Deformation Time Series Research Articles

Preliminary Study on InSAR-Based Uplift or Subsidence Monitoring and Stability Evaluation of Ground Surface in the Permafrost Zone of the Qinghai–Tibet Engineering Corridor, China

Against the background of global warming, permafrost areas are facing increasing thawing, and the threat to the surface of the Qinghai–Tibet Engineering Corridor (QTEC) is serious. It is imperative to understand the current surface deformation and analyze the changes spatiotemporal characteristics for future warnings. At present, observation of a long time series and overall coverage of vertical ground deformation in QTEC are lacking. This paper takes the permafrost deformation of the QTEC as its research object. It uses the pretreated LiCSAR product and combines it with the LiCSBAS package to obtain monitoring results of the long time series deformation of the engineering corridor’s surface. The SAR image acquisition date is taken as the constraint, the results covering the whole processing area are selected, and then the vertical deformation information covering the entire engineering corridor area by ignoring the north–south displacement is calculated. The results show that the surface of the study area, as a whole, slightly subsided between May 2017 and March 2022, and the vertical deformation rate was mostly distributed at −27.068 mm/yr − 18.586 mm/yr, with an average of −1.06 mm/yr. Vertical deformation dominated at 52.84 percent of the study area, of which settlement accounted for 27.57 percent and uplift accounted for 25.27 percent. According to the statistics of the normal distribution of deformation velocity per pixel, a total of 77% of the engineering corridor was stable, with a vertical deformation rate between −6.964 mm/yr and −4.844 mm/yr, and 17.7% of the region was sub-stable, with a settling rate of −12.868 mm/yr − –6.964 mm/yr. The unstable regions included areas with settlement rates greater than 12.868 mm/yr and uplift rates greater than 10.748 mm/yr, representing 4.4 percent and 0.9 percent of the total area, respectively, for a total of 5.3 percent. The results of this paper can be used as the theoretical basis and as basic data for decision making and scientific research in various departments, and they are of great significance for surface stability assessment and early warnings along engineering corridors and traffic projects.

Hydrocarbon production induced land deformation over Permian Basin; analysis using persistent scatterer interferometry and numerical modeling

The Permian Basin, encompassing Southeastern New Mexico and Western Texas, has been an important source of oil and natural gas for over a century for the United States. With recent advancements in petroleum extraction methods, such as hydraulic fracturing and horizontal drilling, production has increased exponentially. Such rapid developments, along with the disposal of wastewater back into the subsurface, have caused significant changes in the stress regime of the region’s subsurface, resulting in various geohazards including land subsidence. In this work, we used the Sentinel-1C-band SAR dataset acquired between 2016 and 2021 to measure the basin-wide deformation over the entire Permian Basin. We utilized Persistent Scatterer Interferometry (PSI) technique to produce the time-series deformation at millimeter-level accuracy. The average monthly production and injection volumes and well depths over the Delaware Basin in New Mexico were calculated for each well and the impact of the hydrocarbon activities on the deformation was modeled using a distributed point source model from fluid volume and well depth data. The results show that the Permian Basin was deforming at a rate of 3–4 cm/yr., with two large pockets of deformation to the north of the Grisham Fault Zone (GFZ) in the Delaware Basin. The region south of the GFZ shows complex deformation features with linear patterns, indicating the presence of faults hindering fluid flow. Some areas in the Delaware Basin also show an uplift, especially along the Texas side of the border driven by shallow wastewater injection. The Midland Basin is also affected by subsidence with several localized subsidence zones identified in the region. Further, the Midland Basin experienced an increase in intensity and the spatial distribution of the deformation over the last few years. Given both production and injection well data, the modeled deformation results agree with the observed except in two pockets. When only production data is used for the modeling, the modeled deformation shows better agreement with the observed, especially in the interior of New Mexico. The modeling results suggest that the deep wastewater injection has minimal impact on surface deformation in the region. Additionally, it is also possible that the region has undergone inelastic deformation. While we used an elastic model in this preliminary study, a more accurate study requires regional poroelastic modeling to study the interaction between the solid subsurface and fluid hydrocarbons in detail.

Reconstruction of spatially continuous time-series land subsidence based on PS-InSAR and improved MLS-SVR in Beijing Plain area

ABSTRACT Beijing has undergone severe settlement in recent years. Persistent Scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) technique has been widely used to derive time-series land deformation. However, existing studies have faced two challenges: (1) the nonlinear characteristics of time-series subsidence has not been fully investigated; (2) since PS points are normally distributed in urban areas with high building density, measurement gaps usually exist in nonurban areas. To address the challenges, we presented a new method to reconstruct spatially continuous time-series deformation. First, PS-InSAR was used to retrieve the deformation based on 135 scenes of Envisat ASAR and Radarsat-2 images from 2003 to 2020. Polynomial Curve Fitting (PCF) was then used to model nonlinear time-series deformation for the PS points. In the PS measurement gaps, Iterative Self-Organizing Data Analysis Technique (ISODATA) and Multi-output Least Squares Support Vector Regression (MLS-SVR) were used to estimate the PCF coefficients and then time-series deformation considering 40 features including thickness of the compressible layers, annual groundwater level, etc. The major results showed that (1) compared to linear, quadratic, and quartic models, cubic polynomial model generated better fit for the time-series deformation (R2 ≈0.99), suggesting obvious nonlinear temporal pattern of deformation; (2) the time-series deformation over measurement gaps reconstructed by ISODATA and MLS-SVR had satisfactory accuracy (R2 = 0.92, MAPE < 15%) and yielded higher accuracy (R2 = 0.947) than IDW (R2 = 0.687) and Ordinary Kriging (R2 = 0.688) interpolation methods. The reconstructed results maintain the nonlinear characteristics and ensure the high spatial resolution (120 m) of time-series deformation. Among the 40 predictor variables, ground water level datasets are the most influential predictors of time-series deformation.

Genetic algorithm-based parameter inversion and pipeline subsidence prediction

To invert mine subsidence parameters and predict the timing of associated risk to underground pipelines passing through mining areas with incoherent subsidence information, this study proposes a model based on combining the advantages of interferometric synthetic aperture radar (InSAR) technology, the probability integral method (PIM), segmented Knothe time function, and genetic algorithm. Firstly, InSAR technology is used to analyze a time series of surface deformation in a mining area, circle the range of large-gradient deformation, and distinguish the subsidence edge from the subsidence center. In the case of the subsidence edge (small-scale deformation), InSAR results are retained, whereas in the case of the subsidence center (large-scale deformation), subsidence parameters, such as the subsidence coefficient and influence radius, are obtained via the genetic algorithm. The subsidence of the working face in the mining area is determined by combining InSAR information and the PIM parameter inversion-based basin model. This allows us to obtain continuous surface subsidence information, study surface subsidence trends over time, and predict future pipeline subsidence. The researchers subsequently test the efficacy of this model in the Qinchi mining area in Shanxi Province, China. Sentinel-1A images were used as the data source, combine the PIM and segmentation time function, and use the genetic algorithm to obtain the subsidence mining parameters. The inversion results show that the subsidence coefficient is 0.5917 (with an error of 2%), and the main influence angle tangent value is 1.927 (with an error of 2.18%), indicating high inversion accuracy. Based on this model, the study predict that subsidence in the Qinchi mining area will threaten the structural integrity and operation of the intersecting pipeline by November 2025.

Evaluation of Post-Tunneling Aging Buildings Using the InSAR Nonuniform Settlement Index

Tunneling work, including the construction of municipal tunnels and metro lines, may disturb the structural health of aging buildings in densely built urban areas. Deformation monitoring and risk assessments of aging buildings are crucial to mitigate incidents and prevent losses of people’s lives and properties. Time-series InSAR reveals spatio-temporal information about observed targets by extracting persistent scatterers of the structures, which can achieve the wide-range monitoring of buildings and infrastructure. However, solely relying on InSAR-derived general parameters (deformation rates and time series of specific points) cannot objectively assess the safety conditions of buildings. To address this issue, this study proposes an InSAR Nonuniform Settlement Index. First, the point targets of buildings are extracted through time-series InSAR processing. Then, using the points as inputs, the Nonuniform Settlement Index calculates the 3D settlement plane and the inclination angle of the plane corresponding to each building. In this way, the proposed Nonuniform Settlement Index acts as a subsequent analysis method of time-series InSAR to characterize the safety statuses of buildings. In our study, 147 scenes of COSMO-SkyMed images from 2013 to 2022 were used to inverse the nine-year deformation evolution of the tested area. After time-series InSAR processing and index analysis based on the above SAR datasets, cross-validation was implemented with static-level and manual investigation data. The approach was to use one aging, collapsed building affected by tunneling work, as well as the eight adjacent aging buildings. The results showed high consistency with the in situ data, which proves the efficiency of the proposed approach.

Convolutional neural network-based spatiotemporal prediction for deformation behavior of arch dams

Deformation directly reflects the behavior of arch dams, and deformation behavior models have been widely used for structural health monitoring (SHM) in accordance with monitoring data. The deformation field brings advantages to the SHM of arch dams in terms of the spatiotemporal scale. In this paper, a spatiotemporal deformation field behavior model based on a convolutional neural network (CNN) was proposed. The temperature field of the dam fusing the climatological data, including air temperature, weather conditions, and solar radiation, was introduced. Furthermore, the water pressure and constraint fields were also input into the CNN-based model. The physical law and structural continuity were considered, and the parameters of the CNN-based model were general and uniform for each predicted location. The accuracy and extrapolation ability of the proposed model were evaluated. The results illustrate that the CNN-based model predicts accurate deformation at the spatiotemporal scale of the arch dam. The accuracy of the prediction of single-point deformation time series from the CNN-based model is close to that from the traditional hydraulic-seasonal-time (HST) model and finite element model (FEM). For the prediction of the spatial deformation field, the mean MAE of the CNN-based model is 2.68 mm, while it is 5.66 mm for the FEM model, showing much higher prediction accuracy of the CNN-based model. The proposed globally uniform CNN-based model can derive a high-precision and high-density deformation field of arch dams with sparse monitoring data. It can also be continuously updated in real-time according to the latest monitoring data without inverse analysis and parameter adjustment like the FEM model.

Exploring the potential for groundwater-related ground deformation in Southern New South Wales, Australia

Unsustainable groundwater extraction can lead to aquifer compaction, damages to infrastructure, changes of water accumulation in rivers and lakes and to a decrease of the aquifer's ability to store water for future generations. While this phenomenon is well identified across the globe, the potential for groundwater-related ground deformation is still largely unknown for most of the heavily exploited aquifers of Australia. This study fills that science gap by exploring signs of this phenomenon across a large region comprising seven of Australia's most intensively exploited aquifers, in the New South Wales Riverina region. To detect ground deformation, we processed 396 Sentinel-1 swaths acquired during 2015–2020 using a multitemporal spaceborne radar interferometry (InSAR), leading to the production of a near-continuous ground deformation maps covering ~280,000 km2. To explore potential groundwater-induced deformation hotspots, four criteria are used in a multiple-line of evidence approach: (1) the amplitude, shape, and extent of the InSAR ground displacement anomaly, (2) the spatial correspondence with groundwater extraction hotspots. (3) The correlations between InSAR deformation time series and change in head levels in 975 wells. Four areas are identified as potentially prone to inelastic, groundwater-related deformations, with average deformation rates ranging from −10 to −30 mm/yr, high rates of groundwater extraction, and ample critical head drops. Comparison of ground deformation and groundwater level time series also suggests potential for elastic deformation in some of these aquifers. This study will help water managers mitigating the groundwater-related ground deformation risk.

Concrete Dam Deformation Prediction Model Research Based on SSA–LSTM

In the context of dam deformation monitoring, the prediction task is essentially a time series prediction problem that involves non-stationarity and complex influencing factors. To enhance the accuracy of predictions and address the challenges posed by high randomness and parameter selection in LSTM models, a novel approach called sparrow search algorithm–long short-term memory (SSA–LSTM) has been proposed for predicting the deformation of concrete dams. SSA–LSTM combines the SSA optimization algorithm with LSTM to automatically optimize the model’s parameters, thereby enhancing the prediction performance. Firstly, a concrete dam was used as an example to preprocess the historical monitoring data by cleaning, normalizing, and denoising, and due to the specificity of the data structure, multi-level denoising of abnormal data was performed. Second, some of the data were used to train the model, and the hyperparameters of the long and short-term memory neural network model (LSTM) were optimized by the SSA algorithm to better match the input data with the network structure. Finally, high-precision prediction of concrete dam deformation was carried out. The proposed model in this study significantly improves the prediction accuracy in dam deformation forecasting and demonstrates effectiveness in long-term time series deformation prediction. The model provides a reliable and efficient approach for evaluating the long-term stability of dam structures, offering valuable insights for engineering practices and decision-making.

GNSS/accelerometer integrated deformation monitoring algorithm based on sensors adaptive noise modeling

The Global Navigation Satellite System (GNSS) and accelerometer integrated system can obtain more stable and reliable deformation displacement with the full use of the complementary technical characteristics of these two types of sensors. The Kalman Filter (KF) method is often applied to integrate GNSS three-dimensional (3D) coordinates and high-rate accelerometer measurements. The key to obtaining accurate solutions is the reasonable setting of measurement noise and process noise of KF. However, in some complex deformation monitoring scenarios such as landslides, both GNSS and accelerometers are susceptible to interference from factors like vegetation cover and external dynamic disturbance. Traditional adaptive filtering methods is hard to simultaneously and appropriately adjust the two types of noise matrices. To address this problem, an adaptive noise model based on sensors is proposed. The adaptive measurement noise is constructed using the posterior coordinate covariance derived from GNSS-RTK process. The random walk coefficient corresponding to the process noise is estimated online via the standard deviation (STD) of acceleration time-varying information. A series of numerical experiments about simulated three-dimensional (3D) deformation displacement are carried out to validate the proposed algorithm. The results show that: (1) When the GNSS signal is disturbed by complex environment, the adaptive measurement noise model effectively contributes to the accuracy improvement, from decimeter level to centimeter level. (2) In the presence of unstable deformable bodies, the adaptive process noise algorithm significantly suppresses the divergent deformation time series caused by external accelerometer disturbances. (3) Even if suffering the overlying effects of electromagnetic interference and external disturbance, the centimeter-level deformation monitoring accuracy can be achieved by using the joint adaptive measurement and process noise model. The improvement effect is particularly significant in the main deformation direction.

Brief communication: Landslide activity on the Argentinian Santa Cruz River mega dam works confirmed by PSI DInSAR

Abstract. Safety and environmental aspects are crucial beyond production goals in the hydropower industry. By monitoring landslides associated with the construction of a hydropower dam in the Santa Cruz River in Argentine Patagonia, this paper contributes to the assessment of the project structural integrity of the construction and safety risks. Ground deformation is monitored using synthetic aperture radar (SAR) satellite data and the persistent scatterer interferometry technique, and it is contrasted with optical imagery, geological and technical reports, and fieldwork. The results include maps of accumulated deformation and deformation time series for the locations of the anchorages of the dam, providing a new and independent dataset to assess the integrity of the construction.

Integrating MODIS LST and Sentinel-1 InSAR to monitor frozen soil deformation in the Qumalai-Zhiduo area, Qinghai-Tibet Plateau

ABSTRACT Owing to continuous global warming, frozen soil degradation has become a universal phenomenon, leading to large-scale ground deformation that affects engineering construction and the fragile ecological balance. Geodetic observations, especially temporal InSAR, can quantify ground deformation. However, the accuracy of InSAR modelling in capturing the spatial–temporal variability of the freeze–thaw process depends on the spatial resolution of temperature measurements. This paper proposes a freeze–thaw amplitude model incorporating MODIS LST based on a single-master InSAR time-series deformation to calculate frozen soil deformation. We applied this model to the Qumalai-Zhiduo area of the Qinghai-Tibet Plateau and compared its results with those of the model using weather station temperature in terms of frozen soil deformation parameters, RSME, and characteristic targets. Our study found that the model incorporating MODIS LST performed better in areas far from weather stations, while both models produced similar results in areas of close proximity. Finally, we evaluated another commonly used method for calculating frozen soil deformation parameters and found that the method incorporating MODIS LST based on a single-master time-series deformation is more accurate and precise than the method based on a multi-master SBAS network.

Integration of DInSAR-PS-Stacking and SBAS-PS-InSAR Methods to Monitor Mining-Related Surface Subsidence

Over-exploitation of coal mines leads to surface subsidence, surface cracks, collapses, landslides, and other geological disasters. Taking a mining area in Nalintaohai Town, Ejin Horo Banner, Ordos City, Inner Mongolia Autonomous Region, as an example, Sentinel-1A data from January 2018 to October 2019 were used as the data source in this study. Based on the high interference coherence of the permanent scatterer (PS) over a long period of time, the problem of the manual selection of ground control points (GCPs) affecting the monitoring results during refinement and re-flattening is solved. A DInSAR-PS-Stacking method combining the PS three-threshold method (the coherence coefficient threshold, amplitude dispersion index threshold, and deformation velocity interval) is proposed as a means to select ground control points for refinement and re-flattening, as well as a means to obtain time-series deformation by weighted stacking processing. A SBAS-PS-InSAR method combining the PS three-threshold method to select PS points as GCPs for refinement and re-flattening is also proposed. The surface deformation results monitored by the DInSAR-PS-Stacking and SBAS-PS-InSAR methods are analyzed and verified. The results show that the subsidence location, range, distribution, and space–time subsidence law of surface deformation results obtained by DInSAR-PS-Stacking, SBAS-PS-InSAR, and GPS methods are basically the same. The deformation results obtained by these two InSAR methods have a good correlation with the GPS monitoring results, and the MAE and RMSE are within the acceptable range. The error showed that the edge of the subsidence basin was small and that the center was large. Both methods were found to be able to effectively monitor the coal mine, but there were also shortcomings. DInSAR-PS-Stacking has a strong ability to monitor the settlement center. SBAS-PS-InSAR performed well in monitoring slow and small deformations, but its monitoring of the settlement center was insufficient. Considering the advantages of these two InSAR methods, we proposed fusing the time-series deformation results obtained using these two InSAR methods to allow for more reliable deformation results and to carry out settlement analysis. The results showed that the automatic two-threshold (deformation threshold and average coherence threshold) fusion was effective for monitoring and analysis, and the deformation monitoring results are in good agreement with the actual situation. The deformation information obtained by the comparison, and fusion of multiple methods can allow for better monitoring and analysis of the mining area surface deformation, and can also provide a scientific reference for mining subsidence control and early disaster warning.

Bayesian Monte Carlo Inversion of InSAR Time Series Deformation Induced by Wastewater Injection: A Case Study in West Texas

AbstractWastewater disposal can induce detectable surface uplift, which may cause ground instability and threaten infrastructure. The distributions of local hydro‐geomechanical parameters, especially Young's modulus and hydraulic conductivity, play an essential role in these geohazards. To constrain these parameters, we have inverted spatio‐temporal deformation measured by Interferometric Synthetic Aperture Radar (InSAR) and injection information using a Bayesian Monte Carlo approach with a poroelastic finite element model. Sentinel‐1A/B imagery from 2014 to 2020 is processed to track the spatio‐temporal deformation in Winkler county, West Texas, USA. The posterior distribution of subsurface effective volumes reveals under‐reported volumes in the well near the deformation center. In addition, the inversion results provide better constraints for the parameters than those solely obtained based on the cumulative spatial deformation or temporal development of the deformation center.

Observation of spatial and temporal patterns of seasonal ground deformation in central Yakutia using time series InSAR data in the freezing season

The seasonal freeze/thaw variations of the active layer cause seasonal ground deformation, such as thaw subsidence in summer and frost heave in winter due to the cyclic phase changes of soil water. The Synthetic Aperture Radar (SAR) remote sensing, especially interferometric SAR (InSAR) techniques, has been proven to be a very useful tool for observing surface displacements of permafrost environments. Most previous studies on seasonal InSAR displacement have been concentrated in several study sites in the Arctic tundra regions. The objective of this study is to extend the InSAR analysis of seasonal ground deformations to boreal forest regions, where monitoring ground deformation is particularly important in terms of better understanding active layer dynamics under various ecological and geological conditions. In this study, we presented a strategy to analyze the time series SAR data in the freezing season to estimate the differential phase for seasonal ground deformation while overcoming the problem of coherence losses due to the rapid growth of vegetation during the short growing season. We applied the SBAS-based InSAR processing method to Sentinel-1 SAR in the central Yakutian lowlands, where boreal forests and thermokarst landforms are widely distributed, and retrieved frost-related displacement signals successfully. In addition to the magnitude of ground deformation in the freezing season, we further examined the temporal deformation pattern related to the progression of the freezing front in the soil during active freezing. Based on the Stefan solution, we proposed a piecewise linear model for the time series deformation. The proposed model divides the time series displacement according to the progression of freezing into three linear segments, and the slope parameter of each linear model can be interpreted as indicating the hydrological characteristics of the upper, middle, and lower parts of the soil. The results for the Yakutian lowlands illustrated the potential of SAR remote sensing to observe and quantify spatial details of the thermal and hydrological properties inside the active layer soil even in highly vegetated subarctic boreal forest areas.

Ground Surface Subsidence Monitoring Using Sentinel-1 in the “Kostolac” Open Pit Coal Mine

Open pit coal mining affects surrounding populated areas, resulting in terrain surface deformation. Surface deformation should be monitored as often as possible to control deformations and prevent potential incidents. This paper analyzes time series deformation estimated from the Sentinel-1 satellite images using the Persistent Scatterer Interferometry method to monitor subsidence rates caused by open pit mining activities. It is possible to measure deformations using classical geodetic methods, but those are rarely used in practice because they are time-consuming and expensive for application in large areas. Using the open access radar images from the Sentinel-1 mission, 513 images from the repository were downloaded between October 2016 and the end of December 2020. We present the processing steps in detail in order to establish a workflow for the automated processing of vertical displacement estimation using open source tools; a total of 402 images were processed: 215 images belonged to the ascending satellite orbit, 187 images belonged to the descending orbit, and 111 images were rejected because of adverse weather conditions. The PS InSAR technique has never been used for the mines of the Republic of Serbia or for land surveying practices related to deformation monitoring. The results based on the Sentinel-1 images were compared with results from geodetic leveling and with neotectonic uplift trends. The trend lines of vertical displacement obtained from PS and corresponding leveling are significantly similar (a Pearson correlation of 85% with a p-value of 0.015). The final evaluation reported results of vertical displacements at the leveling benchmark of −3.4 mm/year with the PS InSAR method and −2.7 mm/year with the leveling method. A comparison of the PS vertical displacements with a settlement model fits reasonably, suggesting that the measurements are valid. As four years of PS time series data is insufficient to establish undisputable conclusions on the neotectonics uplift, extending the time series (covering at least a decade) implies that this approach will become attractive in future neotectonic uplift trend estimations. This study illustrates not only the ability of Sentinel-1 data in mapping vertical deformations, but the obtained results could also be used for geohazard monitoring and land monitoring in general for the area of interest.

Isolating the Poroelastic Response of the Groundwater System in InSAR Data From the Central Valley of California

AbstractWorking with 5 years (2015–2019) of high quality interferometric synthetic aperture radar (InSAR) data covering the Central Valley of California, we developed a new approach to isolate the poroelastic component in InSAR data by estimating and removing the hydrologic loading component. K‐means clustering was used to identify the InSAR deformation time series dominated by the loading response and those dominated by the poroelastic response. The former time series include seasonal oscillations of uplift and subsidence related to mass changes in snow and ice in the adjacent mountain range, while the latter include interannual deformation and seasonal oscillations related to changing head in the valley. The loading component accounts for, on average, 56 ± 12% of the deformation in the Central Valley. Without correcting for the loading response, the deformation due to the poroelastic response (and therefore any derived estimate of change in head) is underestimated by 50%–60% in areas of the valley.

Landslide surface horizontal displacement monitoring based on image recognition technology and computer vision

The deformation and displacement of landslides has always been the priority of landslide monitoring and early warning. With the development of image processing technology in recent years, the analysis of landslide surface deformation through image data has become a popular method and different methods of image analysis are rapidly increasing. However, the current method of analyzing landslide deformation and displacement using a single image processing method tends to analyze the displacement of the local deformation area of the landslide, which lacks overall monitoring of the slope and is difficult to use for pre-disaster monitoring of landslide hazards, easily leading to missed detection of landslide potential hazard points. Therefore, this paper combines image recognition technology and computer vision (IR-CV) to propose a method for monitoring and identifying the deformation displacement of landslide surfaces, which has been validated by indoor landslide model tests and unmanned aerial vehicle field simulation displacement recognition tests. In the model tests, the IR-CV method successfully identified the displacements generated during the deformation and damage of the landslide model while using the model deformation time series image data recorded by the camera to analyze the overall deformation of the surface of the landslide model. Further, in this paper, a field simulation displacement identification test was conducted using an unmanned aerial vehicle (UAV), and the orthophoto data obtained by the UAV verified that the IR-CV method can be used for overall deformation displacement identification of landslides in the field. The results of indoor model tests and field UAV simulation tests show that the monitoring method of IR-CV can effectively identify the deformation and displacement of landslide surfaces, which has good application prospects in pre-disaster monitoring and displacement identification of landslides, thus providing a cost-effective and feasible solution for monitoring the deformation and displacement of landslide surfaces.

Seismic Source Characterization From GNSS Data Using Deep Learning

AbstractThe detection of deformation in Global Navigation Satellite System (GNSS) time series associated with (a)seismic events down to a low magnitude is still a challenging issue. The presence of a considerable amount of noise in the data makes it difficult to reveal patterns of small ground deformation. Traditional analyses and methodologies are able to effectively retrieve the deformation associated with medium to large magnitude events. However, the automatic detection and characterization of such events is still a complex task, because traditionally employed methods often separate the time series analysis from the source characterization. Here we propose a first end‐to‐end framework to characterize seismic sources using geodetic data by means of deep learning, which can be an efficient alternative to the traditional workflow, possibly overcoming its performance. We exploit three different geodetic data representations in order to leverage the intrinsic spatio‐temporal structure of the GNSS noise and the target signal associated with (slow) earthquake deformation. We employ time series, images, and image time series to account for the temporal, spatial, and spatio‐temporal domain, respectively. Thereafter, we design and develop a specific deep learning model for each dataset. We analyze the performance of the tested models both on synthetic and real data from North Japan, showing that image time series of geodetic deformation can be an effective data representation to embed the spatio‐temporal evolution, with the associated deep learning method outperforming the other two. Therefore, jointly accounting for the spatial and temporal evolution may be the key to effectively detect and characterize fast or slow earthquakes.

Spatiotemporal Evolution of Ground Subsidence and Extensional Basin Bedrock Organization: An Application of Multitemporal Multi-Satellite SAR Interferometry

Since the early 1990s, the European (ESA) and Italian (ASI) space agencies have managed and distributed a huge amount of satellite-recorded SAR data to the research community and private industries. Moreover, the availability of advanced cloud computing services implementing different multi-temporal SAR interferometry techniques allows the generation of deformation time series from massive SAR images. We exploit the information provided by a large PS dataset to determine the temporal trend of ground deformation and the relative deformation rate with millimetric accuracy to analyze the spatial and temporal distribution of land subsidence induced by water pumping from a deep confined aquifer in the Northern Valle Umbra Basin (Central Italy), exploiting 24 years of Permanent Scatterers—interferometric SAR data archives. The SAR images were acquired between 1992 and 2016 by satellites ERS1/2 and ENVISAT, the Sentinel 1 ESA missions and the COSMO-SkyMed ASI mission. We observed ground velocities and deformation geometries between 1992 and 2016, with displacements of more than 70 cm and velocities of up to 55 mm/yr. The results suggest that the shape and position of the surface ground displacement are controlled by the fault activity hidden under the valley deposits.

Slow Deformation Time-Series Monitoring for Urban Areas Based on the AWHPSPO Algorithm and TELM: A Case Study of Changsha, China

Health monitoring is important for densely distributed urban infrastructures, particularly in cities undergoing rapid economic progress. Permanent scatterer interferometry (PSI) is an advanced remote sensing observation technique that is commonly used in urban infrastructure monitoring. However, the rapid construction of infrastructures may easily cause a loss of coherence for radar interferometry, inducing a low density of effective permanent scatterer (PS) points, which is the main limitation of PSI. In order to address these problems, a novel time-series synthetic aperture radar interferometry (InSAR) process based on the adaptive window homogeneous pixel selection and phase optimization (AWHPSPO) algorithm and thermal expansion linear model (TELM) is proposed. Firstly, for homogeneous point selection, information on both the time-series intensity and deformation phases is considered, which can compensate for the defects of insufficient homogeneous samples and low phase quality in traditional distributed scatterer interferometric synthetic aperture radar (DS-InSAR) processing. Secondly, the physical, thermal expansion component, which reflects the material properties of the infrastructures, is introduced into the traditional linear model, which can more rationally reflect the temporal evolution of deformation variation, and the thermal expansion coefficients can be estimated simultaneously with the deformation parameters. In order to verify our proposed algorithm, the Orange Island area in Changsha City, China, was selected as the study area in this experiment. Three years of its historical time-series deformation fields and thermal expansion coefficients were regenerated. With the use of high-resolution TerraSAR-X radar satellite images, a maximum accumulated settlement of 12.3 mm and a minor uplift of 8.2 mm were detected. Crossvalidation with small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) results using Sentinel 1A data proved the reliability of AWHPSPO. The proposed algorithm can provide a reference for the control of the health and safety of urban infrastructures.