Displacement Time Series Research Articles

ANALYSIS OF MINING-INDUCED TERRAIN DEFORMATION USING MULTITEMPORAL DISTRIBUTED SCATTERER SAR INTERFEROMETRY

Abstract. This work addresses a methodology based on the Interferometric Synthetic Aperture Radar (InSAR) to analyse and monitor ground motion phenomena induced by underground mining activities, in the Legnica-Glogow Copper District, south-western Poland. Two stacks of ascending and descending Sentinel-1 Synthetic Aperture Radar (SAR) images are processed with a small baseline multitemporal approach. A simple method to select interferograms with high coherence and eliminated images with low redundancy is implemented to optimize the interferogram netwrork. The estimated displacement maps and time series show the effect of both linear and impulsive ground motion and are validated against Global Navigation Satellite System (GNSS) measurements.

Spatio-Temporal Evolution Law of Surface Subsidence Basin with Insufficient Exploitation of Deep Coal Resources in Aeolian Sand Area of Western China

Coal is one of the fundamental fossil energy supporting the world’s economy. The synergistic development between efficient coal mining and ecological environment protection is the inevitable requirement for the preservation of global harmony. As the world’s largest coal producer, China has conducted a strategic shift from east to west in terms of the exploitation of its energy resources, posing a serious threat to the fragile ecological environment of the western region. In particular, the surface subsidence caused by coal mining is the root of the ecological deteriotation and the destruction of ground structures. However, it is difficult to reveal the law of large-scale surface subsidence in western mining areas merely by conventional measurement methods such as leveling, on account of the high intensity of coal seam mining, the weakness of the lithology of overlying rock and the large thickness of wind-blown sand strata. In view of this, small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was used in this study to obtain the time series of surface vertical displacement during the whole mining process of the 2401 working face in the Yingpanhao coal mine, Inner Mongolia. Based on the deformation data, the dynamic evolution characteristics of surface subsidence under high intensity mining in the western mining area were analyzed exhaustively. It was found that the surface subsidence is characterized by an extensive coverage range (48.52 km2) with minimal ground settlement (250 mm) in the study area. Meanwhile, the boundary shape of the subsidence basin followed a “circular-parallelogram-trapezoid” changeable process and the coverage area of the basin experienced three stages: a linear increasing period, a temporary stagnation period, and a re-expansion period. Furthermore, there existed an abnormal uplift phenomenon on the east side of the open-off cut in the 2401 working face. Combined with the structure of overlying strata, this paper carried out a preliminary analysis on the reasons of the abovementioned phenomenon. The research results are of vital realistic significance for ground buildings and ecological environmental protection in the aeolian sand mining area in Western China.

A tracking method of equivalent constraint boundary for offshore monopile wind turbines from measured accelerations

For offshore wind turbines, the foundation constraint boundary is important for safe operation yet difficult to determine due to the invalidity of the fixed point assumption and influences from soil variability, liquidation, scour, etc. In this paper, a tracking method of equivalent constraint boundary is proposed on the basis of a limited numbers of measured responses, which are entirely different from the results by fixed constraints. The most theoretical achievement of the proposed method is that the time series of so-called first-order displacements can be accurately retrieved from the measured accelerations, leading to the significant difference from the first-order mode shape of conventional modal identification methods. The obvious advantage of the proposed method is that the change in the equivalent constraint boundary can be easily tracked just from the assembled accelerometers, which means that the difficulty and uncertainty of considering soil and water can be avoided. To verify the performance of the proposed method, two numerical examples and a beam experiment are conducted, and measured sea test data from a monopile wind turbine is processed. In detail, the first example is a cantilever beam with a theoretical fixed constraint position, and the results show that the fixed boundary position of the cantilever beam can be accurately estimated with a maximum relative error of 5.3% when six sensors and a fifth-order polynomial are used. The second example is a physical experiment of a cantilever beam and the change of constraint is considered by removing segments one by one. The experimental results show that the predicted constraint position is consistent with the cut length, with a maximum error of 0.009 m at 1.9 m. Furthermore, a 4 MW offshore monopile wind turbine evaluated by commercial software Ansys considering pile–soil interaction is analyzed, and the changes of the predicted equivalent constraint position are in good agreement with the preset values. Finally, the proposed method is applied to the field test data of a 6 MW monopile wind turbine, and a stable estimation of the equivalent constraint position can be obtained. The good accuracy of the proposed method demonstrates its capability in monitoring structural health for offshore wind turbines.

PSDEFOPAT – TOWARDS AUTOMATIC MODEL BASED PSI POST-PROCESSING

Abstract. The deformation of the surface of the earth is triggered by numerous naturally occurring and artificial processes such as global isostatic adjustment, aseismic and coseismic movement, varying amounts of groundwater or gas stored underground, and soil consolidation due to urbanization. Monitoring these surface deformations is essential to understand the underlying processes and provide authorities and the public with hazard assessments. Remote sensing techniques, such as Persistent Scatterer Interferometry (PSI), have the capability of mapping these deformations. Besides a spatial analysis of the deformation patterns, PSI also provides information on their temporal evolution. Post-processing strategies to analyze the displacement time series have gained interest in recent years. This paper presents our PSI post-processing strategy, which incorporates different deformation models and automatically chooses the best-fitting one based on statistical tests.

The Effectiveness of Coaching the Australian Recommended Fundamental Overarm Throwing Skill Criteria for Less-Skilled Adolescents

ABSTRACT Purpose: The aim of this study was to investigate the effects of coaching the Australian recommended fundamental overarm throwing skill criteria for less-skilled adolescents on movement competency using three-dimensional (3D) motion analysis. Methods: Less-skilled non-sports-active and skilled sports-active pupils were assigned to FMS Group (n = 97) and Sports Group (n = 129), respectively. Demographic, anthropometric, whole-body kinematic (discrete and time-series angular displacement and angular velocity data), motor coordination (segmental sequencing) and motor performance (maximum ball release speed) were analyzed. Baseline data were compared using independent T-tests and Chi-Square tests; interaction effects were assessed using repeated-measure ANOVAs. Changes in time-series kinematic data were analyzed by comparing the areas enclosed by the 95% confidence intervals. Results:There was merely a modest improvement in motor competency for the less-skilled pupils. The FMS group significantly acquired 13/75 discrete kinematic variables (p < .01) and four movement patterns but not motor coordination (p = .469) and motor performance (p = .756). Prior to the intervention, the pupils acquired 4/7 recommended skill criteria. The descriptions of another three criteria were not appropriate with no improvement in motor coordination and performance. Two new skills were identified and improved after the FMS intervention. Conclusions: The recommended skill criteria for children were not developmentally and age-appropriate for early adolescents. The use of quantitative 3D motion data provided a more precise and developmentally applicable fundamental overarm throwing criteria than content validated skill criteria. The recommended skill criteria should be modified to facilitate teachers and coaches for effective education.

Influence of Meteorological Processes on cGPS Measurements of Crustal Movements

Surface displacement measurements collected using continuous Global Positioning System (cGPS) stations include the contribution of both endogenic processes (e.g., plate tectonics, isostasy) and exogenic processes that show seasonal variation (e.g., climate). These seasonal variations distort the tectonic signals and limit the usefulness of short-term investigations. This paper explores cGPS and meteorological time series collected in southern Africa and investigates whether the usefulness of cGPS time series can be improved by accounting for the seasonal effects of the dominant meteorological processes. The study is carried out using time, frequency, and time-frequency domain signal processing (inferential) analysis techniques. It was found that from the considered atmospheric processes, thermally-induced effects, which are not corrected for in the cGPS data, are the most prominent meteorological contributors in the vertical annual deformation component observed in cGPS time series. The effects of heave action (due to seasonal subsurface water infiltration and absorption, as well as changes in the water table) and changes of water mass distribution (caused by infiltration, transportation, extractions, and evaporation) on the displacement time series are much smaller. This suggests that correcting for thermal expansion, contraction, and thermal-induced errors could reduce the annual seasonal deformation component observed in cGPS position measurements in southern Africa and, most probably, in other parts of the world subjected to large seasonal variations in atmospheric temperatures. Reducing the magnitude of the seasonal components would increase the usefulness of short-term cGPS campaigns.

Landslide deformation and temporal prediction of slope failure in Himalayan terrain using PSInSAR and Sentinel-1 data

Landslides in hilly terrains are frequent phenomenon due to various triggering factors such as heavy rainfall, flash flood, slope instability, tectonic disturbances etc., and result in loss of human lives and damage of infrastructure and property. The continuous monitoring of slope instability and temporal prediction of slope failure is of paramount importance for assessing landslide-triggering factors and mitigating landslide hazards. This research presented a comprehensive methodology for time-series displacement measurement of a landslide-affected area prior to its actual failure and temporal prediction of slope failure in a hilly terrain using persistent scatterer SAR interferometry (PSInSAR) technique and Sentinel-1 data in an open source computational environment. On 30 July 2021, a massive landslide witnessed in Sirmaur district of Himachal Pradesh, India near Barwas and brought down around 180 m stretch of national highway (NH) 707 connecting Paonta Sahib with Shillai-Hatkoti. We used 52 single look complex (SLC) images in ascending and 41 SLC images in descending orbit with VV polarization, acquired for a period of almost two years (2019–2021) for time-series displacement measurement along radar line of sight (LOS), horizontal and vertical directions. The pre-failure displacement-time series derived from ascending and descending data stacks was critically analyzed to identify the reliable and accelerating persistent scatterer (PS) locations for predicting temporal failure of slope material and to understand kinematics of the study area. We used inverse velocity (INV) and modified inverse velocity (T-INV) methods at three different accelerating and reliable PS locations for temporal prediction of slope failure. The results show that the mean LOS velocity ranges from −25.49 to + 31.35 mm/y in ascending data and from −20.97 to + 24.16 mm/y in descending data, and horizontal and vertical components of motion vector vary from −30.23 to + 18.99 mm/y and –23.16 to 25.79 mm/y respectively in the study area. Displacement time-series at selected measurement points (PSs) depict the initiation of slope instability from 31 July 2020 and 3 July 2020 using ascending and descending stacks respectively, which are almost one year before the actual failure. INV method predicted temporal windows of slope failure between 27 July 2021 to 1 August 2021 for ascending stack and 24 July 2021 to 29 July 2021 using descending stack, while those of T-INV method lye between 28 July 2021 to 1 August 2021 for ascending data and between 22 July 2021 to 30 July 2021 for descending data. The deformation-time series and predicted days of slope failure are in good agreement at different reliable PS locations. Results in this study show that the approach used for time failure may evolve as an effective tool for landslide early warning systems.

A Multi-Temporal Small Baseline Interferometry Procedure Applied to Mining-Induced Deformation Monitoring

This work addresses a methodology based on the interferometric synthetic aperture radar (InSAR) applied to analyze and monitor ground-motion phenomena induced by underground mining activities in the Legnica-Glogow copper district, south-western Poland. The adopted technique employs an InSAR processing chain that exploits a stack of Sentinel-1 synthetic aperture radar (SAR) images using a small baseline multitemporal approach. Interferograms with small temporal baselines are first selected, then their network is optimized and reduced to eliminate noisy data, in order to mitigate the effect of decorrelation sources related to seasonal phenomena, i.e., snow and vegetation growth, and to the radar acquisition geometry. The atmospheric disturbance is mitigated using a spatio-temporal filter based on the nonequispaced fast Fourier transform. The estimated displacement maps and time series show the effect of both linear and impulsive ground motion and are validated against global navigation satellite system (GNSS) measurements. In this context, a significant threat to the built environment is represented by seismic tremors triggered by underground mining activities, which are analyzed using the proposed method to integrate the information gathered by in situ seismometer devices.

Towards big SAR data era: An efficient Sentinel-1 Near-Real-Time InSAR processing workflow with an emphasis on co-registration and phase unwrapping

Sentinel-1 Near-Real-Time (NRT) processing embraces great potential in its applications to a wide range of research topics. Towards higher quality NRT products, it is crucial to avoid data reprocessing and to dynamically update InSAR time series. Although state-of-the-art techniques, enabled by high-performance computers and high-capacity storage platforms, make the highly multi-looked data available online quickly, the technique and procedure for efficiently obtaining relatively high resolution NRT data for a specified region are still lacking. Here we propose a workflow in a review way towards efficient and high-resolution Sentinel-1 NRT InSAR processing. In addition, we are committed to addressing issues that are often overlooked but indispensable in NRT data processing: co-registration and phase unwrapping. Through this workflow, not only NRT results in the radar Line-of-Sight (LOS) direction can be obtained, but also along-track NRT observations in overlap regions of images can be achieved. Unlike deriving LOS time series displacements which requires additional operations after co-registration, along-track displacements, a by-product of co-registration, can be directly obtained without further processing. We test and validate the proposed workflow in three regions with representative geomorphological processes including volcanic activity, city subsidence, and landslides. Experimental results demonstrate that our workflow can perform NRT monitoring at a relatively high resolution and a relatively low computational and storage “burden”. The time efficiency is increased by ∼ 65% and the storage space is saved by ∼ 21.8%.

Seasonal driving sources and hydrological-induced secular trend of the vertical displacement in North China

Study regionNorth China (NC) is an important social and economic center in China. Study focusNC is suffering from obvious land deformation. The vertical time series of 20 Global Positioning System (GPS) stations are explored to determine the seasonal driving sources and hydrological-induced displacement trends together with the mass loading models. A method named SSA-ICA is developed to extract the source signal using singular spectrum analysis (SSA) in conjunction with independent component analysis (ICA). New hydrological insightsThe independence of extracted components by SSA-ICA is significantly stronger than that by SSA alone through the mutual information evaluation, and the separation ability of source signals by SSA-ICA is greatly superior to SSA. The good agreements between SSA-ICA-derived independent reconstructed components (IRCs) and mass loading displacements confirm that the atmospheric loading (ATML) and hydrological loading (HYDL) are the primary driving sources of GPS seasonal displacement. Due to the phase biases, we adjust the phases of displacement time series from ATML and HYDL by referring to IRC1 and IRC2, respectively. The average weighted root-mean-squares (WRMS) reduction after phase adjustment is 12.14% higher than that before adjustment, significantly improving the consistency between GPS and mass loading measurements. Moreover, quantitative estimates of the secular displacement trends induced by hydrological mass changes suggest that extensive exploitation of groundwater causes severe land subsidence, especially in the east-central plain.

On the Joint Exploitation of Satellite DInSAR Measurements and DBSCAN-Based Techniques for Preliminary Identification and Ranking of Critical Constructions in a Built Environment

The need for widespread structural safety checks represents a stimulus for the research of advanced techniques for structural monitoring at the scale of single constructions or wide areas. In this work, a strategy to preliminarily identify and rank possible critical constructions in a built environment is presented, based on the joint exploitation of satellite radar remote sensing measurements and artificial intelligence (AI) techniques. The satellite measurements are represented by the displacement time series obtained through the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique known as full resolution Small BAseline Subset (SBAS) approach, while the exploited AI technique is represented by the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) methodology. The DBSCAN technique is applied to the SBAS-DInSAR products relevant to the achieved Persistent Scatterers (PSs), to identify clusters of pixels corresponding to buildings within the investigated area. The analysis of the deformation evolution of each building cluster is performed in terms of velocity rates and statistics on the DInSAR measurements. Synthetic deformation maps of the areas are then retrieved to identify critical buildings. The proposed methodology is applied to three areas within the city of Rome (Italy), imaged by the COSMO-SkyMed SAR satellite constellation from ascending and descending orbits (in the time interval 2011–2019). Starting from the DInSAR measurements, the DBSCAN algorithm provides the automatic clustering of buildings within the three selected areas. Exploiting the derived deformation maps of each study area, a preliminary identification and ranking of critical buildings is achieved, thus confirming the validity of the proposed approach.

Surface Displacements Mechanism of the Dobi Graben from ASAR Time-Series Analysis of InSAR: Implications for the Tectonic Setting in the Central Afar Depression, Ethiopia

The Dobi graben is a Quaternary, NW-trending continental rift found within the East-Central Block (ECB) of the Afar Depression (AD) in Ethiopia. The AD might be the only place where three active rifts meet on land. This diffused, Rift–Rift–Rift (RRR) triple junction in the ECB comprises the overlap zone between the Red Sea and the Gulf of Aden propagators. Rifting is ongoing in the Dobi graben as evidenced by the August 1989 earthquakes (of magnitude 5.7 &lt; MW &lt; 6.2). This study carried out a surface displacement time-series analysis to examine the kinematics of the Dobi graben and the surrounding area using 18 ascending orbit scenes (between May 2005 and March 2010) along tract 257 and 15 along the descending orbit (tract 006) of the Advanced Synthetic Aperture Radar (ASAR), C-band (λ = 5.6 cm) acquired by the ENVIronmental SATellite (ENVISAT). We utilized the Small Baseline Algorithm (SBA) techniques of the distributed scatterer, which were implemented independently to generate Line of Sight (LOS) displacement maps. These LOS displacement surface movements, identified in both geometries, can be interpreted as ± signs of predominantly vertical movement in both geometries: positive for uplifting and negative for subsidence. Additionally, opposite signs of ± horizontal movement in both geometries indicate that the movement is from East to West (or vice versa). Results from the velocity and displacement maps and time series analysis suggest that creeping is associated mainly with normal faulting and could be the primary mechanism for strain distribution for the Southeastern part of the Dobi graben. The anomalous, continuous uplifting exhibited at the rift shoulder and in the horst area might be linked to the presence of temporary reactivation of normal faulting in the region. The oblique, positive LOS signals observed in different parts of the Dobi graben might serve as a proxy for understanding how strain is accommodated as normal faulting and is distributed in a distinct northeast direction. This explanation supports both the arguments for the Northeast migration of the triple junction and the transfer of strain from the southernmost Red Sea Rift (RSR) to the Central AD.

Machine Learning for Defining the Probability of Sentinel-1 Based Deformation Trend Changes Occurrence

The continuous monitoring of displacements occurring on the Earth surface by exploiting MTInSAR (Multi Temporal Interferometry SAR) Sentinel-1 data is a solid reality, as testified by the ongoing operational ground motion service in the Tuscany region (Central Italy). In this framework, anomalies of movement, i.e., accelerations or deceleration as seen by the time series of displacement of radar targets, are identified. In this work, a Machine Learning algorithm such as the Random Forest has been used to assess the probability of occurrence of the anomalies induced by slope instability and subsidence. About 20,000 anomalies (about 7000 and 13,000 for the slope instability and the subsidence, respectively) were collected between 2018 and 2020 and were used as input, while ten different variables were selected, five related to the morphological and geological setting of the study area and five to the radar characteristics of the data. The resulting maps may provide useful indications of where a sudden change of displacement trend may occur, analyzing the contribution of each factor. The cross-validation with the anomalies collected in a following timespan (2020–2021) and with official landslide and subsidence inventories provided by the regional authority has confirmed the reliability of the final maps. The adoption of a map for assessing the probability of the occurrence of MTInSAR anomalies may serve as an enhanced geohazard prevention measurement, to be periodically updated and refined in order to have the most precise knowledge possible of the territory.

Multi-temporal analysis of groundwater depletion-induced land subsidence in Central Ganga Alluvial plain, Northern India

This study explores the Persistent Scatterer Interferometry technique to identify the developing pattern of groundwater depletion-induced land subsidence in Lucknow, northern India. The results show the development of two significant subsidence zones, one each in the north and south of Lucknow. The situation is potentially alarming as ∼9.3% (∼27.6 km2) of the total area is under the expression of a weak degree of subsidence (∼10–40 mm/yr). The detected displacement trends substantially correspond to the constant dwindling of groundwater levels in the city, further indicating that the first unconfined aquifer is under high stress and faces a continuous storage capacity loss with time. The time-series displacement trends have been later forecasted using the Autoregressive Integrated Moving Average method, indicating the city might face subsidence at the rate of ∼25–40 mm/yr. The findings of this study are significant to take precise mitigation measures in the areas facing continuous groundwater depletion on a long-term basis.

Separate modeling technique for deformation monitoring of concrete dams

Deformation monitoring is an important aspect of safety control for concrete dams. Deformation monitoring models (such as statistical models and hybrid models) are extensively applied to predict concrete dam deformation and derive confidence interval of normal deformation for anomaly detection. Deformation monitoring models for concrete dams mainly consist of hydrostatic component, temperature component, and aging component. The optimum parameters of individual components are simultaneously determined by least square method for monitoring data fitting. Thus, significant over-fitting of deformation monitoring models may be induced by mutual compensation among the parameters of model components. In this paper, the Separate Modeling Technique (SMT) is proposed for mitigating the over-fitting problem of deformation monitoring models for concrete dams. Firstly, the Empirical Mode Decomposition (EMD) is adopted to extract and visualize the aging component of displacement sequence. Proper mathematical formulation of the aging component can be established, and the problem of improperly presupposing the mathematical form of aging component in the process of constructing traditional deformation monitoring models is well addressed. In this study, the hydrostatic component is represented by the Hybrid Response Surface (HRS), which is formulated using numerical simulation with varying water levels and material parameters. The displacement variation caused by water level fluctuation is identified in terms of isothermal conditions and is used to calibrate the material parameters in the HRS. The temperature component is separated through subtracting the hydrostatic and aging components from displacement time series and then is expressed with proper mathematical formulations. Finally, hybrid models for displacement monitoring of concrete dams are established by combining the separately formulated components. The Separate modeling technique is applied to formulate crest displacement of the YL concrete gravity dam. The false alarm rate of displacement monitoring and a new model selection criterion (namely over-fitting coefficient) are adopted to compare various deformation monitoring models. It is shown that the over-fitting levels of deformation monitoring models can be effectively reduced using the SMT. The deformation monitoring models constructed with the SMT are of better accuracy in displacement prediction and present no false alarm of displacement monitoring for the tested period.

Assessing the Potential of Long, Multi-Temporal SAR Interferometry Time Series for Slope Instability Monitoring: Two Case Studies in Southern Italy

Multi-temporal SAR interferometry (MTInSAR), by providing both mean displacement maps and displacement time series over coherent objects on the Earth’s surface, allows analyzing wide areas, identifying ground displacements, and studying the phenomenon evolution at a long time scale. This technique has also been proven to be very useful for detecting and monitoring slope instabilities. For this type of hazard, detection of velocity variations over short time intervals should be useful for early warning of damaging events. In this work, we present the results obtained by using both COSMO-SkyMed (CSK) and Sentinel-1 (S1) data for investigating the ground stability of two hilly villages located in the Southern Italian Apennines (Basilicata region), namely the towns of Montescaglioso and Pomarico. In these two municipalities, landslides occurred in the recent past (in Montescaglioso in 2013) and more recently (in Pomarico in 2019), causing damage to houses, commercial buildings, and infrastructures. SAR datasets acquired by CSK and S1 from both ascending and descending orbits were processed using the SPINUA MTInSAR algorithm. Mean velocity maps and displacement time series were analyzed, also by means of innovative ad hoc procedures, looking, in particular, for non-linear trends. Results evidenced the presence of nonlinear displacements in correspondence of some key infrastructures. In particular, the analysis of accelerations and decelerations of PS objects corresponding to structures affected by recent stabilization measures helps to shed new light in relation to known events that occurred in the area of interest.

GNSS-corrected InSAR displacement time-series spanning the 2019 Ridgecrest, CA earthquakes

SUMMARYInSAR displacement time-series are emerging as a valuable product to study a number of Earth processes. One challenge to current time-series processing methods, however, is that when large earthquakes occur, they can leave sharp coseismic steps in the time-series. These discontinuities can cause current atmospheric correction and noise smoothing algorithms to break down, as these algorithms commonly assume that deformation is steady through time. Here, we aim to remedy this by exploring two methods for correcting earthquake offsets in InSAR time-series: a simple difference offset estimate (SDOE) process and a multiparameter offset estimate (MPOE) parametric time-series inversion technique. We apply these methods to a 2-yr time-series of Sentinel-1 interferograms spanning the 2019 Ridgecrest, CA earthquake sequence. Descending track results indicate that the SDOE method precisely corrects for only 20 per cent of the coseismic offsets at 62 study locations included in our scene and only partially corrects or sometimes overcorrects for the rest of our study sites. On the other hand, the MPOE estimate method successfully corrects the coseismic offset for the majority of sites in our analysis. This MPOE method allows us to produce InSAR time-series and data-derived estimates of deformation during each phase of the earthquake cycle. In order to better isolate and estimate the signal of post-seismic lithospheric deformation in the InSAR time-series, we apply a GNSS-based correction to our interferograms. This correction ties the interferograms to median-filtered weekly GNSS displacements and removes additional atmospheric artefacts. We present InSAR-based estimates of post-seismic deformation for the area around the Ridgecrest rupture, as well as a 2-yr coseismic-corrected, GNSS-corrected InSAR time-series data set. This GNSS-corrected InSAR time-series will enable future modelling of post-seismic processes such as afterslip in the near field of the rupture, poroelastic deformation at intermediate distances and viscoelastic deformation at longer timescales in the far field.

The Most Intense Deflation of the Last Two Decades at Mt. Etna: The 2019–2021 Evolution of Ground Deformation and Modeled Pressure Sources

AbstractWe analyze Global Navigation Satellite System (GNSS) and tilt data from the permanent monitoring networks of Etna volcano starting just after the 24 December 2018 eruption to an unusual two‐month period of deflation in February–March, 2021, which coincided with the occurrence of 17 lava fountain episodes. Based on changes in slope in the GNSS displacement time series, we divide the period starting 7 months after the eruption into five phases, spanning the continued inflation of the edifice punctuated by short periods of effusive and strombolian activity (four phases) and a 2‐month phase of intensive deflation. Our model indicates a progressive deepening of the internal pressure sources followed by a fast ascending source starting two‐months before the first 2021 paroxysms. We explain these results in light of a recent volcanological model on the nature and behavior of magma ascending through the Etnean feeding system.

Adaptive Fusion of Multi-Source Tropospheric Delay Estimates for InSAR Deformation Measurements

Atmospheric propagation delay correction is the key to improving the accuracy of deformation measurement of satellite interferometric synthetic aperture radar (InSAR). The empirical phase-elevation models and external data-based models present uneven performances of atmospheric delay correction for InSAR deformation monitoring. In this study, based on our previous fusion of delays predicted by multiple weather models (FDWM), we propose a new approach of adaptive fusion of multi-source tropospheric delay (AFMTD) estimates derived from multiple models over wide areas, i.e., ERA5, GACOS, WRF, MERRA2, NARR, MODIS, Linear model, and Powerlaw model. The spatially varying scaling algorithm is employed to refine the tropospheric delays predicted by the weather models. Meanwhile, we adopt a multiple-window strategy to cope with the spatially lateral variation of tropospheric delays. The AFMTD not only improves the spatial heterogeneity of tropospheric delay, but also adaptively combines multiple models to achieve a more reliable delay estimation. This AFMTD method is incorporated into the StaMPS-SBAS procedure. We compared the AFMTD with other single models using ENVISAT ASAR and Sentinel-1 datasets over Los Angeles of Southern California. The result of ASAR first demonstrates the effectiveness and reliability of the AFMTD method by referring to the assumed ground truth of simultaneous MERIS observations. The results of Sentinel-1 data show that over 95% of unwrapped interferograms have the minimum root-mean-square values after AFMTD correction for both descending and ascending tracks. The validation against GPS observation presents that the RMSEs of InSAR displacement time series after AFMTD correction decreases at more than 90% of 125 GPS stations. The average reductions of RMSE are 35.79% and 36.28% for descending and ascending data, respectively, and the maximum improvement is more than 70%. Overall, the proposed AFMTD method outperforms any single model for InSAR tropospheric delay correction and provides an open framework to fuse multi-source tropospheric delay estimates.

A Satellite Data Mining Approach Based on Self-Organized Maps for the Early Warning of Ground Settlements in Urban Areas

Structural failure prevention is a crucial issue in civil engineering. The causes of structure or infrastructure collapse include phenomena that slowly deform the ground and could affect the stability of foundations such as differential settlements, subsidence, groundwater changes, slope failure, or landslides. When large urban areas need to be monitored, such phenomena are hard to be mapped by means of classical structural health monitoring methods due to the unaffordable quantity of in situ measurements these methods would entail. A very effective alternative is exploiting multitemporal interferometric synthetic aperture radar (MT-InSAR) displacement timeseries which would enable the monitoring of wide geographical areas over a weekly basis and extended spatial coverage. Analyzing the enormous amount of data produced by MT-InSAR may help to assess the time evolution of phenomena but can barely highlight “anomalous” ground deformations in time, to prevent likely structural failure. This paper proposes a method which analyzes the InSAR data through an unsupervised learning paradigm with the purpose of detecting critical events at their early stage. On the basis of similarities among time sequences, this method allows the finding of precursors of anomalous ground settlement behaviors, the correct framing of which should be directed to specialist evaluation and in situ inspections.