weather parameters for landslide hazard mitigation.

Wide-Area Subsidence Monitoring and Analysis Using Time-Series InSAR Technology: A Case Study of the Turpan Basin

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.

Experimental study on progressive interfacial mechanical behaviors using fiber optic sensing cable in frozen soil

Frost heave and thaw settlement in cold regions pose a significant threat to engineering construction. Optical frequency domain reflectometry (OFDR) based on Rayleigh scattering can be applied to monitor ground deformation in frozen soil areas, where the interface behavior of soil-embedded fiber optic sensors governs the monitoring accuracy. In this paper, a series of pullout tests were conducted on fiber optic (FO) cables embedded in the frozen soil to investigate the cable‒soil interface behavior. An experimental study was performed on interaction effects, particularly focused on the water content of unfrozen soil, freezing duration, and differential distribution of water content in frozen soil. The high-resolution axial strains of FO cables were obtained using a sensing interrogator, and were used to calculate the interface shear stress. The interfacial mechanical response was analytically modeled using the ideal elasto‒plastic and softening constitutive models. Three freezing periods, correlating with the phase change process between ice and water, were analyzed. The results shows that the freezing effect can amplify the peak shear stress at the cable-soil interface by eight times. A criterion for the interface coupling states was proposed by normalizing the pullout force‒displacement information. Additionally, the applicability of existing theoretical models was discussed by comparing the results of theoretical back‒calculations with experimental measurements. This study provides new insights into the progressive interfacial failure behavior between strain sensing cable and frozen soil, which can be used to assist the interpretation of FO monitoring results of frozen soil deformation.

Mitigation of tropospheric delay induced errors in TS-InSAR ground deformation monitoring

ABSTRACT Interferometric Synthetic Aperture Radar (InSAR) is capable of detecting crust deformation. However, the accuracy is limited by spatiotemporal changes in the lower troposphere. In this paper, we constructed a periodic zenith total delay negative exponential function (PZTD-NEF) model of atmospheric spatiotemporal variation characteristics based on ERA-5 data to alleviate the temporal oscillation bias introduced by tropospheric delay and improve the accuracy of time series InSAR (TS-InSAR) inversion of surface deformation. We evaluated the model’s performance using the phase standard deviation (STD), atmospheric delay correlation coefficient with topography and the spatial structure function. The results were compared with a linear topography-dependent empirical model, generic atmospheric correction online service (GACOS) and ERA-5 methods. Our method reduces the STD of the phase of 83% of the interferograms by 12.8%. For vertical stratification delay correction, the correlation between the proposed method and the Linear, GACOS, and ERA-5 reached 0.734, 0.708, and 0.729, respectively. We found that accounting for spatiotemporal variation characteristics of tropospheric delay can alleviate the seasonal oscillations of vertical stratification delay and improve the accuracy of the deformation time series solution by 40.04%. We also used the Kunming continuous operation reference station system (KMCORS) to verify the displacement results of our method.

Ground deformation monitoring via PS-InSAR time series: An industrial zone in Sacco River Valley, central Italy

Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) is an advanced technique enabling effective ground deformation monitoring. In this study, PS-InSAR time series of Sentinel-1 ascending and descending orbits for period 2015–2022 are utilized for an industrial zone in Sacco River Valley, Central Italy. The Sequential Turning Point Detection (STPD) is applied to estimate the trend turning points and their directions in PS-InSAR time series. In addition, river flow and climate time series for Sacco River near the industrial zone are analyzed using the coefficient of determination and the Least-Squares Cross-Wavelet Analysis (LSCWA) to investigate their potential impact on ground deformation. A significant land subsidence was observed prior to Fall 2016 likely as the result of drought and excessive water extraction followed by land uplift after Fall 2017 likely due to groundwater rebound. The LSCWA showed statistically significant seasonal coherency between precipitation and streamflow in 2018 when relatively much higher precipitation and streamflow were observed in this year compared to 2016 and 2017, potentially contributing to the land uplift in the study zone during 2018. These results not only highlight the capabilities of STPD for detecting trend turning points and LSCWA for analyzing streamflow and precipitation time series but also can help policymakers and stakeholders for developing a sustainable city and environment.

Automated deformation detection and interpretation using InSAR data and a multi-task ViT model

Many geological hazards are associated with ground deformations. Prompt and accurate detection and interpretation of ground deformation is therefore vital to geohazard mitigation. Multitemporal Interferometric Synthetic Aperture Radar (MT-InSAR) is an effective geodetic technique for monitoring ground deformation. However, accurate computation and interpretation of deformation using InSAR are often hindered by various errors and a lack of expert knowledge. We present a new advanced deep learning model based on a multi-task vision transformer (MT-ViT) to automatically detect, locate, and interpret deformation using single interferograms. To address the issue of limited training data in InSAR applications, the proposed model utilizes pre-trained weights from optical images and transfers them to a simulated InSAR dataset. Then real interferograms are used to fine-tune the weights in the network. An overall loss function is designed, which considers the classification and localization losses in the model. The effectiveness of the proposed model is demonstrated using both simulated and real InSAR datasets that contain either coseismic or volcanic deformation. The experimental results from the model are also compared with the state-of-the-art convolutional neural network (CNN) based techniques. The results show significant improvement in both the accuracy of the results and the computational efficiency over the CNN-based approaches. The MT-ViT model achieved 99.4 % classification accuracy, 54.1 % mean intersection over union (IOU), and 0.9 km localization accuracy. A comprehensive evaluation of the hyperparameters in training the MT-ViT model was carried out, which will inform future research in this direction. The research results highlight the promising capabilities of MT-ViT in near real-time deformation monitoring and automated deformation interpretation.

Analysis and prediction of ground deformation in Yinxi Industrial Park based on time-series InSAR technology.

Monitoring ground deformation in industrial parks is of great importance for the economic development of urban areas. However, limited research has been conducted on the deformation mechanism in industrial parks, and there is a lack of integrated monitoring and prediction models. Therefore, this study proposes a comprehensive monitoring and prediction model for industrial parks, utilizing time-series Interferometry Synthetic Aperture Radar (InSAR) technology and the Whale Optimization Algorithm-Back Propagation (WOA-BP) neural network algorithm. Taking Yinxi Industrial Park in Baiyin District as a case study, we used 68 scenes of Sentinel-1A ascending and descending orbit data from June 2018 to April 2021. The Stanford Method for Persistent Scatterers-Permanent Scatterers (StaMPS-PS) and the Small Baseline Subsets-Interferometry Synthetic Aperture Radar (SBAS-InSAR) technologies were employed to obtain the surface deformation information of the park. The deformation information obtained by the two technologies was cross-validated in terms of temporal and spatial distribution, and the vertical and east-west deformation of the park was obtained by combining the ascending and descending orbit data. The results show that the deformation feature points in the line of sight (LOS) direction obtained by the two technologies have a high consistency in spatial distribution, using the ascending orbit data as an example. Additionally, the SBAS-InSAR technology was used to obtain the east-west and vertical deformation results of the park after merging the ascending and descending orbit data for the same period. It was found that the park is mainly affected by vertical deformation, with a maximum subsidence rate of 14.67mm/yr. The subsidence areas correspond to the deformation positions observed in field survey photos. Based on the ascending orbit deformation data, the two technologies were validated with 585 points of the same latitude and longitude, and the coefficient of determination R2 was found to be 0.82, with a root mean square error (RMSE) of 2.20mm/a. The deformation rates were also highly consistent. Due to the 47% increase in the number of sampling points provided by the StaMPS-PS technique compared to the SBAS-InSAR technique, the former was found to be more applicable in the industrial park. Based on the ground deformation mechanism in the park, we combined the StaMPS-PS technique with the WOA-BP neural network to construct a deformation zone prediction model. We conducted predictive studies on the deformation zones of buildings and roads within the park, and the results showed that the WOA-optimized BP neural network achieved higher accuracy and lower overall error compared to the unoptimized network. Finally, we analyzed and discussed the geological conditions and inducing factors of ground deformation in the park, providing a reference for a better understanding of the deformation mechanism and early warning of disasters in the industrial park.

A fast and robust method for detecting trend turning points in InSAR displacement time series

Ground deformation monitoring is a crucial task in geohazard management to ensure the safety of lives and infrastructure. Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is an advanced technique for measuring small displacements on the Earth’s surface. Estimated PS-InSAR time series acquired by Sentinel-1 satellites provide a great opportunity for effective monitoring of ground deformation in recent years. However, challenges arise when processing these time series due to their non-uniform sampling, noise from atmosphere and preprocessing issues including phase unwrapping and others. Therefore, estimating the location and direction of trend turning in such time series, as an indicator of ground deformation, is not an easy task. In this work, a sequential turning point detection method (STPD) is proposed and compared with other change point detection methods. Using a large set of simulated time series with various noise types, it is shown that STPD outperforms other methods in terms of overall accuracy and root mean square error for location and direction of trend turnings. As a case study, STPD is applied to detect turning points within PS-InSAR time series for the province of Frosinone in Italy and classified using topography and land cover/use. In addition, an area susceptible to landslides is selected to estimate the starting dates of potential slow-moving landslides. It is also shown that the turning points in the local precipitation time series have a high correlation with the ones in the PS-InSAR time series, indicating that precipitation is a major triggering factor of the displacements in the area. The STPD can rapidly and effectively detect locations and directions of trend turnings and is freely available online in both MATLAB and python.

SAR interferometry on full scatterers: Mapping ground deformation with ultra-high density from space

Spaceborne time series SAR interferometry (TS-InSAR) technology has been widely applied in ground deformation monitoring. The current popular TS-InSAR are carried out mainly on permanent scatterers (PS) or distributed scaterrers (DS), which can map the ground deformation on sparse targets with high or moderate coherence. However, high-precision and high-density deformation monitoring is always restricted by atmospheric artifacts and surface decorrelation, especially over nonurban areas. Here we show that the ground deformation can be precisely mapped with high density by spaceborne InSAR technique. We proposed a full scatterers InSAR (FS-InSAR) methodology, which can significantly improve the quality of the interferograms by applying a dual-scale temporal low-pass filter (DTLF) to separate both atmospheric and noisy phases from deformation phases, without external atmospheric data. Simulation experiments were conducted to determine the small-scale and large-scale window sizes and evaluate the effectiveness of DTLF. Then we applied the FS-InSAR method to the Tianjin-Tangshan region, China using Sentinel-1 data, assessed the regenerated differential low-frequency phases, and validated the results with leveling measurements and groundwater depth data. What's more, detailed ground deformation was retrieved over the Tangshan mining area with an unprecedented density of 98.55%. Our results demonstrate that the FS-InSAR strategy contrasts sharply with the previous PS-InSAR or DS-InSAR methods by simultaneously solving the two bottleneck problems of atmospheric artifacts and decorrelation in most cases except for water bodies or dense vegetations such as rainforest, thereby it is of great importance for future monitoring and understanding the ground deformation to prevent and control geological disasters.

Oilfield Reservoir Parameter Inversion Based on 2D Ground Deformation Measurements Acquired by a Time-Series MSBAS-InSAR Method

Time-series ground deformation monitoring and reservoir parameter inversion are crucial for the dynamic assessment of oilfield resources and sustainable exploitation in oilfields. As some of the regions with the richest oil reserves in China, the oilfield areas in the western Qaidam Basin were selected as a typical study area. Firstly, we used SAR images collected by the Sentinel-1A satellite from January 2021 to December 2022 and applied the multidimensional small baseline subset (MSBAS) method to obtain vertical and east–west deformation measurements. On this basis, a nonlinear Bayesian inversion method was applied to model the shallow reservoir in a series of complex deformation areas, based on a single-source model and a multi-source model, respectively. As a result, the ground deformation monitoring results obtained by long time-series InSAR clearly reflect the uneven ground deformation caused by the oil extraction and water injection operation processes. There was slight subsidence in the Huatugou oilfield, while significant uplift deformation occurred in the Ganchaigou oilfield and the Youshashan oilfield, with a maximum uplift rate of 48 mm/year. Further analysis indicated that the introduction of the 2D deformation field helps to improve the robustness of oilfield reservoir parameter inversion. Moreover, the dual-source model is more suitable than the single-source model for inverting reservoir parameters of complex deformation. This study not only fills the gap of InSAR deformation monitoring for the oilfields in the western Qaidam Basin but also provides a theoretical reference for the model and method selection of reservoir parameter inversion in other oilfields.

A Statistical Approach for the Integration of Multi-Temporal InSAR and GNSS-PPP Ground Deformation Measurements.

Determining and monitoring ground deformations is critical for hazard management studies, especially in megacities, and these studies might help prevent future disaster conditions and save many lives. In recent years, the Golden Horn, located in the southeast of the European part of Istanbul within a UNESCO-protected region, has experienced significant changes and regional deformations linked to rapid population growth, infrastructure work, and tramway construction. In this study, we used Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) techniques to investigate the ground deformations along the Golden Horn coastlines. The investigated periods are between 2015 and 2020 and 2017 and 2020 for InSAR and GNSS, respectively. For the InSAR analyses, we used sequences of multi-temporal synthetic aperture radar (SAR) images collected by the Sentinel-1 and ALOS-2 satellites. The ground displacement products (i.e., time series and velocity maps) were then cross-compared with those achievable using the Precise Point Positioning (PPP) technique for the GNSS solutions, which can provide precise positions with a single receiver. In the proposed analysis, we compared the ground displacement velocities obtained by both methods by computing the standard deviations of the difference between the relevant observations considering a weighted least square estimation procedure. Additionally, we identified five circle buffers with different radii ranging between 50 m and 250 m for selecting the most appropriate coherent points to conduct the cross-comparison analysis. Moreover, a vertical displacement rate map was produced. The comparison of the vertical ground velocities derived from PPP and InSAR demonstrates that the PPP technique is valuable. For the coherent stations, the vertical displacement rates vary between -4.86 mm/yr and -23.58 mm/yr and -9.50 and -27.77 mm/yr for InSAR and GNSS, respectively.

Kalman filter-based integration of GNSS and InSAR observations for local nonlinear strong deformations

The continuous monitoring of ground deformations can be provided by various methods, such as leveling, photogrammetry, laser scanning, satellite navigation systems, Synthetic Aperture Radar (SAR), and many others. However, ensuring sufficient spatiotemporal resolution of high-accuracy measurements can be challenging using only one of the mentioned methods. The main goal of this research is to develop an integration methodology, sensitive to the capabilities and limitations of Differential Interferometry SAR (DInSAR) and Global Navigation Satellite Systems (GNSS) monitoring techniques. The fusion procedure is optimized for local nonlinear strong deformations using the forward Kalman filter algorithm. Due to the impact of unexpected observations discontinuity, a backward Kalman filter was also introduced to refine estimates of the previous system’s states. The current work conducted experiments in the Upper Silesian coal mining region (southern Poland), with strong vertical deformations of up to 1 m over 2 years and relatively small and horizontally moving subsidence bowls (200 m). The overall root-mean-square (RMS) errors reached 13, 17, and 35 mm for Kalman forward and 13, 17, and 34 mm for Kalman backward in North, East, and Up directions, respectively, in combination with an external data source - GNSS campaign measurements. The Kalman filter integration outperformed standard approaches of 3-D GNSS estimation and 2-D InSAR decomposition.

Nationwide urban ground deformation in Japan for 15 years detected by ALOS and Sentinel-1

InSAR time series analysis has become a major tool for nationwide land deformation monitoring. Sentinel-1 SAR data have enabled us to measure and monitor ground deformation globally with high accuracy and resolution through InSAR time series analysis, due to its constant and frequent global coverage and open data policy since 2014. Although several datasets from previous SAR satellites were available before Sentinel-1, such comprehensive deformation monitoring was not performed due to several limitations such as data quality, analysis technique, data policy, and processing capacity at that time. However, since a large amount of ALOS InSAR products and an open-source InSAR time series analysis tool LiCSBAS have become openly and freely available, we can easily derive the deformation from 2006 to 2011 by using them. In this study, we detected the deformation time series and velocity in all major urban areas in Japan from 2006 to 2011 and compared the results with the deformation from 2014 to 2020 detected by Sentinel-1 data. The two deformation datasets with different time periods revealed various 15-year deformation histories, such as long-term constant subsidence in Tomakomai and Niigata, changes in deformation areas and/or velocities in Hirosaki, Kujyukuri, Kanazawa, and Matsushiro, and appearance or disappearance of deformation in Joso, Yoyogi, and Kyoto. Future abundant and continuous SAR data acquisitions will reveal more long-term deformation transitions and help to understand the details of the mechanisms.

Ground Deformation Monitoring for Subway Structure Safety Based on GNSS

Ground deformation poses a serious threat to the safety of subway structures. Consequently, intelligent and efficient automated safety monitoring of ground deformation along the subway has become urgent. Traditional engineering observation methods have the disadvantages of difficulties with datum selection, non-automation, and poor reliability. A ground deformation monitoring system for subway structure safety based on the Global Navigation Satellite System (GNSS) was established and validated through experimental comparisons with traditional precision leveling in this study. Based on the GNSS monitoring points, the continuous kinematic observation GNSS data of ground deformation along the subway line were obtained; a joint robust local mean decomposition (RLMD)–singular value decomposition (SVD) noise-reduction processing method for GNSS signals was proposed to realize the real-time and high-precision monitoring of ground deformation. The results show that the proposed combined noise-reduction method can reduce the maximum noise amplitude by 86%. When compared with the accuracy of the traditional precision leveling method, it was determined that the vertical positioning accuracy of the deformation monitoring system is greater than 2.7 mm, the horizontal positioning accuracy is greater than 1.3 mm, and the measurement error is less than 1.5 mm. The deformation monitoring system has the advantages of convenience, automation, and high accuracy and can be applied to ground deformation monitoring for subway structures.

Understanding spatially nonstationary effects of natural and human-induced factors on land subsidence based on multi-temporal InSAR and multi-source geospatial data: a case study in the Guangdong-Hong Kong-Macao Greater Bay Area

ABSTRACTLand subsidence, a common geological phenomenon in deltaic regions, poses significant risks to infrastructures, environments, and human lives. Monitoring and understanding land subsidence are crucial for establishing resilient, adaptive, and sustainable environments. In this study, a robust multi-temporal interferometric synthetic aperture radar (MTInSAR) method was employed to monitor land subsidence in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) using 541 Sentinel-1 SAR images. Spatial auto- and cross-correlation analysis were applied to select the most reasonable explanatory variables from nine candidates in each city of the GBA. City-level geographically weighted regression (GWR) models were then constructed to explore the spatially nonstationary effects of natural and human-induced factors. The findings revealed subsidence velocities ranging from 0 to 83.7 mm/yr in the GBA from 2015 to 2021, with the dominant factors affecting subsidence varying among regions. Natural and human-related factors accounted for 51.13% and 48.87%, respectively. Further analysis of representative subsidence areas highlights the importance of continuous monitoring of ground deformation using MTInSAR, region-specific land subsidence mitigation strategies, and regular maintenance of urban infrastructure. These insights are valuable for policymakers and urban planners in comprehending the complex processes underlying land subsidence and informing decision-making processes for sustainable and resilient urban development.

Underground Gas Storage Monitoring Using Free and Open Source InSAR Data: A Case Study from Yela (Spain)

Gas consumption is subject to large seasonal fluctuations between the summer season (period with lower request) and the winter season (time with increased consumer demand). Underground gas storage applications (UGS) help to ensure a steady and reliable supply of natural gas, even during periods of peak demand, smoothing price fluctuations and providing a means of balancing the supply and demand of natural gas on a daily, weekly, or seasonal basis. However, UGS activities can induce vertical ground displacement, which is usually strictly associated with the injection and withdrawal of gas into/from the reservoir. It is necessary to carefully monitor and manage the potential impact of UGS activities on the subsurface and surface to ensure the stability and safety of the local environment. The Interferometric Synthetic Aperture Radar (InSAR) technique can provide a wide range of high-precision information on seasonal surface deformation associated with UGS activities useful for increasing the amount of information on ground deformation monitoring. This study introduces a unique and replicable approach to investigating freely available ground movement data for a fractured aquifer reservoir located in the Madrid Basin (Guadalajara, Spain), which is currently employed for seasonal underground gas storage applications. Notably, this study gives a comprehensive comparison of InSAR results of UGS activity in a deep aquifer, leveraging data that are entirely open-source and easily accessible. The Yela UGS project exploits a carbonate reservoir (dolomite) managed, since 2012, by Enagás, the Spanish main Transmission System Operator (TSO). InSAR data from 2015 to 2021 provided a full and coherent ground deformation pattern of the area. Based on this data, a fully integrated volumetric variation model was developed, elucidating the effects of gas storage activity. A significant correlation between the periodic injection/withdrawal rates of natural gas and InSAR ground deformation over time was identified.

A distributed fibre optic monitoring method for ground subsidence induced by water pipeline leakage

After a water supply pipeline experiences leakage due to damage, the surrounding soil is prone to erosion, leading to the formation of underground voids. As the voids gradually expand, there is a high possibility of triggering ground subsidence accidents. Monitoring ground deformation is the simplest and most effective method for capturing the process of ground subsidence development. Among various methods of ground deformation monitoring, distributed optical fibre sensing has advantages such as real-time performance, distributed measurement, and ease of networking. This paper investigates the monitoring effectiveness of distributed optical fibre strain sensing technology on ground subsidence induced by underground voids under different conditions through indoor model experiments. To convert the measured strain of the optical fibre into ground subsidence, this paper proposes a strain–displacement conversion method based on the sequence-to-sequence (Seq2Seq) model. The method is validated using data from indoor model experiments. The research results indicate that distributed optical fibre sensing technology can effectively monitor the strain distribution of soil during the subsidence process. It can identify the development location of ground subsidence and reveal the deformation evolution patterns. The strain–displacement intelligent conversion model based on Seq2Seq can convert the strain monitored by the optical fibre during the ground subsidence process into ground subsidence. Compared to traditional methods, this conversion model exhibits higher accuracy.