基于InSAR技术对长白山进行地表形变监测

Monitoring the surface deformation is of great significance, in order to explore the activity and geophysical features of the underground deep pressure source in the volcanic regions. In this study, the time series surface deformation of the Changbaishan volcano is retrieved via Sentinel-1B SAR data, using the SBAS-InSAR method. The main results are as follows. (1) The mean surface deformation velocity in the Changbaishan volcano is uplifted as a whole, while the uplift is locally distributed, which shows a strong correlation with faults. (2) The time series surface deformation of the Changbaishan volcano indicates an apparently seasonal change. (3) The cumulative surface deformation shows a strong correlation with the maximal magnitude and number of annual earthquakes, and it is likely dominated by the maximal magnitude of the annual earthquakes. (4) The single Mogi source model is appropriate to evaluate the deep pressure source in the Changbaishan volcano, constrained by the calculated surface deformation. The optimal estimated depth of the magma chamber is about 6.2 km, and the volume is increased by about 3.2 × 106 m3. According to the time series surface deformation, it is concluded that the tectonic activity and faults, related to the deep pressure source, are pretty active in the Changbaishan volcano.

Interferometric synthetic aperture radar (InSAR) technology plays a crucial role in monitoring surface deformation and has become widely used in volcanic and earthquake research. With the rapid advancement of satellite technology, InSAR now generates vast volumes of deformation data. Deep learning has revolutionized data analysis, offering exceptional capabilities for processing large datasets. Leveraging these advancements, automatic detection of volcanic and earthquake deformation from extensive InSAR datasets has emerged as a major research focus. In this paper, we first introduce several representative deep learning architectures commonly used in InSAR data analysis, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and Transformer networks. Each architecture offers unique advantages for addressing the challenges of InSAR data. We then systematically review recent progress in the automatic detection and identification of volcanic and earthquake deformation signals from InSAR images using deep learning techniques. This review highlights two key aspects: the design of network architectures and the methodologies for constructing datasets. Finally, we discuss the challenges in automatic detection and propose potential solutions. This study aims to provide a comprehensive overview of the current applications of deep learning for extracting InSAR deformation features, with a particular focus on earthquake and volcanic monitoring.

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Landslide geological disasters, occurring globally, often result in significant loss of life and extensive economic damage. In recent years, the severity of these disasters has increased, likely due to the frequent occurrence of extreme rainstorms associated with global warming. This escalating trend emphasizes the urgent need for a simple and efficient method to identify hidden dangers related to landslide geological disasters. Areas experiencing seasonal heavy rainfall are particularly susceptible to such disasters, posing a serious threat to the lives and property of local residents. In response to the challenging characteristics of landslide geological hazards, such as their strong concealment and the high vegetation coverage in the Liupan Mountain area of the Loess Plateau, this study focuses on the integrated remote sensing identification and research of hidden landslide dangers in Longde County. The methodology combines differential interferometric synthetic aperture radar technology (D-InSAR) and high-resolution optical remote sensing. Surface deformation information of Longde County was obtained by analyzing 85 Sentinel-1A data from 2019 to mid-2020 using Stacking-InSAR, in conjunction with high-resolution optical remote sensing image data from GF-2 in 2019. Furthermore, the study conducted integrated remote sensing identification and field verification of landslide hazards throughout the entire county. This involved interpreting the shape and deformation marks of landslide hazards, identifying the disaster-bearing bodies, and expertly interpreting the environmental factors contributing to the hazards. As a result, 47 suspected landslide hazards and 21 field investigation points were identified, with 16 hazards verified with an accuracy of 76.19%. This outcome directly confirms the applicability and accuracy of the integrated remote sensing identification technology in the study area. The research results presented in this paper provide an effective scientific and theoretical basis for the monitoring and treatment of landslide geological disasters in the future stages. They also play a pivotal role in the prevention of such disasters.

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This study investigates ground displacement in Underground Gas Storage (UGS) areas using Interferometric Synthetic Aperture Radar (InSAR) techniques applied to free and open-source Sentinel-1 data extracted from the European Ground Motion Service (EGMS). Three UGS facilities in Lower Saxony, Germany, are examined to understand how reservoir type plays a key role in surface deformation. In pore storage reservoirs, such as those at Uelsen and Rehden, UGS operations cause pressure changes propagating to the surface and resulting in observable seasonal uplift and subsidence. Conversely, in the Etzel salt caverns, subsidence is inherent, with deformation rates influenced by the balance of gas withdrawal and injection. The analysis includes mapping the displacement velocity in both ascending and descending geometries, as well as the vertical and horizontal components of the displacement. The extracted time series of ground displacement reveal temporal relationships between UGS operations and surface fluctuations. By integrating InSAR data with the UGS operational records, this study highlights significant differences in ground displacement behaviours between geological settings. The Etzel site exhibited the most significant deformations, while the Uelsen and Rehden sites showed seasonal ‘breathing’ patterns. This work represents a novel application, filling a significant gap in the literature by using free and open-source data to investigate UGS activities over different geological contexts. The results highlight the complexity of the interactions between UGS operations and the environment, emphasising the need for further research to improve the sustainable management of subsurface resources.

Global assessments of landslide impact on critical communication infrastructure have become urgent because of rising occurrences related to human activities and climate change. The landslide and glacial slide susceptibility along the Karakoram Highway poses a significant threat to the infrastructure ecosystem, local communities, and the critical China–Pakistan Economic Corridor. This research paper utilized the Small Baseline Subset InSAR technique to monitor the deformation patterns over the past 5 years, yielding high-resolution insights into the terrain instability in this geologically active region. The SBAS time series results reveal that the substantial cumulative deformation in our study area ranges from 203 mm to −486 mm, with annual deformation rates spanning from 62 mm/year to −104 mm/year. Notably, the deformation that occurred is mainly concentrated in the northern section of our study area. The slope’s aspect is responsible for the maximum deformed material flow towards the Karakoram Highway via steep slopes, lost glacial formations, and the climate variations that cause the instability of the terrain. The given pattern suggests that the northern area of the Karakoram Highway is exposed to a greater risk from the combined influence of glacial slides, landslides, and climatic shifts, which call for the increased monitoring of the Karakoram Highway. The SBAS-InSAR method is first-rate in deformation monitoring, and it provides a scientific basis for developing real-time landslide monitoring systems. The line of sight limitations and the complexity and imprecision of weather-induced signal degradation should be balanced through additional data sources, such as field surveys to conduct large slide and glacial slide susceptibility evaluations. These research results support proactive hazard mitigation and infrastructure planning along the China–Pakistan Economic Corridor by incorporating SBAS-InSAR monitoring into the original planning. The country’s trade policymakers and national level engineers can enhance transport resilience, efficiently manage the landslide and glacial slide risks, and guarantee safer infrastructure along this strategic trade route.

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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.

Coastal subsidence is a geological disaster that has devastating consequences. However, an accurate understanding of its risks involves more than simply assessing the amount or rate of land subsidence. The existing methods used to evaluate geological disaster risks depend on extensive data collection, entail substantial workloads, suffer from error estimation challenges, and lack regional adaptability. These limitations prevent us from fully understanding coastal subsidence risks in estuarine deltas. Therefore, in this study, we propose a new subsidence risk assessment method that addresses the challenges of traditional geological risk assessments in terms of spatial coverage, spatiotemporal resolution, and data collection difficulty. First, Sentinel-1 multitemporal interferometric synthetic aperture radar (MT-InSAR) and cluster analysis were used to estimate the subsidence hazards. Subsequently, Landsat-8 imagery and a random forest (RF) classifier were used to obtain land use and land cover (LULC), and the analytic hierarchy process (AHP) was used to obtain settlement vulnerability. Thereafter, subsidence vulnerability was derived from the sediment layer thickness. By combining subsidence hazard, vulnerability, and susceptibility, the first subsidence risk map with a 30 m resolution was generated. The results showed that 4.54 % of the Yellow River Delta (YRD) area was high-risk, 8.75 % was medium-risk, and 10.14 % was low-risk. Notably, the risk map shows a clear overlap between high-risk and saltwater mining areas in the YRD. The proposed method is expected to improve our understanding of the coastal subsidence risk in estuarine deltas. Considering that the risk in high-value economic areas in the YRD is increasing, whereas the risk in low-value economic areas may change owing to human activity, early preventive measures are required.

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The fragile Loess Plateau of China suffers substantial gully erosion. It is imperative to elucidate gully erosion patterns for implementing effective erosion control strategies. However, high spatiotemporal resolution quantification of gully dynamics remains limited across the Loess Plateau landscape. We utilized the small baseline subset interferometric synthetic aperture radar (SBAS InSAR) technique to investigate the phenomenon of gully erosion and deposition on the Dongzhiyuan tableland, which sits within the vast expanse of the Loess Plateau in China, over the period spanning 2020-2022. The tableland edges subsided while gully bottoms uplifted due to sedimentation. Low elevations underwent active deformation. Slope, aspect, and curvature modulated uplift and subsidence patterns by affecting runoff and sediment transport. Gentle downstream slopes displayed enhanced sedimentation. Southern gullies showed pronounced uplift compared to northern gullies. Heavy rainfall triggered extensive erosion followed by rapid uplift, reflecting an adaptive oscillation between erosion and deposition. Basin hydrology correlated with spatial patterns of deformation. Vegetation cover above 60 % of the maximum substantially increased InSAR error. Our study reveals intricate spatiotemporal behaviors of erosion and deposition in loess gullies using time-series InSAR. The findings provide new insights into gully geomorphology and evolution, and our study quantifies gully erosion and deposition patterns at high spatiotemporal resolution, enabling identification of the most vulnerable areas and prioritization of conservation efforts.

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Changbaishan volcano (CBV), located on the border between China and North Korea, has undergone violent eruptions since the Oligocene, making it one of the most captivating and explosive volcanoes on Earth. However, the lack of precise characterization regarding the magmatic system makes it difficult to decipher its eruption risk and mechanism, despite its significant size and past devastating effects. In this study, we employed newly developed teleseismic receiver function techniques, including Ps and Sp waves, to construct a lithospheric structure model beneath the CBV region. The results show a thick crust (∼37 km) and a weak, thin lithosphere under the CBV, with a low-amplitude width of 80 km at the Moho depth and 200 km at the depth of the lithosphere-asthenosphere boundary. These features depict the seismological response of a translithospheric magmatic system beneath the CBV, where hot upwelling material rises through the lithospheric mantle, underplates at the base of the crust, and forms the magma chamber(s) at shallow depth. Such magmatic system features can be taken as a unifying paradigm for large volcanic regions worldwide.

The accurate prediction of landslide susceptibility relies on effectively handling landslide absence samples in machine learning (ML) models. However, existing research tends to generate these samples in feature space, posing challenges in field validation, or using physics-informed models, thereby limiting their applicability. The rapid progress of interferometric synthetic aperture radar (InSAR) technology may bridge this gap by offering satellite images with extensive area coverage and precise surface deformation measurements at millimeter scales. Here, we propose an InSAR-based sampling strategy to generate absence samples for landslide susceptibility mapping in the Badong–Zigui area near the Three Gorges Reservoir, China. We achieve this by employing a Small Baseline Subset (SBAS) InSAR to generate the annual average ground deformation. Subsequently, we select absence samples from slopes with very slow deformation. Logistic regression, support vector machine, and random forest models demonstrate improvement when using InSAR-based absence samples, indicating enhanced accuracy in reflecting non-landslide conditions. Furthermore, we compare different integration methods to integrate InSAR into ML models, including absence sampling, joint training, overlay weights, and their combination, finding that utilizing all three methods simultaneously optimally improves landslide susceptibility models.

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Continental intraplate volcano is an ideal probe to unravel the composition and structure of the deep Earth. The intraplate Changbaishan‐Tianchi volcano was one of the most hazardous eruptions on the Earth's planet. The long‐term activity of this volcano from the Pleistocene to 946 CE has erupted materials with a broad compositional range from basalt to rhyolite, which are expected to be associated with the continuous northeastward subduction of the Pacific plate, but the magma source remains controversial. In this paper, we present a comprehensive data set of in situ zircon Hf and O isotope data, combined with whole‐rock element and Sr‐Nd‐Pb isotope compositions, for selected eruptions of the Changbaishan‐Tianchi volcano, aiming to provide new insights into their magma source and the associated geodynamics. Radiogenic isotopic ratios and incompatible trace element compositions indicate that the erupted volcanic rocks at different stages, although with a varied differentiation degree, were derived from a common magma source characterized by a mixture of DM and EM1 end‐members. Zircon Hf and O isotopes are both relatively homogeneous for different lithologies and eruption stages, with the εHf(t) values varying between −5 and +5, and δ18O values between 3.58‰ and 5.97‰. Modeling of source mixing indicates that high‐temperature altered oceanic crust materials are an important component in the source of Changbaishan‐Tianchi volcano, likely derived from an ancient stagnant slab that has been reactivated by the subduction of the Pacific plate. This study demonstrates that the recycling of deeply subducted oceanic crust is potentially an important source and trigger for continental intraplate volcanism.

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This study focuses on the Badui region in eastern Tibet, an area with complex topography featuring numerous valleys, ravines, and frequent geological hazards. Given the economic expansion in this region, advanced techniques are essential for analyzing the distribution of geological hazards and developing early warnings of geological hazards. The research employs enhanced small baseline subset interferometric synthetic aperture radar (ESBAS-InSAR) technology, which provides more ascending and descending data than traditional small baseline subset-InSAR (SBAS-InSAR), allowing reprojection into vertical and horizontal components. Following dimensionality reduction through principal component analysis (PCA) and k-means clustering, the horizontal displacements were categorized into four clusters, and the vertical displacements were categorized into five clusters. Time-series data of vertical and horizontal displacements, rainfall, and normalized difference vegetation index (NDVI) were then used to assess 16 displacement prediction models. The temporal fusion transformer (TFT) model demonstrated the best predictive performance. To further improve accuracy, 11 static variables such as clusters, elevation, slope, aspect, distance from faults, time-varying known categorical variable, and earthquake times, were added as the TFT input variables. Results indicate that the optimized TFT model reduces the root-mean-square error (RMSE) from 3.4842 to 2.1707, the mean absolute percentage error (MAPE) from 2625.6399 to 2154.5505, and the mean absolute error (MAE) from 2.4392 to 2.3731. Overall, this study provides a framework for multivariate, multistep forecasting of diverse deformation modes across large areas and identifies distinct landslide deformation patterns through clustering, thereby enhancing the prediction of landslide deformation.

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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.

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Reservoir impoundment significantly impacts the hydrogeological conditions of reservoir bank slopes, and bank slope deformation or destruction occurs frequently under cyclic impoundment conditions. Ground deformation prediction is crucial to the early warning system for slow-moving landslides. Deep learning methods have developed rapidly in recent years, but only a few studies are on combining deep learning and landslide warning. This paper proposes a slow-moving landslide displacement prediction method based on the Informer deep learning model. Firstly, the Sentinel-1 (S1) data are processed to obtain the cumulative displacement time-series image of the bank slope by the Small-BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) method. Then, combining data on rainfall, humidity, and horizontal and vertical distances of pixel points from the water table line, this study created a dataset with landslide displacement as the target feature. After that, this paper improves the Informer model to make it applicable to our dataset. This study chose the Dawanzi landslide in the Baihetan reservoir area, China, for validation. After training with 50-time series deformation data points, the model can predict the displacement results of 12-time series deformation data points using 12-time series multi-feature data, and compared with the monitoring values, its Mean Square Error (MSE) was 11.614. The results show that the multivariate dataset is better than the deformation univariate data in predicting the displacement in the large deformation zone of bank slopes, and our model has better complexity and prediction performance than other deep learning models. The prediction results show that among zones I–IV, where the Dawanzi Tunnel is located, significant deformation with the maximum deformation rate detected exceeding –100mm/year occurs in Zones I and III. In these two zones, the initiation of deformation relates to the drop in water level after water storage, with the deformation rate of Zone III exhibiting a stronger correlation with the change in water level. It is expected that deformation in Zone III will either remain slow or stop, while deformation in Zone I will continue at the same or a decreased rate. Our proposed method for slow-moving landslide displacement forecasting offers fast, intuitive, and economically feasible advantages. It can provide a feasible research idea for future deep learning and landslide warning research.

Differential interferometric synthetic aperture radar (InSAR) is a remote sensing technique for measuring surface displacements with precision down to millimeters, most commonly from satellites. In permafrost landscapes, InSAR measurements can provide valuable information on geomorphic processes and hazards, including thaw subsidence and frost heave, thermokarst, and permafrost creep. We first review recent progress in InSAR data availability, InSAR processing and uncertainty analysis methods relevant to permafrost studies. These technical advances have contributed to our understanding of surface deformation in flat and sloping terrain in polar and mountainous regions. We emphasize two emerging trends. First, InSAR increasingly enables insight into the mechanisms, controls, and drivers of permafrost landscape dynamics on subseasonal to decadal time scales. Second, InSAR observations in conjunction with models enable novel ways to infer subsurface parameters, such as near‐surface ground ice content and active layer thickness. We anticipate that in the coming decade, InSAR will mature into a widely used operational tool for monitoring, modeling, and planning across rapidly changing permafrost landscapes.

Landslide susceptibility mapping (LSM) can accurately estimate the location and probability of landslides. An effective approach for precise LSM is crucial for minimizing casualties and damage. The existing LSM methods primarily rely on static indicators, such as geomorphology and hydrology, which are closely associated with geo-environmental conditions. However, landslide hazards are often characterized by significant surface deformation. The Small Baseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology plays a pivotal role in detecting and characterizing surface deformation. This work endeavors to assess the accuracy of SBAS-InSAR coupled with ensemble learning for LSM. Within this research, the study area was Shiyan City, and 12 static evaluation factors were selected as input variables for the ensemble learning models to compute landslide susceptibility. The Random Forest (RF) model demonstrates superior accuracy compared to other ensemble learning models, including eXtreme Gradient Boosting, Logistic Regression, Gradient Boosting Decision Tree, and K-Nearest Neighbor. Furthermore, SBAS-InSAR was utilized to obtain surface deformation rates both in the vertical direction and along the line of sight of the satellite. The former is used as a dynamic characteristic factor, while the latter is combined with the evaluation results of the RF model to create a landslide susceptibility optimization matrix. Comparing the precision of two methods for refining LSM results, it was found that the method integrating static and dynamic factors produced a more rational and accurate landslide susceptibility map.

On 2 April 2024, an MW 7.4 earthquake struck the northern Longitudinal Valley in eastern Taiwan, about 18 km SSW of Hualien, causing damage and casualties. In this study, we investigated a comprehensive geodetic data set, employing Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) measurements to assess the rupture geometry associated with earthquake sequence. Although geodetic data can be satisfactorily reproduced by simple single‐fault models (i.e., a high‐angle E‐dipping plane related to the Longitudinal Valley Fault (LVF), or a gentle W‐dipping surface associated with the Central Range Fault, CRF), a composite model involving the rupture of different fault segments (a major CRF‐related W‐dipping fault, a deep segment of the E‐dipping LVF, and the Milun Fault) is able to explain the observations, the distribution of seismicity, and the complex structural arrangement of the northernmost sector of the Longitudinal Valley.

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.

We present average ground‐surface velocities and strain rates for the 1.7 million km2 area of Iran, from the joint inversion of InSAR‐derived displacements and GNSS data. We generate interferograms from 7 years of Sentinel‐1 radar acquisitions, correct for tropospheric noise using the GACOS system, estimate average velocities using LiCSBAS time‐series analysis, tie this into a Eurasia‐fixed reference frame, and perform a decomposition to estimate East and Vertical velocities at 500 m spacing. Our InSAR‐GNSS velocity fields reveal predominantly diffuse crustal deformation, with localized interseismic strain accumulation along the North Tabriz, Main Kopet Dagh, Main Recent, Sharoud, and Doruneh faults. We observe signals associated with recent groundwater subsidence, co‐ and postseismic deformation, active salt diaprism, and sediment motion. We derive high‐resolution strain rate estimates on a country‐ and fault‐scale, and discuss the difficulties of mapping diffuse strain rates in areas with abundant non‐tectonic and anthropogenic signals.

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Whakaari (White Island) volcano is the most active volcano in New Zealand with a dynamic hydrothermal system. The volcano has had four eruptive periods since 2014. In this study, our aim is to understand the pre‐and post‐eruption deformation processes occurring at Whakaari using interferometric synthetic aperture radar (InSAR). We analyze Copernicus Sentinel‐1 Bursts from 2014 to 2024 from ascending and descending passes using small baseline subset (SBAS) InSAR time‐series analysis. Four stacks are analyzed, one spanning approximately a decade from 2014 to 2024, and three short‐term periods approximately 6 months before and after the 2016 and 2019 eruptions, and 6 months before the 2024 eruption. Together, these provide insights into the long‐and short‐term evolution of deformation at Whakaari. Results show spatially and temporally varied inflation‐deflation cycles around the active crater lake area pre‐and post eruptions. Long‐term gradual uplift is observed east of the crater lake, whereas subsidence is observed south south‐west of the crater lake. The nature of inflationary signatures vary prior to eruptions, which is interpreted as an effect of the pressure source (hydrothermal pressurization from a deep magma source, shallow magma, or crater lake‐related processes). The nature of deflationary signatures is inferred to be related to post‐eruption contraction of materials in the subsurface and movement and collapse of crater walls. The spatial and temporal variability in the observed deformation is correlated well with reported observations of gas emissions, eruptions, lava extrusion, and slope instabilities showing the usefulness of InSAR for volcano monitoring.

In recent decades, the interferometric synthetic aperture radar (InSAR) technique has emerged as a powerful tool for monitoring ground subsidence and geohazards. Various satellite SAR systems with different modes, such as Sentinel-1 and Lutan-1, have produced abundant SAR datasets with wide coverage and large historical archives, which have significantly influenced long-term deformation monitoring applications. However, large-scale InSAR data have posed significant challenges to conventional InSAR methods. These issues include the computational burden and storage of multi-temporal InSAR (MT-InSAR) methods, as well as temporal decorrelation for coherent scatterers with long temporal baselines. In this study, we propose a stepwise MT-InSAR with a temporal coherent scatterer method to address these problems. First, a batch sequential method is introduced in the algorithm by grouping the SAR dataset in the time domain based on the average coherence distribution and then applying permanent scatterer interferometry to each temporal subset. Second, a multi-layer network is employed to estimate deformation for partially coherent scatterers using small baseline subset interferograms, with permanent scatterer deformation parameters as the reference. Finally, the final deformation rate and displacement time series were obtained by incorporating all the temporal subsets. The proposed method efficiently generates high-density InSAR deformation measurements for long-time series analysis. The proposed method was validated using 9 years of Sentinel-1 data with 229 SAR images from Jakarta, Indonesia. The deformation results were compared with those of conventional methods and global navigation satellite system data to confirm the effectiveness of the proposed method.

This study employs advanced synthetic aperture radar (SAR) techniques, specifically the small baseline subset (SBAS) method, to analyze ground deformation dynamics on Aegina, a volcanic island within the Hellenic Volcanic Arc. Using Sentinel-1 satellite data spanning January 2016 to May 2023, this research reveals different deformation behaviors. The towns of Aegina and Saint Marina portray regions of stability, contrasting with central areas exhibiting subsidence rates of up to 1 cm/year. The absence of deformation consistent with volcanic activity on Aegina Island aligns with geological records and limited seismic activity, attributing the observed subsidence processes to settlement phenomena from past volcanic events and regional geothermal activity. These findings reinforce the need for continuous monitoring of the volcanic islands located in the Hellenic Volcanic Arc, providing important insights for local risk management, and contributing to our broader understanding of geodynamic and volcanic processes.

The eruption in Fagradalsfjall Volcano, located in Reykjanes Peninsula, Iceland, from several centuries’ dormant states, occurred for the first time on March 19, 2021. Observations of Fagradalsfjall Volcano were conducted in 2021, and the eruption period lasted for six months until 18 September 2021. Six days pair of interferograms were generated from ninety synthetic aperture radar (SAR) data. Thus, the SAR data will be acquired from the Sentinel-1 satellite from January until December 2021. Time-series measurements were conducted using a combination of persistent scatterer (PS) and distributed scatterer (DS) points to produce denser measurement points (MPs) in the study area. The improved combined scatterers interferometry with optimized point scatterers (ICOPS) algorithm is the time-series method that utilizes both PS and DS MPs and optimizes those combined MPs using a deep learning algorithm over different temporal intervals and using a statistical clustering approach to optimize the MPs spatially. Validation was conducted by comparing the ICOPS result with GPS measurement in Reykjavik. The comparison with the GPS measurement was performed to validate the line-of-sight (LOS) deformation from the ICOPS measurement, which resulted in an RMSE value of about 0.58 cm, which is considered a good correlation. Besides the time-series Interferometry SAR (InSAR) measurement, we used the integrated InSAR and multiple aperture interferometry (MAI) methods to estimate both LOS and along-track surface deformation, respectively, during the Fagradalsfjall, Iceland volcanic eruption. A pair of ALOS-2 data was used between 28 February 2021 and 23 May 2021. The result from the MAI method shows a deformation of approximately ± 2 mm in the azimuth direction around Fagradalsfjall Volcano. The deformation around Fagradalsfjall Volcano was suggested to be due to the activity of the magma reservoir beneath the Earth’s surface, which was formed by dike intrusion. The analysis of the seismicity in Fagradalsfjall was discussed by visualization of the distribution of earthquakes during the deformation occurrence. Further analysis can be conducted by applying multitrack analysis to find the 3D deformation pattern due to the eruption.

SBAS-InSAR technology is effective in obtaining surface deformation information and is widely used in monitoring landslides and mining subsidence. However, SBAS-InSAR technology is susceptible to various errors, including atmospheric, orbital, and phase unwrapping errors. These multiple errors pose significant challenges to precise deformation monitoring over large areas. This paper examines the spatial characteristics of these errors and introduces a spatially constrained SBAS-InSAR method, termed SSBAS-InSAR, which enhances the accuracy of wide-area surface deformation monitoring. The method employs multiple stable ground points to create a control network that limits the propagation of multiple types of errors in the interferometric unwrapped data, thereby reducing the impact of long-wavelength signals on local deformation measurements. The proposed method was applied to Sentinel-1 data from parts of Jining, China. The results indicate that, compared to the traditional SBAS-InSAR method, the SSBAS-InSAR method significantly reduced phase closure errors, deformation rate standard deviations, and phase residues, improved temporal coherence, and provided a clearer representation of deformation in time-series curves. This is crucial for studying surface deformation trends and patterns and for preventing related disasters.

The aim of this exploratory research is to capture spatial evolution patterns in the Bucharest metropolitan area using sets of single polarised synthetic aperture radar (SAR) satellite data and multi-temporal radar interferometry. Three sets of SAR data acquired during the years 1992–2010 from ERS-1/-2 and ENVISAT, and 2011–2014 from TerraSAR-X satellites were used in conjunction with the Small Baseline Subset (SBAS) and persistent scatterers (PS) high-resolution multi-temporal interferometry (InSAR) techniques to provide maps of line-of-sight displacements. The satellite-based remote sensing results were combined with results derived from classical methodologies (i.e., diachronic cartography) and field research to study possible trends in developments over former clay pits, landfill excavation sites, and industrial parks. The ground displacement trend patterns were analysed using several linear and nonlinear models, and techniques. Trends based on the estimated ground displacement are characterised by long-term memory, indicated by low noise Hurst exponents, which in the long-term form interesting attractors. We hypothesize these attractors to be tectonic stress fields generated by transpressional movements.

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Urban expansion and subsurface resource exploitation have intensified ground subsidence, posing significant geological risks. Conventional prediction models often overlook multi-scale spatiotemporal effects that critically influence accuracy. This study proposes an integrated MGTWR-CNN-BiLSTM-AM (MGCBA) model to address this gap. Utilizing SBAS-InSAR-derived deformation data from Shanghai’s primary subsidence zones, validated through GNSS and PS-InSAR observations, we developed a Multi-scale Geographically and Temporally Weighted Regression (MGTWR) framework. This model quantifies nonlinear spatiotemporal relationships between subsidence and driving factors, including monthly-scale variables (groundwater extraction, precipitation) and annual-scale parameters (land use, soil type), generating dynamic weight matrices. The integrated CNN-BiLSTM-AM (CBA) deep learning network extracts critical time-series features to optimize spatiotemporal weights adaptively. Experimental results demonstrate a prediction accuracy of 0.99347 (RMSE: 1.8643 mm), outperforming the standalone CBA model (0.98494) by 0.85%. SHAP value analysis identifies monthly groundwater levels, soil moisture, and annual-scale soil type/DEM as dominant contributors to Shanghai’s urban core subsidence. The proposed multi-scale spatiotemporal modeling framework advances surface deformation prediction by enhancing the interpretability of key drivers under spatiotemporally variable conditions.

No abstract available

Abstract Ground subsidence, the gradual sinking of the earth’s surface, poses a significant challenge globally, affecting regions like Midvaal, South Africa. This study employed Persistent Scatterer (PS) and Small Baseline Subset (SBAS) InSAR time-series analysis, continuous Global Navigation Satellite Systems (cGNSS) validation, and Random Forest machine learning to monitor and model ground subsidence using Sentinel-1 data (2019–2021). SBAS InSAR showed higher ground subsidence rates than PS InSAR, with cGNSS validation revealing InSAR limitations. Factor analysis identified NDVI, NDWI, lithology, and elevation as key drivers. The Random Forest model generated a susceptibility map, classifying areas into five vulnerability levels. Results indicate that Midvaal experiences significant ground subsidence, likely due to groundwater extraction for agricultural activities and mining, impacting approximately 3.4 % of the study area (118.85 sq. km). This study provides critical insights for sustainable land management and infrastructure planning in ground subsidence-prone regions.