Search

Previous studies have shown that underground natural gas storage (UGS) in California has served a critical role in meeting energy demands in California, and there is no immediate alternative. Therefore, it is important to ensure the safety of UGS infrastructure, especially considering that many of the UGS sites are using a combination of new and old wells, some of which were installed decades ago and re-purposed for UGS. The purpose of this project is to develop an integrated risk management and decision support system (IRMDSS) to manage risks associated with this heterogeneous subsurface infrastructure. The approach of the IRMDSS is to take advantage of the predictive capability of mechanistic models, with support from data acquired from advanced monitoring technologies, for evaluation and analysis of various incident scenarios or potential threats. In this project, we have demonstrated data collection by four advanced monitoring technologies. These include two downhole monitoring technologies, distributed temperature sensing (DTS), distributed acoustic sensing (DAS), and two surface monitoring technologies, Interferometric Synthetic Aperture Radar (InSAR), and unmanned aerial vehicle (UAV). DTS and DAS data are collected continuously, providing information related to individual wells. InSAR data are collected frequently (~every 24 days), and UAV data can be collected as frequently as is practical depending on need. Together, these subsurface and surface monitoring technologies provide near real-time information useful for risk management of UGS facilities.

Abstract: Rock glaciers are common in alpine landscapes, but their evolution over time and their significance as agents of debris transport are not well‐understood. Here, we assess the movement of an ice‐cemented rock glacier over a range of timescales using GPS surveying, satellite‐based radar, and cosmogenic 10Be surface‐exposure dating. GPS and InSAR measurements indicate that the rock glacier moved at an average rate of ∼10 cm yr−1 in recent years. Sampled boulders on the rock glacier have cosmogenic surface‐exposure ages from 1.2 to 10 ka, indicating that they have been exposed since the beginning of the Holocene. Exposure ages increase linearly with distance downslope, suggesting a slower long‐term mean surface velocity of 3 ± 0.3 cm yr−1. Our findings suggest that the behavior of this rock glacier may be dominated by episodes of dormancy punctuated by intervals of relatively rapid movement over both short and long timescales. Our findings also show that the volume of the rock glacier corresponds to ∼10 m of material stripped from the headwall during the Holocene. These are the first cosmogenic surface‐exposure ages to constrain movement of a North American rock glacier, and together with the GPS and satellite radar measurements, they reveal that rock glaciers are effective geomorphic agents with dynamic multi‐millennial histories.

Interferometric synthetic aperture radar (InSAR) is a remote sensing tool used for monitoring urban areas affected by geological hazards. Here we analysed the effectiveness of stabilization works on a slow-moving landslide in Arcos de La Frontera (Cádiz, Spain) using a persistent scatterer interferometric approach. The works consisted on jet grouting of cement-based injections and were applied locally to stabilize the most damaged neighbourhood. We processed a large stack of Sentinel-1 SAR satellite acquisitions covering the period January, 2016, to March, 2023, and obtained surface velocity and displacement trends measured along the line of sight (LOS) of the satellite on both ascending and descending orbits. The results show a clear deceleration of the landslide head after mid-2018, suggesting the local stabilization works were effective after that time. Prior to mid-2018, the maximum LOS velocity of the landslide head was 2.2 cm/year in ascending orbit and 1.3 cm/year in the descending orbit, decreasing to 0.43 cm/year and 0.23 cm/year, respectively. The InSAR results were compared to in-situ monitoring data and revealed that the extent of the stabilization has influenced a much larger area beyond the zone of the local interventions. Overall, InSAR has proved a powerful and versatile tool to be implemented in operational geotechnical monitoring. [ABSTRACT FROM AUTHOR]

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

On 2 April 2024, an MW 7.4 earthquake struck the northern Longitudinal Valley in eastern Taiwan, about 18 km SSW of Hualien, causing damage and casualties. In this study, we investigated a comprehensive geodetic data set, employing Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) measurements to assess the rupture geometry associated with earthquake sequence. Although geodetic data can be satisfactorily reproduced by simple single‐fault models (i.e., a high‐angle E‐dipping plane related to the Longitudinal Valley Fault (LVF), or a gentle W‐dipping surface associated with the Central Range Fault, CRF), a composite model involving the rupture of different fault segments (a major CRF‐related W‐dipping fault, a deep segment of the E‐dipping LVF, and the Milun Fault) is able to explain the observations, the distribution of seismicity, and the complex structural arrangement of the northernmost sector of the Longitudinal Valley. Plain Language Summary: On 2 April 2024, a powerful magnitude 7.4 earthquake hit the northern Longitudinal Valley in eastern Taiwan, near Hualien, causing significant damage and loss of life. This study examined a detailed set of geodetic data, including measurements from Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR), to understand the geometry of the earthquake's rupture. While simple models focused on single faults (like the Longitudinal Valley Fault (LVF) or the Central Range Fault) can replicate the geodetic data to some extent, a more complex model involving the rupture of different fault segments (including a major Central Range Fault segment, a deep segment of the LVF, and the Milun Fault) better explains the data, seismic activity distribution, and the intricate structural layout of the northern part of the Longitudinal Valley. Key Points: We developed a composite rupture geometry of the MW 7.4 2024 Hualien (eastern Taiwan) earthquake sequenceThe earthquake primarily ruptured a major W‐dipping fault, while secondary affecting both deep and shallow segments of an E‐dipping faultOur results support the clear partitioning of the seismic release on different structures [ABSTRACT FROM AUTHOR]

The accuracy of Interferometric Synthetic Aperture Radar surface deformation solutions depends on the quality of the chosen interferogram subset. We present a method to select interferogram subsets based on unwrapping errors rather than temporal baseline thresholds. Using Sentinel‐1 interferograms over the Tulare Basin, California, we show that subtle phase noise can lead to up to 31.5 mm/yr line‐of‐sight (LOS) errors in short temporal baseline subset solutions, while decorrelation leads to a systematic underestimation of LOS rates (up to 92.3 mm/yr) in long temporal baseline subset solutions. Our new workflow better mitigates these noise sources at the same time. In the Eagle Ford region, Texas, our strategy better reconstructed up to 11.9 cm of cumulative LOS deformation between 2017 and 2021 over a ∼ ${\sim} $900 km2 $\mathrm{k}{\mathrm{m}}^{\mathrm{2}}$ region. This deformation feature can be linked to the total volume of produced oil and water through a linear relationship. Plain Language Summary: Surface deformation estimates derived from spaceborne imaging radar data are often impacted by weather conditions and surface vegetation changes. While it is common to select high‐quality data based on the time separation between two image passes, we designed a new data selection strategy based on measurable phase artifacts. We applied the new method to two vegetated regions that experienced land subsidence due to agricultural groundwater pumping (Tulare Basin, California) or oil and gas production (Eagle Ford region, Texas). In both cases, we show that surface deformation estimates can vary substantially depending on the choice of data subsets. Our strategy better mitigates different InSAR measurement noise terms at the same time. We showed that the observed subsidence in the Eagle Ford region is linearly proportional to the volume of produced oil and water from unconventional oil and gas resources exploitation. Furthermore, ongoing production activities have led to an increase in human‐induced earthquakes. Based on these findings, accurate surface deformation derived from InSAR data is now achievable in densely vegetated regions and can play an important role in future induced seismicity studies. Key Points: InSAR phase coherence does not always decrease with temporal baselinesChoosing interferograms based on phase unwrapping errors rather than temporal baselines better mitigates InSAR measurement noiseThe improved InSAR analysis strategy reveals up to 11.9 cm of LOS deformation signals related to oil and gas operations over the Eagle Ford [ABSTRACT FROM AUTHOR]

Since the 1970s, extensive halite extraction in Maceió, Brazil, has resulted in significant geological risks, including ground collapses, sinkholes, and infrastructure damage. These risks became particularly evident in 2018, following an earthquake, which prompted the cessation of mining activities in 2019. This study investigates subsidence deformation resulting from these mining operations, focusing on the collapse of Mine 18 on 10 December 2023. We utilized the Quasi-Persistent Scatterer Interferometric Synthetic Aperture Radar (QPS-InSAR) technique to analyze a dataset of 145 Sentinel-1A images acquired between June 2019 and April 2024. Our approach enabled the analysis of cumulative displacement, the loss of amplitude stability, the evolution of amplitude time series, and the amplitude change matrix of targets near Mine 18. The study introduces an innovative QPS-InSAR approach that integrates phase and amplitude information using amplitude time series to assess the lifecycle of radar scattering targets throughout the monitoring period. This method allows for effective change detection following sudden events, enabling the identification of affected areas. Our findings indicate a maximum cumulative displacement of −1750 mm, with significant amplitude changes detected between late November and early December 2023, coinciding with the mine collapse. This research provides a comprehensive assessment of deformation trends and ground stability in the affected mining areas, providing valuable insights for future monitoring and risk mitigation efforts. [ABSTRACT FROM AUTHOR]

Land subsidence is an environmental hazard occurring gradually over time, potentially posing significant threats to the structural stability of civilian buildings and essential infrastructures. This study presented a workflow using the SBAS-PSInSAR approach to analyze surface deformation in the Choushui River Fluvial Plain (CRFP) based on Sentinel-1 SAR images and validated against precise leveling. Integrating the InSAR results with hydrogeological data, such as groundwater levels (GWLS), multilayer compactions, and borehole loggings, a straightforward model was proposed to estimate appropriate groundwater level drops to minimize further subsidence. The results showed a huge subsidence bowl centered in Yunlin, with maximal sinking at an average 60 mm/year rate. High-resolution subsidence maps enable the quantitative analyses of safety issues for Taiwan High-Speed Rail (THSR) across the areas with considerable subsidence. In addition, the analysis of hydrogeological data revealed that half of the major compaction in the study area occurred at shallow depths that mainly included the first and second aquifers. Based on a maximal subsidence control rate of 40 mm/year specified in the CRFP, the model results indicated that the groundwater level drops from wet to dry seasons needed to be maintained from 3 to 5 m for the shallowest aquifer and 4–6 m for Aquifers 3 and 4. The workflow demonstrated the compatibility of InSAR with traditional geodetic methods and the effectiveness of integrating multiple data sources to assess the complex nature of land subsidence in the CRFP. [ABSTRACT FROM AUTHOR]

The Cerro Prieto basin, a tectonically active pull-apart basin, hosts significant geothermal resources currently being exploited in the Cerro Prieto Geothermal Field (CPGF). Consequently, natural tectonic processes and anthropogenic activities contribute to three-dimensional surface displacements in this pull-apart basin. Here, we obtained the Cerro Prieto Step-Over 3D surface velocity field (3DSVF) by accomplishing a weighted least square algorithm inversion from geometrically quasi-orthogonal airborne UAVSAR and RADARSAT-2, Sentinel 1A satellite Synthetic Aperture-Radar (SAR) imagery collected from 2012 to 2016. The 3DSVF results show a vertical rate of 150 mm/yr and 40 mm/yr for the horizontal rate, where for the first time, the north component displacement is achieved by using only the Interferometric SAR time series in the CPGF. Data integration and validation between the 3DSVF and ground-based measurements such as continuous GPS time series and precise leveling data were achieved. Correlating the findings with recent geothermal energy production revealed a subsidence rate slowdown that aligns with the CPGF's annual vapor production. [ABSTRACT FROM AUTHOR]

In this study, we utilize C‐band Sentinel‐1 radar images from 2015 to 2022, combined with interseismic horizontal GNSS velocities, to construct large‐scale, high‐resolution, 3‐D velocity and strain rate maps over a vast region of southern Tibet. We show the distribution of prevailing dilatational strain accumulation along the seven major rift zones. Using 2‐D elastic dislocations invoking a two‐fault model in a Bayesian framework, we quantified the decadal extension rates across the seven rift zones, and we suggest a total extension rate of 18.4 ± 1.7 mm/yr, consistent with geological and geodetic estimates. The resulting strain rate maps, combined with the earthquake catalog, help us identify areas with high earthquake potential. Our study enhances our understanding of the present‐day tectonics and kinematics in southern Tibet and provides important constraints for seismic hazard assessment in this region. Plain Language Summary: In this research, we used satellite radar images from 2015 to 2022 and GNSS data to study the crustal deformation and strain distribution in southern Tibet, where the Earth's crust is actively stretching due to the collision of the Indian and Eurasian plates and the extrusion of crustal materials. By analyzing high‐resolution 3D velocities, we provided new high‐resolution surface strain maps over southern Tibet. We found that the widespread dilatational strain is mainly localized along seven major N‐S trending rift zones. Seven major rift zones are experiencing extension at a total rate of 18.4 ± 1.7 mm/yr. The strain rate maps, combined with historical earthquakes, helped us identify fault segments that are more likely to host earthquakes in the future. By mapping deformation and strain in greater detail, we provided valuable data that can improve our understanding of kinematics and earthquake risk assessments in geologically complex southern Tibet. Key Points: We present InSAR‐based, high‐resolution maps of 3‐D velocities and strain rates in Southern TibetThere is prevailing dilatational strain along seven rift zones in Southern Tibet, with a total extension rate of 18.4 ± 1.7 mm/yrWe show the distribution and spatial variations of extension rates for seven rift zones [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar Interferometry (InSAR), which can map subtle ground displacement over large areas, has been widely utilized to recognize active landslides. Nevertheless, due to various origins of subtle ground displacement, their presence on slopes may not always reflect the occurrence of active landslides. Therefore, interpretation of exact landslide-correlated deformation from InSAR results can be very challenging, especially in mountainous areas, where natural phenomenon like soil creep, anthropogenic activities and erroneous deformational signals accumulated during InSAR processing can easily lead to misinterpretation. In this paper, a two-phase interpretation method applicable to regional-scale active landslide recognition utilizing InSAR results is presented. The first phase utilizes statistical threshold and clustering analysis to detect unstable regions mapped by InSAR. The second phase introduces landslide susceptibility combined with empirical rainfall threshold, which are considered as causative factors for active landslides triggered by rainfall, to screen unstable regions indicative of active landslides. A case study validated by field survey indicates that the proposed interpretation method, when compared to a baseline model reported in the literature, can achieve better interpretation accuracy and miss rate. [ABSTRACT FROM AUTHOR]

Objectives: Lutan-1 (LT-1), as the first constellation of civil L-band interferometric synthetic aperture radar (InSAR) satellites in China, primarily focuses on surface deformation measurement by leveraging the differential interferometric, while also possessing height measurement and fully polarimetric imaging capabilities. With its dual-satellite formation imaging mode, it can achieve a strict revisit period of four days, which is highly beneficial for surface deformation monitoring and emergency response to natural disasters such as earthquakes. From the perspective of the LT-1 SAR satellite's imaging capabilities and the demands of the seismic industry, we primarily highlight the preliminary applications of the satellite system during the testing phase. The main objective is to evaluate the satellite data's capabilities in co-seismic deformation monitoring, emergency observations, and remote sensing interpretation of active fault zones. The ultimate purpose is to provide a reference for the future commercialization of seismic applications, based on the potential applications of the satellite data in these fields. Methods: Taking the examples of the 2022 Luding earthquake in Sichuan Province, China, the 2023 Turkey earthquake, and the Jishishan earthquake in Gansu Province, China, coseismal deformation monitoring will be conducted. The deformation maps will be assessed in terms of accuracy and monitoring capabilities. Additionally, emergency assessments of secondary earthquake hazards and remote sensing investigations of active fault zones will be carried out based on the high-resolution SAR data from the LT-1 satellite. Results: By comparing the co-seismic deformation results derived from LT-1 satellites with the results from concurrent other SAR satellites and global navigation satellite system (GNSS) measurements, it has been verified that the LT-1 satellite has significant advantages in terms of monitoring accuracy and spatiotemporal resolution. In addition, leveraging the high-resolution advantage of LT-1 SAR satellite imagery, it is possible to quickly obtain information on the distribution of earthquake-induced landslides and accurately delineate the accessibility of roads in the affected area. This capability effectively supports earthquake emergency response and disaster assessment efforts. Furthermore, thanks to the L-band sensor, the LT-1 satellite exhibits strong penetration capabilities, thus enabling the detection of hidden faults. Conclusions: Through multiple case studies in the seismic industry, encompassing various areas such as coseismic deformation monitoring, emergency response, and hidden fault detection, the results have demonstrated that the LT-1 satellite constellation has a significant breadth and depth of applications in the seismic industry. In the future, it will effectively support scientific research on seismic cycles and earthquake emergency operations. [ABSTRACT FROM AUTHOR]

The deformation behavior of slow‐moving large landslides is often governed by rainfall characteristics. Based on observational data such as precipitation, deformation measurement, and pore water pressure measurements in the slip zone, in many cases a strong correlation between strong rainfall events, a time‐delayed increase of pore water pressures in the slip zone, and, simultaneously to this, an increase of the deformation rate of the landslide can be found. Based on such detailed data, calculation models, which couples the relation between rainfall characteristics and the development of pore water pressures in the slip zone on one hand and the deformation behavior of the slope on the other, can be developed and be used for a better understanding and a prediction of deformation behavior of such slow‐moving landslides. Climate change issues will lead to a change in rainfall frequency and magnitude and annual temperature distribution characteristics in several regions worldwide, which will also lead to changes in the deformation behavior of such large landslides. In this contribution, satellite‐based interferometric synthetic aperture radar (InSAR) data are discussed to be used as source for deformation measurements as bases for prediction models describing the rainfall‐triggered deformation behavior of slow‐moving landslides. [ABSTRACT FROM AUTHOR]

Due to a heavy reliance on groundwater, Bangladesh is experiencing a severe decline in groundwater storage, with some areas even facing land subsidence. This study aims to investigate the relationship between groundwater storage changes and land subsidence in Bangladesh, utilizing a combination of GRACE and InSAR technologies. To clarify this relationship from a macro perspective, the study employs GRACE data merged with GLDAS to analyze changes in groundwater storage and SBAS-InSAR technology to assess land subsidence. The Dynamic Time Warping (DTW) method calculates the similarity between groundwater storage and land subsidence time series, incorporating precipitation and land cover types into the data analysis. The findings reveal the following: (1) Groundwater storage in Bangladesh is declining at an average rate of −5.55 mm/year, with the most significant declines occurring in Rangpur, Mymensingh, and Rajshahi. Notably, subsidence areas closely match regions with deeper groundwater levels; (2) The similarity coefficient between the time series of groundwater storage and land subsidence changes exceeds 0.85. Additionally, land subsidence in different regions shows an average lagged response of 2 to 6 months to changes in groundwater storage. This study confirms a connection between groundwater dynamics and land subsidence in Bangladesh, providing essential knowledge and theoretical support for further research. [ABSTRACT FROM AUTHOR]

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

The present study investigates the reactivation of a paleolandslide due to the expansion of a community in an area covered by plastic Pliocene marls in the southwestern part of Cyprus. The landslide, which takes place in an area with gently sloping ground and relatively shallow water table, affects more than 100 residential buildings. In the context of the study, building damages and ground surface ruptures were mapped through field work campaigns. Remote sensing data from InSAR (Interferometric Synthetic Aperture Radar) analysis were evaluated in conjunction with available geological, geotechnical and hydrogeological data. Subsequently, the landslide was backanalyzed using the finite element method to examine possible failure mechanism scenarios and shed light on the influence of potential triggering factors. The results indicate that the paleolandslide has been almost fully reactivated, with the main cause of the reactivation being the rising of the phreatic water table due to long-term discharges of wastewater through the absorption pits of the residential developments. The water table rise was further amplified by rainwater infiltration during rainy years. According to the backanalysis results, the slip surface follows the bedding planes of weak marl horizons with residual friction angle of the order of 10°. [ABSTRACT FROM AUTHOR]

Spatial slope deformation pattern is crucial for landslide early warning and management. Quantitative modelling of the effects of multiple driving factors on the landslide spatial pattern has not yet been fully explored. To address this, this study utilizes data-driven models and Interferometric Synthetic Aperture Radar (InSAR) techniques to unveil the spatial pattern of landslides based on SUs (slope units). Specifically, we propose a methodology that establishes a correlation between InSAR deformation and topographic/meteorological factors by constructing a Generalized Additive Mixed Model (GAMM). Using this method, we analysed ALOS-2 ascending SAR images from April 2020 to September 2021 and successfully obtained cumulative minimum deformation during the period. Through the GAMM, we linked the observed cumulative minimum deformation in each SU to various factors, including topographic and meteorological factors. In terms of accuracy, there is an excellent agreement between the observed and fitted deformation, with a Pearson Correlation Coefficient (PCC) of 0.83. In terms of interpretability, our method can well explain the contribution of each factor to landslide deformation. We conclude that using the data-driven GAMM and InSAR techniques can effectively reveal the spatial pattern of landslides based on SUs. Furthermore, based on the method proposed in this study, it is possible to predict landslide deformation in the future. [ABSTRACT FROM AUTHOR]

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

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

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

This study analyzed the Democratic People's Republic of Korea's (DPRK) fourth (DPRK4, 6 January 2016 MW 4.49) and sixth (DPRK6, 7 September 2017 MW 5.2) declared nuclear tests, employing a joint seismic and Interferometric Synthetic Aperture Radar (InSAR) inversion to improve understanding of these events and enhance moment tensor (MT) inversion capabilities. The recent efforts have focused on employing seismic waveform and InSAR geodetic deformation data separately to analyze these and the previous nuclear tests (e.g., Chiang et al., 2018; Myers et al., 2018; Wang et al., 2018). Building upon our previous work (Chi-Durán et al., 2021), we performed a joint regional waveform, first-motion (FM) polarity, and surface displacement inversion, which demonstrated improved source-type discrimination, a revised MT solution with reduced scalar moment uncertainty, and an independently constrained location. In this article, we build on the previous results for DPRK6 by including an analysis using a four-layered velocity model with free-surface topography to compute the near-source static deformation Green's functions. The model consists of a 50 m basalt layer (VP = 2.07km/s, VS = 1.2km/s), a 250 m stratified volcanic deposit layer (VP = 1.73km/s VS = 1km/s), a 700 m weathered granodiorite layer (VP = 2.5km/s, VS = 1.3km/s) and a granodiorite half-space (VP = 5.35km/s, VS = 3.09km/s). The half-space shares the velocity of the regional MDJ2 velocity model (Ford et al., 2010), which has proven effective for waveform inversion in the region. This model considers the range of reported values for various lithologies and weathering effects. Our findings show that using the layered velocity model enhances the recovery of source location and depth for both the explosions by improving fits and reducing uncertainties. The joint inversion also improves source-type discrimination and better constrains the scalar seismic moment necessary for downstream yield estimation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Landslides are characterized by intricate formation mechanisms and multiple triggering factors, posing serious threats to human life and property. Interferometric synthetic aperture radar (InSAR) has been extensively applied in landslide monitoring due to its all-weather operation, high precision, and wide spatial coverage. However, one-dimensional line-of-sight (LOS) deformation limits the ability to measure actual landslide displacement. Presently, many regions without the latest descending data, but they are in the overlapping position of two adjacent latest ascending track. Compared to using a single track, the method measuring three-dimensional deformation of landslides, combined with two ascending track InSAR datasets and pixel-level surface-parallel flow (PSPF) assumption, is presented. Based on the small baseline subset (SBAS) InSAR, the LOS deformation in two observations in the middle reaches of the Qingjiang River in China was measured. With pixel-level surface-parallel flow model, the three-dimensional deformation was obtained. It was further converted into the slope surface coordinate system, thus determining the direction of landslide movement. The findings effectively elucidate the primary displacement mode of landslides, with deformation rates ranging from −80 to 20 mm yr$^{ - 1}$ −1, which are significantly influenced by the slope angle and slope aspect. It is highly important to remotely identify the movement direction and analyse the failure mode of landslides. [ABSTRACT FROM AUTHOR]

The 2017 Jiuzhaigou earthquake (Ms = 7.0) struck the eastern Tibetan Plateau and caused extensive concern. However, the reported slip models of this earthquake have distinct discrepancies and cannot provide a good fit for GPS data. The Jiuzhaigou earthquake also presents a good opportunity to investigate the question of how to avoid overfitting of InSAR observations for co-seismic slip inversions. To comprehend this shock, we first used pre-seismic satellite optical images to extract a surface trace of the seismogenic fault, which constitutes the northern segment of the Huya Fault. Then, we collected GPS observations as well as to measure the co-seismic displacements. Lastly, joint inversions were carried out to obtain the slip distribution. Our results showed that the released moment was 5.3 × 1018 N m, equivalent to Mw 6.4 with a rigidity of 30 GPa. The maximum slip at a depth of ~6.8 km reached up to 1.12 m, dominated by left-lateral strike-slip. The largest potential surface rupture occurred in the center of the seismogenic fault with strike- and dip-slip components of 0.4 m and 0.2 m, respectively. Comparison with the focal mechanisms of the 1973 Ms 6.5 earthquake and the 1976 triplet of earthquakes (Mw > 6) on the middle and south segments of the Huya Fault indicated different regional motion and slip mechanisms on the three segments. The distribution of co-seismic landslides had a strong correlation with surface displacements rather than surface rupture. [ABSTRACT FROM AUTHOR]

Correcting the signal delay caused by electromagnetic waves passing through the atmosphere is crucial in InSAR. Atmospheric delays are significant enough to obscure the true deformation signal, making it difficult for InSAR to detect deformation signals at the millimetre scale. Therefore, it is imperative to mitigate atmospheric interference appropriately. To interpolate sparse GNSS networks and capture small-scale atmospheric delays, this study presents an enhanced method for constructing an atmospheric correction model based on GNSS Zenith Total Delay (ZTD) and its horizontal gradient, which is integrated within a predefined window. To validate the effectiveness of the improved model, we applied it to 27 InSAR short-time and short-spatial baseline unwrapped interferograms. These interferograms are composed of images observed by the Sentinel-1A satellite from January to December 2022 in Southern California. The corrective impact of the enhanced model is also compared against that of the initial inversion model and GACOS to confirm the viability of the approach. Results of the evaluation, considering root mean square error (RMSE), standard deviation (STD), and the correlation between phase and elevation, indicate that our enhanced method reduces correction errors by 35.10%, 20.05%, and 63.52%, respectively, compared to GACOS. Compared to the initial model, these reductions are 58.19%, 57.23%, and 4.34%, respectively. By integrating the outcomes from all three evaluation methods and analysing the corrected images, our enhanced model effectively resolves the inconsistency in correction effects resulting from the spatial variability of atmospheric delay. Moreover, incorporating the horizontal gradient in the inversion method has proven effective in capturing small-scale atmospheric delay signals. This highlights the feasibility and benefits of our enhanced model. [ABSTRACT FROM AUTHOR]

Disturbances in the ionosphere can severely affect synthetic aperture radar (SAR) acquisition and also the accuracy of differential SAR interferometry (D-InSAR) techniques. Focusing on the mid-latitudes, we report for the first time the effect of small-scale ionospheric irregularities on the interferograms generated by the C-band Sentinel-1 satellite. The interferograms obtained during the geomagnetic storm on 4 November 2021 showed finger-like stripes of phase artefacts, while the interferograms obtained on non-storm days did not show such anomalies. Such phase artefacts introduce an error greater than ± 3π in the interferogram. By further checking the gridded total electron content (TEC) from the ground-based global navigation satellite system (GNSS) network, as well as the in-situ electron density measured by ESA's Swarm satellite, the finger-like stripes in the interferograms are found to be correlated with ionospheric bulge structures generated during the geomagnetic storm. Since both the interferogram stripes and the ionospheric bulges exhibit a comparable east-west extension, it is believed that the interferograms can also be used to reflect the small-scale structures of ionospheric bulge fringes. For example, in this case, the latitudinal width of each finger-like stripe reaches about 8 km. Our result implies that the C-band interferogram has the potential to probe the two-dimensional ionospheric structures with unprecedented resolution, which cannot be achievable by the gridded GNSS-TEC. [ABSTRACT FROM AUTHOR]

The river-blocking landslide disasters are widely distributed in the mountainous areas of southwest China, characterized by high-elevation long-runout movements with significant destructive power. The identification and monitoring of high-elevation long-runout landslides, as well as the prediction of unstable landslide movements, hold great significance for regional disaster mitigation and prevention. In this study, we used interferometric synthetic aperture radar to identify and monitor potential landslides in the Gongjue segment of the Jinsha River Basin in China. The Sela landslide was selected for rainfall infiltration simulation, predict the entire process of landslide instability, and explore its failure characteristics from a dynamic perspective. The monitoring results revealed the presence of four typical landslides in Gongjue County, situated within the Jinsha River area, with slope deformation rates exceeding 17 cm/yr. The maximum observed slope deformation rate reaches 46 cm/yr. Decomposition results of the time-series deformation characteristics of the landslide feature measurement points show that rainfall was the primary factor influencing the periodic deformations of landslides. Simulation results indicated that rainfall promotes landslide deformation. Under the influence of rainfall, the movement speed of the landslide increases rapidly, and the front sliding body slides first, followed by the instability of the upper sliding body. This is consistent with the damage mode caused by a traction-type landslide. Subsequently, the particle movement speed maintains a short period of rapid sliding, gradually decreasing and eventually reaching a stable state. The results of this study provide an important reference for carrying out remote monitoring and risk prediction for high-elevation long-runout landslides. [ABSTRACT FROM AUTHOR]

Land subsidence, marked by a decline in surface elevation, poses a significant threat to urban infrastructure and safety. Accurate subsidence information and a reliable prediction model are crucial for prevention and control. In this study, we used persistent scatterer interferometric synthetic aperture radar (PSInSAR) technology to obtain long-term land subsidence data and analyzed subsidence characteristics in Decheng District. By integrating hydrogeological and groundwater data, we developed a three-dimensional groundwater flow and one-dimensional compaction model through numerical simulation. Furthermore, the subsidence data monitored by PS-InSAR were used to further constrain and validate the model. The evolution trend of land subsidence under different groundwater exploitation scenarios was predicted and analyzed. The results showed that from May 2017 to December 2021, the cumulative maximum subsidence in Decheng District reached -173 mm. The subsidence area is mainly concentrated in the northern area, and its subsidence center is near Qiaoyuan Town. According to the Land Subsidence Prevention and Control Plan of Dezhou City, Shandong Province (2018-2025), we set up different groundwater mining scenarios with the goal that the rate of land subsidence in the key prevention and control area is less than 35 mm/yr in 2025.The Fluidsolid coupled model prediction analysis results indicated that a 30% reduction in groundwater exploitation is reasonable. [ABSTRACT FROM AUTHOR]

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

We study the interrelations between salt karst and landscape evolution at the Ze'elim and Hever alluvial fans, Dead Sea (DS), Israel, in an attempt to characterize the ongoing surface and subsurface processes and identify future trends. Using light detection and ranging, interferometric synthetic aperture radar, drone photography, time-lapse cameras, and direct measurements of floodwater levels, we document floodwater recharge through riverbed sinkholes, subsurface salt dissolution, groundwater flow, and brine discharge at shoreline sinkholes during the years 2011–2023. At the Ze'elim fan, most of the surface floodwater drains into streambed sinkholes and discharges at shoreline sinkholes, whereas at the Hever fan, only a small fraction of the floodwater drains into sinkholes, while the majority flows downstream to the DS. This difference is attributed to the low-gradient stream profiles in Ze'elim, which enable water accumulation and recharge in sinkholes and their surrounding depressions, in contrast with the higher-gradient Hever profiles, which yield high-energy floods capable of carrying coarse gravel that eventually fill the sinkholes. The rapid drainage of floodwater into sinkholes also involves slope failure due to pore-pressure drop and cohesion loss within hours after each drainage event. Surface subsidence lineaments detected by InSAR indicate the presence of subsurface dissolution channels between recharge and discharge sites in the two fans and in the nearby Lynch straits. Subsidence and streambed sinkholes occur in most other fans and streams that flow to the DS; however, with the exception of Ze'elim, all other streams show only minor or no recharge along their course. This is due to either the high-gradient profiles, the gravelly sediments, the limited floods, or the lack of conditions for sinkhole development in the other streambeds. Thus, understanding the factors that govern the flood-related karst formation is of great importance for predicting landscape evolution in the DS region and elsewhere and for sinkhole hazard assessment. [ABSTRACT FROM AUTHOR]

Landslide hazard mapping is important to urban construction and landslide risk management. Dynamic landslide hazard mapping considers landslide deformation with changes in the environment. It can show more details of the landslide process state. Landslides in highly vegetated areas are difficult to observe directly, which makes landslide hazard mapping much more challenging. The application of multi-InSAR opens new ideas for dynamic landslide hazard mapping. Specifically, landslide susceptibility mapping reflects the spatial probability of landslides. For rainfall-induced landslides, the scale exceedance probability reflects the temporal probability. Based on the coupling of them, dynamic landslide hazard mapping further considers the landslide deformation intensity at different times. Zigui, a highly vegetation-covered area, was taken as the study area. The landslide displacement monitoring effect of different band SAR datasets (ALOS-2, Sentinel-1A) and different interpretation methods (D-InSAR, PS-InSAR, SBAS-InSAR) were studied to explore a combined application method. The deformation interpreted by SBAS-InSAR was taken as the main part, PS-InSAR data were used in towns and villages, and D-InSAR was used for the rest. Based on the preliminary evaluation and the displacement interpreted by fusion InSAR, the dynamic landslide hazard mappings of the study area from 2019 to 2021 were finished. Compared with the preliminary evaluation, the dynamic mapping approach was more focused and accurate in predicting the deformation of landslides. The false positives in very-high-hazard zones were reduced by 97.8%, 60.4%, and 89.3%. Dynamic landslide hazard mapping can summarize the development of and change in landslides very well, especially in highly vegetated areas. Additionally, it can provide trend prediction for landslide early warning and provide a reference for landslide risk management. [ABSTRACT FROM AUTHOR]

A large number of mines have been closed due to resource depletion, failure to meet safety production requirements, and other reasons. To effectively ensure the safety of the ecological environment above these closed mines along with the safety of engineering construction, it is necessary to monitor the secondary deformation of closed mines. Based on TerraSAR-X, Sentinel-1A data, and InSAR technology, this study obtained high-density secondary surface deformation data on the Jiahe Coal Mine and Pangzhuang Coal Mine in the western Xuzhou area. Combining mining geological data, we analyzed the spatiotemporal variation patterns and mechanisms of secondary deformation in multi-seam mining of closed mines. It was found that when mining multiple seams involves large interlayer spacing, the secondary deformation pattern shows a "W" shape. In this situation, the deformation can be divided into five stages: subsidence, uplift, re-subsidence, re-uplift, and relative stability. This study provides technical support for the evaluation and prevention of secondary deformation hazards in closed mines. [ABSTRACT FROM AUTHOR]

Reservoir impoundment imposes a significant triggering effect on bank landslides. Studying the early identification of landslides and their stability concerning reservoir water levels and rainfall is vital for guaranteeing the safety of residents and infrastructure in reservoir regions. This study proposed a method for establishing a dynamic inventory of active landslides at large hydropower stations using integrated remote sensing techniques, demonstrated at Lianghekou Reservoir. We employed interferometric stacking synthetic aperture radar (stacking-InSAR) technology, small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology, and optical satellite images to identify and catalogue active landslides. Moreover, we conducted field investigations to examine the deformation characteristics of landslides. Finally, Pearson's correlation analysis was employed to evaluate the response between deformation values, reservoir water levels, and rainfall. The results revealed 75 active landslides, including 12 long-term active landslides before impoundment and 63 new landslides after impoundment, which were primarily concentrated in the Waduo and Yazho–Zatou regions. The correlation coefficient between landslide deformation values and the reservoir level was high (0.93), while the correlation coefficient with rainfall was low (0.57). The results of this research offer a crucial foundation for preventing and mitigating landslides in reservoir areas. [ABSTRACT FROM AUTHOR]

Landslides represent a major problem in human activities, especially in the proximity of cities or infrastructure. In this paper, we present the peculiar case of Pietracamela, Italy. Pietracamela is a small village located in the Central Apennines, a few kilometers north of the Gran Sasso Mountain. The peculiarity of the case study is the fact that the village is simultaneously affected by two different types of slope instabilities. The southwest sector, representing the historical part of the village, has been affected by large rockfalls generated from the "Capo le Vene" cliff located in the south of the village. The northeastern sector of the village represents the most recent urbanized area and is involved in a deep-seated landslide that, in the last decades, has damaged buildings and infrastructure. In this context, we have used two different types of remote sensing techniques to study the two phenomena. The rockfall area has been surveyed through the use of an Unmanned Aerial Vehicle (UAV) that allowed the definition of main joint sets and the volume of blocks associated with the most recent (2011) rockfall event. Three main joint sets have been highlighted, which are responsible for the failure of the "Capo le Vene" cliff. The volume of blocks that failed during the last rock failure in 2011 ranged from a few to 1500 m3. The deep-seated landslide has been studied by analyzing borehole data and 20 years of InSAR data from ERS1/2, ENVISAT, COSMO-SkyMed, and SENTINEL-1. It has been highlighted by InSAR analysis that the northeast sector of the village shows a perfectly linear displacement trend that generates movements up to about 230 mm (about 1 cm/year). [ABSTRACT FROM AUTHOR]

In this paper, ground deformation measurements from synthetic aperture radar interferometry (InSAR) and ground-based prism monitoring are compared to finite-element (FE) simulation results for a recent tailings dam collapse. The InSAR monitoring demonstrated the complex spatial and temporal variability of the tailings dam deformation that is not captured in traditional, point-based monitoring approaches. Potentially anomalous deformation behaviour could have been detected from InSAR 1 year preceding the dam failure. A two-dimensional coupled-consolidation analysis was used to predict the dam behaviour during construction stages. The FE modelling results were in broad agreement with the deformation measurement sources, both in terms of magnitude and trends preceding failure. The results, however, deviate significantly immediately before failure, following the construction of the buttresses. Moreover, the FE modelling is sensitive to parameter uncertainties, such as spatial variability of foundation soil properties. The FE results revealed that deformation in both the upstream and downstream parts of the dam are sensitive to changing foundation properties. However, this impact is potentially much more significant on the downstream compared to the upstream parts of the dam, which are dominated by the behaviour of the tailings. This study highlights the efficacy and complementarity of geotechnical and remote sensing techniques for the monitoring of tailings dams. [ABSTRACT FROM AUTHOR]

The eastern Himalayan syntaxis (EHS) region is characterized by steep topography, strong tectonic activity, and strong disaster activity, and the disaster activity has a temporal correlation with the change of seasonal climate factors. Different from other disaster-prone areas, the disasters in the EHS area are more destructive and more susceptible to changes in climate factors. However, previous studies on disaster susceptibility in the region mainly focused on geohazard samples, regional topography, and geological conditions and seldom considered the characteristics of disaster activity and the characteristics of seasonal temperature and rainfall changes, which often led to an underestimation of disaster susceptibility in this region. Therefore, in this study, SBAS-InSAR technology and the GEE (Google Earth Engine) platform were used to identify 317 geological disasters and construct the spatial–temporal motion field and spatial-temporal background information of disasters in the last 5 years. Based on the random forest and optimized evaluation data model, the disaster susceptibility in the EHS area was quantitatively evaluated. The AUC value of the model was 0.89, indicating that the results have high reliability. The results showed that the high-susceptibility areas were mainly distributed in the riverbank and high-altitude areas. The SHAP model revealed that the factors of temperature, precipitation, slope, and elevation in the EHS area had a great negative influence on the disaster. The quantitative results of the influence of seasonal temperature and precipitation on disaster susceptibility showed that the area of susceptibility in summer was significantly higher than that in winter, and the area of high and very high susceptibility increased by about 13%. Finally, the dynamic failure process of typical landslide and glacier disasters in highly susceptible areas was quantitatively predicted, which provides a scientific basis for disaster prevention. [ABSTRACT FROM AUTHOR]

Copyright of Urban Geology is the property of Urban Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Monitoring of dam reservoirs is crucial for developing effective management strategies especially in regions with slope instabilities. Recent developments in satellite synthetic aperture radar (SAR) sensors and their processing techniques enabled frequent observations, allowing monitoring of landslides and revealing the triggering factors. In this study, we analyzed the slope movements in Kalekoy dam reservoir built in 2018 in Bingol Province, Türkiye using weekly Sentinel-1 SAR time series data. We used LiCSAR (Looking into Continents from Space with SAR) products freely served by the COMET-LiCS (Center for the Observation and Modeling of Earthquake Volcanoes and Tectonics) web portal to perform interferometric SAR (InSAR) time-series analysis. Using land use land cover, snow and vegetation indices derived from the Sentinel-2 optical imagery, and combining with precipitation, lake level measurements, and seismic and anthropogenic activities between 2015 and 2022, our findings revealed that the use of SAR data with regular global coverage is essential for deformation monitoring in dam reservoirs and ensure a comprehensive understanding of landslide phenomena in regions prone to such events. The slope movements were clearly observable in the InSAR time series with seasonal cycles especially in winter, and changes of snow cover were linked to them. Vegetation cover decreased over time in the region, likely influenced by the dam reservoir. Sudden changes in slope deformations were related to lake water levels and spring snowmelt as the saturation of landslide toe zones increased pore water pressure. Despite varying characteristics, changes in lake water levels and snowmelt were common triggers for all landslides analyzed. [ABSTRACT FROM AUTHOR]

Quantifying seasonal deformation is essential for accurately determining the thickness of the active layer and the distribution of water content within it, providing insights into the freeze‐thaw dynamics of permafrost environments and their sensitivity to climate change. Due to the limited hydraulic conductivity of the underlying permafrost, the freeze‐thaw processes are largely confined to the active layer, allowing for predictable seasonal deformations. This study employed Independent Component Analysis to isolate large‐scale seasonal deformation from Interferometric Synthetic Aperture Radar (InSAR) measurements taken from 2016 to 2020 in the Yangtze River Source Region (YRSR) of the Qinghai‐Tibet Plateau (QTP), covering 18,500 km2. We developed dedicated machine learning (ML) models that integrate these InSAR‐derived measurements with various environmental proxies. By applying these models to the YRSR, we generated a comprehensive, full‐coverage deformation map for permafrost terrains, achieving an R2 value of 0.91 and an Root Mean Squared Error of approximately 0.5 cm, thus confirming the model's strong predictability of seasonal deformation in permafrost regions. Deformation magnitude varied from less than 1 cm to over 10 cm. Our analysis suggests that terrain attributes, influenced by climate and soil conditions, are the primary factors driving these deformations. This research provides valuable insights into quantifying permafrost‐related seasonal deformation across expansive and rural landscapes. It also aids in assessing subsurface hydrological processes and the resilience and vulnerability of permafrost. The developed ML algorithm, with access to precise environmental data, is capable of forecasting seasonal deformations across the entire QTP and potentially throughout the Arctic. Plain Language Summary: Seasonal ground deformation, including both subsidence and uplift, is common in areas with a layer of ground that freezes and thaws seasonally, underlain by permafrost‐a type of ground that remains at or below 0°C for at least 2 years. These deformations are crucial indicators of changes in water content and thickness of this layer, offering insights into the freeze‐thaw dynamics of cold environments and their sensitivity to climate change. However, accurately mapping ground deformation over large areas has been challenging. In this study, we developed machine learning (ML) models that use radar remote sensing data, statistical methods, and a set of environmental variables to predict these seasonal ground movements. Our models can accurately forecast seasonal deformation using readily available environmental data. We find that slope of the terrain is the main factor influencing seasonal deformation, with climate and soil conditions also playing significant roles. This research offers new ways to measure and understand ground deformation in remote permafrost regions and demonstrates how ML can be used to predict such deformations on a continental or even global scale large. Our findings provide valuable insights for environmental scientists and could help inform strategies for managing these regions under changing climatic conditions. Key Points: Our results underscore the predictability of seasonal deformation with high accuracy in permafrost terrainsMachine learning models predict full‐coverage seasonal deformation with high accuracy (R2 = 0.91, Root Mean Squared Error [RMSE] = 0.5 cm)Seasonal deformation is primarily determined by terrain slope and regulated by climate and soil conditions [ABSTRACT FROM AUTHOR]

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

On 18 August 2020, Masbate Island was struck by a magnitude (MW) 6.6 earthquake. This seismic event represents the second occurrence of a strong earthquake (M > 6) in 17 years, emphasizing the necessity for further investigation into the characteristics of this event. In this study, we employ Interferometric Synthetic Aperture Radar, seismicity analysis, and field investigations to comprehensively characterize the coseismic and postseismic slip associated with the event. Our findings reveal a 50‐km‐long fault rupture along the Masbate segment of the Philippine Fault, with ∼23 km surface rupture mapped onshore, despite the occurrence of interseismic creep. The slip distribution demonstrates decreasing displacements northwestward toward the creeping section, with a maximum left‐lateral displacement of 0.97 m near the epicenter. Toward the southeast offshore, the rupture terminates at a left stepover of a fault. While the surface rupture appears relatively straight and narrowly concentrated, the secondary ruptures and mapped offshore faults reveal a more complex transtensional fault structure in the southeastern part of Masbate Island. This fault complexity represents an asperity that facilitates high‐stress accumulation and rupture initiation. Postseismic slip persists for several months along the onshore creeping segment. Based on comprehensive measurements of both cumulative and coseismic slip along the Masbate fault segment, we calculate a slip rate ranging between 2.8 and 3.8 cm/year and a recurrence interval of 16–41 years for earthquakes similar to the 2020 earthquake. Our study highlights how heterogeneity in fault properties, including geometry and coupling state, influences the distribution of slip and magnitude of earthquakes. The 2020 Masbate earthquake provides valuable insights into the rupture dynamics and fault behavior of the Philippine Fault in the Masbate region. Plain Language Summary: The 2020 earthquake on Masbate Island, Philippines, provided an opportunity to study how faults behave during earthquakes. We used different methods to understand the movement of the ground during and after the earthquake. We found that the fault ruptured for about 23 km on land, despite its slow movement before the earthquake. The rupture continued along the fault's offshore extension. The fault structure becomes more complex in the southeastern part of Masbate Island. After the earthquake, the ground continued to move for several months along the fault. Our research helps us understand how different factors affect earthquakes, which is important for predicting and preparing for future earthquakes in the region. Key Points: The 2020 Masbate earthquake nucleated offshore along the Philippine Fault, rupturing 23 km onshore accompanied by postseismic slipThe Masbate segment of the Philippine Fault is characterized by both aseismic and seismic slip which account for the total slip budgetThe earthquake demonstrated how creeping behavior and fault geometry complexities control rupture extent [ABSTRACT FROM AUTHOR]

We developed a robust InSAR processing strategy that can effectively mitigate severe decorrelation noise in a large volume of InSAR data. We mapped the average land subsidence rate (2017–2020) over the 131,572 km2 Upper Texas and Louisiana coasts from Sentinel‐1 data, with ∼2 mm/yr accuracy based on independent GPS and tide gauge validation at 189 locations. The improved InSAR observations reveal widespread subsidence that was previously undetected in coastal wetlands and rural areas with small communities. Our InSAR surface deformation map is at the spatial scale that overlaps with the scale of hydrodynamic model grids. This allows us to integrate InSAR observations into operational storm surge models to analyze future flooding risks due to relative sea level change. We found that these subtle millimeter‐to‐centimeter subsidence features can substantially increase hurricane‐induced inundation, and passive flood mapping (known as the "bathtub" approach) can lead to inaccurate flood risk predictions. Plain Language Summary: The analysis of future storm surge risks along the Gulf Coast often focuses only on projected sea level rise estimates due to the lack of spatially dense land subsidence data. Here we show advances in spaceborne Interferometric Synthetic Aperture Radar (InSAR) techniques can enable the retrieval of coastal subsidence patterns over a 131,572 km2 region along the Gulf Coast of the United States, a challenging terrain for InSAR analysis due to the presence of dense vegetation coverage. Our results reveal widespread subsidence features due to oil and gas production, groundwater pumping, and wetland degradation that were previously undetected. Physics‐based storm surge modeling shows that the detected subtle millimeter‐to‐centimeter land subsidence can substantially elevate flood water depth and flood extent. We demonstrate that the relationship between coastal subsidence and flood depth increase is complex, and communities vulnerable to future flood risk are highly variable along the Gulf Coast of the United States. Key Points: A robust InSAR processing algorithm enables accurate retrieval of subtle surface deformation signals over densely vegetated Gulf CoastInSAR reveals many coastal subsidence features that were previously undetected by existing GPS and tide gauge stationsStorm surge modeling suggests that passive flood mapping based on topography can lead to inaccurate flood risk predictions [ABSTRACT FROM AUTHOR]

Abstract Landslide disasters have characteristics of frequent occurrence, widespread impact, and high destructiveness, posing serious threats to human lives, property, and the ecological environment. Timely and accurate early identification of landslides remains an urgent issue within the disaster prevention field. This study focuses on Jianzha County, Qinghai Province, integrating PS-InSAR, SBAS-InSAR, and optical remote-sensing techniques to delineate potential landslide-prone areas. Utilizing Google Earth imagery and existing landslide datasets, potential landslide points were identified through a deep learning model. Results indicate the following: (1) In Jianzha County, the variation trend of the average surface velocity monitored by PS-InSAR and SBAS-InSAR technology is consistent, and the deformation monitoring results are reliable. (2) Utilizing the deep learning model, 56 potential landslide points were identified, comprising 39 high-risk points and 17 medium-risk points. By integrating the spatial distribution data of historical geological disaster points, 10 out of 13 previously occurred landslide disaster points were found to be located at the identified high-risk landslide points, achieving a detection accuracy of 76.92%. (3) The spatial distribution of landslide points exhibits clustering, with slopes ranging from 10° to 40°, elevations between 15 and 30 m, and slope orientations predominantly toward the northeast. (4) Landslide formation is correlated with seasonal precipitation concentrations and temperature fluctuations. This method can provide a crucial basis for large-scale surface deformation monitoring and early identification of landslide risks.

Abstract Spaceborne-based monitoring for environmental purposes has become a well-established practice. The recent progress of synthetic aperture radar (SAR) sensors, including through the European Space Agency’s (ESA) Sentinel-1 constellation, has enabled the scientific community to identify and monitor several geohazards, including subsidence ground deformations. A case study in the Tuscany Region, Italy, highlights the effectiveness of interferometric synthetic aperture radar (InSAR) in detecting abrupt increases in ground deformation rates in an industrial area of Montemurlo municipality. In this case, InSAR data enabled prompt identification of the phenomenon, supporting the authorities in charge of environmental management to thoroughly investigate the situation. First, an on-site validation was performed via field surveys confirming the presence of cracks and fissures on some edifices. Further analysis, including water pumping rates, settlement gauge and topographic levelling, corroborated the InSAR data's findings regarding vertical deformation. Integration of collected data allowed for spatial identification and assessment of the subsidence bowl and its source depth recognized by the remote sensing data. The Montemurlo case offers a procedural guideline for managing abrupt accelerations, identified by InSAR data in subsidence-prone areas due to fluid overexploitation. In fact, these data proved useful in helping local authorities responsible for hydrogeomorphological risk management. With the exacerbation of deformation issues in subsidence-prone regions due to climate change, early detection and monitoring of such phenomena are increasingly crucial, with InSAR data playing a central role in achieving this goal.

Land subsidence is a worldwide geological hazard. Differential land subsidence has posed the major threat to urban infrastructure, linear rail transit and underground space development and utilization, and also restricted the sustainable development of the economy and society. This paper systematically elaborates on the research progresses on land deformation acquisition, evolution mechanism and simulation of land subsidence, and focuses on the land deformation acquisition technology based on InSAR monitoring and multi-source deformation data fusion, as well as the correlation analysis, statistical analysis, machine learning and other methods to analyze the relationship between the evolution of land subsidence and various influencing factors based on geotechnical experiment and long time series observation data. On this basis, the advantages and disadvantages of land subsidence simulation models such as groundwater flow field-land deformation model, mathematical statistical model and machine learning model are explored. It is found that multi-source deformation data fusion can improve the spatiotemporal resolution of land deformation. The differences in geological structure, lithology, groundwater exploitation, and dynamic and static loads are factors contributing to the differential evolution of land subsidence. The difficulty in balancing the computational efficiency and interpretability of mathematical models for land subsidence is the main problem in simulation. According to the literature review, the current researches mainly focus on land subsidence caused by groundwater over-exploitation. This paper further proposes the future research directions for land subsidence, under the background of climate change, new hydrological condition and dataset, and based on the fusion of data through remote sensing and field observations, integrating the latest progress of InSAR, GeoAI, cloud platform and other technologies to reveal the evolution mechanism of land subsidence considering the climate change and anthropic activities and provide technical support for regional land subsidence prevention and urban safety.

The Southern Highland Fold and Thrust Belt (SHFTB), the boundary of the Australian plate and the New Guinea Highland block, significantly contributes to the convergent deformation along the plate boundary. However, due to the lack of observation data, the detailed slip pattern of the SHFTB and the orogenic mechanism beneath the New Guinea Highlands remains controversial. On 25 February 2018, the MW7.5 Papua New Guinea (PNG) earthquake struck the southeastern segment of the SHFTB. The detailed rupture characteristics of this event is significant for further clarifying the inter-seismic slip pattern along the SHFTB. Here, the coseismic deformation field of this earthquake was obtained using high-resolution ALOS-2 satellite images. We find that the 2018 MW7.5 PNG earthquake ruptured a large-scaled fault (SHFTB) extending to the lower crust (deeper than 20 km) beneath the New Guinea Highlands, with a dip angle of 24°. The slips on the fault plane are equivalent to moment magnitudes of MW7.51. Three major asperities with thrust-dominated slip of up to 3.94 m are detected on the fault plane. This finding implies that the slip pattern on the eastern segment of the SHFTB is dominated by thrust, rather than with significant sinistral movement, as previously reported. The tectonic deformation across the New Guinea Highlands is possibly concentrated on the large-scale fault SHFTB and primarily controls the intra-continental orogeny in the central Papua New Guinea.

Forest above-ground biomass (AGB) is one of the critical measures of forest resources. Therefore, it is crucial to identify a reliable method to estimate the AGB, especially in the tropics, where forest ecosystems are exposed to several depleting factors, including deforestation, climate change and replacing natural forests with palm oil tree plantations. We investigated the digital elevation data over the forest and uses an artificial intelligence-based approach to develop a method for quick and cost-effective assessment of the AGB. The study was conducted in the tropical peatland rainforest of Brunei Darussalam. The Shuttle Radar Topography Mission (SRTM) elevation data product and Light Detection and Ranging (LiDAR) digital elevation data were used. A linear regression (LR) model and three different machine learning (ML) algorithms, i.e., Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machines (SVM), were tested and compared. As model inputs, the SRTM elevation and distance from the peat dome’s center, a feature of a peatland swamp forest, were used. ML methods were trained on the samples taken from the LiDAR elevations. The validation results showed that the SVM was the best method to predict AGB in the study area with R2 = 0.70, RMSE = 83.65 Mg ha-1, and MAE = 74.43 Mg ha-1, which in relative terms corresponds to approximately 6% of the AGB of the forest of interests. This study demonstrated the potential of ML algorithms in AGB estimation based on canopy height derived from the InSAR-based DEM in tropical forests.

Subsidence in southeastern Louisiana is a significant geological issue caused by natural and human-induced factors like low-lying topography and groundwater pumping. Human activities also led to coastal land loss and reduced sediment supply. Satellite-based technologies such as Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) are used to monitor subsidence. Louisiana has about 130 continuously operating reference stations (CORS) monitoring subsidence statewide. GNSS provides accurate point measurements but limited spatial coverage. InSAR, however, detects ground deformation over large areas using satellite-based radar imagery. In response to this advantage, we employed Sentinel-1 SAR images from 2017 to 2021 to estimate the vertical displacement in East Baton Rouge (EBR) Parish. Significant displacement is found in urban and industrial areas, particularly in high- and medium-density residential areas. The significant subsidence area is between Denham Spring and Baton Rouge faults, where residential areas experience displacement of -0.7 to -1 cm/year. The displacement variation in land use indicates significant annual subsidence in some buildings and infrastructure. Three strategic facilities in Baton Rouge Downtown experienced displacement, with -6.1 mm/yr in Downtown, -2.99 mm/yr at Horace Wilkinson Bridge, and -4.94 mm/yr at central railway station. In addition, machine learning is employed to estimate the vertical displacement in the study area. The K-Nearest Neighbors (KNN) model provides a comprehensive understanding of subsidence estimation among the GBR (Gradient Boosting Regression), RFR (Random Forest Regression), and KNN models. Machine learning models revealed that proximity to fault lines and precipitation are the most influential factors in displacement.

The China Loess Plateau (CLP) is the world's most extensive and thickest region of loess deposits. The inherently loose structure of loess makes the CLP particularly vulnerable to geohazards such as landslides, collapses, and subsidence, resulting in substantial geological and environmental challenges. Xining City, situated at the northwest edge of the CLP, is especially prone to frequent geological hazards due to intensified human activities and natural forces. Synthetic Aperture Radar Interferometry (InSAR) has become a widely used tool for identifying landslide hazards and displacement monitoring because of its high accuracy, low cost, and wide coverage. In this study, we utilized the small baseline subset (SBAS) InSAR technique to derive the line of sight (LOS) displacements of Xining City using Sentinel-1 datasets from ascending and descending orbits between October 2014 and September 2022. By integrating LOS displacements from the two datasets, we retrieved the eastward and vertical displacements to characterize the kinematics of active slopes. To identify the active areas semi-automatically, we applied the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster InSAR measurement points (IMPs). Forty-eight active slopes with areas ranging from 0.0049 to 0.5496 km2 and twenty-five subsidence-dominant areas ranging from 0.023 to 3.123 km2 were identified across Xining City. Kinematics analysis of the Jiujiawan landslide indicated that acceleration started in August 2016, likely triggered by rainfall, and continued until the landslide. The extreme rainfall in August 2022 may have pushed the Jiujiawan landslide beyond its critical threshold, leading to instability. Additionally, the study identified nine active slopes that threaten the normal operation of the Lanzhou–Xinjiang High-Speed Railway, with kinematic analysis suggesting rainfall-related accelerations. The influence of anthropogenic activities on ground displacements in loess areas was also confirmed through time series displacement analysis. Our results can be leveraged for geohazard prevention and management in Xining City. As SAR image data continue to accumulate, InSAR can serve as a regular tool for maintaining up-to-date landslide inventories, thereby contributing to more sustainable geohazard management. [ABSTRACT FROM AUTHOR]