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Accurate monitoring of mining three-dimensional deformation plays an important role in analyzing the subsidence mechanism and assessing potential geological hazards in the mining area. This paper combines TerraSAR-X and RADARSAT-2 heterogeneous SAR images employing MSBAS technology to obtain the three-dimensional time-series of deformation of the 132 610 working face in the Fengfeng mine from December 2015 to February 2016. The monitoring results show that the maximum surface subsidence and the subsidence rate during the monitoring period were –243 mm and 4.42 mm/day, and the maximum east-west horizontal movement was –80~30 mm. The surface subsidence basin is in the active stage, which is prone to pose a threat to the safety of the surrounding structures. Comparison with the leveling data shows that the root-mean-square deviation of the vertical deformation obtained by MSBAS technology is 10.7 mm, and the accuracy can reach the centimeter level. Meanwhile, based on the vertical deformation obtained by MSBAS technology, combined with the proportionality relationship between horizontal movement and tilt, the north-south horizontal movement was estimated as –80~70 mm, which provided a solution for obtaining the north-south horizontal movement of the mining area by InSAR. Analysis of the three-dimensional time-series of the surface deformation at the main sections shows that the surface deformation features during the mining process of the 132610 working face match well with the general model of mining subsidence, but the working face inclination angle has an influence on the surface deformation. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar interferometry (InSAR) is a powerful technique for quantifying the co- and postseismic deformation of large earthquakes at the Earth's surface. However, surface deformation caused by small- to moderate-sized earthquakes is rarely revealed by InSAR because their coseismic slip occurs mostly at significant depths (> 5 km), with limited deformation on the Earth's surface. In this work, we investigate the surface deformation associated with the Mw 5.4 May 28, 2016, Mihoub (Algeria) earthquake and its source parameters. Interferograms calculated from Sentinel-1 TOPSAR images of both ascending and descending orbits show that, despite its small size, the earthquake produced evident deformation on the Earth's surface, suggesting that the coseismic slip took place at a relatively shallow depth. We model the coseismic displacement fields extracted from InSAR time series using Bayesian approaches in two stages: 1) we model the coseismic slip data using uniform slip on a single fault to constrain the fault parameters. 2) We explore a variable slip model with varying rakes on the discretized fault obtained in the first stage. The modelling results indicate that the earthquake was associated with a ~ 0.5 m shallow oblique reverse slip, mostly between depths of 1.5 and 4.5 km, on a NE–SW-trending and SE-dipping thrust fault, which is in good agreement with the focal mechanism solutions of the earthquake deduced from seismology. This study demonstrates that the multitemporal InSAR (MTI) method may constrain surface displacements when the coseismic interferograms of moderate- to small-sized earthquakes are noisy and hence difficult to unwrap. The newly identified seismogenic Mihoub fault has implications for seismic hazard assessment in northern Algeria. Highlights: InSAR observations reveal surface deformation during a moderate earthquake (Mw 5.4) in the Tell Atlas of Algeria. The surface deformation of an earthquake is better revealed by a time series of noisy interferograms. Bayesian modelling highlights the importance of utilizing multiple interferograms with different viewing geometries and reveals shallow coseismic rupture. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) technology is capable of detecting large areas of potentially unstable slopes. However, traditional time-series InSAR methods yield fewer valid measurement points (MPs) in alpine canyon regions. Distributed Scatterer (DS) Interferometry (DSI) technology serves as a potent tool for monitoring surface deformation in complex land cover areas; nonetheless, it grapples with high computational demands and low efficiency when interpreting deformation across extended time series. This study proposes an image compression–based DSI (ICDSI) method, which, building upon the DSI method, utilizes principal component analysis (PCA) to compress multi-temporal SAR images in the time dimension. It develops a module for compressing long-time sequence SAR images, acquires the compressed image (referred to as a virtual image), and integrates the developed image compression module into the DSI data processing flow to facilitate the inversion of long-time sequence InSAR land surface deformation information. To validate and assess the credibility of the ICDSI method, we processed a total of 78 ascending and 81 descending scenes of Sentinel-1A images spanning the period 2019–2021 using Small Baseline Subset (SBAS), DSI, and the ICDSI method proposed in this paper. Subsequently, these methods were applied to detect landscape displacements on both coasts of the Jinsha River Basin. The investigation reveals that the ICDSI method outperforms SBAS and DSI significantly in monitoring landslide displacements, enabling the detection of more measurement points (MPs) while utilizing less raw data. The accomplishments of this research program carry crucial theoretical implications and practical application value for the detection of surface deformation using long-time series InSAR. [ABSTRACT FROM AUTHOR]

We performed an extensive analysis of C-band SAR datasets provided by the European Space Agency (ESA) satellites ERS-1/2, Envisat ASAR, and Sentinel-1 in the period 1992–2020 aiming at reconstructing the multi-decadal spatial and temporal evolution of the surface displacements at the Brienz/Brinzauls landslide complex, located in canton Graubünden (Switzerland). To this end, we analyzed about 1000 SAR images by applying differential interferometry (InSAR), multitemporal stacking, and persistent scatterer interferometry (PSI) approaches. Moreover, we jointly considered digital image correlation (DIC) on high-resolution multi-temporal digital terrain models (DTM) generated from airborne surveys and InSAR results to compute 3-D surface deformation fields. The extensive network of GNSS stations across the Brienz landslide complex allowed us to extensively validate the deformation results obtained in our remote sensing analyses. Here, we illustrate the limitations occurring when relying on InSAR and/or PSI measurements for the analysis and interpretation of complex landslide scenarios, especially in cases of relevant spatial and temporal heterogeneities of the deformation field. The joint use of InSAR and DIC can deliver a better picture of the evolution of the deformation field, however, not for all displacement components. Since InSAR, PSI and DIC measurements are nowadays routinely used in the framework of local investigations, as well as in regional, national, and/or continental monitoring programs, our results are of major importance for users aiming at a comprehensive understanding of these datasets in landslide scenarios. [ABSTRACT FROM AUTHOR]

Global climate change highlights the significance of renewable clean energy. Geothermal energy from oilfields is abundant and can be utilized for residential heating and oil transportation. However, unreasonable heat exchange methods lead to groundwater level decline. Differences and delays between water extraction and reinjection inevitably cause land subsidence, hindering the sustainable utilization of geothermal energy resources. It is crucial to effectively monitor large-scale ground surface deformation characteristics in geothermal fields. In the research, 55 Sentinel-1A images from October 2018 to May 2022 were collected, and the ground deformation of Caofeidian new district in Bohai Bay Basin, where Nanpu Oilfield is located, in North China, was inverted by short baseline subsets-interferometric synthetic aperture radar (SBAS-InSAR) technology. The maximum subsidence velocity in Caofeidian new district exceeded 50 mm/year. Since April 2019, there has been concentrated ground subsidence in the Caofeidian new district, with a maximum cumulative subsidence exceeding 50 mm. The maximum cumulative subsidence exceeded 150 mm by May 2022. Combined with pumping and recharging tests near heating project in Caofeidian new district, the temporal subsidence mechanisms were revealed. The water extraction from the thermal reservoir caused drainage consolidation of the strata, triggering rapid subsidence in November. In April, discrepancy between water extraction and reinjection flow rate in thermal reservoir caused drainage consolidation of the strata, further exacerbating the subsidence trend. During rainy seasons, despite water reinjection and groundwater replenishment from precipitation, the strata in the Caofeidian new district underwent irreversible plastic deformation, with a slow rebound process, lagging behind urban areas without geothermal development by one month. InSAR is a reliable technology for understanding the ground deformation process in geothermal fields. Clarifying the subsidence mechanisms guides the selection of water-heat exchange methods, ensuring the sustainable development of geothermal resources. [ABSTRACT FROM AUTHOR]

Land subsidence caused by human engineering activities is a serious problem worldwide. We selected Qian'an County as the study area to explore the evolution of land subsidence and predict its deformation trend. This study utilized synthetic aperture radar interferometry (InSAR) technology to process 64 Sentinel-1 data covering the area, and high-precision and high-resolution surface deformation data from January 2017 to December 2021 were obtained to analyze the deformation characteristics and evolution of land subsidence. Then, land subsidence was predicted using the intelligence neural network theory, machine learning methods, time-series prediction models, dynamic data processing techniques, and engineering geology of ground subsidence. This study developed three time-series prediction models: a support vector regression (SVR), a Holt Exponential Smoothing (Holt) model, and multi-layer perceptron (MLP) models. A time-series prediction analysis was conducted using the surface deformation data of the subsidence funnel area of Zhouzi Village, Qian'an County. In addition, the advantages and disadvantages of the three models were compared and analyzed. The results show that the three developed time-series data prediction models can effectively capture the time-series-related characteristics of surface deformation in the study area. The SVR and Holt models are suitable for analyzing fewer external interference factors and shorter periods, while the MLP model has high accuracy and universality, making it suitable for predicting both short-term and long-term surface deformation. Ultimately, our results are valuable for further research on land subsidence prediction. [ABSTRACT FROM AUTHOR]

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The movement and deformation of rock strata and the ground surface is a dynamic deformation process that occurs as underground mining progresses. Therefore, the dynamic prediction of three-dimensional surface deformation caused by underground mining is of great significance for assessing potential geological disasters. Synthetic aperture radar interferometry (InSAR) has been introduced into the field of mine deformation monitoring as a new mapping technology, but it is affected by many factors, and it cannot monitor the surface deformation value over the entire mining period, making it impossible to accurately predict the spatiotemporal evolution characteristics of the surface. To overcome this limitation, we propose a new dynamic prediction method (InSAR-DIB) based on a combination of InSAR and an improved Boltzmann (IB) function model. Theoretically, the InSAR-DIB model can use information on small dynamic deformation during mining to obtain surface prediction parameters and further realize a dynamic prediction of the surface. The method was applied to the 1613 (1) working face in the Huainan mining area. The results showed that the estimated mean error of the predicted surface deformation during mining was between 80.2 and 112.5 mm, and the estimated accuracy met the requirements for mining subsidence monitoring. The relevant research results are of great significance, and they support expanding the application of InSAR in mining areas with large deformation gradients. [ABSTRACT FROM AUTHOR]

The southern margin of the South Tian Shan has drawn attention due to the intense compressional deformation and seismic activity associated with its thrust structures. However, the deformation and seismic activity in the thick-skinned thrust sheets of the root zones are minimal. The Mw 7.1 Wushi earthquake on 23 January 2024 serves as a window to reveal these unknown aspects of the seismic mechanisms in this structural setting. Using the Leveraging Interferometric Synthetic Aperture Radar (InSAR) technique, we unlock critical insights into the coseismic deformation fields. The seismogenic fault is an unmapped segment within the Maidan Fault system, exhibiting a strike ranging from 241° to 222°. It is characterized by a shallow dip angle of 62° and a deeper dip angle of 56°. Remarkably, the seismic rupture did not propagate to the Earth's surface. The majority of slip distribution is concentrated within a range of 4 to 26 km along the strike, indicating that this earthquake was a thrust event on a blind fault within the thick-skinned tectonics of the South Tian Shan. Coulomb stress changes indicate that aftershocks primarily occur in the stress-loading region. Interestingly, some aftershocks are very shallow, causing clear surface deformation. Inversion results show that the fault planes of two aftershocks are located above the main shock fault plane at extremely shallow depths (<6 km). Combining geophysical profile data, we infer that ruptures in the deep-seated thick-skinned structures during the main shock triggered ruptures in the shallow thrust structures. This triggering relationship highlights the potential for combined ruptures of the main shocks and aftershocks in the deep-seated thick-skinned structures beneath the South Tian Shan to result in larger disasters than typical seismic events. [ABSTRACT FROM AUTHOR]

Conventional geodetic methods rely on point measurements, which have drawbacks for detecting and tracking geologic disasters at specific locations. In this study, the time series Interferometric Synthetic Aperture Radar (InSAR) approach was incorporated to estimate non-linear surface deformation caused by tectonic, shoreline reclamation, and other anthropogenic activities in economically important urban regions of Pakistan's southern coast, which possesses around 270 km. The shoreline is extended from the low-populated area on the premises of the Hub River in the west to the highly populated Karachi City and Eastern Industrial Zone, where we collected the Sentinel-1A C-band data from 2017 to 2023 to address urban security and threats to human life and property. The main advantage of opting for the non-linear persistent scatterer interferometric SAR (PSInSAR) approach for this study is that it exposes minute movements without any prior consideration of conventional monitoring techniques, making it valid in continuously varying regions. An average vertical displacement range of − 170 to + 82 mm per year was found, which was used to investigate the potential correlation with the most effective causative parameters of deformation. The densely populated areas of the study area experience an annual subsidence of 170 mm, and the less populated western region experiences an uplift of 82 mm annually. Land deformation varies along the coast of the study area, where the eastern region is highly reclaimed and is affected by erosion. Groundwater table-depleting regions experienced high levels of land subsidence, and tectonic activities controlled vertical displacement in the region. Major variation was detected after an earthquake occurred along fault lines. This study was designed because a non-linear approach is required to address ground movement activities acutely, and it will make it possible to plan surface infrastructure and handle issues brought on by subsidence more effectively. [ABSTRACT FROM AUTHOR]

The Heihe River Basin (HRB), located on the northeast margin of the Qilian Mountains, is China's second largest inland river basin. It is a typical oasis-type agricultural area in northwest China's arid and semiarid areas. It is important to monitor and investigate the spatiotemporal distribution characteristics and mechanisms of surface deformation in HRB for the ecology of inland river basins. In recent years, research on HRB has mainly focused on hydrology, meteorology, geology, or biology. Few studies have conducted wide-area monitoring and mechanism analysis of the surface stability of HRB. In this study, an improved interferometric point target analysis InSAR (IPTA-InSAR) technique is used to process 101 Sentinel-1 SAR images from two adjacent track frames covering the HRB from 2019 to 2020. The wide-area deformation of the HRB is obtained first for this period. The results show that most of the surface around the HRB is relatively stable. There are six areas with an extensive deformation range and magnitude in the plain oasis area. The maximum deformation rate is more than 50 mm/year. The maximum seasonal subsidence and uplift along the satellites' line-of-sight (LOS) direction can be up to −70 mm and 60 mm, respectively. Moreover, we use the Google Earth Engine platform to process the multisource optical images and analyze the deformation areas. The remote sensing indicators of the deformation areas, such as the normalized difference vegetation index (NDVI), soil moisture (SMMI), and precipitation, are obtained during the InSAR monitoring period. We combine these integrated remote sensing results with soil type and precipitation to analyze the surface deformations of the HRB. The spatiotemporal relationships between soil moisture, vegetation cover, and surface deformation of the HRB are revealed. The results will provide data support and reference for the healthy and sustainable development of the inland river basin economic zone. [ABSTRACT FROM AUTHOR]

Time-series Interferometric Synthetic Aperture Radar (InSAR) technology, renowned for its high-precision, wide coverage, and all-weather capabilities, has become an essential tool for Earth observation. However, the quality of the interferometric baseline network significantly influences the monitoring accuracy of InSAR technology. Therefore, optimizing the interferometric baseline is crucial for enhancing InSAR's monitoring accuracy. Surface vegetation changes can disrupt the coherence between SAR images, introducing incoherent noise into interferograms and reducing InSAR's monitoring accuracy. To address this issue, we propose and validate an optimization method for the InSAR baseline that considers changes in vegetation coverage (OM-InSAR-BCCVC) in the Yuanmou dry-hot valley. Initially, based on the imaging times of SAR image pairs, we categorize all interferometric image pairs into those captured during months of high vegetation coverage and those from months of low vegetation coverage. We then remove the image pairs with coherence coefficients below the category average. Using the Small Baseline Subset InSAR (SBAS-InSAR) technique, we retrieve surface deformation information in the Yuanmou dry-hot valley. Landslide identification is subsequently verified using optical remote sensing images. The results show that significant seasonal changes in vegetation coverage in the Yuanmou dry-hot valley lead to noticeable seasonal variations in InSAR coherence, with the lowest coherence in July, August, and September, and the highest in January, February, and December. The average coherence threshold method is limited in this context, resulting in discontinuities in the interferometric baseline network. Compared with methods without baseline optimization, the interferometric map ratio improved by 17.5% overall after applying the OM-InSAR-BCCVC method, and the overall inversion error RMSE decreased by 0.5 rad. From January 2021 to May 2023, the radar line of sight (LOS) surface deformation rate in the Yuanmou dry-hot valley, obtained after atmospheric correction by GACOS, baseline optimization, and geometric distortion region masking, ranged from −73.87 mm/year to 127.35 mm/year. We identified fifteen landslides and potential landslide sites, primarily located in the northern part of the Yuanmou dry-hot valley, with maximum subsidence exceeding 100 mm at two notable points. The OM-InSAR-BCCVC method effectively reduces incoherent noise caused by vegetation coverage changes, thereby improving the monitoring accuracy of InSAR. [ABSTRACT FROM AUTHOR]

Monitoring active volcanoes is necessary to analyze their current status to pose a mitigation hazard. Mount Melbourne is an active volcano that has erupted in the past, and future eruptions are possible. This condition could threaten future eruptions, particularly near scientific bases. Jang Bogo, a South Korean research station, is located only 30 km from the summit and could be affected by significant ash fallout in case of an explosive eruption. This condition leads to the necessity of observing Mount Melbourne's activity frequently. This study used Sentinel-1 SAR data acquired from 2017 to 2024 to monitor the volcanic activity of Mount Melbourne by utilizing InSAR multitemporal time-series analysis implementing the improved combined scatterers interferometry with optimized point scatterers (ICOPS) method. The ICOPS method combined persistent scatterer (PS) and distributed scatterer (DS) with measurement point (MP) optimization based on convolutional neural network (CNN) and optimized hot spot analysis (OHSA). The ICOPS measurement results maintain reliable MP along the Mount Melbourne summit and around Jang Bogo station. The absence of GPS stations around these two areas makes it difficult to validate the result with the ground truth measurement, so the comparison with another method, small baseline (SBAS) measurement, is made to evaluate the reliability of the ICOPS measurement points. The comparison between the MP from ICOPS and the SBAS methods shows a good correlation with R2 of about 0.8134 in the Melbourne area and 0.8678 in the Jang Bogo area. The selected time-series plot around the summit of Mount Melbourne and the Jang Bogo area shows a stable trend of surface deformation. Thus, a total accumulated deformation of around 0.82 cm and an average deformation of about 0.10 cm/year was found around Mount Melbourne. Meanwhile, the Jang Bogo area exhibits a total deformation of about 0.15 cm with an average deformation of about 0.02. Overall, this research is a preliminary study of the ability of the ICOPS algorithm to monitor volcanic activity in snow-covered areas. [ABSTRACT FROM AUTHOR]

Affected by geological factors, the scale of surface deformation in a hilly semi-desertification mining area varies. Meanwhile, there is certain dense vegetation on the ground, so it is difficult to construct a high-precision and complete image of a subsidence basin by using a single monitoring method, and hence the laws of the deformation and inversion of mining parameters cannot be known. Therefore, we firstly propose conducting collaborative monitoring by using InSAR (Interferometric Synthetic Aperture Radar), UAV (unmanned aerial vehicle), and 3DTLS (three-dimensional terrestrial laser scanning). The time-series complete surface subsidence basin is constructed by fusing heterogeneous data. In this paper, SBAS-InSAR (Small Baseline Subset) technology, which has the characteristics of reducing the time and space discorrelation, is used to obtain the small-scale deformation of the subsidence basin, oblique photogrammetry and 3D-TLS with strong penetrating power are used to obtain the anomaly and large-scale deformation, and the local polynomial interpolation based on the weight of heterogeneous data is used to construct a complete and high-precision subsidence basin. Compared with GNSS (Global Navigation Satellite System) monitoring data, the mean square errors of 1.442 m, 0.090 m, 0.072 m are obtained. The root mean square error of the high-precision image of the subsidence basin data is 0.040 m, accounting for 1.4% of the maximum subsidence value. The high-precision image of complete subsidence basin data can provide reliable support for the study of surface subsidence law and mining parameter inversion. [ABSTRACT FROM AUTHOR]

Objectives: The continuous exploitation of the Liaohe Delta oilfield has resulted in severe surface subsidence, impacting oil recovery rates, production operations, and posing threats to surface infrastructure and ecological environments. To ensure the safe exploitation of underground fluid resources and protect the regional environment, monitoring implementation is needed for this region. Methods: The method of small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology with coherence information as weights was used to analyze the surface deformation. Using the fusion decomposition of synthetic aperture radar descending and ascending orbit results to extract both vertical and horizontal east-west deformation within the Shuguang oilfield area. Subsequently, a reservoir compaction-induced subsidence inversion model is applied to the Shuguang oilfield to simulate and interpret the observed subsidence phenomena, linking them to the underground fluid resource exploitation activities.Results: The results reveal significant ground subsidence throughout the Liaohe Delta region, particularly in the Shuguang oilfield and Huanxiling oilfield. The average line of sight subsidence rates reaching 158 mm/a and 73 mm/a, respectively. In the Shuguang oilfield, there is horizontal movement towards the subsidence center, with approximately equal magnitudes of movement on the east and west sides. The maximum horizontal movement rate is observed to be − 62 mm/a (westward motion). Furthermore, the reservoir compaction and subsidence model based on Shuguang oilfield reservoir parameters effectively invert the maximum subsidence position at the center of the oilfield, with subsidence range and magnitude consistent with the InSAR observation results. Conclusions: The study concludes that continuous exploitation of oil has led to significant ground subsidence in the Liaohe Delta region, especially in Shuguang oilfield and Huanxiling oilfield, with clear patterns of subsidence and horizontal movement detected using SBASInSAR technology. The developed reservoir compaction-induced subsidence inversion model proves effective in simulating subsidence phenomena associated with oilfield operations. These findings underscore the importance of monitoring and managing subsidence risks to ensure the safe exploitation of underground resources and to protect regional ecological environments in the Liaohe Delta region. [ABSTRACT FROM AUTHOR]

Aged hydrocarbon wells, if proper care is not ensured, can crack, get corroded, and leak subsurface fluids. Permian Basin in Texas, home to thousands of such wells, has seen numerous blowouts and wastewater leaks. Our study employs surface deformation derived from satellite observations, and injection well records to investigate these events. The results reveal an over‐pressurized wastewater aquifer producing a surface uplift of 20 cm/yr, likely due to wastewater being injected tens of kilometers away. Focusing on a January 2022 blowout resulting in 3 cm subsidence in 2 weeks, our geophysical model suggests aquifer over‐pressurization as the cause. With an excess pressure of over 3 MPa in the aquifer, several more such blowouts are possible in the near future. This research highlights the urgent need to better understand the impact of subsurface fluid injection and calls for prompt action to mitigate the environmental effects of oil and gas production. Plain Language Summary: Wastewater, a byproduct during oil extraction, is generally injected back into the ground. Permian Basin has witnessed several incidents of leakage of wastewater in the last 3 years. Recently in January 2022, huge amounts of wastewater were expelled at a high pressure from an old well. We used satellite data and wastewater injection data to understand the cause of these events. We found that the wastewater injection happening nearly several kilometers away is responsible for these leakages. We also discovered a highly pressurized wastewater lake below the surface in this region using geophysical modeling. Due to high pressures, the land in this region rose by 40 cm in just 2 years. Meanwhile, a part of this region sunk by 3 cm because of the leakage in January 2022. Our findings raise concerns about the potential for more leakages in the near future if action is not taken. Key Points: We utilized satellite observations of land surface deformation to explore the subsurface anomalies in the Permian BasinWe established a significant link between wastewater injection and oil well blowouts in the Permian BasinWe discovered an over‐pressurized aquifer responsible for the reported blowouts, with a potential for more blowouts in the near future [ABSTRACT FROM AUTHOR]

The joint action of human activities and environmental changes contributes to the frequent occurrence of landslide, causing major hazards. Using Interferometric Synthetic Aperture Radar (InSAR) technique enables the detailed detection of surface deformation, facilitating early landslide detection. The growing availability of SAR data and the development of artificial intelligence have spurred the integration of deep learning methods with InSAR for intelligent geological identification. However, existing studies using deep learning methods to detect landslides in InSAR deformation often rely on single InSAR data, which leads to the presence of other types of geological hazards in the identification results and limits the accuracy of landslide identification. Landslides are affected by many factors, especially topographic features. To enhance the accuracy of landslide identification, this study improves the existing geological hazard detection model and proposes a multi-source data fusion network termed MSFD-Net. MSFD-Net employs a pseudo-Siamese network without weight sharing, enabling the extraction of texture features from the wrapped deformation data and topographic features from topographic data, which are then fused in higher-level feature layers. We conducted comparative experiments on different networks and ablation experiments, and the results show that the proposed method achieved the best performance. We applied our method to the middle and upper reaches of the Yellow River in eastern Qinghai Province, China, and obtained deformation rates using Sentinel-1 SAR data from 2018 to 2020 in the region, ultimately identifying 254 landslides. Quantitative evaluations reveal that most detected landslides in the study area occurred at an elevation of 2500–3700 m with slope angles of 10–30°. The proposed landslide detection algorithm holds significant promise for quickly and accurately detecting wide-area landslides, facilitating timely preventive and control measures. [ABSTRACT FROM AUTHOR]

The Jing-Mi Diversion Canal is a large-scale water diversion project in Beijing. Routine monitoring is crucial for the reliability and stability of urban water supply. Compared with traditional monitoring methods, interferometric synthetic aperture radar (InSAR) has the advantages of large scale and high accuracy. Based on the small baseline subset InSAR, 187 ascending and 102 descending SAR images obtained from Sentinel-1 were used to detect the deformation along the diversion canal from 2017 to 2023. The results show that there was a sinking trend along the diversion canal. The subsidence was serious in the first half of the canal, and continued to sink from 2019 to 2020. The subsidence was alleviated in 2023. Combined with leveling measurements, the InSAR deformation monitoring results of important pumping station buildings were verified. The measurement accuracy of InSAR can reach the millimeter level. We extracted the groundwater level time series and subsidence for risky canal segments. Through pixel-by-pixel comparison, it was found that fluctuations in groundwater level would have some impact on surface deformation. Severe local subsidence or uplift deformation occasionally occurred. To ensure the safety of water diversion, the monitoring and maintenance of relevant pump station buildings in risky areas should be increased in the future. [ABSTRACT FROM AUTHOR]

Three-dimensional coseismic surface deformation fields are important for quantifying the geometric and kinematic characteristics of earthquake rupture faults. However, traditional geodetic techniques are constrained by intrinsic limitations: Interferometric synthetic aperture radar (InSAR) can only extract far-field deformation fields owing to incoherence; global navigation satellite systems (GNSSs) can only acquire displacement at discrete points. The recently developed optical pixel correlation technique, which is based on high-resolution remote sensing images, can acquire near-field coseismic horizontal deformation. In this study, InSAR line-of-sight (LOS) and azimuth direction far-field deformation, horizontal near-field deformation determined using optical pixel correlation based on pre- and post-earthquake GaoFen (GF)-2/7 images, and vertical deformation determined by differencing pre- and post-earthquake GF-7 digital elevation models (DEMs) were combined to comprehensively provide the three-dimensional deformation field of the 2022 Mw 6.6 Menyuan earthquake. The results show that the near-field deformation field calculated by optical pixel correlation quantified displacements distributed over the rupture fault zone, which were not available from the InSAR deformation maps. We identified significant vertical displacements of ~1–1.5 m at a bend region, which were induced by local compressive stress. The maximum uplift (>2.0 m) occurred near the epicenter, on the southern sides of the main and secondary faults along the middle segment of the ruptured Lenglongling fault. In addition, surface two-dimensional strain derived from the displacement maps calculated by optical pixel correlation revealed high strain concentration on the rupture fault zone. The method described herein provides a new tool for a better understanding of the characteristics of coseismic surface deformation and rupture patterns of faults. [ABSTRACT FROM AUTHOR]

Time series dynamic prediction of surface deformation in mining areas can provide reference data for coal mine safety and production, which has important impacts. The combination of interferometric synthetic aperture radar (InSAR) technology and the probability integral method (PIM) is commonly used for predicting deformation. However, most surface subsidence prediction in mining areas is based on the static PIM parameters, failing to achieve the three-dimensional (3D) dynamic deformation prediction. This paper proposed a 3D deformation dynamic prediction model (InSAR-3D-CTPIM) between InSAR deformation observations and dynamic coordinate-time PIM (CTPIM) parameters, which can realize the prediction of east–west, north–south, and vertical series deformation caused by mining. The method has been validated by simulation experiments and real experiments in the mining area of Jiansheng Coal Mine in Baisha Reservoir, Henan Province, China. The results showed that the modeling accuracy was improved by 34.3% compared to the traditional multi-rate model, and the accuracy was improved by 28.5% compared to the vertical deformation obtained by the traditional static PIM method. The InSAR-3D-CTPIM model can be used to predict the evolutionary history of basin-wide surface deformation dynamics in coal mining areas, and provide a reference for the early warning and prediction of geological hazards in coal mining areas. [ABSTRACT FROM AUTHOR]

Detecting surface deformation associated with low-magnitude ( M w ≤ 5 ) seismicity using interferometric synthetic aperture radar (InSAR) is challenging due to the subtlety of the signal and the often challenging imaging environments. However, low-magnitude earthquakes are potential precursors to larger seismic events, and thus characterizing the crustal displacement associated with them is crucial for regional seismic hazard assessment. We combine InSAR time-series techniques with a Deep Learning (DL) autoencoder denoiser to detect the magnitude and extent of crustal deformation from the M w = 3.4 Gallina, New Mexico earthquake that occurred on 30 July 2020. Although InSAR alone cannot detect event-related deformation from such a low-magnitude seismic event, application of the DL method reveals maximum displacements as small as (±2.5 mm) in the vicinity of both the fault and earthquake epicenter without prior knowledge of the fault system. This finding improves small-scale displacement discernment with InSAR by an order of magnitude relative to previous studies. We additionally estimate best-fitting fault parameters associated with the observed deformation. The application of the DL technique unlocks the potential for low-magnitude earthquake studies, providing new insights into local fault geometries and potential risks from higher-magnitude earthquakes. This technique also permits low-magnitude event monitoring in areas where seismic networks are sparse, allowing for the possibility of global fault deformation monitoring. [ABSTRACT FROM AUTHOR]

On 2 December 2020 10:54 UTC a shallow earthquake of MW (NOA) = 4.6 occurred near the village of Kallithea (to the east of Thiva), central Greece, which, despite its modest size, was locally damaging. Using InSAR and GNSS data, we mapped a permanent change on the ground surface, i.e., a subsidence of 7 cm. Our geodetic inversion modelling indicates that the rupture occurred on a WNW–ESE striking, SSW-dipping normal fault, with a dip-angle of ~ 54°. The maximum slip value was 0.35 m, which was reached at a depth of about 1100 m. The analysis of broadband seismological data also provided kinematic source parameters such as moment magnitude MW = 4.6 (± 0.1), rupture area 6.3 km2 and mean slip 0.16 m, which agree with the values obtained from the geodetic model. The effects of the earthquake were disproportionate to its moderate magnitude, probably due to its unusually shallow depth (slip centroid at 1.1 km) and the high efficiency of the earthquake (radiation efficiency η = 0.62). The geodetic data inversion also indicates that within the uncertainty limits of the technique, three scenarios are possible (a) the earthquake responsible for the mapped surface deformation may have occurred on a ~ 2-km long, blind normal fault different from the well-known active Kallithea normal fault or (b) could have occurred along a secondary fault that branches off the Kallithea fault or (c) it may have occurred along the Kallithea fault itself, but with its geometrical configuration could not be modelled with available data. We have also concluded that with a high dip-angle Kallithea Fault forward model it is not possible to fit the geodetic data. The rupture initiated at a very shallow depth (1.1 km) and it could not propagate deeper possibly because of a structural barrier down-dip. The 2020 event near Kallithea highlighted the structural complexity in this region of the Asopos Rift valley as the reactivation of the WNW–ESE structures indicates their significant role in strain accommodation and that they still represent a seismic hazard for this region. [ABSTRACT FROM AUTHOR]

Characterizing the coseismic slip behaviors of earthquakes could offer a better understanding of regional crustal deformation and future seismic potential assessments. On 18 December 2023, an Mw 6.0 earthquake occurred on the Lajishan–Jishishan fault system (LJFS) in the northeastern Tibetan Plateau, causing serious damage and casualties. The seismogenic fault hosting this earthquake is not well constrained, as no surface rupture was identified in the field. To address this issue, in this study, we use Interferometric Synthetic Aperture Radar (InSAR) data to investigate the coseismic surface deformation of this earthquake and invert both ascending and descending line-of-sight observations to probe the seismogenic fault and its slip characteristics. The InSAR observations show up to ~6 cm surface uplift caused by the Jishishan earthquake, which is consistent with the thrust-dominated focal mechanism. A Bayesian-based dislocation modeling indicates that two fault models, with eastern and western dip orientations, could reasonably fit the InSAR observations. By calculating the coseismic Coulomb failure stress changes (∆CFS) induced by both fault models, we find that the east-dipping fault scenario could reasonably explain the aftershock distributions under the framework of stress triggering, while the west-dipping fault scenario produced a negative ∆CFS in the region of dense aftershocks. Integrating regional geological structures, we suggest that the seismogenic fault of the Jishishan earthquake, which strikes NNE with a dip of 56° to the east, may be either the Jishishan western margin fault or a secondary buried branch. The optimal finite-fault slip modeling shows that the coseismic slip was dominated by reverse slip and confined to a depth range between ~5 and 15 km. The released seismic moment is 1.61 × 1018 N·m, which is equivalent to an Mw 6.07 earthquake. While the Jishishan earthquake ruptured a fault segment of approximately 20 km, it only released a small part of the seismic moment that was accumulated along the 220 km long Lajishan–Jishishan fault system. The remaining segments of the Lajishan–Jishishan fault system still have the capability to generate moderate-to-large earthquakes in the future. [ABSTRACT FROM AUTHOR]

The Longitudinal Valley (LV) in the eastern Taiwan is located at the convergent boundary between the Philippine Sea Plate and the Eurasian Plate. Within the LV, two significant fault systems, the Longitudinal Valley Fault (LVF) in the eastern part and the Central Range Fault (CeRF) in the western part, have been recognized. However, understanding the precise mechanisms and spatial relationships of the two fault systems in the middle segment of the LV has remained a challenge. In this study, we integrate various data sources, including geodetic measurements, seismic data, field investigations, and simulation results, all obtained during the 2022 Guangfu earthquake event. Our goal is to shed light on the complex structural interactions between the two fault systems. We introduce a coupled structural model consisting of a main fault plane and a coupled ramp plane. The deep-seated fault plane is constrained using seismic data, while the shallow ramp plane is determined through field investigations. Geodetic measurements are utilized to constrain the surface deformation resulting from dislocation model. The simulation outcomes reveal that the 2022 Guangfu earthquake was primarily triggered by the activity along the CeRF. The CeRF experienced an eastward reverse slip of approximately 58 cm along the fault plane. This fault not only intersects the LVF beneath the Coastal Range but also initiates the development of a ramp plane along pre-existing fault planes of the LVF. This intricate interplay results in approximately 20 cm of vertical surface deformation within the Coastal Range. The linear connection observed in the simulation residuals is associated with pre-existing faults within the LVF system. This alignment corresponds to the ground distribution of ramp plane, suggesting that older fault systems within this area exhibit characteristics of reactivation in response to earthquake-induced processes. Our study not only pinpoints the potential location of the CeRF intersecting with the LVF in the middle segment of the LV but also provides insight into one of the interaction mechanisms between the CeRF and the LVF. These findings hold significant implications for assessing seismogenic structural activity and enhancing the mitigation of regional earthquake hazards in the future. [ABSTRACT FROM AUTHOR]

Mauna Loa volcano (Hawaii) is the world’s largest subaerial volcano. Interferometric analysis of ISRO’s EOS-04 and ESA’s Sentinel-1 SAR images provided a detailed view on the surface deformation associated with the 2022 Mauna Loa volcano eruption. Co-eruptive InSAR analysis of Sentinel-1 data revealed Line-ofSight deformation of ±50 cm along the NE and SW rift zones. The complex bipolar deformation pattern in ascending and descending tracks suggests vertical and horizontal deformation associated with the sub-surface magma ascent and spreading respectively. InSAR analysis of EOS-04 data revealed concentric interferometric fringes east of the caldera. This near-circular deformation lobe (~ –13 cm) could be interpreted either as post-eruptive volcanic deflation or topographically correlated atmospheric artefact. Polarimetric and coherence based analysis revealed extensive lava flow along the SW and NE rifts (~15 km) and further towards the north along a topographic channel for about 16 km. Polarimetric radar analysis of a fresh lava flow channel associated with the NE rift zone revealed a transition between smooth pāhoehoe and rough a’a flow textures along its current extent. The present study demonstrates the interferometric and polarimetric capabilities of EOS-04 satellite for geophysical applications. [ABSTRACT FROM AUTHOR]

Surface deformation caused by an earthquake is very important to study for a better understanding of the development of geological structures and seismic hazards in an active tectonic area. In this study, we estimated the surface deformation due to an earthquake along an active blind fault using Sentinel-1 SAR data. On 24 September 2019, an earthquake with 5.6 Mw and 10 km depth stroke near Mirpur, Pakistan. The Mirpur area was highly affected by this earthquake with a huge collapse and the death of 34 people. This study aims to estimate the surface deformation associated with this earthquake in Mirpur and adjacent areas. The interferometric synthetic aperture radar (InSAR) technique was applied to study earthquake-induced surface motion. InSAR data consisting of nine Sentinel-1A SAR images from 11 August 2019 to 22 October 2019 was used to investigate the pre-, co- and post-seismic deformation trends. Time series investigation revealed that there was no significant deformation in the pre-seismic time. In the co-seismic time, strong displacement was observed and in post-seismic results, small displacements were seen due to 4.4 and 3.2 Mw aftershocks. Burst overlap interferometry and offset-tracking analysis were used for more sensitive measurements in the along-track direction. Comprehensive 3D displacement was mapped with the combination of LOS and along-track offset deformation. The major outcome of our results was the confirmation of the existence of a previously unpublished blind fault in Mirpur. Previously, this fault line was triggered during the 2005 earthquake and then it was activated on 24 September 2019. Additionally, we presented the co-seismically induced rockslides and some secondary faulting evidence, most of which occurred along or close to the pre-existing blind faults. The study area already faces many problems due to natural hazards where additional surface deformations, particularly because of the earthquake with activated blind fault, have increased its vulnerability. [ABSTRACT FROM AUTHOR]

Surface movements pose a critical issue requiring prompt detection and monitoring to ensure safety. To address this concern, the detection and continuous monitoring of surface deformations have gained importance. While numerous point-based surveying methods exist, multi-temporal interferometric synthetic aperture radar (InSAR) analysis has emerged as a powerful technique capable of detecting deformations across large areas with freely available SAR images and software. However, the significance of surface movement forecasting must be addressed, as it plays a significant role in preventing accidents and implementing precautionary measures. In light of this, a comprehensive study was conducted at the new Istanbul Airport in Türkiye, employing the Persistent Scatterer InSAR (PSI) method with a collection of 211 Sentinel-1 SAR images. The results obtained were investigated, and six test case regions were determined for the forecasting analysis in terms of time series movement type (subsidence, uplift, and stable) and structure type (runway and building) using various machine learning (ML) algorithms. To accomplish this, regression-based and sequential-based ML algorithms were employed and compared, showing the potential of cutting-edge techniques. The Stacked LSTM algorithm was the most effective in generating accurate forecasts. Furthermore, by incorporating exogenous variables into the forecasting analysis, the study improved the accuracy of the results. To gain deeper insights into the impact of these exogenous variables, feature importance analysis was conducted using the permutation feature importance method. The results obtained were examined, considering both the structure type and the characteristics of the time series data. [ABSTRACT FROM AUTHOR]

A considerable number of coal mines employed room and pillar mining in the last century in northern China, where the goaf remained stable for a period of time; however, with the increased exposure of coal pillars, their collapse may gradually increase. The stability assessment of these old rooms and pillar goafs is challenging due to their concealment, irregular mining patterns, and the long passage of time. The methodology developed in this study, based on "space-sky-earth" remote sensing such as InSAR to trace historical deformation, the UAV observation of current surface damage, and comparison of mining spaces, can rapidly detect on a large scale the collapse of old goafs and the trend of damage. This study is conducted with an example of a coal mine in Yulin, Northern China, where obtained quantitative surface deformation values were integrated with qualitative surface damage interpretation results, followed by a yearly analysis of the overlying rock movement in accordance with the underground coal mining process. The results show that from 2007 to 2021, corresponding surface deformation and damage occurred following mining progress. However, the room and pillar goaf areas had not undergone any surface deformation, nor had there been incidents of landslides or ground fissures; therefore, it was speculated that no roof collapse had occurred in this region. The surface deformation and damage associated with underground coal mining are complex and influenced by the coal seam occurrence, mining methods, strata lithology, terrain slope, temporal evolution, and anthropogenic modifications. These phenomena are representative of the coal mining area, and this methodology can provide a reference for similar endeavors. [ABSTRACT FROM AUTHOR]

A 3D surface deformation field for an earthquake can aid in understanding fault behaviors and earthquake mechanisms. However, SAR-based 3D surface deformation estimates are often limited by insufficient observations and hampered by various error sources. In this study, we demonstrate the derivation of a 3D coseismic displacement field from different InSAR processing algorithms. The azimuth displacements from Multiple Aperture Interferometry (MAI) and Pixel Offset Tracking (POT) were integrated to ensure reliable displacements at low coherent areas. The 3D displacement field was inverted pixel-by-pixel by Line-of-Sight (LOS) displacement and integrated azimuth displacement. The results showed that MAI and POT could compensate for the weaknesses of each algorithm. Also, pixels with less than three sets of observations showed higher noise levels. Such noisy pixels were removed by a denoising criterion proposed herein. For the vertical direction, the proportion of pixels inverted with two sets of azimuth and one set of LOS displacements was 26.1%. After denoising, the proportion dropped to 2.4% due to the insufficiency of LOS displacements. This shows that the viewing angle influences the overall performance of 3D surface displacement inversion. Implementing various displacement vectors should reduce such limitations. [ABSTRACT FROM AUTHOR]

Toppling is among the most common deformation types in steeply bedded rock slopes. With the construction of high dams in large rivers, various toppling deformations have occurred in reservoir areas, and it is still unclear how toppling deformation varies with long-term water-level fluctuations. To identify the deformation characteristics and different responses to the water-level fluctuations of the two types of toppling in underdip bedding and anti-dip bedding slopes, stacking interferometric synthetic aperture radar (stacking-InSAR) and small baseline subset InSAR (SBAS-InSAR) technologies were used based on Sentinel-1 SAR data from 8 years following reservoir impoundment. Initially, topplings that deformed locally after impoundment and those that deformed later exhibited complete deformation. According to the stacking-InSAR deformation profile and the deformation characteristics of typical permanent scatter (PS) points from SBAS-InSAR, both Xingguangsanzu (XGSZ) and Yanwan (YW) toppling instabilities can be divided into two deformation zones. According to the annual stacking-InSAR results and the deformation rates of the two zones of topplings, the deformation mode of the XGSZ toppling instability was retrogressive and that of the YW toppling instability was progressive. The crack distributions were related to the surface deformation and the slope topography, and the main tension cracks were very consistent with the large deformation area revealed by InSAR. In terms of long-term deformation, the XGSZ toppling instability mainly suffered from collapse of the front edge before reservoir impoundment, which turned into overall deformation after the first impoundment, while the YW toppling instability deformed after impoundment. The deformation area of the XGSZ toppling instability expanded faster than that of the YW toppling instability in the first 3 years after impoundment. Regarding the relationship with water-level fluctuations, the SBAS-InSAR results showed that the impact of water-level drawdown on the YW toppling instability was more significant than that on the XGSZ toppling instability. The elevation of the YW toppling instability affected by water-level fluctuations was higher than that of the XGSZ toppling instability, and it was speculated that the strong water conductivity of the fault fracture zone in the middle of the slope affected the deformation of the YW toppling instability. [ABSTRACT FROM AUTHOR]

Mount Semeru, one of the world's most active volcanoes, experiences frequent eruptions and at least one significant eruption annually. Traditional monitoring methods, constrained by the existing geodetic infrastructure, have been augmented by Interferometry Synthetic Aperture Radar (InSAR) technology. Previous InSAR applications in Indonesia have primarily relied on Line-of-Sight (LOS) displacement measurements, utilizing a single orbital geometry. This study breaks new applications by leveraging two orbital geometries of the Sentinel-1 satellite, advancing beyond conventional one-dimensional observations to generate comprehensive multidimensional displacement time series. This approach enables the precise capture of vertical, east-west, and north-south components of surface motion at Semeru volcano. Our findings underscore the critical role of multitrack InSAR data in providing a nuanced understanding of volcanic deformation. By encompassing all three displacement components, our methodology delivers a more detailed and accurate depiction of the volcano's dynamics. Although the 2.5D displacement model proves adequate in certain scenarios, the full three-dimensional analysis reveals its superiority in monitoring slow surface deformation. This study not only demonstrates the enhanced capabilities of InSAR in volcanic monitoring but also sets a precedent for its application in other high-risk volcanic and tectonic regions. Furthermore, our research emphasizes the value of integrating InSAR data with other monitoring techniques, like the Global Navigation Satellite System (GNSS), for cross-validation and accuracy enhancement. The synergy of these technologies offers a powerful tool for geoscientists, potentially enhancing our understanding of surface motion and contributing to more effective risk management in geologically active areas. [ABSTRACT FROM AUTHOR]

We developed an open source, extensible Python‐based framework, that we call the Versatile Modeling Of Deformation (VMOD), for forward and inverse modeling of crustal deformation sources. VMOD abstracts from specific source model implementations, data types and inversion methods. We implement the most common geodetic source models which can be combined to model and analyze multi‐source deformation. VMOD supports Global Navigation Satellite System (GNSS), InSAR, electronic distance measurement, Leveling and tilt data. To infer source characteristics from observations, VMOD implements non‐linear least squares and Markov Chain Monte‐Carlo Bayesian inversions, including joint inversions using different sources of data. VMOD's structure allows for easy integration of new geodetic models, data types, and inversion strategies. We benchmark the forward models against other published results and the inversion approaches against other implementations. We apply VMOD to analyze deformation at Unimak Island, Alaska, observed with continuous and campaign GNSS, and ascending and descending InSAR time series generated from Sentinel‐1 satellite radar acquisitions. These data show an inflation pattern at Westdahl volcano and subsidence at Fisher Caldera. We use VMOD to test a range of source models by jointly inverting the GNSS and InSAR data sets. Our final model simultaneously constrains the parameters of two sources. Our results reveal a depressurizing spheroid under Fisher Caldera ∼4–6 km deep, contracting at a rate of ∼2–3 Mm3/yr, and a pressurizing spherical source underneath Westdahl volcano ∼6–8 km deep, inflating at ∼5 Mm3/yr. This and past applications of VMOD to volcanic unrest benefit from an extensible framework which supports jointly inversions of data sets for parameters of easily composable multi‐source models. Plain Language Summary: We developed a computational framework called the Versatile Modeling Of Deformation (VMOD) to calculate surface deformation created by buried pressure, tensile and shear sources, or find parameter values for a model that fits observed surface deformation. The framework has three main components. One component handles the model, one component handles the data, and the remaining component handles the algorithm that finds the most probable values for the parameters of a given model. Deformation can be observed using different techniques such as Global Navigation Satellite System (GNSS) or satellite images. The framework supports most of these techniques and allows users to add new techniques or deformation models. We validate our framework with literature results and contrast it with similar software used in the volcano deformation community. We apply the framework to analyze a deformation episode at Westdahl volcano and Fisher Caldera on Unimak Island, Alaska. The deformation is captured by GNSS and satellite radar images. Our results suggest a deflating spheroid ∼4–6 km deep with a rate of ∼2–3 Mm3/yr for Fisher Caldera and a inflating spherical source underneath Westdahl volcano ∼6–8 km deep, at a rate of ∼5 Mm3/yr. This and previous experiences highlight the advantages of multi‐source model deformation observations from different techniques. Key Points: We introduce a Python‐based object‐oriented modeling and inversion framework for geodetic data (Versatile Modeling Of Deformation (VMOD))VMOD's expandable framework design enables easy integration of new observation types, new deformation models, or inversion strategiesWe validate VMOD against literature and a community exercise, and use it to infer deformation sources at Fisher Caldera and Westdahl volcano [ABSTRACT FROM AUTHOR]

Mountainous landscapes affected by strong earthquakes typically exhibit higher landslide susceptibility in post‐seismic periods compared to pre‐seismic conditions. This concept is referred to as the earthquake legacy effect, which needs to be better understood to develop an accurate post‐seismic landslide hazard assessment. The earthquake legacy effect is mainly assessed by monitoring either rapid landslide occurrences or slow‐moving landslides over time. However, landslide inventories provide discrete information both in spatial and temporal domains, whereas monitoring only selected slow‐moving landslides discards the response of hillslopes exposed to a given earthquake. Therefore, to provide a more comprehensive understanding of the concept, this research focuses on post‐seismic hillslope evolution by examining the deformation time series generated from the Interferometric Synthetic Aperture Radar technique over the area affected by the 2017 Mw 6.4 Nyingchi earthquake, China. We also analyse factors controlling these InSAR‐derived hillslope deformations. Our results show a high coherence between hydrologic conditions (i.e., precipitation and terrestrial water storage) and surface deformation in pre‐ and post‐seismic periods. The earthquake disturbs this strong correlation for a while (~2 years) right after the seismic tremor, and then, a seasonal deformation pattern depending on hydrologic conditions appears again. Our findings show that the average post‐seismic hillslope deformation is still higher than its pre‐seismic counterpart approximately four and a half years after the earthquake. These findings trigger further research questions regarding whether hillslopes could fully recover after a major earthquake or gain a new level of hillslope susceptibility caused by intense ground shaking. [ABSTRACT FROM AUTHOR]

In the past two decades, extensive and in-depth research has been conducted on Time Series InSAR technology with the advancement of high-performance SAR satellites and the accumulation of big SAR data. The introduction of distributed scatterers in Distributed Scatterers InSAR (DS-InSAR) has significantly expanded the application scenarios of InSAR geodetic measurement by increasing the number of measurement points. This study traces the history of DS-InSAR, presents the definition and characteristics of distributed scatterers, and focuses on exploring the relationships and distinctions among proposed algorithms in two crucial steps: statistically homogeneous pixel selection and phase optimization. Additionally, the latest research progress in this field is tracked and the possible development direction in the future is discussed. Through simulation experiments and two real InSAR case studies, the proposed algorithms are compared and verified, and the advantages of DS-InSAR in deformation measurement practice are demonstrated. This work not only offers insights into current trends and focal points for theoretical research on DS-InSAR but also provides practical cases and guidance for applied research. [ABSTRACT FROM AUTHOR]

Unrest began in July 2021 at Askja volcano in the Northern Volcanic Zone (NVZ) of Iceland. Its most recent eruption, in 1961, was predominantly effusive and produced ∼0.1 km3 lava field. The last plinian eruption at Askja occurred in 1875. Geodetic measurements between 1983 and 2021 detail subsidence of Askja, decaying in an exponential manner. At the end of July 2021, inflation was detected at Askja volcano, from GNSS observations and Sentinel‐1 interferograms. The inflationary episode can be divided into two periods from the onset of inflation until September 2023. An initial period until 20 September 2021 when geodetic models suggest transfer of magma (or magmatic fluids) from within the shallowest part of the magmatic system (comprising an inflating and deflating source), potentially involving silicic magma. A following period when one source of pressure increase at shallow depth can explain the observations. Plain Language Summary: Askja volcano, situated in the Northern Volcanic Zone in Iceland, has been quiet since its last eruption in 1961, with surface deformation measurements from 1983 to 2021 displaying a decaying subsidence signal within the Askja caldera. However, at the end of July 2021, the volcano began to inflate. This was detected on both GNSS and satellite observations. As of September 2023, ∼65 cm of uplift had been measured at GNSS station OLAC. Modeling of surface deformation measurements indicates that the inflation was triggered by upward migration of melt (or magmatic fluids). Key Points: At the end of July 2021, Askja volcano began to inflate—detected on both GNSS and satellite observations, ending 1983–2021 subsidenceGeodetic modeling indicates upward migration of magma, feeding a magma body at an inferred depth of 2.5–3.1 km under the main Askja calderaStart of unrest was associated with magma transfer within the upper part of the system, followed by possible additional influx from depth [ABSTRACT FROM AUTHOR]

As urban economies flourish and populations become increasingly concentrated, urban surface deformation has emerged as a critical factor in city planning that cannot be overlooked. Surface deformation in urban areas can lead to deformations in structural supports of infrastructure such as road bases and bridges, thereby posing a serious threat to public safety and creating significant safety hazards. Consequently, research focusing on the monitoring of urban surface deformation holds paramount importance. Interferometric synthetic aperture radar (InSAR), as an important means of earth observation, has all-day, wide-range, high-precision, etc., characteristics and is widely used in the field of surface deformation monitoring. However, traditional solitary InSAR techniques are limited in their application scenarios and computational characteristics. Additionally, the manual selection of ground control points (GCPs) is fraught with errors and uncertainties. Permanent scatterers (PS) can maintain high interferometric coherence in man-made building areas, and distributed scatterers (DS) usually show moderate coherence in areas with short vegetation; the combination of DS and PS solves the problem of manually selecting GCPs during track re-flattening and regrading, which affects the monitoring results. In this paper, 45 Sentinel-1B data from 16 February 2019 to 14 December 2021 are used as the data source in the urban area of Horqin District, Tongliao City, Inner Mongolia Autonomous Region, for example. A four-threshold (coherence coefficient threshold, FaSHPS adaptive threshold, amplitude divergence index threshold, and deformation velocity interval) GCPs point screening method for PS-DS, as well as a Small Baseline Subset-Permanent Scatterers-Distributed Scatterers-Interferometric Synthetic Aperture Radar (SBAS-PS-DS-InSAR) method for selecting PS and DS points as ground control points for orbit refinement and re-flattening, are proposed. The surface deformation results obtained using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the SBAS-PS-DS-InSAR proposed in this paper were comparatively analysed and verified. The maximum cumulative line-of-sight settlements were −90.78 mm and −83.68 mm, and the maximum cumulative uplifts are 74.94 mm and 97.56 mm, respectively; the maximum annual average line-of-sight settlements are −35.38 mm/y and −30.38 mm/y, and the maximum annual average uplifts are 25.27 mm/y and 27.92 mm/y. The results were evaluated and analysed in terms of correlation, mean absolute error (MAE), and root mean square error (RMSE). The deformation results of the two InSAR methods were evaluated and analysed in terms of correlation, MAE, and RMSE. The errors show that the Pearson correlation coefficients between the vertical settlement results obtained using the SBAS-PS-DS-InSAR method and the GPS monitoring results were closer to 1. The maximum MAE and RMSE were 13.7625 mm and 14.8004 mm, respectively, which are within the acceptable range; this confirms that the monitoring results of the SBAS-PS-DS-InSAR method were better than those of the original SBAS-InSAR method. SBAS-InSAR method, which is valid and reliable. The results show that the surface deformation results obtained using the SBAS-InSAR, SBAS-PS-DS-InSAR, and GPS methods have basically the same settlement locations, extents, distributions, and temporal and spatial settlement patterns. The deformation results obtained using these two InSAR methods correlate well with the GPS monitoring results, and the MAE and RMSE are within acceptable limits. By comparing the deformation information obtained using multiple methods, the surface deformation in urban areas can be better monitored and analysed, and it can also provide scientific references for urban municipal planning and disaster warning. [ABSTRACT FROM AUTHOR]

Landslides are a common geological disaster, which cause many economic losses and casualties in the world each year. Drawing up a landslide list and monitoring their deformations is crucial to prevent landslide disasters. Interferometric synthetic aperture radar (InSAR) can obtain millimeter-level surface deformations and provide data support for landslide deformation monitoring. However, some landslides are difficult to detect due to the low-coherence caused by vegetation cover in mountainous areas and the difficulty of phase unwrapping caused by large landslide deformations. In this paper, a method based on multi-direction phase gradient stacking is proposed. It employs the differential interferograms of small baseline sets to directly obtain the abnormal region, thereby avoiding the problem where part of landslide cannot be detected due to a phase unwrapping error. In this study, the Sentinel-1 satellite ascending and descending data from 2018 to 2020 are used to detect landslides around Zhouqu County, China. A total of 26 active landslides were detected in ascending data and 32 active landslides in the descending data using the method in this paper, while the SBAS-InSAR detected 19 active landslides in the ascending data and 25 active landslides in the descending data. The method in this paper can successfully detect landslides in areas that are difficult for the SBAS-InSAR to detect. In addition, the proposed method does not require phase unwrapping, so a significant amount of data processing time can be saved. [ABSTRACT FROM AUTHOR]

Slant range geometry plays a crucial role in interpreting synthetic aperture radar (SAR) observations, especially in converting line-of-sight (LOS) surface deformations to actual vertical subsidence. This paper proposes a new conversion model to retrieve vertical settlements of the embankment slopes using the geometrical parameters of the dam and the SAR sensor. The simulation results highlight the impact of slope foreshortening and heading direction of the satellite on deformation retrieval. Various SAR data with different resolutions and bands are used to analyze the model's performance, revealing a high conformity of the model with practical conversion parameters exceeding 80% for TerraSAR-X and Cosmo-SkyMed data. [ABSTRACT FROM AUTHOR]

Permafrost, known for its high sensitivity to climate change and human activities, plays a crucial role in comprehending the environmental sustainability of Arctic cities. Yakutsk, being the largest city situated in continuous permafrost, has suffered irreversible damage to surface buildings due to the widespread permafrost thaw. Therefore, it is imperative to conduct detailed monitoring and assessment of surface deformation to ensure the stable operation of building facilities and to plan urban development situated above the permafrost. This study utilized 138 scenes of Sentinel‐1B images captured between 2017 and 2021 to process the surface deformation variations of Yakutsk using the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS‐InSAR) method. The surface deformation rate map reflects that the study area is undergoing extensive surface subsidence. The cumulative deformation time series maps effectively trace the temporal and spatial evolution of surface deformation, with the highest surface subsidence rates exceeding 40 mm/year and maximum cumulative subsidence exceeding 250 mm. In‐depth analysis of the obtained results indicated that the permafrost in Yakutsk is undergoing degradation, and the primary reason for surface subsidence is the thawing of foundation soils and the degradation of ice‐bearing permafrost. This study provides basic data for the investigation of geohazards caused by surface subsidence in Yakutsk. [ABSTRACT FROM AUTHOR]

Coseismic deformation of large earthquakes causes significant property damages and fatalities, which requires quantitative research of multiple disciplines such as geodesy, geological investigation, seismic tomography, and seismic dislocation theory. The finite element method accounts for material heterogeneity and geometric complexity, making it suitable for studying the coseismic deformation of large earthquakes. This paper develops a parallel elastic finite element program that utilizes split nodes and high-performance parallel computing technology on the FELAC software platform to study the coseismic deformation of large earthquakes. We verify the accuracy of the parallel elastic finite element program by comparing its results with the analytical solutions from seismic dislocation theory for four ideal earthquake cases. Finally, we established parallel elastic finite element models to study the coseismic deformation of the 2008 Wenchuan earthquake. The simulation results are consistent with the GPS and InSAR data. Coseismic surface deformation results are significantly influenced by medium regional heterogeneity with different layered structures besides the Longmenshan fault. The finite element program lay the foundation for the inversion of the coseismic fault rupture process based on the heterogeneous medium model and complex geometric model. [ABSTRACT FROM AUTHOR]

Landslides are common natural disasters that cause serious damage to ecosystems and human societies. To effectively prevent and mitigate these disasters, an accurate assessment of landslide hazards is necessary. However, most traditional landslide hazard assessment methods rely on static assessment factors while ignoring the dynamic changes in landslides, which may lead to false-positive errors in the assessment results. This paper presents a novel landslide hazard assessment method for the Zagunao River basin, China. In this study, an updated landslide inventory was obtained for the Zagunao River basin using data from interferometric synthetic aperture radar (InSAR) and optical images. Based on this inventory, a landslide susceptibility map was developed using a random forest algorithm. Finally, an evaluation matrix was created by combining the results of deformation rates from both ascending and descending data to establish a hazard level that considers surface deformation. The method presented in this study can reflect recent landslide hazards in the region and produce dynamic assessments of regional landslide hazards. It provides a basis for the government to identify and manage high-risk areas. [ABSTRACT FROM AUTHOR]

The combination of InSAR and GNSS data can provide more accurate measurements of coseismic 3D surface deformations, which are crucial in determining earthquake source parameters. Currently, the methods for inverting coseismic 3D surface deformations by integrating GNSS and InSAR data using the strain model (SM) (e.g., Simultaneous and integrated strain tensor estimation from geodetic measurements to obtain 3D displacement maps (SISTEM), SM-VCE) simply select GNSS points based on a certain number or distance, without considering the weight influence of the spatial distribution of G NSS points on the InSAR and GNSS data fusion results. To address this issue, this paper proposes a new method that uses Voronoi diagrams analyze the contribution of GNSS points to the deformation at target points. In this paper, GNSS points with high contribution can be used to construct observation value equations and optimize the internal weight of GNSS data. The proposed approach characterizes the contribution of GNSS data to target points by analyzing the changes in the Delaunay triangulation area formed before and after the target points are added to the GNSS data. Simulation experiments reveal that the proposed method outperforms conventional methods in accuracy and completeness. The proposed method is then applied to map the coseismic 3D surface deformations of the 2020 Mw6.5 Monte Cristo Range earthquake and find that the maximum deformation in the east–west, north–south, and vertical directions is about 13 cm, 11 cm, and 24 cm, respectively. [ABSTRACT FROM AUTHOR]

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Constraining the lithospheric rheology of Tibetan Plateau is important for the physical understanding of its tectonics. Siling Co, the largest high‐altitude lake in the world, has experienced a rapid water level increase in the 2000s. The resulting loading changes stimulate the viscoelastic response of the lower crust, giving access to study the lithospheric rheology in central Tibet. Here, we derive a clear subsidence signal around the lake with peak velocity of 4 mm/yr from two tracks of Sentinel‐1 InSAR images acquired from 2017 to 2022. Our viscoelastic modeling suggests a ductile layer 15 km beneath the surface with a decadal‐scale, steady‐state viscosity of 1–4 × 1019 Pa s. This value is consistent with the viscosity inferred from millennium‐scale shoreline changes, but is about 10 times higher than the viscosities derived from the deformation surrounding Siling Co before 2011, highlighting the role of transient viscosity in controlling the surface deformation. Plain Language Summary: The change of surface loading will cause the response of earth lithosphere, including instantaneous, concentrated elastic and delayed, wide‐range viscoelastic deformation at the surface. With the change of climate, the amount of water in many lakes in the Tibetan Plateau has changed. The resulted water loading changes can cause surface deformation that can be measured by space‐borne radar images. Using the Interferometric Synthetic Aperture Radar (InSAR), we measure the deformation around the Siling Co, the largest high‐altitude lake in the world, with the peak subsidence velocity of up to 4 mm/yr. According to numerical modeling, we find that the deformation mainly comes from the viscoelastic response of the ductile layer 15 km beneath the surface. The inferred viscosity of 1–4 × 1019 Pa s is consistent with the value derived from millennium‐scale shoreline changes. Our research links the surface displacement with the strengths of the deep earth, providing a direct constraint for geodynamics models. Key Points: We observe subsidence around the Siling Co from Sentinel‐1 InSAR images acquired between 2017 and 2022 with a peak rate of 4 mm/yrA ductile layer below 15 km with a viscosity of 1–4 × 1019 Pa s can explain the observed subsidence around the Siling CoThis decadal‐scale viscosity is consistent with the millennial‐scale value derived from paleoshoreline changes around the Siling Co [ABSTRACT FROM AUTHOR]

The coal industry is a crucial component of China's energy sector. However, the persistent exploitation of coal resources has gravely impacted the ecological environment. While the Remote Sensing Ecology Index (RSEI) is predominantly used for assessing ecological quality, its primary focus has been urban or aquatic environments. There is limited research focused on the evaluation of the ecological environment quality in mining areas. Moreover, the information regarding surface deformation caused by coal mining extraction is an essential factor in the ecological monitoring of mining areas. Therefore, this study proposed the Modified Remote Sensing Ecology Index (MRSEI). This enhanced model merges active and passive remote sensing techniques and incorporates a deformation factor (Surface Deformation Index, SDI) to provide a holistic evaluation of mining area ecologies. Furthermore, for comparative verification, we developed the Eco-environmental Quality Index (EQI) model by selecting 12 ecological parameters and employing a hierarchical analysis. The Juye mining area in Shandong Province was selected as the region of study. MRSEI results from 2015 to 2021 indicate a decline in the ecological quality of the Juye mining area, with MRSEI values registering at 0.691, 0.644, and 0.617. The EQI model mirrors this decreasing trend over the same period. Despite MRSEI using fewer indicators, its assessments align closely with the multi-indicator EQI method. This validates the accuracy of the MRSEI method, providing reliable technical support for the monitoring and evaluation of ecological environment quality in mining areas. [ABSTRACT FROM AUTHOR]

InSAR capabilities allow us to understand ground deformations in large metropolitan areas, this is key to assessing site conditions in areas in an inherently expanding context. The multi-temporal interferometry of SAR data records ground surface displacement velocities over large metropolitan areas, identifying anomalous and potential geological hazards. The metropolitan city of Concepción, Chile, is an alluvial basin in one of the world's most seismically active subduction zones, where many subduction earthquakes have occurred throughout history. In this study, we monitored the deformations of the ground surface in the metropolitan area of Concepción using two interferometric techniques, the first being Persistent Scatterer Interferometry (PSI) and the second, the Small Baseline Subset (SBAS) technique. To do this, we have used the same Sentinel-1 dataset, obtaining ground movement rates between 2019 and 2021. The velocities were aligned with the GNSS station available in the area. Ground deformation patterns show local deformations depending on factors such as soil type and heterogeneity, and regional deformations due to geographical location in the subduction area. Our results highlight the similarity of the deformation rates obtained with different processing techniques and have also allowed us to identify areas of deformation and compare them to site conditions. These results are essential to evaluate ground conditions and contribute to urban planning and risk management in highly seismic areas. [ABSTRACT FROM AUTHOR]

Extreme precipitation is one of the most prevalent meteorological disasters occurring today. Its occurrence not only causes significant social and economic losses but also indirectly affects surface deformation, creating safety hazards for diverse ground features. Although there are presently high-precision, comprehensive tools such as continuous scattering interferometry to observe surface deformation, it takes a long time to locate potentially vulnerable objects. A monitoring scheme for surface deformation anomalies was devised to address the timeliness issue of identifying sensitive surface features under extreme rainfall conditions. An SAR image of Sentinel-1A is used to derive the surface deformation in three years before and after a rainstorm in the main urban area of Zhengzhou, and the anomaly surface deformation objects after extreme precipitation are screened to determine the surface deformation-sensitive objects. The results indicate that, in the past three years, a 22.14 km2 area in Zhengzhou City has experienced a settlement speed greater than 10 mm/yr. Under the influence of the "7–20" rainstorm in the main urban area of Zhengzhou City, among them, the area of highly sensitive agricultural land for deformation is 2,581,215 m2, and there are 955 highly sensitive houses for deformation, with an excellent recognition effect. This method is effective in rapidly locating surface deformation-sensitive or potentially damaged features; it can provide a reference for the vulnerability and risk assessment of buildings. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping based on static influence factors often exhibits issues of low accuracy and classification errors. To enhance the accuracy of susceptibility mapping, this study proposes a refined approach that integrates categorical boosting (CatBoost) with small baseline subset interferometric synthetic-aperture radar (SBAS-InSAR) results, achieving more precise and detailed susceptibility mapping. We utilized optical remote sensing images, the information value (IV) model, and fourteen influencing factors (elevation, slope, aspect, roughness, profile curvature, plane curvature, lithology, distance to faults, land use type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), distance to rivers, distance to roads, and annual precipitation) to establish the IV-CatBoost landslide susceptibility mapping method. Subsequently, the Sentinel-1A ascending data from January 2021 to March 2023 were utilized to derive the deformation rates within the city of Lishui in the southern region of China. Based on the outcomes derived from IV-CatBoost and SBAS-InSAR, a discernment matrix was formulated to rectify inaccuracies in the partitioned regions, leading to the creation of a refined information value CatBoost integration (IVCI) landslide susceptibility mapping model. In the end, we utilized optical remote sensing interpretations alongside surface deformations obtained from SBAS-InSAR to cross-verify the excellence and accuracy of IVCI. Research findings indicate a distinct enhancement in susceptibility levels across 165,784 grids (149.20 km2) following the integration of SBAS-InSAR correction. The enhanced susceptibility classes and the spectral characteristics of remote sensing images closely correspond to the trends of SBAS-InSAR cumulative deformation, reflecting a high level of consistency with field-based conditions. These improved classifications effectively enhance the refinement of landslide susceptibility mapping. The refined susceptibility mapping approach proposed in this paper effectively enhances landslide prediction accuracy, providing valuable technical reference for landslide hazard prevention and control in the Lishui region. [ABSTRACT FROM AUTHOR]

The Great Wall of China constitutes a significant cultural treasure of the Chinese nation and a valuable piece of heritage of world civilization. Owing to both natural and anthropogenic factors, the Great Wall is undergoing gradual deformation, thereby posing considerable challenges to the overall preservation of the associated sites. This study aims to investigate techniques for monitoring deformation at large-scale linear heritage sites, leveraging the Great Wall as a representative example, and to offer valuable insights for monitoring surface deformations at extensive cultural heritage sites worldwide. Employing SBAS-InSAR technology, this research analyzes and monitors the deformation of the Great Wall. A series of Sentinel-1A images captured between March 2017 and January 2022, consisting of 161 scenes, were subjected to SBAS-InSAR processing to derive the deformation rate field along the wall. To ensure the reliability of the findings, a representative mountainous segment, spanning approximately 896.53 km within the scenic corridor of the Great Wall, was selected for analysis. The outcomes indicate that 75.8% of the scenic corridor in Shanxi Province, representing the Ming Great Wall, exhibits relative stability with deformation rates ranging from −10 to 10 mm/year. Conversely, 24.2% of the scenic corridor demonstrates significant deformation, with a maximum subsidence rate of 33.1 mm/year and a maximum subsidence of 148.6 mm. Therefore, this research highlights the potential application of SBAS-InSAR technology in the monitoring and assessment of surface deformation at massive linear cultural heritage sites and offers a reference point for similar monitoring efforts on a global scale. [ABSTRACT FROM AUTHOR]