Interferometric Synthetic Aperture Radar Displacement Research Articles

Enhancing InSAR accuracy: Unveiling more accurate displacement fields through 3-D troposphere tomography

Interferometric Synthetic Aperture Radar (InSAR) measurements are susceptible to tropospheric delay induced by changes in tropospheric temperature, pressure, and water vapor. While pressure and temperature temporal variations have a limited impact on phase gradients, variations in water vapor, which contribute to the wet component of tropospheric delay, play a significant role. Therefore, obtaining a more accurate estimation of the wet refractivity of the tropospheric layer is crucial for improving InSAR displacement field accuracy. This paper presents the utilization of Global Navigation Satellite Systems (GNSS) tropospheric tomography for reconstructing three-dimensional (3-D) wet refractivity. Various data sources are employed, including GNSS measurements, ERA5 reanalysis data, radiosonde observations, and Sentinel-1A radar acquisitions. ERA5 reanalysis data and radiosonde observations are used to calculate tropospheric hydrostatic delay and to evaluate the wet refractivity obtained from tomography, respectively. The effectiveness of tomography techniques in mitigating the tropospheric signal in unwrapped interferograms is compared with the total tropospheric delay obtained from Generic Atmospheric Correction Online Service (GACOS) products, which is one of the most common approach for tropospheric correction of InSAR measurements. To ensure a comprehensive analysis, the effects of the correction methods are investigated in two different weather conditions. Based on the results, GNSS tropospheric tomography reduces the mean Root Mean Square Error (RMSE) by about 1.2 cm compared to GACOS products. This technique proves to be highly effective in improving the accuracy of InSAR displacement field estimation, particularly under challenging weather conditions.

A field test of compact active transponders for InSAR geodesy

Abstract Compact active transponders (CATs) – also termed electronic corner reflectors – are compact electronic devices designed to receive, actively amplify and re-transmit a radar signal, e.g. a C-band radar signal received from a Sentinel-1 satellite. CATs can potentially be useful for a number of purposes, e.g. if co-located with geodetic infrastructure. However, CATs have only recently become commercially available, and therefore, the usability and long-term performance of CATs are not well known. In this study, two CATs are tested under realistic operating conditions for a period of 14 months, from July 2020 to September 2021. The displacement time series of the CATs are determined from a persistent scatterers interferometric synthetic aperture radar processing of four tracks of Sentinel-1A/-1B data with a passive corner reflector (CR) as the spatial reference. The displacement time series of the CATs are evaluated against a ground truth established from repeated levellings between the CR and the CATs. Based on the results of this study, it is found that a sudden vertical displacement of a CAT can be determined with an accuracy better than 1 cm, possibly a few millimetres. Furthermore, it is found that the mean vertical velocity of a CAT, calculated from 14 months of interferometric synthetic aperture radar displacement time series, can be determined with an accuracy of a few mm/year. Finally, the line of sight (LoS) phase error is generally found to be moderately correlated with temperature, with an instrument-specific linear relationship between LoS error and temperature ranging between approx. 0.1 and 0.2 mm/°C. This correlation between LoS phase error and temperature can in principle be used for instrument-specific calibrations, which is a topic that should be addressed in future studies.

Characterization and prediction of InSAR-derived ground motion with ICA-assisted LSTM model

Interferometric Synthetic Aperture Radar (InSAR) is a highly effective and widely used approach for monitoring large-scale ground deformation. The precise and timely prediction of deformation holds significant importance in mitigating and preventing geological hazards, particularly considering the long revisit cycle of satellites and the considerable time required for data processing. In this study, we propose a strategy that predicts spatiotemporal InSAR time series based on Independent Component Analysis (ICA) and the Long Short-Term Memory (LSTM) machine learning model. Unlike traditional methods that rely on physical or statistical models, the proposed strategy leverages the power of ICA and LSTM to achieve accurate predictions without such dependencies. ICA is employed to decompose and capture the InSAR displacement signals of interest caused by various natural or anthropogenic processes and to characterize each individual signal. The spatiotemporal unsupervised K-mean cluster method is then applied to partition large-scale deformation fields into homogeneous subregions, considering the spatial variations and temporal nonlinearities of time series. This process facilitates the refinement of the model, thereby enhancing the accuracy of large-scale predictions. The neural network models are then individually constructed for each cluster, and the optimal parameters are determined through a grid search strategy. Subsequently, the proposed framework is implemented and assessed using two datasets featuring distinct deformation patterns: Case I involves land subsidence in Willcox Basin, USA, while Case II focuses on post-seismic deformation following the 12 November 2017 Mw 7.3 Sarpol-e Zahab earthquake. The results demonstrate that our proposed ICA-assisted LSTM outperforms the original LSTM model on large-scale deformation prediction, with the average prediction accuracy for one-step prediction (12 days in our case) being improved by 34% and 17% for cases I and II, respectively. Furthermore, we perform iterative predictions on the spatiotemporal InSAR measurements with varying temporal characteristics for the subsequent five steps using Sentinel-1 data and evaluate its performance and limitations. The successful prediction of land subsidence and post-seismic deformation provides further evidence that the proposed prediction strategy can be effectively employed in monitoring other large-scale geohazards characterized by prolonged and gradual deformation. This capability enables expedited decision-making and timely implementation of risk mitigation measures.

A Computation to Generate 2D and 3D Vector Displacements Using InSAR Ascending and Descending Source Data (Case Studies: Landslides and Earthquake in Indonesia)

Interferometric Synthetic Aperture Radar (InSAR) is a radar technique to generate map of surface deformation using phase differences acquired at different times. The InSAR technique measures these phase differences in its line of sight (LOS). Regarding LOS projection to a ground coordinate system, the combination of SAR data from ascending and descending can estimate at least two dimensional fields which are vertical and east-west components and three dimensional fields with either an additional motion model or vector displacements from along-track direction. The purpose of this work is to generate 2D and 3D vector displacements from two different InSAR tracks (e.g. ascending and descending) with a calculation programme both for the mean velocity and time series data named as PS DISP. 2D mode is calculated in which the north-south component is neglected due to insensitive along the flight direction, thus, assuming to be zero (0). On the other hand, 3D mode is calculated with an assumption that the northsouth component moves along the elevation downward. The experimental results were conducted for the Puncak Pass and Puncak Highway landslides and the Lombok earthquake in Indonesia. For the landslide study, the computation generated the movement towards the slope surface in the 3D Cartesian coordinate (dU,dE, and dN (psuedo)) and found both the crown body and cumulative zone of the landslide. For the earthquake study, PS DISP decomposed 2D vectors (dU and dE). The area of earthquake rupture was lifted by 42 cm and other northern Lombok areas by 10 - 25 cm. Regarding the west-eastern motion, it shows that the west of northern Lombok was displaced ±5 cm toward the west direction, while the other side to the east direction. Keywords: InSAR, 2D and 3D displacements, slope aspect, ground motion, landslide, earthquake

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.

Understanding the Spatial Variability of the Relationship between InSAR-Derived Deformation and Groundwater Level Using Machine Learning

The interferometric synthetic aperture radar (InSAR) technique was used in this study to derive the temporal and spatial information of ground deformation and explore its temporal correlation with groundwater dynamics. The random forest (RF) machine learning method was used to model the spatial variability of the temporal correlation and understand its influential contributors. The results showed that groundwater dynamics appeared to be an important factor in InSAR deformation at some bores where strong and positive correlations were observed. The RF model could explain up to 72% of spatial variances between InSAR deformation and groundwater dynamics. The spatial and temporal InSAR coherence (a proxy for the noise in InSAR results that is strongly related to vegetation) and soil moisture (difference, trend, and amplitude) were the most important factors explaining the spatial pattern of the temporal correlation between InSAR displacements and groundwater levels. This result confirms that noise sources (including deformation model fitting errors and radar signal decorrelation) and perturbation of the InSAR signal related to vegetation and surficial soils (clay content, moisture changes) should be accounted for when interpreting InSAR to support groundwater-related risk assessments and in groundwater resource management activities.

EZ-InSAR: An easy-to-use open-source toolbox for mapping ground surface deformation using satellite interferometric synthetic aperture radar

Satellite Interferometric Synthetic Aperture Radar (InSAR) is a space-borne geodetic technique that can map ground displacement at millimetre accuracy. Via the new era for InSAR applications provided by the Copernicus Sentinel-1 SAR satellites, several open-source software packages exist for processing SAR data. These packages enable one to obtain high-quality ground deformation maps, but still require a deep understanding of InSAR theory and the related computational tools, especially when dealing with a large stack of images. Here we present an open-source toolbox, EZ-InSAR (easy-to-use InSAR), for a user-friendly implementation of InSAR displacement time series analysis with multi-temporal SAR images. EZ-InSAR integrates the three most popular and renowned open-source tools (i.e., ISCE, StaMPS, and MintPy), to generate interferograms and displacement time series by using these state-of-art algorithms within a seamless Graphical User Interface. EZ-InSAR reduces the user’s workload by automatically downloading the Sentinel-1 SAR imagery and the digital elevation model data for the user’s area of interest, and by streamlining preparation of input data stacks for the time series InSAR analysis. We illustrate the EZ-InSAR processing capabilities by mapping recent ground deformation at Campi Flegrei (> 100 mm·yr−1) and Long Valley (~ 10 mm·yr−1) calderas with both Persistent Scatterer InSAR and Small-Baseline Subset approaches. We also validate the test results by comparing the InSAR displacements with Global Navigation Satellite System measurements at those volcanoes. Our tests indicate that the EZ-InSAR toolbox provided here can serve as a valuable contribution to the community for ground deformation monitoring and geohazard evaluation, as well as for disseminating bespoke InSAR observations for all.

Determination of periodic deformation from InSAR results using the FFT time series analysis method in Gediz Graben.

Permanent Scatterers (PS) point velocities obtained by the interferometric synthetic aperture radar (InSAR) method are generally determined using the linear regression model, which ignores periodic and seasonal effects. In this study, software was developed that can detect periodic effects by applying fast Fourier transformation (FFT) time series analysis to InSAR results. Using the FFT time series analysis, the periodic components of the surface movements at the PS points were determined, and then the annual velocity values free from periodic effects were obtained. The study area was chosen as the Gediz Graben, a tectonically active region where aseismic surface deformations have been observed in recent years. As a result, using the developed method, seasonal effects were successfully determined with the InSAR method at the PS points in the study area with a period of 384days and an average amplitude of 19mm. In addition, groundwater level changes of a water well in the region were modeled, and 0.93 correlation coefficient values were calculated between seasonal InSAR displacement values and water level changes. Thus, using the developed methodology, the relationship between the tectonic movement in the Gediz Graben in Turkey and the seasonal movements and the change in the groundwater level was determined.

SARClust—A New Tool to Analyze InSAR Displacement Time Series for Structure Monitoring

Interferometric Synthetic Aperture Radar (InSAR) has proved its efficiency for displacement monitoring in urban areas. However, the large volume of data generated by this technology turns the retrieval of information useful for structure monitoring into a big data problem. In this study, a new tool (SARClust) to analyze InSAR displacement time series is proposed. The tool performs the clustering of persistent scatterers (PSs) based on dissimilarities between their displacement time series evaluated through dynamic time warping. This strategy leads to the formation of clusters containing PSs with similar displacements, which can be analyzed together, reducing data dimensionality, and facilitating the identification of displacement patterns potentially related to structural damage. A proof of concept was performed for downtown Lisbon, Portugal, where ten distinct displacement patterns were identified. A relationship between clusters presenting centimeter-level displacements and buildings located on steep slopes was observed. The results were validated through visual inspections and comparison with another tool for time series analysis. Agreement was found in both cases. The innovation in this study is the attention brought to SARClust’s ability to (i) analyze vertical and horizontal displacements simultaneously, using an unsupervised procedure, and (ii) characterize PSs assisting the displacement interpretation. The main finding is the strategy to identify signs of structure damage, even on isolated buildings, in a large amount of InSAR data. In conclusion, SARClust is of the utmost importance to detect potential signs of structural damage in InSAR displacement time series, supporting structure safety experts in more efficient and sustainable monitoring tasks.

Surface Deformation Analysis of the Houston Area Using Time Series Interferometry and Emerging Hot Spot Analysis

Cities in the northern Gulf of Mexico, such as Houston, have experienced one of the fastest rates of subsidence, with groundwater/hydrocarbon withdrawal being considered the primary cause. This work reports substantial ground subsidence in a few parts of Greater Houston and adjoining areas not reported before. Observation of surface deformation using interferometric synthetic aperture radar (InSAR) data obtained from Sentinel-1A shows total subsidence of up to 9 cm in some areas from 2016 to 2020. Most of the area within the Houston city limits shows no substantial subsidence, but growing suburbs around the city, such as Katy in the west, Spring and The Woodlands in the north and northwest, and Fresno in the south, show subsidence. In this study, we performed emerging hot spot analysis on InSAR displacement products to identify areas undergoing significant subsidence. To investigate the contributions of groundwater to subsidence, we apply optimized hot spot analysis to groundwater level data collected over the past 31 years from over 71,000 water wells and look at the correlation with fault surface deformation patterns. To evaluate the contribution of oil/gas pumping, we applied optimized hot spot analysis to known locations of oil and gas wells. The high rate of water pumping in the suburbs is the main driver of subsidence, but oil/gas withdrawal plays an important role in areas such as Mont Belvieu. Displacement time series shows that the Clodine, Hockley, and Woodgate faults are active, whereas the Long Point Fault shows no motion, although it was once very active.

Post‐Rifting Relaxation During 2015–2020 Following the Bárðarbunga‐Holuhraun Dike Intrusion and Eruption in Iceland

AbstractPost‐rifting ground deformation may be driven by viscoelastic relaxation of stresses generated by dike intrusions. The single‐dike intrusion of the 2014–2015 Bárðarbunga eruption in Iceland presents an opportunity for a detailed study of this process. We use continuous Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) velocity fields to analyze the 2015–2020 post‐rifting deformation, showing uplift on both sides of the dike and horizontal displacement away from the dike after correcting for background deformation. Two GNSS stations experience baseline lengthening at a rate of 19 mm/yr in the direction perpendicular to the strike of the dike. A two‐layer viscoelastic model with a 0.4 × 1019 Pa s viscoelastic half‐space overlain by an 18 km thick elastic layer best explains the observed horizontal and vertical InSAR and GNSS displacements. The model misfit space shows a second regime of good fit, likely driven by deformation near the dike that may result from cooling compaction of the emplaced dike.

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.

Accuracy of Sentinel-1 PSI and SBAS InSAR Displacement Velocities against GNSS and Geodetic Leveling Monitoring Data

Correct use of multi-temporal Interferometric Synthetic Aperture Radar (InSAR) datasets to complement geodetic surveying for geo-hazard applications requires rigorous assessment of their precision and accuracy. Published inter-comparisons are mostly limited to ground displacement estimates obtained from different algorithms belonging to the same family of InSAR approaches, either Persistent Scatterer Interferometry (PSI) or Small BAseline Subset (SBAS); and accuracy assessments are mainly focused on vertical displacements or based on few Global Navigation Satellite System (GNSS) or geodetic leveling points. To fill this demonstration gap, two years of Sentinel-1 SAR ascending and descending mode data are processed with both PSI and SBAS consolidated algorithms to extract vertical and horizontal displacement velocity datasets, whose accuracy is then assessed against a wealth of contextual geodetic data. These include permanent GNSS records, static GNSS benchmark repositioning, and geodetic leveling monitoring data that the National Institute of Statistics, Geography, and Informatics (INEGI) of Mexico collected in 2014−2016 in the Aguascalientes Valley, where structurally-controlled land subsidence exhibits fast vertical rates (up to −150 mm/year) and a non-negligible east-west component (up to ±30 mm/year). Despite the temporal constraint of the data selected, the PSI-SBAS inter-comparison reveals standard deviation of 6 mm/year and 4 mm/year for the vertical and east-west rate differences, respectively, thus reassuring about the similarity between the two types of InSAR outputs. Accuracy assessment shows that the standard deviations in vertical velocity differences are 9−10 mm/year against GNSS benchmarks, and 8 mm/year against leveling data. Relative errors are below 20% for any locations subsiding faster than −15 mm/year. Differences in east-west velocity estimates against GNSS are on average −0.1 mm/year for PSI and +0.2 mm/year for SBAS, with standard deviations of 8 mm/year. When discrepancies are found between InSAR and geodetic data, these mostly occur at benchmarks located in proximity to the main normal faults, thus falling within the same SBAS ground pixel or closer to the same PSI target, regardless of whether they are in the footwall or hanging wall of the fault. Establishing new benchmarks at higher distances from the fault traces or exploiting higher resolution SAR scenes and/or InSAR datasets may improve the detection of the benchmarks and thus consolidate the statistics of the InSAR accuracy assessments.

Integrated Sentinel‐1 InSAR and GNSS Time‐Series Along the San Andreas Fault System

AbstractMeasuring crustal strain and seismic moment accumulation, is crucial for understanding the growth and distribution of seismic hazards along major fault systems. Here, we develop a methodology to integrate 4.5 years (2015–2019.5) of Sentinel‐1 Interferometric Synthetic Aperture Radar (InSAR) and continuous Global Navigation Satellite System (GNSS) time series to achieve 6 to 12‐day sampling of surface displacements at ∼500 m spatial resolution over the entire San Andreas fault system. Numerous interesting deformation signals are identified with this product (video link:https://www.youtube.com/watch?v=SxNLQKmHWpY). We decompose the line‐of‐sight InSAR displacements into three dimensions by combining the deformation azimuth from a GNSS‐derived interseismic fault model. We then construct strain rate maps using a smoothing interpolator with constraints from elasticity. The resulting deformation field reveals a wide array of crustal deformation processes including, on‐ and off‐fault secular and transient tectonic deformation, creep rates on all the major faults, and vertical signals associated with hydrological processes. The strain rate maps show significant off‐fault components that were not captured by GNSS‐only models. These results are important in assessing the seismic hazard in the region.

The Detection of Active Sinkholes by Airborne Differential LiDAR DEMs and InSAR Cloud Computing Tools

InSAR (Interferometric Synthetic Aperture Radar) cloud computing and the subtraction of LiDAR (Light Detection and Ranging) DEMs (Digital Elevation Models) are innovative approaches to detect subsidence in karst areas. InSAR cloud computing allows for analyzing C-band Envisat and Sentinel S1 SAR images through web platforms to produce displacement maps of the Earth’s surface in an easy manner. The subtraction of serial LiDAR DEMs results in the same product but with a different level of accuracy and precision than InSAR maps. Here, we analyze the capability of these products to detect active sinkholes in the mantled evaporite karst of the Ebro Valley (NE Spain). We found that the capability of the displacement maps produced with open access, high-resolution airborne LiDAR DEMs was up to four times higher than InSAR displacement maps generated by the Geohazard Exploitation Platform (GEP). Differential LiDAR maps provide accurate information about the location, active sectors, maximum subsidence rate and growing trend of the most rapid and damaging sinkholes. Unfortunately, artifacts and the subsidence detection limit established at −4 cm/yr entailed important limitations in the precise mapping of the sinkhole edges and the detection of slow-moving sinkholes and small collapses. Although InSAR maps provided by GEP show a worse performance when identifying active sinkholes, in some cases they can serve as a complementary technique to overcome LiDAR limitations in urban areas.

Development of an Imagewise Stacking Method to Mitigate Atmospheric Effect With an Application to Lianyan Railway

Much attention has been paid to the subsidence monitoring of large-scale man-made linear features, which are usually characterized by long-distance, large-scale, and nonuniform distribution. The multitemporal interferometric synthetic aperture radar (InSAR) technique can provide ground deformation maps for a large area with high spatial resolution and millimeter precision at low costs. The technique overcomes the limitations of ground-based survey instruments. However, the variable atmospheric turbulent component in flat areas has impacts on InSAR detection capability, especially over low-amplitude quasi-stable deformation areas. We propose an imagewise stacking method, which can improve the InSAR time-series analysis by reducing residual noise while retaining the ground deformation signal. We integrate persistent scatterer and distributed scatterer in our processing strategy and conduct a tentative test on the roadbed subsidence monitoring of the Lianyan high-speed railway using 47 medium-resolution Sentinel-1 images. Comparing the InSAR displacement time series and the results with a continuous BeiDou navigation satellite system network, the standard deviation of 3.3 mm and the mean absolute error of 2.2 mm are obtained. The proposed method reduced the above two error measures by 66.3% and 65.6%, respectively, compared with the spatiotemporal filtering. Moreover, the analysis of the spatiotemporal characteristics of the railway deformations also tells that the railway under study is stable.

Triggered afterslip on the southern Hikurangi subduction interface following the 2016 Kaikōura earthquake from InSAR time series with atmospheric corrections

The 2016 Mw 7.8 Kaikōura earthquake represents an extremely complex event involving over ten major crustal faults, altering conventional understanding of multi-fault ruptures. Although evidence for coseismic slip on the Hikurangi subduction interface is controversial, we present afterslip on the subduction zone beneath Marlborough using 13 months of Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System (GPS) observations. The spatially and temporally correlated atmospheric errors in SAR interferograms are problematic, and hence a new InSAR time series approach, combining the Generic Atmospheric Correction Online Service (GACOS) with a spatial-temporal Atmospheric Phase Screen (APS) filter to facilitate the InSAR time series analysis, is developed. For interferograms with over 250 km spatial extent, we achieve a 0.77 cm displacement RMS difference against GPS, improving 61% from the conventional InSAR time series method (TS). Comparisons between the overlapping region of two independent tracks show an RMS difference of 1.1 cm for the TS-GACOS-APS combined method, improving 54% from the TS method and 27% from using TS with an APS filter only. The APS filter reduces the short wavelength residuals substantially, but fails to remove the long wavelength error even after the ramp removal, revealing that the GACOS correction has played a key role in mitigating long wavelength atmospheric effects. The resultant InSAR displacements, together with the GPS displacements, are used to recover the time-dependent afterslip distribution on the Hikurangi subduction interface, which provides insights for reviewing the co-seismic slip sources, the present status of the subduction plate boundary and future seismic hazards.

Toward Sustained Monitoring of Subsidence at the Coast Using InSAR and GPS: An Application in Hampton Roads, Virginia

AbstractHampton Roads is among the regions along the U.S. Atlantic Coast experiencing high rates of relative sea level rise. Partly to mitigate subsidence from aquifer compaction, Hampton Roads is injecting treated wastewater into the underlying aquifer. However, the GPS (Global Positioning System) station spacing (∼30 km) is too coarse to capture the spatial variability of subsidence and potential uplift from the injection. We present a cost‐effective workflow for generating an InSAR (interferometric synthetic aperture radar) and GPS combined displacement product. We leverage a live, open‐access archive of InSAR products generated from Sentinel‐1 data. We find an overall subsidence rate of −3.6 ± 2.3 mm/year with considerable spatial variability. The effects of groundwater injection are currently below detection. The workflow presented here is an asset for sustained monitoring of the injection effort and regional subsidence that is applicable along the U.S. coasts for assisting in mitigation and adaptation of relative sea level rise.

Rapid Geodetic Observations of Spatiotemporally Varying Postseismic Deformation Following the Ridgecrest Earthquake Sequence: The U.S. Geological Survey Response

Abstract The U.S. Geological Survey’s geodetic response to the 4–5 July 2019 (Pacific time) Ridgecrest earthquake sequence comprised primarily the installation and/or reoccupation of Global Navigation Satellite System (GNSS) monumentation. Our response focused primarily on the United States’ Navy’s China Lake Naval Air Weapons Station base (NAWSCL). This focus was because much of the surface rupture occurred on the NAWSCL and because of NAWSCL access restrictions only permitting Federal and State of California personnel. In total, we measured or are still measuring at 24 sites, 14 of which were on the NAWSCL and, as of this writing, operational. The majority of sites were set up as continuous stations logging at either 1 sample per second or 1 sample per 15 s. Two stations were recording a 200 m cross-rupture aperture starting ∼10 hr after the M 6.4 event, and they recorded the coseismic displacements of the M 7.1. Approximately, 1 hr after the M 7.1 event, two new stations were recording a ∼200 m cross-rupture aperture of the surface rupture. In the days following, we established the rest of the stations ranging to a distance of ∼15 km from the M 7.1 principal rupture trace. The lack of differential displacement across the M 6.4 rupture during the M 7.1 event suggests that it did not reactivate the M 6.4 plane. The lack of differential cross-fault displacement for both events suggests that rapid shallow afterslip did not occur at those two locations. The postseismic time series from these stations shows centimeters of horizontal displacement over periods of a few months. They record a mixture of fault-parallel and fault-normal displacements that, in conjunction with analysis of more spatially complete Interferometric Synthetic Aperture Radar displacement fields, suggest that both poroelastic and afterslip phenomena occur along the M 6.4 and 7.1 rupture planes. Using preliminary data from these and other regional stations, we also explore the Ridgecrest sequence’s effect on regional GNSS time series and the differentiation of long-term postseismic motions and secular deformation rates. We find that redefining a common-mode noise filter using different GNSS stations that are assumed to be unaffected by the earthquakes results in small but systematic differences in the regional velocity field estimate.

Atmospheric phase screen estimation for land subsidence evaluation by InSAR time series analysis in Kurdistan, Iran

Atmospheric phase screen (APS) is one of the main error sources of interferometric synthetic aperture radar (InSAR) measurements. In order to accurately retrieve displacement fields, it is necessary to use advanced methods to eliminate the tropospheric effect of interferograms. In this paper, the land subsidence in Kurdistan province of Iran is investigated using Sentinel-1A acquisitions on a single track for the period 2014–2018. The accurate and applicable 3D ray tracing technique is used to accurately estimate the APS. The ERA-I reanalysis data generated by European Centre for Medium Range Weather Forecasts (ECMWF) are used to implement the 3D ray tracing technique. In order to determine the effect of using the 3D ray tracing technique, the APSs are also determined using a traditional approach called, spatiotemporal filters method. To evaluate the capability of the two methods, the results are compared with the weather research and forecasting model (WRF) model. Finally, the interferograms are corrected using APSs from 3D ray tracing technique and traditional method and the subsidence rate in the study area is computed. Comparing the subsidence rates obtained from two APS estimation methods with piezometric data, GPS and precise levelling observations shows that the 3D ray tracing technique is significantly more accurate than traditional method in computing InSAR displacement fields.