Multi-Method Analysis of Surface Deformations in Western Anatolia with InSAR and GNSS Data

This study proposes a new set of processing procedures based on the strain model and the Kalman filter (SM-Kalman) to obtain high-precision three-dimensional surface deformation time series from interferometric synthetic aperture radar (InSAR) and global navigation satellite system (GNSS) data. Implementing the Kalman filter requires the establishment of state and observation equations. In the time domain, the state equation is generated by fitting the pre-existing deformation time series based on a deformation model containing linear and seasonal terms. In the space domain, the observation equation is established with the assistance of the strain model to realize the spatial combination of InSAR and GNSS observation data at each moment. Benefiting from the application of the Kalman filter, InSAR and GNSS data at different moments can be synchronized. The time and measurement update steps are performed dynamically to generate a 3-D deformation time series with high precision and a high resolution in the temporal and spatial domains. Sentinel-1 SAR and GNSS datasets in the Los Angeles area are used to verify the effectiveness of the proposed method. The datasets include twenty-seven ascending track SAR images, thirty-four descending track SAR images and the daily time series of forty-eight GNSS stations from January 2016 to November 2018. The experimental result demonstrates that the proposed SM-Kalman method can produce high-precision deformation results at the millimeter level and provide two types of 3-D deformation time series with the same temporal resolution as InSAR or GNSS observations according to the needs of users. The new method achieves a high degree of temporal and spatial fusion of GNSS and InSAR data.

The fusion of global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR) deformation data can leverage the advantages of GNSS high temporal resolution and InSAR high spatial resolution, and obtain more abundant deformation data for constraints on geophysical structural and mechanical parameters. Existing studies seldom consider the spatial heterogeneity of largescale deformation data, which easily leads to obvious spatial aggregation of errors in the results of fusion. Here, we propose a novel multiresolution segmentation fusion (MRSF) method that uses a multiresolution segmentation algorithm to automatically classify the spatial heterogeneity of InSAR deformation data with similar deformation characteristics. We applied the MRSF method to the fusion of GNSS and InSAR deformation data covering the central valley aquifer system (CVAS) in southern California to verify its precision and robustness. Results show that the MRSF method can accurately reflect spatiotemporal evolution characteristics of displacement data and reliably estimate deformation for the times and locations of missing data. We then tested this method for geophysical parameter estimation by constructing three different sets of data, including dense GNSS sites, sparse GNSS sites, and sparse GNSS sites fused with InSAR data using MRSF, to invert the slip distribution of the Cascadia subduction zone. Results show that the inverted slip of the fused InSAR and GNSS data is comparable to that of the dense GNSS sites. Therefore, the MRSF method can obtain deformation results with high precision and high spatiotemporal resolution and effectively compensate for the lack of data caused by sparse GNSS sites during the geophysical inversion process.

We analyze Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) data to characterize ground deformation and dike opening associated with the May 2021 Nyiragongo eruption. Despite documented eruptions in 1977 and 2002, Nyiragongo's magmatic system and its interaction with regional rifting remain poorly understood. Here, we use Sentinel‐1 (ESA Copernicus) and ALOS‐2 (JAXA) InSAR data and GNSS observations from the KivuGNet network, that cover the period of the eruption and dike intrusion. Deformation is dominated by a co‐eruptive dike intrusion extending over 25 km, accompanied by ground fissures of about 1.5 m opening in Goma (Democratic Republic of Congo) and Gisenyi (Rwanda). Joint inversions of four interferograms with GNSS data resolved a maximum dike opening near 10 m and a volumetric increase of about 180 Mm 3 . The inferred dike geometry aligns with observed ground deformation and seismicity, underscoring the coupling between magmatic and tectonic processes.

The Wadi Hagul region in the eastern desert of Egypt is facing seismic hazards and increased human activity. This study uses remote sensing and geodetic methods to monitor and analyze recent deformation in the area. Interferometric Synthetic Aperture Radar (InSAR) data from the Sentinel-1A satellite and Global Navigation Satellite System (GNSS) data were combined to track surface movements and deformations accurately. The study analyzed InSAR data from February 4, 2020, to February 07, 2024, and GNSS data from the Wadi Hagul geodetic network established in July 2022 and monitored until January 2024. Despite the relatively short GNSS monitoring period, it provided valuable insights into recent deformation trends. By integrating data from ten GNSS stations, including International Geodetic stations (IGS), and InSAR scenes from the Sentinel-1A mission, the study estimated recent ground deformation in the region. The main objectives were to analyze recent crustal movements by identifying spatial and temporal patterns of deformation and assess implications for geological processes. In Key Findings, horizontal movement fluctuates between 0.5 and 2.5 ± 0.1 mm annually across the geodetic network. The estimated velocity of the area was 1.5–2 ± 0.5 mm per year. Integrating GNSS and InSAR data helped calculate movement rates along fault lines and create a fault map. In conclusion, the results suggest that while current deformation rates are moderate, they could increase significantly due to human activity, leading to higher seismic activity and potential earthquakes. Limiting human activity in the region is advisable to prevent negative impacts on nearby populated areas.

The Choushui River alluvial fan is an important agricultural production region in western Taiwan. This insufficient level of rainfall replenishment, combined with the unauthorized use of wells to irrigate crops, exacerbated land subsidence in the region. In this study, levelling, global navigation satellite system (GNSS), and interferometric synthetic aperture radar (InSAR) observation data were collected from 2018 to 2021 for analysis. The GNSS data were processed using the precise point positioning method, and the results were compared with data processed using the traditional static relative positioning method. The 3-year cumulative subsidence at the subsidence centre was approximately 9 cm in Changhua and 19 cm in Yunlin. To compensate for the lack of spatial resolution of GNSS and improve the accuracy of InSAR, the InSAR, GNSS, and levelling data were integrated, and these results were validated against levelling check points. They showed an average error of 0.4 cm/year in the annual subsidence rate. Overall, the GNSS had high precision and continuity, making it suitable for real-time early warning of land subsidence. InSAR obtained data with higher spatial resolution, allowing for early detection of new land subsidence areas and comprehensive monitoring of changes in land subsidence across the entire region.

The primary goal of operational volcano monitoring is the timely identification of volcanic unrest. This provides critical information to decision makers tasked with mitigating the societal impacts of volcanic eruptions. Volcano deformation is recognized as a key indicator of unrest at many active volcanoes and can be used to provide insight into the depth and geometry of the magma source. Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technique that has detected deformation at many volcanoes globally, but most often with hindsight. To date, the use of InSAR for operational volcano monitoring has been limited to a few cases and only in high income countries. Yet a vast number of active volcanoes are located in low- and middle-income countries, where resources for operational monitoring are constrained. In these countries, InSAR could provide deformation monitoring at many active volcanoes, including those that have no existing ground monitoring infrastructure. Several barriers combine to make uptake of InSAR into operational volcano monitoring difficult in most countries, but particularly in resource-constrained environments. To overcome some of these limiting factors, we propose a simplified processing chain to better incorporate InSAR and Global Navigation Satellite Systems (GNSS) data into the decision-making process at volcano observatories. To combine the InSAR and GNSS data we use a joint modelling procedure that infers volume changes of a spherical source beneath the volcano. The benefits of our approach for operational use include that the algorithm is computationally lightweight and can be run quickly on a standard desktop or laptop PC. This enables a volcano observatory to interpret geodetic data in a timely fashion, and use the information as part of frequent reporting procedures. To demonstrate our approach we combine ALOS-PALSAR InSAR data and continuous GNSS data from the Rabaul Caldera, Papua New Guinea between 2007 and 2011. Joint inversion of the two datasets indicates volume loss of ~1x107 m3 (deflation) occurring between February 2008 and November 2009, followed by volume gain of ~2.5x106 m3 (inflation) until February 2011 in a magma body situated ~1.5 km beneath the caldera.

Modelling of combined Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) data was performed to characterize the source of the Mw6.9 earthquake that occurred to the north of Samos Island (Aegean Sea) on 30 October 2020. Pre-seismic analysis revealed an NNE–SSW extensional regime with normal faults along an E–W direction. Co-seismic analysis showed opening of the epicentral region with horizontal and vertical displacements of ~350 mm and ~90 mm, respectively. Line-of-sight (LOS) interferometric vectors were geodetically corrected using the GNSS data and decomposed into E–W and vertical displacement components. Compiled interferometric maps reveal that relatively large ground displacements had occurred in the western part of Samos but had attenuated towards the eastern and southern parts. Alternating motions occurred along and across the main geotectonic units of the island. The best-fit fault model has a two-segment listric fault plane (average slip 1.76 m) of normal type that lies adjacent to the northern coastline of Samos. This fault plane is 35 km long, extends to 15 km depth, and dips to the north at 60° and 40° angles for the upper and lower parts, respectively. A predominant dip-slip component and a substantial lateral one were modelled.

The regions of California and Nevada are shaped by dynamic tectonic and hydrologic processes that drive significant crustal deformation. In this study we use the method of Shen and Liu (2020), https://doi.org/10.1029/2019ea001036 to integrate Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) observations to investigate deformation from both tectonic and hydrologic activities. This method includes interpolating GNSS velocity data into a continuous velocity field, evaluating uncertainties in GNSS and InSAR measurements, correcting orbital errors in InSAR data, and combining InSAR Line‐of‐Sight measurements with GNSS data to resolve 3‐D deformation using least squares regression. We apply this method to three decades of GNSS and InSAR data across California and western Nevada. Key findings include: (a) Faulting along the San Andreas and Walker Lane fault systems drives dextral shear motions of 30–40 and 8–12 mm/yr, respectively. Residual deformation, however, is broadly distributed within the fault systems, particularly in the Walker Lane region, suggesting contributions from ductile flow in the lower crust. (b) Significant subsidence, caused by drought and excessive groundwater withdrawal, is observed in the San Joaquin and Sacramento Valleys, at rates of 150–250 and 10–25 mm/yr, respectively. Uplift rebound of 3–8 mm/yr is observed in the mountains surrounding the San Joaquin Valley. Notable subsidence of 6–14 mm/yr is also seen along the California coastline, while the Santa Maria Basin and Oxnard Plain experience subsidence of up to 12–18 mm/yr. (c) Abrupt vertical offsets are observed across various tectonically active faults, suggesting fault‐modulated hydrological deformation.

Monitoring volcanoes involves a variety of data sources and methods to maintain complete continuity of coverage. Global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR) are commonly used complementary methods to assess the deformation state of a volcano as magma migrates beneath the surface. The amount of data these methods produce, however, is growing rapidly beyond human analysis capabilities and is becoming difficult to manage. Here, we create a novel multimodal deep learning framework to ingest InSAR and GNSS data simultaneously and classify the deformation state of the system. We apply this methodology to Mauna Loa, Hawai‘i given its wealth of InSAR and GNSS data as well as its propensity to deform on multiple timescales. Our model performs with high accuracy and is able to identify both slow and fast deformation from 2015 to 2023. The multimodal nature of our model also allows us to identify the presence of atmospheric noise in InSAR data. Furthermore, we employ explainability algorithms to show that our model is making decisions for the right reasons and to connect complex black-box machine learning mappings to current real-world geodetic interpretations of the Mauna Loa magmatic system.

Spaceborne interferometric synthetic aperture radar (InSAR) techniques are important for landslide detection and monitoring; however, several limitations and uncertainties, such as the unique north–south flying direction and side-look radar observing geometry, currently limit the ability of InSAR to credibly detect landslides, especially those related to high and steep slopes. Here, we conducted experimental and statistical analysis on the feasibility of time-series InSAR monitoring for steep slopes using ascending and descending SAR images. First, the theoretical (TGNSS), practical (PGNSS), and terrain (Hterrain) (T-P-H) indices for sensitivity evaluations of the slope displacement monitoring results from time-series InSAR were proposed for slope monitoring. Subsequently, two experimental and statistical studies were conducted for the cases with and without Global Navigation Satellite System (GNSS) monitoring data. Our experimental results of two high and steep open-pit mines showed that the defined theoretical and practical sensitivity indices can quantitatively evaluate the feasibility of ascending and descending InSAR observations in steep-slope deformation monitoring with GNSS data, and the terrain sensitivity index can qualitatively evaluate the feasibility of landslide monitoring results from ascending and descending Sentinel-1 satellite data without GNSS data. We further demonstrate the generalizability of these proposed indices using four landslide cases with both public GNSS and InSAR monitoring data and 119 landslide cases with only InSAR monitoring data. The statistical results indicated that greater indices correlated with higher reliability of the monitoring results, suggesting that these novel indices have wide suitability and applicability. This study can help to improve the practice of slope deformation monitoring using spaceborne InSAR, especially for high and steep slopes.

Correcting interferometric synthetic aperture radar (InSAR) interferograms using Global Navigation Satellite System (GNSS) data can effectively improve their accuracy. However, most of the existing correction methods utilize the difference between GNSS and InSAR data for surface fitting; these methods can effectively correct overall long-wavelength errors, but they are insufficient for multiple medium-wavelength errors in localized areas. Based on this, we propose a method for correcting InSAR interferograms using GNSS data and the K-means spatial clustering algorithm, which is capable of obtaining correction information with high accuracy, thus improving the overall and localized area error correction effects and contributing to obtaining high-precision InSAR deformation time series. In an application involving the Central Valley of Southern California (CVSC), the experimental results show that the proposed correction method can effectively compensate for the deficiency of surface fitting in capturing error details and suppress the effect of low-quality interferograms. At the nine GNSS validation sites that are not included in the modeling process, the errors in the ascending track 137A and descending track 144D are mostly less than 15 mm, and the average root mean square error values are 11.8 mm and 8.0 mm, respectively. Overall, the correction method not only realizes effective interferogram error correction, but also has the advantages of high accuracy, high efficiency, ease of promotion, and can effectively address large-scale and high-precision deformation monitoring scenarios.Graphical

Although geodetic techniques like Gravity Recovery and Climate Experiment have been widely applied to investigate terrestrial water storage (TWS) variations at regional or basin scales on the Tibetan Plateau (TP) caused by global warming, their coarse spatial has limited the study of individual lakes. In this study, we overcome this limitation by jointly using Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) data to generate a high‐precision, high‐resolution surface deformation field, enabling the quantitative assessment of TWS changes for Qinghai Lake, from January 2016 to December 2022. By leveraging Independent Component Analysis to extract surface deformation induced by lake hydrological loads, we find that the deformation caused by Qinghai Lake's hydrological changes is spatially limited to within approximately 25 km of the lake and is largely overshadowed by regional background loads of the TP. The region surrounding Qinghai Lake exhibited an overall trend of initial subsidence (from January 2016 to August 2019, −2.89 to −0.30 mm/yr) followed by uplift (from September 2019 to December 2022, 2.20 to 4.89 mm/yr), primarily driven by variations in precipitation patterns. Notably, we found that lake water volume increase accounts for up to 86% of the total TWS changes in Qinghai Lake, underscoring the relatively marginal role of groundwater compared to previous assessments in Inner TP where groundwater accounted for 34% of TWS changes. This study demonstrates the effectiveness of integrating GNSS and InSAR data to overcome spatial resolution limitations, providing detailed insights into the hydrological dynamics of individual lakes like Qinghai Lake, and contributes to a more comprehensive understanding of TP's hydrological changes under the influence of climate change.

Interferometric synthetic aperture radar (InSAR) is a primary geodetic technique for monitoring surface deformation, achieving high spatial resolution and extensive coverage. However, long-wavelength errors – such as orbital errors, atmospheric delays, and tidal loading – mask subtle deformation signals. Furthermore, reliance on a single reference station neglects plate motion gradients, causing time series displacement biases. These factors limit millimeter-level monitoring accuracy. Our study integrates InSAR and global navigation satellite system (GNSS) data, leveraging GNSS measurements to correct interferograms for long-wavelength errors and thereby enhance InSAR deformation accuracy. This integration also anchors InSAR-derived deformation to GNSS motions and helps establish a reference frame. Using Tianjin in northern China as our case study, we corrected long-wavelength errors in both ascending and descending Sentinel-1 interferograms with continuous GNSS observations. To evaluate the effectiveness of our long-wavelength error correction method, we compared the corrected interferograms against results derived from both the general atmospheric correction online service (GACOS) atmospheric correction and trend surface modeling. We conducted a systematic evaluation of correction accuracy and its impacts on four key results: phase changes, velocity estimates, cumulative displacement, and seasonal signals. Results show that the correction by using GNSS data improved accuracy in 84% of the 862 processed interferograms, while the performance of the correction depended on the baseline distance of the GNSS stations. The root mean square error (RMSE) of the time series displacement for the GNSS validation stations was maintained within the millimeter-level, improving the displacement accuracy by at least 80%. Regarding reference stability, the GNSS correction method proved to be more reliable than the single-reference approach. These results highlight the benefits of multiple GNSS stations in InSAR correction. Finally, our analysis of the fused vertical velocities, derived from both ascending and descending tracks, reveals a notable reduction in land subsidence across Tianjin since 2016, with subsidence rates and areas decreasing annually.

To explore the degree of constraint by Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) data on the Maduo earthquake within a layered earth model structure and to gain an insight into the seismogenic mechanism and the seismic risk in the surrounding area, this study employs D-InSAR technology to acquire the InSAR co-seismic deformation field of the Maduo earthquake on 22 May 2021. Utilizing both GNSS and InSAR data, the inversions constrained by single and joint data are conducted and compared to determine the co-seismic slip model and fault plane stress distribution of the Maduo earthquake. Additionally, this paper calculates the Coulomb stress changes induced by 14 M ≥ 7 strong earthquakes, considering co-seismic effects, post-seismic viscoelastic relaxation, and inter-seismic tectonic stress loading, on 19 fault segments within the Bayan Har block research area (96°E~106°E, 29°N~36°N) since 1900. The findings are as follows: (1) The maximum line-of-sight (LOS) deformation was approximately 0.9 m. The joint inversion rupture was primarily located in the Dongcao Along Lake section (~98.6°E), aligning with previous research outcomes. (2) The cumulative Coulomb stress at the Maduo earthquake’s source location was −0.1333 MPa, while the inter-seismic stress loading amounted to 0.0745 MPa. The East Kunlun Fault, Maduo–Gande Fault, Ganzi–Yushu Fault, and Dari Fault C exhibited considerable stress loading, warranting attention due to heightened seismic risk. (3) Based on three different co-seismic slip models, the stress disturbance results caused by the Maduo earthquake to the surrounding area and fault did not differ significantly. After the earthquake, the seismogenic fault still has high seismic risk.

The North American Plate is subducted under the Cocos and Rivera plates, while the Pacific Plate divides from the North American Plate near the spreading centre of the Baja California Gulf, placing the Mexican Republic in a seismically active area of the world. The earthquake of a magnitude of Mw 7.6–7.7 occurred 37 km southeast of the town of Aquila (near the municipality of Coalcoman) at a depth of 15.1 km on 19.09.2022 at 18:05:06 UTC (13:05:06 local time (LT)). This study focuses on the use of GNSS (Global Navigation Satellite System) data to investigate the Mexican earthquake, and the results (using static-kinematic methods) are presented in this paper. The TNCC and COL2 IGS stations, which are situated to the north and northwest of the fault, recorded the largest displacements after GNSS data processing. At five points, 9–25 cm horizontal motions were obtained in the southwest, northwest, and west directions. The quantity of horizontal motions, however, was smaller in the south of the fault stations UCOE (approximately 9–10 cm) and PENA (about 9 cm). A comparison between the GNSS and InSAR (Interferometric Synthetic Aperture Radar) results from the COMET-LiCSAR analysis showed that the GNSS and InSAR solution mirrors the pattern of earthquakes. The GNSS and InSAR data were aligned by standardizing to a common spatial and temporal grid, with corrections for atmospheric delays and noise. The mirroring of patterns was evaluated by using correlation analysis, displacement magnitude comparison, and assessment of spatial gradients. Error tolerances were considered to validate the alignment and highlight any discrepancies.