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Evaluating building damage in mining areas using Interferometric Synthetic Aperture Radar (InSAR) has inherent challenges. This work presents a Delaunay Triangulation of Surface Deformation (DTSD) model that utilizes Permanent Scatter InSAR to analyze various types of deformations in villages. This model created a network of irregular triangles using Delaunay triangulation and PS points as the input. The calculation involved determining the inclination of triangular edges and the torsion of spatial Bezier curves fitted by these edges. The PS points' maximum inclination and curvature values were identified by solving over-determined equations. Results from a slurry injection mine in Huaibei, China, indicate that the various types of deformations in the neighboring communities were below 8 %, and the buildings experienced minimal damage. The DTSD model offers a theoretical framework for monitoring and assessing various types of surface deformation and designing essential zones for building protection. [ABSTRACT FROM AUTHOR]

Guangdong Province, home to 21 cities and a permanent population of 127.06 million people, boasts the largest provincial economy in China, contributing 11.76% to the national GDP in 2023. However, it is prone to geological hazards due to its geological conditions, extreme weather, and extensive human activities. Geohazards not only endanger lives but also hinder regional economic development. Monitoring surface deformation regularly can promptly detect geological hazards and allow for effective mitigation strategies. Traditional ground subsidence monitoring methods are insufficient for comprehensive surveys and rapid monitoring of geological hazards in the whole province. Interferometric Synthetic Aperture Radar (InSAR) technology using satellite images can achieve wide-area geohazard monitoring. However, current geological hazard monitoring in Guangdong Province based on InSAR technology lacks regional analysis and statistics of surface deformation across the entire province. Furthermore, such monitoring fails to analyze the spatial–temporal characteristics of surface deformation and disaster evolution mechanisms by considering the local geological features. To address these issues, current work utilizes Sentinel-1A/B satellite data covering Guangdong Province from 2015 to 2022 to obtain the wide-area surface deformation in the whole province using the multi-temporal (MT) InSAR technology. Based on the deformation results, a wide-area deformation region automatic identification method is used to identify the surface deformation regions and count the deformation area in each city of Guangdong Province. By analyzing the results, we obtained the following findings: (1) Using the automatic identification algorithm we identified 2394 deformation regions. (2) Surface subsidence is concentrated in the delta regions and reclamation areas; over a 4 cm/year subsidence rate is observed in the hilly regions of northern Guangdong, particularly in mining areas. (3) Surface deformation is closely related to geological structures and human activities. (4) Sentinel-1 satellite C-band imagery is highly effective for wide-area geological hazard monitoring, but has limitations in monitoring small-area geological hazards. In the future, combining the high-spatial–temporal-resolution L-band imagery from the NISAR satellite with Sentinel-1 imagery will allow for comprehensive monitoring and early warning of geological hazards, achieving multiple geometric and platform perspectives for geological hazard monitoring and management in Guangdong Province. The findings of this study have significant reference value for the monitoring and management of geological disasters in Guangdong Province. [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]

The EarthExplorer 10 mission Harmony by the European Space Agency ESA, scheduled for launch around 2029–2030, consists of two passive C-band synthetic-aperture-radar companion satellites flying in a flexible constellation with one Sentinel-1 radar satellite as an illuminator. Sentinel-1 will serve as transmitter and receiver of radar waves, and the two Harmonys will serve as bistatic receivers without the ability to transmit. During the first and last year of the 5-year mission, the two Harmony satellites will fly in a cross-track interferometric constellation, such as that known from TanDEM-X, about 350 km ahead or behind the assigned Sentinel-1. This constellation will provide 12-day repeat DEMs, among other regions, over most land-ice and permafrost areas. These repeat DEMs will be complemented by synchronous lateral terrain displacements from the well-established offset tracking method. In between the cross-track interferometry phases, one of the Harmony satellites will be moved to the opposite side of the Sentinel-1 to form a symmetric bistatic "stereo" constellation with ±~350 km along-track baseline. In this phase, the mission will provide opportunity for radar interferometry along three lines of sight, or up to six when combining ascending and descending acquisitions, enabling the measurement of three-dimensional surface motion, for instance sub- and emergence components of ice flow, or three-dimensional deformation of permafrost surfaces or slow landslides. Such measurements would, for the first time, be available for large areas and are anticipated to provide a number of novel insights into the dynamics and mass balance of a range of mass movement processes. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping (LSM) can accurately estimate the location and probability of landslides. An effective approach for precise LSM is crucial for minimizing casualties and damage. The existing LSM methods primarily rely on static indicators, such as geomorphology and hydrology, which are closely associated with geo-environmental conditions. However, landslide hazards are often characterized by significant surface deformation. The Small Baseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology plays a pivotal role in detecting and characterizing surface deformation. This work endeavors to assess the accuracy of SBAS-InSAR coupled with ensemble learning for LSM. Within this research, the study area was Shiyan City, and 12 static evaluation factors were selected as input variables for the ensemble learning models to compute landslide susceptibility. The Random Forest (RF) model demonstrates superior accuracy compared to other ensemble learning models, including eXtreme Gradient Boosting, Logistic Regression, Gradient Boosting Decision Tree, and K-Nearest Neighbor. Furthermore, SBAS-InSAR was utilized to obtain surface deformation rates both in the vertical direction and along the line of sight of the satellite. The former is used as a dynamic characteristic factor, while the latter is combined with the evaluation results of the RF model to create a landslide susceptibility optimization matrix. Comparing the precision of two methods for refining LSM results, it was found that the method integrating static and dynamic factors produced a more rational and accurate landslide susceptibility map. [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]

This study aimed to map and analyze the spatial pattern of the surface deformation associated with the 2024 Noto Peninsula earthquake (Mw7.5) using structure-from-motion/multi-view-stereo (SfM–MVS), an advanced photogrammetric technique. The analysis was conducted using digital aerial photographs with a ground pixel dimension of 0.2 m (captured the day after the earthquake). Horizontal locations of GCPs were determined using pre-earthquake data to remove the wide-area horizontal crustal deformation component. The elevations of the GCPs were corrected by incorporating quasi-vertical values derived from a 2.5-dimensional analysis of synthetic aperture radar (SAR) results. In the synclinorium structure area, where no active fault had previously been identified, we observed a 5 km long uplift zone (0.1 to 0.2 km in width), along with multiple scarps that reached a maximum height of 2.2 m. The area and shape of the surface deformation suggested that the induced uplift and surrounding landslides were related to fold structures and their growth. Thus, our study shows the efficacy of SfM–MVS with respect to accurately mapping earthquake-induced deformations, providing crucial data for understanding seismic activity and informing disaster-response strategies. [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: On September 20, 2019, the Jungong ancient landslide in Lajia Town, Maqin County, Qinghai Province, China suffered a local failure, which led to the interruption of traffic and seriously threatened the safety of local resident's lives and properties. It is urgent to find out the deformation area and deformation law of the ancient landslide, and to provide the support for prevention and control engineering design, monitoring and early warning. Methods: First, using high-resolution satellite images, digital elevation model data and combined with field investigations, the ancient landslide were zoned based on landslide morphological characteristics and signs of deformations; further, Sentinel-1 radar satellite descending data from January 2017 to December 2020 were used to analyze the surface deformation characteristics and deformation patterns of the ancient landslide based on small baseline subset interferometric synthetic aperture radar technology (SBAS-InSAR). Results: Based on the morphological characteristics and deformation signs of the landslide, the ancient landslide was divided into four sub-areas. The SBAS-InSAR results show that the ancient landslide is in a continuous slow creeping state. The strong deformation area of the landslide is mainly located in the road excavation section. Human activities greatly disturb the stability of the ancient landslide. The deformation rate of the strong deformation area of the landslide has a good relationship with rainfall. Conclusions: Although the ancient landslide has been partially treated with anti-slide piles and other projects, the ancient landslide has multi-level sliding surfaces, and the depth of the existing anti-slide piles is not enough. Although the ancient landslide has played a certain role in anti-slide, it has not completely prevented the creep deformation of the land‐ slide. It is suggested that the subsequent treatment projects should use drilling and other exploration techniques to find out the depth of the multi-level sliding surfaces before designing, and installing onsite real-time monitoring and early warning equipment such as crack meters in the strongly deformed areas. Combined with the medium and long-term monitoring of radar satellite InSAR, a point-surface monitoring and early warning system could be realized [ABSTRACT FROM AUTHOR]

Objectives: The response law of ancient (old) landslides in the reservoir area is an important research topic. Previous research primarily analyzed real-time surface displacement and reservoir water level data. However, professional monitoring conditions are often lacking on most reservoir bank slopes. This complicates tracking the landslide's historical deformation. Satellite and airborne remote sensing platforms enable multi-scale, long-term monitoring of landslide deformation and damage. Methods: This study employs multi-source three-dimensional observation technologies including aerial, space-based, and terrestrial platforms to monitor the deformation and evolution of the Pubugou Hydropower Station's Hongyanzi landslide over approximately 10 years. It utilizes unmanned aerial vehicle photography (optical imaging) and light detection and ranging (LiDAR) for detailed topographic mapping and deformation analysis from 2009 to 2020. Additionally, time-series interferometric synthetic aperture radar (InSAR) technology is used to track long-term surface deformations from October 2014 to July 2020. Field investigations have identified typical deformation and failure characteristics of the landslide, incorporating geological conditions and external factors such as rainfall and reservoir water levels to analyze causal mechanisms and dynamic trends. Results: The irregularly semicircular Hongyanzi landslide spans 20-50 m in thickness, encompasses approximately 15.53 million m³ in volume, and slides at an approximate bearing of 340°. Composed of quaternary pebbled stones, silty sand, and clay, the landslide's bed slopes between 20° and 25°. Its lithology includes Emeishan Formation basalt and Yangxin Formation dolomite. Existing since 2006 or earlier, the landslide features elements like walls and steps. LiDAR imagery from 2009 clearly delineates its boundaries, though it shows no new signs of deformation or failure. Following reservoir impoundment, the reactivated landslide develops new, widening cracks along its rear edge. Post-reactivation, the landslide predominantly undergos uniform deformation, with more significant movement at the trailing edge than the leading edge, without marked acceleration. Heavy rainfall is the most significant control factor, imparting stepwise deformation characteristics to the landslide. Conclusions: A comprehensive analysis of multi-source data reveals that phenomena like the Hongyanzi landslide exhibit typical long-term, gradual, and seasonal movements. Long-term InSAR effectively captures these characteristics. Multi-stage optical remote sensing and surface point cloud data from LiDAR, after vegetation removal, enable more intuitive comparisons of macroscopic deformation across different landslide areas. Integrating this with geological assessments and field investigations allows for detailed engineering analyses to ascertain the causes, patterns, and future trends of landslide activity. [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]

Landslide hazards pose a significant threat to lives and infrastructure, especially in mountainous regions like the Three Gorges Reservoir area. While the mechanisms driving landslide initiation and progression in reservoir environments are not fully understood. This study aimed to leverage the capabilities of Sentinel-1 satellite imagery and the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to detect and monitor potential landslide deformations in Yunyang County, Chongqing, China. We utilized Sentinel-1 data acquired between January 1, 2020, and December 28, 2022, to generate deformation velocity maps. Twelve potential landslides were identified, primarily concentrated near residential areas and along the Yangtze River. Precipitation emerged as the primary driver of surface deformation and landslide initiation, with potential landslides in residential vicinities and along the river exhibiting significantly higher deformation rates during the wet season compared to the dry season. These sites are susceptible to slope failures and geological disasters upon reaching critical antecedent rainfall thresholds, highlighting the necessity for continuous monitoring. Other potential landslides maintained consistent deformation rates across seasons but experienced brief accelerations following heavy precipitation events. Notably, potential landslides adjacent to the Yangtze River experienced accelerated deformation during periods of significant river water level reductions, suggesting that the river's cyclical water level fluctuations influence slope stability. The study demonstrated the effectiveness of SBAS-InSAR in detecting millimetric deformations in incipient landslides, a crucial step in averting landslide disasters and ensuring public safety. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) is a novel method of ground observation, which is widely used in fields such as surface deformation monitoring due to the fact that it is unrestricted of day and night, as well as cloud and rain. However, due to measurement errors, InSAR technology still has some limitations, especially in the densely vegetated southwest mountainous areas. Time series InSAR, such as SBAS-InSAR receives insufficient observation points, making it difficult to carry out deformation monitoring in mountainous areas. In response to the above issues, this paper considers the atmospheric characteristics of the southwest mountainous areas and designs an improved SBAS-InSAR method to optimize the interferometric phase. The Lijiang-Shangrila Railway is an electrified railway connecting Lijiang City and Shangri-La in Diqing Tibetan Autonomous Prefecture in Yunnan Province. It is an important component of the Yunnan Tibet Railway in the Western Railway Network. In order to ensure the safety and stability of the Lijiang-Shangrila Railway and verify the availability of the above method, this paper takes a certain section along the Lijiang-Shangrila Railway as the experimental area, uses archived SAR data to estimate historical surface deformation, calculates the historical time series deformation of the target area for nearly six and a half years (May 16, 2015 to December 10, 2021), and analyzes their influence range, deformation factors, change patterns, and other information. Finally, the improved SBAS InSAR method proposed in this article is compared with conventional InSAR method, which fully demonstrates the superiority of the proposed method. [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 accurate prediction of landslide susceptibility relies on effectively handling landslide absence samples in machine learning (ML) models. However, existing research tends to generate these samples in feature space, posing challenges in field validation, or using physics-informed models, thereby limiting their applicability. The rapid progress of interferometric synthetic aperture radar (InSAR) technology may bridge this gap by offering satellite images with extensive area coverage and precise surface deformation measurements at millimeter scales. Here, we propose an InSAR-based sampling strategy to generate absence samples for landslide susceptibility mapping in the Badong–Zigui area near the Three Gorges Reservoir, China. We achieve this by employing a Small Baseline Subset (SBAS) InSAR to generate the annual average ground deformation. Subsequently, we select absence samples from slopes with very slow deformation. Logistic regression, support vector machine, and random forest models demonstrate improvement when using InSAR-based absence samples, indicating enhanced accuracy in reflecting non-landslide conditions. Furthermore, we compare different integration methods to integrate InSAR into ML models, including absence sampling, joint training, overlay weights, and their combination, finding that utilizing all three methods simultaneously optimally improves landslide susceptibility models. [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]

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]

In the early stages, uncontrolled groundwater extraction led to the Hangjiahu (HJH) Plain becoming one of the areas with the most severe land subsidence in China. Since the beginning of this century, comprehensive measures have been taken to control the continuous aggravation of large land subsidence patterns in some areas; however, urban land subsidence issues, influenced by various factors, still persist and exhibit complex geographical distribution characteristics. In this study, we utilized Sentinel-1A images and the SBAS-InSAR technique to capture surface deformation over the HJH Plain in Zhejiang from 16 March 2017 to 20 January 2023. Through a comparative analysis with geological conditions, changes in surface mass loading, rainfall and groundwater, and land use types, we discussed the contributions of natural and anthropogenic factors to land subsidence. Augmented with optical remote sensing images and field investigations, we conducted a correlation analysis of the land subsidence status. The preliminary findings suggest that changes in surface mass loading and short-term heavy rainfall under extreme weather conditions can lead to periodic land subsidence changes in the region. Additionally, extensive infrastructure construction triggered by urbanization has resulted in significant and sustained land subsidence deformation. The research findings play an important guiding role in formulating scientifically effective strategies for land subsidence prevention and control, as well as urban planning and construction. [ABSTRACT FROM AUTHOR]

The Kumaun Himalaya is considered as the most active part of the Central Seismic gap in the Indian Sub-continent. In this paper, we presented active surface deformation rates of the Kumaun region from February 2017 to February 2021 using the Persistent Scatterer Interferometric Synthetic Aperture Radar (PSI) technique. The cumulative displacement that occurred during the span of 4 years is ±55 mm, whereas the Line of Sight (LOS) deformation velocity rate ranges is ±7 mm/yr. Apart from PSI, we also estimated the b-value of the Kumaun region from 1803 to 2021 (399 events) and its value is 0.54 ± 0.03. A distinct NE-SW trend of b-value is observed where earthquakes with M>6 occurred towards NE of this trend. The PSIderived deformation reveals that the central part of the Inner Lesser Himalaya along with the Main Central Thrust (MCT) zone is dominated by uplift. The zone between the Munsiari Thrust (MT) and MCT in the central region shows the maximum uplift ranging 5-7 mm/yr which exactly lies above the mid-crustal ramp of the Main Himalayan Thrust (MHT). Our results are well corroborated with available observations of geodetic strain and peak ground acceleration values. However, the deformation patterns and highvelocity rates in the central part of the study area between MT and MCT indicate the accumulation of high stress. [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]

Introduction: This study aims to differentiate local and regional ground uplift, as well as sub-regional subsidence induced by groundwater level drawdown, which are possibly enhanced across fault structures, as monitored by various synthetic aperture radar interferometry (InSAR) processing methods. A buoyant mantle plume under the Eifel may be responsible for the regional ground uplift, including the Weser-Geul (BE) and South Limburg regions (NL), which could negatively affect the area proposed for the future Einstein Telescope. Methods: Different InSAR processing techniques are compared to evaluate their limits in tracking fault structures on a time series of Copernicus Sentinel-1 images while detecting and measuring ground motion based on their phase signature. The results present an overall stable ground for the Euregio Meuse-Rhine region, especially at the Belgian-Dutch border, while tectonic activity is observed along the German side of the Rhine Graben. Results: As the current neotectonic activity in the target area was not well known, we performed a spatiotemporal analysis of ground deformation associated with the presence of NW-SE-trending normal faults where karst also develops, as well as along the Variscan NE-SW-trending thrust faults. This work demonstrates that the identification of deformation hazards using satellite remote sensing (and connected seismological) techniques is challenging mainly due to (very) small regional scale deformation, terrain conditions, and SAR properties. Discussion: Thus, the results mostly indicate ground stability over the area; however, also some agricultural activities were observed, as was deformation along some infrastructure such as railways. Displacements of millimetric order measured along the faults located near the Geul valley (BE) are probably related to old mining activities. [ABSTRACT FROM AUTHOR]

Ground subsidence often occurs over a large area. Although traditional monitoring methods have high accuracy, they cannot perform wide-area ground deformation monitoring. Synthetic Aperture Radar (SAR) interferometry (InSAR) technology utilizes phase information in SAR images to extract surface deformation information in a low-cost, large-scale, high-precision, and high-efficiency manner. With the increasing availability of SAR satellite data and the rapid development of InSAR technology, it provides the possibility for wide-area ground deformation monitoring using InSAR technology. Traditional time-series InSAR methods have cumbersome processing procedures, have large computational requirements, and rely heavily on manual intervention, resulting in relatively low efficiency. This study proposes a strategy for wide-area InSAR multi-scale deformation monitoring to address this issue. The strategy first rapidly acquires ground deformation information using Stacking technology, then identifies the main subsidence areas by setting deformation rate thresholds and visual interpretation, and finally employs advanced TS-InSAR technology to obtain detailed deformation time series for the main subsidence areas. The Turpan Basin in Xinjiang, China, was selected as the study area (7474.50 km2) to validate the proposed method. The results are as follows: (1) The basin is generally stable, but there is ground subsidence in the southern plain area, mainly affecting farmland. (2) From 2016 to 2019, the maximum subsidence rate in the farmland area was approximately 0.13 m/yr, with a maximum cumulative subsidence of about 0.25 m, affecting a total area of approximately 952.49 km2. The subsidence mainly occurred from late spring to mid-autumn, while lifting or subsidence mitigation occurred from late autumn to early spring. The study also analyzed the impacts of rainfall, geographical environment, and human activities on subsidence and found that multiple factors, including water resource reduction, overexploitation, geological characteristics, and the expansion of human activities, contributed to the subsidence problem in the Turpan Basin. This method contributes to wide-area InSAR deformation monitoring and the application of InSAR technology in engineering. [ABSTRACT FROM AUTHOR]

The mining-induced subsidence has caused severe casualties and poses a threat to the safe operation of coal extraction, especially under the roads, buildings and rivers. This alarming situation demands the large-area subsidence detection and the large-gradient deformation monitoring to ensure safety. Although the traditional Interferometric Synthetic Aperture Radar (InSAR) technology can monitor surface deformation in high accuracy, unfortunately, it often fails to recover the sufficient deformation when facing the effects of decorrelation and large gradient displacement. Therefore, this study firstly proposes a Stacking-InSAR and coherence gradient fusion method to detect the mining-induced subsidence over large area. Secondly, the robust offset tracking method is applied to retrieve the large-gradient deformation in Buertai mining site. Both Gaofen-3 and Sentinel-1A Synthetic Aperture Radar (SAR) datasets are acquired over the border of Shaanxi and Inner Mongolia, China, for experimental test. The results show that 179 mining-induced subsidence sites are detected with the proposed method, and the complete spatial temporal deformation fields are obtained by both Gaofen-3 and Sentinel-1A offset tracking method with maximum deformation of 3 m in range direction. This study provides a valuable tool for detecting the fine boundary of mining subsidence and recovering the accurate deformation time series. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanisation, urban areas are subject to the combined effects of the accumulation of various natural factors, such as changes in temperature leading to the thermal expansion or contraction of surface materials (rock, soil, etc.) and changes in precipitation and humidity leading to an increase in the self-weight of soil due to the infiltration of water along the cracks or pores in the ground. Therefore, the subsidence of urban areas has now become a serious geological disaster phenomenon. However, the use of traditional neural network prediction models has limitations when examining the causal relationships between time series surface deformation data and multiple influencing factors and when applying multiple influencing factors for predictive analyses. To this end, Sentinel-1A data from March 2017 to February 2023 were used as the data source in this paper, based on time series deformation data acquired using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique. A sparrow search algorithm–convolutional neural network–long short-term memory (SSA-CNN-LSTM) neural network prediction model was built. The six factors of temperature, humidity, precipitation, and ground temperature at three different depths below the surface (5 cm, 10 cm, and 15 cm) were taken as the input of the model, and the surface deformation data were taken as the output of the neural network model. The correlation between the spatial and temporal evolution characteristics of the ground subsidence in urban areas and various influencing factors was analysed using grey correlation analysis, which proved that these six factors contribute to some extent to the deformation of the urban surface. The main urban area of Hohhot City, Inner Mongolia Autonomous Region, was used as the study area. In order to verify the efficacy of this neural network prediction model, the prediction effects of the multilayer perceptron (MLP), backpropagation (BP), and SSA-CNN-LSTM models were compared and analysed, with the values of the correlation coefficients of the feature points of A1, B1, and C1 being in the range of 0.92, 0.83, and 0.93, respectively. The results show that compared with the traditional MLP and BP neural network models, the SSA-CNN-LSTM model achieves a higher performance in predicting time series surface deformation data in urban areas, which provides new ideas and methods for this area of research. [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]

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]

Interferometric synthetic aperture radar (InSAR) decorrelation that creates great challenges to phase unwrapping has been a critical issue for mapping large earthquake deformation. Some studies have proposed a 'remove-and-return model' solution to tackle this problem, but it has not been fully validated yet, and therefore has rarely been applied to real earthquake scenarios. In this study, we use the 2023 M w 7.8 and 7.6 earthquake doublet in Turkey and Syria as a case example to develop an iterative modelling method for InSAR-based coseismic mapping. We first derive surface deformation fields using Sentinel-1 offset tracking and Sentinel-2 optical image correlation, and invert them for an initial coseismic slip model, based on which we simulate InSAR coseismic phase measurements. We then remove the simulated phase from the actual Sentinel-1 phase and conduct unwrapping. The simulated phase is added back to the unwrapped phase to produce the final phase measurements. Comparing to the commonly used unwrapping method, our proposed approach can significantly improve coherence and reduce phase gradients, enabling accurate InSAR measurements. Combining InSAR, offset tracking and optical image correlation, we implement a joint inversion to obtain an optimal coseismic slip model. Our model shows that slip on the Çardak Fault is concentrated on a ∼100 km segment; to both ends, slip suddenly diminished. On the contrary, rupture on the East Anatolian Fault Zone propagated much longer as its geometry is fairly smooth. The iterative coseismic modelling method is proven efficient and can be easily applied to other continental earthquakes. [ABSTRACT FROM AUTHOR]

Earthquake source parameters can be estimated using seismological observations, but the identification of the fault responsible is often complicated by location uncertainties and the inherent ambiguity between nodal planes. Satellite Interferometric Synthetic Aperture Radar (InSAR) can be used to observe ground deformation and model fault geometry but is limited by climate conditions (water vapour) and ground coverage (dense vegetation). In the tropics, the atmosphere is dynamic and most regions are densely vegetated, making detecting coseismic deformation challenging. Here, we perform a systematic inspection of coseismic interferograms from Sentinel-1 SAR images, to assess their suitability for detecting coseismic deformation in Costa Rica. Using data from the seismological network, we target seven earthquakes between 2016 and 2020 with depths |$\le \, 20$| km and magnitudes M w 5.3–6.2. For each event, we use the seismic parameters to compute line-of-sight displacements for ascending and descending geometries and for both nodal planes and generate 12- and 24-d coseismic interferograms where available. We obtain interferograms with coseismic displacement signals for three of the seven earthquakes. We invert the geodetic data to retrieve the earthquake source parameters but the lack of offshore geodetic coverage causes trade-offs between parameters and large uncertainties. The Jacó and Golfito earthquakes likely occurred on the subduction interface and the geodetic locations were 6–9 km closer to the coast than previous seismic estimates. The Burica earthquake occurred on a shallow steeply dipping thrust fault in the outer forearc. For the other earthquakes, no coseismic deformation was detected due to atmospheric noise or poor coherence. These results demonstrate the suitability of 12-d Sentinel-1 interferograms for monitoring shallow earthquakes of magnitude > M w 5.7 in Central America. This approach can be used to begin a surface deformation catalogue for the region, which will ultimately help improve the understanding of active deformation processes and improve hazard maps. [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]

Remote sensing techniques, such as Interferometric Synthetic Aperture Radar (InSAR), have the potential to document and monitor surface deformation up to a millimetre scale especially in areas where ground monitoring is not feasible. The InSAR technique has been used to isolate and map some current and potential landslide areas in Kinnaur region of the Satluj Valley in Himachal Pradesh. The fragile ecosystem of Kinnaur has been challenged by the shifting climatic pattern and immense anthropogenic pressure associated with burgeoning hydroelectric projects, leading to destructive landslides of epic proportions. Three recent landslides that occurred at Ribba and Pangi villages of the Satluj River Valley and the Batseri landslide in the Sangla Valley were studied and potential landslide areas along the Satluj Valley were mapped with Sentinel-1 multi-baseline interferograms using data acquired between 2016 and 2021. The analysis suggests significant changes in velocities (±11 mm/y) where the subsidence rate ranges from −6 to −11 mm/y with a cumulative displacement of ±45 mm/y. Our study highlights the potential locations for future landslides and provides an early warning landslide map to support future disaster prevention in highly deformed regions. [ABSTRACT FROM AUTHOR]

In the Eastern Himalayan syntaxis, massive Quaternary loose deposits represent a typical product of abrupt topographic changes. Influenced by the Qinghai‐Tibet Plateau uplift and rapid river incision, the partial or complete failure of these deposits is a recurrent phenomenon in alpine‐gorge regions, posing substantial threats to major infrastructure development. Through topographic interpretation and field investigation, this study clarifies the geological features and genetic mechanisms of the Baimu ancient landslide deposit (BALD), located on the northern margin of the East Himalayan syntaxis. The surface deformation time series spanning October 2017 to December 2019 was estimated using employing multitemporal synthetic aperture radar interferometry. Then, the spatiotemporal deformation characteristics of the BALD was analysed by superposing terrain factors, and hazard division was implemented. The results indicate that high‐hazard zones are mainly distributed on high and steep free faces of the BALD. Deformation evolution and rainfall time series showed obvious correlation. Lastly, a generalized failure model is established for the BALD, which will serve as a valuable reference for disaster prevention and is applicable to the study of similar deposits in the Qinghai‐Tibet Plateau. [ABSTRACT FROM AUTHOR]

The protracted and pervasive incidence of land subsidence emerges as a pivotal factor exerting a substantial impact on the sustainable development of urban landscapes. A nuanced comprehension of the spatiotemporal evolution characteristics of land subsidence within the Northern Henan Plain assumes paramount significance in the context of mitigating potential urban geological disasters. This study endeavors to redress the deficiency in information concerning the temporal and spatial evolution characteristics of enduring deformation in cities within the northern plain of Henan Province. To this end, the authors leveraged Sentinel-1A radar data processed through persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technology to elucidate the distribution patterns of ground deformation and temporal evolution characteristics within the expansive 24-scene coverage research area. Empirical findings illuminate conspicuous surface deformation in Anyang, Puyang, and Hebi throughout the monitoring period. Spatially, land subsidence in the study area predominantly clusters in the suburban peripheries of the cities, with Hebi and Puyang registering a maximum subsidence rate exceeding 25 mm per annum. Temporally, land subsidence manifests predominantly during autumn and winter, whereas spring and summer display relatively stable land subsidence interspersed with a slight ground uplift. In order to rectify the spatial disparities observed between leveling data and PS-InSAR monitoring data, this experiment employed an averaging procedure on the PS-InSAR monitoring data, subsequently subjecting it to comparative analysis with the leveling data. Additionally, through the integration of the singular spectrum analysis (SSA) method and the time series deformation model, this study aspires to attain a comprehensive understanding of the temporal dynamics manifested in the PS-InSAR monitoring outcomes, while concurrently elucidating the factors influencing the observed deformations. Ultimately, this analysis discloses that the monitoring outcomes derived via PS-InSAR technology exhibit a root mean square error of ±12.9 mm and a standard deviation of ±13.31 mm. These statistical metrics furnish valuable insights into the precision and consistency of the PS-InSAR monitoring data. Drawing upon a comparative scrutiny of on-site data and historical remote sensing imagery within the study area, it has been discerned that excessive groundwater extraction and expansive surface engineering initiatives stand as the principal instigators of land subsidence in the research domain. Consequently, this experiment assumes the role of a salient reference for the mitigation of urban ground subsidence within the study area. [ABSTRACT FROM AUTHOR]

Objectives: On February 22, 2023, a large area of slope collapsed in Xinjing Open-Pit Mine, Inner Mongolia Autonomous Region, China. In order to prevent this kind of disaster scientifically, it is necessary to carry out fine monitoring of slope stability in large open-pit mine, so as to obtain the temporal and spatial characteristics of slope deformation comprehensively, and take corresponding prevention measures. Methods: The full scatterer interferometric synthetic aperture radar (FS-InSAR) technique was applied for detailed monitoring of Xinjing Open-Pit Mine, with 24 Sentinel-1 synthetic aperture radar images acquired from September 9, 2021 to August 11, 2022. Fine ground deformation of Xinjing Open-Pit Mine was obtained, and the temporal and spatial distribution characteristics of the deformation were analyzed. Results: For the low-coherence open-pit area, the FS-InSAR technique can obtain fine surface deformation with a density of 4 758 points per km². The deformation anomaly signal of bottom acceleration had appeared since February 2022, before the collapse occurred. The deformation at the bottom of the west side of the south slope is the most serious, with deformation rate more than 200 mm/a, and there is a collapse risk. Conclusions: The FS-InSAR technique can fully retrieve the spatial-temporal distribution of slope deformation before collapse disaster in open-pit mines, which is of great significance for mine safety production in the future. [ABSTRACT FROM AUTHOR]

Numerous significant earthquakes have occurred along the North Anatolian Fault Zone (NAFZ), Türkiye's most important fault zone. An earthquake with magnitude of Mw 6.0 occurred near the Gölyaka District of Düzce Province at 04:08 local time on November 23, 2022. According to the moment tensor solutions published by national and international seismology centers, the earthquake occurred at the northeast end of the Karadere segment within the NAFZ and was caused by a right-lateral strike-slip fault in the NW-SE direction. In this study, the differential interferometric synthetic aperture radar (InSAR) method was employed to analyze the deformation. Four Sentinel-1A complex datasets were used, two of which were ascending (2022/11/15 and 2022/11/27) and two of which were descending (2022/11/14 and 2022/11/26) pre- and postearthquake, to determine the deformation that occurred after the earthquake. In addition, surface deformation movements in both the east-west and vertical directions were calculated using these two data sets. Elastic dislocation modeling was performed by applying linear and nonlinear inversion to determine slip distribution related to the fault structure, using the displacement amount obtained from DInSAR results and data obtained from the Global Centroid Moment Tensor (GCMT) catalog. Based on the findings from the DInSAR analysis, subsidence of approximately 5 cm and eastward movement of 8 cm were detected on the northern block of the Düzce segment. This right-lateral movement corresponds to an area of approximately 105 km² northwest of the Düzce segment. On the southern block of the Düzce segment, elevation of about 3 cm and westward movement of about 3 cm were observed. Elastic dislocation modeling shows that the observed deformation model can be explained by slip on a single planar fault for the Gölyaka-Düzce earthquake. A right-lateral strike-slip fault with northwest dip was observed, extending down to a slip-centroid depth approximately 9.2 km within the upper crust region. The elastic slip modeling in this half-space resulted in a slip of 0.35 m, Mw of 6.1, strike of 264°, dip of 66°, and rake of --165°. This study reveals that the Düzce segment was the location where the earthquake occurred, but it is also believed that the Karadere segment was impacted by this seismic event. Overall, the study provides information about the occurrence of the Gölyaka-Düzce earthquake, subsequent deformation, and the elastic fault model in half-space. [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]

Synthetic Aperture Radar (SAR) can image the ground with a wide area and high resolution at all times and in all weather and has become an important means of remote sensing. Differential interferometry SAR technology can obtain high-precision surface deformation information by processing more than two SAR images before and after deformation. In recent years, it has attracted widespread attention and research. However, due to the increasing number of ground electronic devices, ground radio frequency interference (RFI) has become one of the main problems in differential interferometry SAR processing, seriously affecting the performance of differential interferometry SAR imaging and differential interferometry surface deformation monitoring applications. In this paper, a clutter cancellation interference enhancement detection algorithm is proposed. By clutter suppression in the primary and secondary images, the interference-to-signal ratio is increased, which effectively improves the interference detection capabilities. The effectiveness of the algorithm in this paper is verified by the on-orbit measured data of the Lutan-1 satellites. [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]

The analysis of the degree of surface deformation can be a relevant aspect in the study of surface stability conditions, as it provides added value in the construction of risk management plans. This analysis provides the opportunity to establish the behaviors of the internal dynamics of the earth and its effects on the surface as a prediction tool for possible future effects. To this end, this study was approached through the analysis of Synthetic Aperture Radar (SAR) images using the Differential Interferometry (DInSAR) technique, which, in turn, is supported by the Small Baseline Subset (SBAS) technique to take advantage of the orbital separation of the Sentinel-1 satellite images in ascending and descending trajectory between the years 2014 and 2021. As a result, a time series was obtained in which there is a maximum uplift of 117.5 mm (LOS-ascending) or 49.3 mm (LOS-descending) and a maximum subsidence of −86.2 mm (LOS-ascending) or −71.5 mm (LOS-descending), with an oscillating behavior. These deformation conditions are largely associated with the kinematics of the Bucaramanga Fault, but a recurrent action of deep seismic activity from the Bucaramanga Seismic Nest was also observed, generating a surface deformation of ±20 mm for the period evaluated. These deformations have a certain degree of impact on the generation of mass movements, evaluated by the correlation with the LOS-descending images. However, their action is more focused as an inherent factor of great weight, which makes it possible to respond to early care and allows real-time follow-up, giving positive feedback to the system. [ABSTRACT FROM AUTHOR]

Objectives: When the conventional time-series interferometric synthetic aperture radar (InSAR) technology is applied to the surface deformation monitoring of mining areas, it is difficult to fully reflect the surface deformation characteristics of the goaf due to the uneven distribution and small number of monitoring points. In addition, distributed scatterers are seriously affected by the temporal and spatial incoherence. Thus the corresponding phase optimizing operation is a key step in fusing distributed scatterers (DS) points into time-series InSAR analysis. The current constraints of phase space continuity in the phase optimization of distributed targets are insufficient. Methods: A phase optimization method based on the region growing algorithm is proposed. The detailed surface deformation information in complex environment could be obtained by optimizing phase in both time and spatial domain. The optimized phase of distributed scatter is evaluated qualitatively and quantitatively in Pan'an Lake area. Results: The proposed method and Sentinel-1 data are used to investigate the surface deformation of abandoned mining areas around Xuzhou from May 2018 to May 2020. Besides, the accuracy and reliability of this method are proved by continuously operating reference stations results and bedrock-exposed area. Conclusions: The result indicates that the method gives a good performance on reducing phase noise by two-dimensional phase optimization of distributed scatterers, which would be good for obtaining the surface deformation information in the abandoned mining areas. [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]

The Chengdu Plain serves as the central hub for economic development in Sichuan Province. The study of its surface deformation plays a pivotal role in disaster prevention and ensuring the safety of lives and property. Based on SBAS-Interferometric Synthetic Aperture Radar technology, this paper derived the land surface deformation in the Chengdu Plain from January 2020 to October 2022, and analyzed it in relation to urban development and other natural environmental factors. Results are as follows: (1) The overall surface deformation in the study area remains stable, with a noticeable trend of westward uplift and eastward decline. Significant uplift is identified in Dujiangyan and Pengzhou, located near the Longmen Mountain, with a maximum uplift rate of +61 mm/year. Conversely, subsidence is evident from Qingbaijiang District in Chengdu to Deyang, with subsidence rates reaching a maximum of −87.8 mm/year. In the areas of Dayi and Qionglai, subsidence is relatively minor. The surface subsidence phenomenon in the study area is primarily associated with urban development. (2) The mean grey correlation coefficient between the time-series cumulative surface deformation and precipitation is 0.71, which suggests a strong relationship between the surface deformation and precipitation in the Chengdu Plain. However, a time delay is observed between the changes in surface deformation and precipitation. (3) The Longmen Mountain tectonic belt extrudes and lifts the premountain plain, playing a crucial role in shaping its topography. Additionally, the concealed fracture zone of Xinjin-Chengdu-Deyang may have contributed to surface subsidence. (4) After eliminating the effect of human factors, the cultivated land and grass land cover types exhibit a subsided state, whilst the artificial surface deformation is relatively stable, and the forest land exhibits an uplifted state. Surface deformation on different land cover types is correlated with water permeability, but it is also influenced by other factors. [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]