Search Results (171)

Reservoir-induced landslides threaten infrastructures and downstream communities, making continuous deformation monitoring vital. Time-series InSAR, notably the SBAS algorithm, provides high-precision surface-displacement mapping but suffers from voids due to layover/shadow effects and temporal decorrelation. Existing deep-learning approaches often operate on fixed-size patches or ignore irregular spatio-temporal dependencies, limiting their ability to recover missing pixels. With this objective, a hybrid spatio-temporal Graph Attention (ST-GAT) framework was developed and trained on SBAS-InSAR values using 24 influential features. A unified spatio-temporal graph is constructed, where each node represents a pixel at a specific acquisition time. The nodes are connected via inverse distance spatial edges to their K-nearest neighbors, and they have bidirectional temporal edges to themselves in adjacent acquisitions. The two spatial GAT layers capture terrain-driven influences, while the two temporal GAT layers model annual deformation trends. A compact MLP with per-map bias converts the fused node embeddings into normalized LOS estimates. The SBAS-InSAR results reveal LOS deformation, with 48% of missing pixels and 20% located near the Dasu dam. ST D-GAT reconstructed fully continuous spatio-temporal displacement fields, filling voids at critical sites. The model was validated and achieved an overall R² (0.907), ρ (0.947), per-map R² ≥ 0.807 with RMSE ≤ 9.99, and a ROC-AUC of 0.91. It also outperformed the six compared baseline models (IDW, KNN, RF, XGBoost, MLP, simple-NN) in both RMSE and R². By combining observed LOS values with 24 covariates in the proposed model, it delivers physically consistent gap-filling and enables continuous, high-resolution landslide monitoring in radar-challenged mountainous terrain. Full article

Using interferometric synthetic aperture radar (InSAR) to observe slow ground deformation can be challenging due to many sources of error, with tropospheric phase delay and unwrapping errors being the most significant. While analytical methods, weather models, and data exist to mitigate tropospheric error, most of these techniques are unsuitable for all InSAR applications (e.g., complex tropospheric mixing in the tropics) or are deficient in spatial or temporal resolution. Likewise, there are methods for removing the unwrapping error, but they cannot resolve the true phase when there is a high prevalence (>40%) of unwrapping error in a set of interferograms. Applying tropospheric delay removal techniques is unnecessary for C-band Sentinel-1 InSAR time series studies, and the effect of unwrapping error can be minimized if the full dataset is utilized. We demonstrate that using interferograms with long temporal baselines (800 days to 1600 days) but very short perpendicular baselines (<5 m) (LTSPB) can lower the velocity detection threshold to 2 mm y⁻¹ to 3 mm y⁻¹ for long-term coherent permanent scatterers. The LTSPB interferograms can measure slow deformation rates because the expected differential phases are larger than those of small baselines and potentially exceed the typical noise amplitude while also reducing the sensitivity of the time series estimation to the noise sources. The method takes advantage of the Sentinel-1 mission length (2016 to present), which, for most regions, can yield up to 300 interferograms that meet the LTSPB baseline criteria. We demonstrate that low velocity detection can be achieved by comparing the expected LTSPB differential phase measurements to synthetic tests and tropospheric delay from the Global Navigation Satellite System. We then characterize the slow (~3 mm/y) ground deformation of the Socorro Magma Body, New Mexico, and the Tampa Bay Area using LTSPB InSAR analysis. The method we describe has implications for simplifying the InSAR time series processing chain and enhancing the velocity detection threshold. Full article

The South Shetland Islands and Antarctic Peninsula (SHETPENANT region) constitute a geodynamically active area shaped by the interaction of major tectonic plates and active magmatic systems. This study analyzes GNSS time series spanning from 2017 to 2024 to investigate surface deformation associated with the 2020–2021 seismic swarm near the Orca submarine volcano. Horizontal and vertical displacement velocities were estimated for the preseismic, coseismic, and postseismic phases using the CATS method. Results reveal significant coseismic displacements exceeding 20 mm in the horizontal components near Orca, associated with rapid magmatic pressure release and dike intrusion. Postseismic velocities indicate continued, though slower, deformation attributed to crustal relaxation. Stations located near the Orca exhibit nonlinear, transient behavior, whereas more distant stations display stable, linear trends, highlighting the spatial heterogeneity of crustal deformation. Stress and strain fields derived from the velocity models identify zones of extensional dilatation in the central Bransfield Basin and localized compression near magmatic intrusions. Maximum strain rates during the coseismic phase exceeded 200 $\mathrm{\nu }$strain/year, supporting a scenario of crustal thinning and fault reactivation. These patterns align with the known structural framework of the region. The integration of GNSS-based displacement and strain modeling proves essential for resolving active volcano-tectonic interactions. The findings enhance our understanding of back-arc deformation processes in polar regions and support the development of more effective geohazard monitoring strategies. Full article

Accurate prediction of dam displacement is essential for structural safety and risk management. To comprehensively address the “accuracy–uncertainty–interpretability” trilemma in dam displacement prediction, this study proposes a deep learning framework that integrates Patch Time Series Transformer (PatchTST), Sand Cat Swarm Optimization (SCSO), Quantile Regression (QR), and SHapley Additive exPlanations (SHAP). The proposed framework first employs PatchTST to capture the nonlinear temporal dependencies between multiple monitoring factors and dam displacement, while SCSO is utilized to adaptively optimize key hyperparameters, enabling the construction of a high-precision point prediction model. On this basis, QR is introduced to model the distributional uncertainty of displacement responses and to generate confidence-based prediction intervals, facilitating the evaluation of displacement anomalies. Furthermore, SHAP is incorporated to quantify the marginal contribution of each input factor to the model outputs, thereby enhancing interpretability and aligning model behavior with physical domain knowledge. The framework is validated using multi-year monitoring data from a double-curvature arch dam located in Southwest China. Comparative experiments demonstrate that the proposed model outperforms five well-established machine learning methods and the traditional linear regression method in terms of point prediction accuracy, reliability of interval estimation, and false alarm rate, exhibiting strong generalization and robustness. The SHAP-based analysis further reveals that water pressure variations and seasonal temperature cycles are the dominant factors influencing radial displacement, consistent with known structural deformation mechanisms. These findings affirm the physical consistency and engineering applicability of the proposed framework, offering a deployable and trustworthy solution for intelligent dam health monitoring and uncertainty-aware forecasting in safety-critical infrastructures. Full article

Hainan Island and its surrounding seabed are located at the intersection of the Eurasian, Indochina, and South China Sea tectonic plates with active Quaternary volcanism and intensive seismicity, such as the 7.6-magnitude earthquake that occurred in northern Hainan in 1605. Based on the newest airborne gravity data of Hainan Island and its adjacent areas, this paper uses wavelet multiscale decomposition followed by power spectral analysis to estimate the average depth of each layer of the source field. We use the Parker–Oldenburg method to invert the Moho structure, incorporating constraints from seismic data to investigate the fine crustal structure and deformation characteristics to elucidate the deep seismogenic mechanism. The regional Moho depth decreases from 30 km in the northwest to 16 km in the southeast. The map of the Moho surface shows three Moho uplift zones, located in the northern Hainan Island, the southern Qiongdongnan Basin, and the southwestern tip of Hainan Island. The following findings are revealed: Firstly, a series of northeastward high-gravity anomaly strips are discovered for the first time in the middle and lower crust of Hainan Island, which may be the remnants within the continental crust of the ancient Pacific northwestward subduction during the Mesozoic era. Secondly, under the Leiqiong volcanic rocks, there is a pronounced northeastward high-value anomaly and shallower Moho depth, which may indicate the deep-seated mantle material that rose and intruded during the activity of the Hainan mantle plume. Thirdly, the seismogenic structure is discussed by combining the wavelet multiscale decomposition results with natural seismic data. The results show that earthquakes occur in the place where the NE-trending gravity anomaly is cut by the NW-trending fault in the upper crust. That place also lies in the gravity anomaly gradient or high-value anomaly in the middle and lower crust. These features reveal that the earthquakes on Hainan Island are controlled by the left strike-slip activity of the Red River Fault and deep mantle upwelling caused by Hainan Plume. Full article

In the past three decades, the city of Addis Ababa, a capital city of Africa, has grown significantly in population, facilities, and infrastructure. The area involved in the recent urbanization is prone to slow natural subsidence phenomena that can be accelerated due to anthropogenic factors such as groundwater overexploitation and loading of unconsolidated soils. The main aim of this study is to identify and monitor the areas most affected by subsidence in a context, such as that of many areas of emerging countries, characterized by the lack of geological and technical data. In these contexts, advanced remote sensing techniques can support the assessment of spatial and temporal patterns of ground instability phenomena, providing critical information on potential conditioning and triggering factors. In the case of subsidence, these factors may have a natural or anthropogenic origin or result from a combination of both. The increasing availability of SAR data acquired by the Sentinel-1 mission around the world and the refinement of processing techniques that have taken place in recent years allow one to identify and monitor the critical conditions deriving from the impressive recent expansion of megacities such as Addis Ababa. In this work, the Sentinel-1 SAR images from Oct 2014 to Jan 2021 were processed through the PS-InSAR technique, which allows us to estimate the deformations of the Earth’s surface with high precision, especially in urbanized areas. The obtained deformation velocity maps and displacement time series have been validated using accurate second-order geodetic control points and compared with the recent urbanization of the territory. The results demonstrate the presence of areas affected by a vertical rate of displacement of up to 21 mm/year and a maximum displacement of about 13.50 cm. These areas correspond to sectors that are most predisposed to subsidence phenomena due to the presence of recent alluvial deposits and have suffered greater anthropic pressure through the construction of new buildings and the exploitation of groundwater. Satellite interferometry techniques are confirmed to be a reliable tool for monitoring potentially dangerous geological processes, and in the case examined in this work, they represent the only way to verify the urbanized areas exposed to the risk of damage with great effectiveness and low cost, providing local authorities with crucial information on the priorities of intervention. Full article

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. Full article

Coastal areas are highly dynamic regions where surface deformation due to natural and anthropogenic activities poses significant challenges. Synthetic Aperture Radar (SAR) interferometry techniques, such as Persistent Scatterer Interferometry (PSInSAR), provide advanced capabilities to monitor surface deformation with high precision. This study applies PSInSAR techniques to estimate surface deformation along coastal zones from 2017 to 2020 using Sentinel-1 data. In the densely populated areas of Pasni, an annual subsidence rate of 130 mm is observed, while the northern, less populated region experiences an uplift of 70 mm per year. Seawater intrusion is an emerging issue causing surface deformation in Pasni’s coastal areas. It infiltrates freshwater aquifers, primarily due to excessive groundwater extraction and rising sea levels. Over time, seawater intrusion destabilizes the underlying soil and rock structures, leading to subsidence or gradual sinking of the ground surface. This form of surface deformation poses significant risks to infrastructure, agriculture, and the local ecosystem. Land deformation varies along the study area’s coastline. The eastern region, which is highly reclaimed, is particularly affected by erosion. The results derived from Sentinel-1 SAR data indicate significant subsidence in major urban districts. This information is crucial for coastal management, hazard assessment, and planning sustainable development in the region. Full article

The detection of potential rural mountain landslide displacements using time-series interferometric Synthetic Aperture Radar has been challenged by both atmospheric phase screens and decoherence noise. In this study, we propose the use of a combined distributed scatterer (DS) and the Prophet_ZTD-NEF model to rapidly map the landslide surface displacements in Diqing Tibetan Autonomous Prefecture, China. We conducted tests on 28 full-resolution SENTINEL-1A images to validate the effectiveness of our methods. The conclusions are as follows: (1) Under the same sample conditions, confidence interval estimation demonstrated higher performance in identifying SHPs compared to generalized likelihood ratio test. The density of DS points was approximately eight times and five times higher than persistent scatterer interferometry and small baseline subset methods, respectively. (2) The proposed Prophet_ZTD-NEF model considers the spatial and temporal variability properties of tropospheric delays, and the root mean square error of measured values was approximately 1.19 cm instead of 1.58 cm (PZTD-NEF). (3) The proposed Prophet_ZTD-NEF method reduced the mean standard deviation of the corrected interferograms from 1.88 to 1.62 cm and improved the accuracy of the deformation velocity solution by approximately 8.27% compared to Global Position System (GPS) measurements. Finally, we summarized the driving factors contributing to landslide instability. Full article

To solve the problems of difficult to model parameter selections, useful signal extraction and improper-signal decomposition in nonlinear, non-stationary dam displacement time series prediction methods, we propose a new predictive model for grey wolf optimization and variational mode decomposition and long short-term memory (GVLSTM). Firstly, we used the grey wolf optimization (GWO) algorithm to optimize the parameters of variable mode decomposition (VMD), obtaining the optimal parameter combination. Secondly, we used multiscale permutation entropy (MPE) as a standard to select signal screening, determining and recon-structing the effective modal components. Finally, the long short-term memory neural network (LSTM) was used to learn the dam deformation characteristics. The result shows that the GVLSTM model can effectively reduce the estimation deviation of the prediction model. Compared with VMDGRU and VMDANN, the average RMSE and MAE value of each station is increased by 19.11%~28.58% and 27.66%~29.63%, respectively. We used determination (R²) coefficient to judge the performance of the prediction model, and the value of R² was 0.95~0.97, indicating that our method has good performance in predicting dam deformation. The proposed method has outstanding advantages of high accuracy, reliability, and stability for dam deformation prediction. Full article

In recent years, phase linking (PL) methods in radar time-series interferometry (TSI) have proven to be powerful tools in geodesy and remote sensing, enabling the precise monitoring of surface displacement and deformation. While these methods are typically designed to operate on a complete network of interferograms, generating such networks is often challenging in practice. For instance, in non-urban or vegetated regions, decorrelation effects lead to significant noise in long-term interferograms, which can degrade the time-series results if included. Additionally, practical issues such as gaps in satellite data, poor acquisitions, or systematic errors during interferogram generation can result in incomplete networks. Furthermore, pre-existing interferogram networks, such as those provided by systems like COMET-LiCSAR, often prioritize short temporal baselines due to the vast volume of data generated by satellites like Sentinel-1. As a result, complete interferogram networks may not always be available. Given these challenges, it is critical to understand the applicability of PL methods on these incomplete networks. This study evaluated the performance of two PL methods, eigenvalue decomposition (EVD) and eigendecomposition-based maximum-likelihood estimator of interferometric phase (EMI), under various network configurations including short temporal baselines, randomly sparsified networks, and networks where low-coherence interferograms have been removed. Using two sets of simulated data, the impact of different network structures on the accuracy and quality of the results was assessed. These patterns were then applied to real data for further comparison and analysis. The findings demonstrate that while both methods can be effectively used on short temporal baselines, their performance is highly sensitive to network sparsity and the noise introduced by low-coherence interferograms, requiring careful parameter tuning to achieve optimal results across different study areas. Full article

Background: Intermittent pneumatic compression is a non-invasive therapeutic technique that has been gaining popularity in recent years due to its potential use in many areas of medicine. It can be successfully used alone or in combination with other therapeutic methods. The aim of this study was to investigate whether and how a series of pressotherapy treatments on the lower limbs affects the rheological properties of blood (blood count, red blood cell deformability and aggregation, and blood viscosity), lipid profile (total cholesterol, triglycerides, low-density lipoprotein, and high-density lipoprotein), and renal profile (urea, creatinine, and estimated glomerular filtration rate) in young, healthy women. Methods: The study group consisted of 15 healthy women aged 20–26 (22.5 ± 1.5), without chronic diseases and not practicing competitive sports. The participants underwent a series of 10 lower limb pressotherapy treatments. A single treatment lasted 30 min and each time the pressure used during the treatment was individually selected according to the participants’ preference. The first blood test was performed a week before the treatments; the second on the day of the start of treatment, but before the pneumatic massage; the third after the completed series of pressotherapy treatments; and the fourth a week after the completed series of treatments. Results: In the conducted study, the analysis of the values of the complete blood count showed the following: a significant decrease in red blood cell count, hemoglobin, average hemoglobin concentration in erythrocytes, average red blood cell volume, average hemoglobin mass in red blood cells; a significant increase in average red blood cell volume; and an average hemoglobin mass in red blood cells. The analysis of the values of rheological parameters showed the following: a significant decrease in elongation indices 0.58, 1.13, 4.24, 15.95, 30.94, and 60.00; blood viscosity; the aggregation index; the degree of complete aggregation; and a significant increase in elongation indices 0.30, 1.13, 8.23, 30.94, 60.00; blood viscosity; the degree of complete aggregation; and the half-time of complete aggregation. A decrease in the concentration of low-density lipoprotein and high-density lipoprotein fractions was also noted. No significant changes were found in the values of total cholesterol and triglycerides, as well as in renal profile elements. Conclusions: The application of a series of 10 lower limb pressotherapy treatments has a beneficial effect, with a decrease in blood viscosity and the aggregation index, and an increase in the elongation index at shear stress from 0.30 [Pa] to 8.23 [Pa] in young, healthy women. A series of 10 lower limb pressotherapy treatments may affect the decrease in the values at high shear stress forces of 30.95 [Pa] and from 60.00 [Pa] in young, healthy women. The use of a series of 10 lower limb pressotherapy treatments increases the values of hemoglobin, the average red blood cell volume, and the average hemoglobin concentration in erythrocytes, and also reduces the values of red blood cell count, average hemoglobin mass in red blood cells and low-density lipoproteins and high-density lipoproteins in young, healthy women (it also does not cause any adverse changes). The use of pressotherapy on the lower limbs seems to be an effective element of the multi-component prevention of circulatory system diseases. Full article

Hyperboloid cooling towers are counted among the largest cast-in-place industrial structures. They are an essential element of cooling systems used in many power plants in service today. Their main structural component, a reinforced-concrete shell in the form of a one-sheet hyperboloid with bidirectional curvature continuity, makes them stand out against other towers and poses very high construction and service requirements. The safe service and adequate durability of the hyperboloid structure are guaranteed by the proper geometric parameters of the reinforced-concrete shell and monitoring of their condition over time. This article presents an original concept for employing terrestrial laser scanning to conduct an end-to-end assessment of the geometric condition of a hyperboloid cooling tower as required by industry standards. The novelty of the proposed solution lies in the use of measurements of the interior of the structure to determine the actual thickness of the hyperboloid shell, which is generally disregarded in geometric measurements of such objects. The proposal involves several strategies and procedures for a reliable verification of the structure’s verticality, the detection of signs of ovalisation of the shell, the estimation of the parameters of the structure’s theoretical model, and the analysis of the distribution of the thickness and geometric imperfections of the reinforced-concrete shell. The idea behind the method for determining the actual thickness of the shell (including its variation due to repairs and reinforcement operations), which is generally disregarded when measuring the geometry of such structures, is to estimate the distance between point clouds of the internal and external surfaces of the structure using the M3C2 algorithm principle. As a particularly dangerous geometric anomaly of hyperboloid cooling towers, shell ovalisation is detected with an innovative analysis of the bimodality of the frequency distribution of radial deviations in horizontal cross-sections. The concept of a complete assessment of the geometry of a hyperboloid cooling tower was devised and validated using three measurement series of a structure that has been continuously in service for fifty years. The results are consistent with data found in design and service documents. We identified a permanent tilt of the structure’s axis to the northeast and geometric imperfections of the hyperboloid shell from −0.125 m to +0.136 m. The results also demonstrated no advancing deformation of the hyperboloid shell over a two-year research period, which is vital for its further use. Full article

Different hyperelastic material models (Mooney-Rivlin, Yeoh, Gent, Arruda-Boyce and Ogden) are able to estimate Treloar’s test data series containing uniaxial and biaxial tension and pure shear stress-strain characteristics of rubber. If the rubber behaviour is only determined for the specific load of the product, which, in the case of rubber bumpers, is the compression, the time needed for the laboratory test can be significantly decreased. The stress-strain characteristics of the uniaxial compression test of rubber samples were used to fit hyperelastic material models. Laboratory and numerical tests of a rubber bumper with a given compound and complex geometry were used to determine the accuracy of the material models. Designing rubber products requires special consideration of the numerical discretization process due to the nonlinear behaviours (material nonlinearity, large deformation, connections, etc.). Modelling considerations were presented for the finite element analysis of the rubber bumper. The results showed that if only uniaxial compression test data are available for the curve fitting of the material model, the Yeoh model performs the best in predicting the rubber product material response under compressive load and complex strain state. Full article

With the enrichment of GNSS data and the improvement in data processing accuracy, GNSS technology has been widely applied in fields such as crustal deformation. The Crustal Movement Observation Network of China (CMONOC) has provided decades of Global Navigation Satellite System (GNSS) data and related data products for crustal deformation research on the Chinese mainland. The coordinate time series of continuously observed reference stations contain abundant information on crustal movements. In contrast, the coordinate time series of periodically observed campaign stations have limited data, making it difficult to separate or remove instantaneous non-tectonic movements from the time series, as performed with reference stations, to obtain a stable and reliable crustal movement velocity field. To address this issue, this paper proposes a method to improve the estimation of crustal movement velocity at campaign stations using the information of neighboring reference stations. This method constructs a Delaunay triangulation of reference stations and fits the periodic movement of each campaign station using an inverse distance weighted interpolation algorithm based on the reference station information. The crustal movement velocity of the campaign stations is then estimated after removing the periodic movement. This method was verified by its application to the estimation of the vertical motion rate at some reference and campaign stations in Yunnan Province. The results show that the accuracy of vertical motion rate estimation for virtual and real campaign stations improved by an average of 24.4% and 9.6%, respectively, demonstrating the effectiveness of the improved method, which can be applied to estimate crustal movement velocity at campaign stations in other areas. Full article