Backfill and Dumping Zones (BDZs) in open-pit mining areas represent geologically fragile anthropogenic landforms where long-term consolidation settlement and potential failure processes are often intertwined. This study proposes an Interferometric Synthetic Aperture Radar (InSAR)-based framework that explicitly accounts for soil consolidation to improve hazard prediction in BDZs. The framework consists of three main components: first, a Digital Elevation Model (DEM) error-based approach is used to reconstruct excavation and backfilling histories, validated against ICESat-2 data (RMSE = 4.20 m); second, multi-track InSAR fusion enables threedimensional deformation monitoring, complemented by a quadratic-based deformation activity factor to capture nonlinear temporal evolution; and third, an ensemble learning model integrates multiple influencing factors to identify key drivers of instability. Results reveal that BDZs are dominated by large-scale consolidation-driven settlement, with vertical rates up to 0.4 m/yr at the Casaio mine (2017-2025) and significant subsidence of 0.13-0.12 m/yr at the failure source zones of the Las Cruces and Copler mines. Strong correlations between backfill thickness and vertical deformation (|r| = 0.61-0.65) further confirm gravity-driven compaction as the prevailing mechanism. Building on this recognition, a Bagging ensemble model integrating seven factors was constructed, achieving recall rates of 89.1% and 90.8% in two independent collapse cases. Feature importance analysis identified backfill thickness, vertical deformation, and deformation activity as the most critical instability predictors. These findings highlight the geologic significance of consolidation processes in BDZs, and further demonstrate that explicitly incorporating them into InSAR analysis. It provides a reliable, interpretable, and geotechnically meaningful framework for hazard assessment and risk prevention in open-pit mining areas.

Potential tropospheric noise is a critical factor that undermines the effectiveness of deformation monitoring in Synthetic Aperture Radar Interferometry (InSAR) technologies. In most scenarios, many point targets within the InSAR deformation monitoring area either do not undergo deformation or exhibit only minimal deformation trends. The phases of densely distributed stable points can effectively respond to spatial tropospheric delays, particularly turbulent atmospheric phases. This study proposes a data-driven InSAR atmospheric correction method by exploring how to use these densely stable InSAR time series to model atmospheric phase delays. Our focus is on selecting stable InSAR time series point targets and evaluating the impact of different densities of stable points on atmospheric correction performance. Analysis of 645 interferograms derived from 217 Sentinel-1A SAR images, spanning from 13 June 2017 to 15 November 2024, demonstrates that the proposed method reduces the Root Mean Square Error (RMSE) by 70%, 59%, and 69% compared to the terrain-related linear approach, the General Atmospheric Correction Online Service, and common scene stacking methods, respectively. In addition, simulation data and leveling data were used to validate the proposed method. This article does not develop an independent InSAR atmospheric correction method. Instead, the proposed approach starts with the InSAR deformation time series, allowing for easy integration into existing InSAR workflows and widely used atmospheric correction strategies. It can serve as a post-processing tool to improve InSAR time series analysis.

Surface subsidence induced by coal mining often exhibits spatially extension. Precise identification and temporal tracking of subsidence field boundaries are essential for safe mining and damage prevention. In traditional interferometric synthetic aperture radar (InSAR) techniques, the deformation field cannot be completely segmented due to invalid deformation in the center of the mining center caused by phase unwrapping errors and large gradient deformation. Moreover, the spatiotemporal tracking of mining-induced deformations is seldom studied. To address these challenges, we propose an automated spatiotemporal tracking framework with an enhanced real-time object segmentation model, i.e., You Only Look Once version 11 (YOLOv11), based on SAR interferograms, where the backbone network is then enhanced by incorporating a double branch-simple attention module attention mechanism, aiming to improve feature extraction capabilities and reduce background interference. After segmentation for each SAR interferogram, time series deformation boundaries are generated to sequentially reveal the dynamic subsidence evolution. The experimental results from the coal mining region of northern Shaanxi Province with Sentinel-1A SAR images acquired from August 22, 2018, to April 7, 2019 indicate that the new method can achieve better segmentation accuracy than common models. Moreover, it can track the changes of the deformation boundary and deformation area dynamically. This article highlights the deep learning capabilities in terms of automation, efficiency, and accuracy in identifying subsidence boundaries caused by underground coal mining activities.

With the acceleration of global warming, coastal cities with high populations and rapid urban construction are more vulnerable to the combined effects of sea level rise and land subsidence. In the Bohai Bay region, which is characterized by significant urbanization and natural resource extraction, monitoring land subsidence is crucial for risk mitigation. This study first validated the InSAR results by comparing them with Global Navigation Satellite System (GNSS) data, showing an average RMSE of 7.22 mm. We then used Intermittent Small Baseline Subsets (ISBAS) InSAR techniques to monitor multi-scale surface deformation over Bohai Bay, employing 662 Sentinel-1A/B images from January 2020 to June 2023. The Independent Component Analysis (ICA) method and Okada model were used to extract deformation components and analyze the mechanisms. Results show significant subsidence in the Yellow River Delta (YRD), with a maximum deformation rate of 235 mm/a. This study provides crucial insights for land subsidence prevention in the Bohai Bay region.

Mining-induced land subsidence poses significant geohazard risks to critical on-site mining operational support infrastructure, such as tailings storage facilities (TSFs). This study investigates the Doornpoort TSF subsidence at the South Deep gold mine in South Africa, using multi-temporal Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and Non-negative Matrix Factorisation (NMF) algorithm approach to split the superimposed subsidence contributing drivers, alongside the incorporation of Global Navigation Satellite System (GNSS) data and underground mining layout plans. 78 Sentinel-1A Satellite Aperture Radar (SAR) ascending acquisitions between May 2022 and December 2024 were obtained and processed to determine the average annual deformation rates and cumulative time-series displacement for the study area. The InSAR-derived subsidence rates at the designated three benchmarks on the embankments of the Doornpoort TSF and TSF 1&2 are −26.09 mm/year, −13.40 mm/year and −16.25 mm/year, while the maximum cumulative subsidence was −57.45 mm, −42.76 mm and −36.44 mm. A comparison of the InSAR results with the GNSS-derived subsidence results showed correlation standard deviations of 1.87 mm, 0.93 mm, and 1.05 mm, respectively. The InSAR results revealed spatially coherent subsidence patterns and a good correlation between deformation boundaries and underground mining layouts, suggesting that mining-induced stress redistribution is the primary driver of regional surface subsidence. The NMF decomposition of the InSAR-derived deformation result at a selected benchmark on the Doornpoort TSF embankment, whose average annual deformation with a cumulative time series deformation of −26.09 mm/year and −57.45 mm, respectively, revealed that 92% of the observed cumulative deformation is associated directly with the underground mining, whilst the remaining 8% is associated with TSF embankment consolidation. Furthermore, the selected decomposition benchmark within the TSF basin showed that underground mining alone accounted for 100% of the observed subsidence there. These findings support a coupled deformation framework in which deep mining activities influence regional subsidence, while localised geological conditions modulate its surface manifestation.

Tropospheric delays constitute the principal error source in the inversion of surface deformation using InSAR techniques. Here, a novel method is proposed to estimate the tropospheric delays and improve the accuracy of deformation inversion. Firstly, a small baseline subset is divided into several common-reference interferometric subsets (CRIS). Subsequently, the CRIS optimally selected by a lookup table were employed to construct an inversion model based on the spatiotemporal distribution characteristics of tropospheric delays. The lookup table was constructed through 540,000 simulation tests. Finally, the estimated tropospheric delays are applied to correct the interferograms, enabling high-accuracy deformation inversion. The performance of the new method was validated through simulated and real tests. Simulation results indicate that the new method accurately estimates tropospheric delays with an average RMSE of 2.4 mm, while achieving reductions of 56% and 62% in deformation RMSE compared to two commonly-used methods. A real-world validation in Southern California confirms that the new method achieves an improvement of 29% to 69% in deformation accuracy over four commonly-used methods. Furthermore, validation on the Qinghai-Tibet Plateau demonstrates that the new strategy can correct the tropospheric delays of co-seismic interferograms with small-to-moderate magnitude earthquakes, enabling the accurate extraction of centimeter- to sub-centimeter-scale deformation fields.

Phase unwrapping (PU) is a key step in synthetic aperture radar interferometry (InSAR) techniques, as its accuracy directly determines the precision of deformation estimation. Despite the widespread use of InSAR, many existing PU techniques, such as minimum cost flow (MCF), Snaphu, and recently developed deep-learning models, struggle to maintain high accuracy when faced with large deformation gradients. To address this issue, this study proposes a phase gradient rate constrained minimum cost flow (PGR-MCF) method. It uses the phase gradient rate (PGR) calculated from time series differential interferograms through stacking and fusion, to constrain weighting of the arcs during MCF unwrapping process. By optimizing the unwrapping path to prioritize low-gradient areas, the PGR-MCF method significantly improves unwrapping accuracy in high-gradient regions. In simulated experiments, the PGR-MCF method correctly unwrapped 99.5 % of the pixels in large deformation zones. In the application to Guobu landslide, the PGR-MCF method reduces the root mean square errors (RMSEs) between InSAR and global navigation satellite system (GNSS) deformation time series by more than 69 %, compared to other tested methods. This method does not require external datasets or prior models, ensuring its broad applicability. Additionally, it reliably unwraps interferograms with large spatiotemporal baselines, thus increasing the number of reliable unwrapped interferograms for time-series deformation inversion and improving deformation monitoring accuracy. Moreover, it has been proven to be an effective PU method for deformation estimation in regions with large deformation gradients.

Slow-moving landslides can trigger severe disasters when activated by earthquakes, torrential rains, or typhoons. Early detection is crucial for mitigating loss of life and property damage. Interferometric Synthetic Aperture Radar (InSAR) technology is among the most effective techniques for detecting slow-moving landslides, though its accuracy can be further improved through integration with optical imagery and Digital Elevation Models (DEM). Current machine learning approaches that combine InSAR and optical data suffer from limited efficiency, poor transferability, and challenges in regional-scale application. To address these limitations, this study proposes a multimodal dual-path network that integrates InSAR products with textural information from optical imagery to detect slow-moving landslides. One path processes InSAR deformation rates and topographic factors, while the other incorporates texture information and auxiliary data. Together, these paths extract semantic information from high-dimensional spatial features and condense it into low-dimensional representations. A pyramid pooling module is employed to capture multi-scale features during low-level semantic extraction. For feature fusion, a rate-constrained adaptive module is introduced to enhance the contribution of deformation rates to slow-moving landslides. According to the results, the proposed method improves the F1-score for landslide detection by 6% compared to using InSAR products alone. These results provide reliable technical support for regional landslide inventory compilation and disaster management, as well as new insights for regional-scale surveys in slow-moving landslide-prone areas.

Land subsidence poses a persistent challenge to Tianjin, a major coastal city in China, with implications for urban infrastructure and sustainable development. This study examines the spatiotemporal evolution of ground subsidence in Tianjin from 2003 to 2024 using multi-source SAR observations from Envisat ASAR (C-band), ALOS PALSAR (L-band), and Sentinel-1 (C-band). Surface deformation was derived using SBAS-InSAR with atmospheric phase correction. Due to limitations in data availability, SAR observations are temporally discontinuous; therefore, the long-term subsidence evolution was reconstructed by integrating multi-sensor deformation rates through a model-based time-series fitting approach. The results show pronounced subsidence during 2003-2010 in inland districts such as Wuqing, Beichen, Jinnan, and Jinghai, with maximum rates exceeding 50 mm/yr. After 2017, regional subsidence rates generally declined, while localized deformation became increasingly concentrated in coastal reclamation areas of the Binhai New Area, particularly around Dongjiang Port and Fuzhuang. Spatial and temporal patterns of subsidence exhibit clear correspondence with changes in groundwater use intensity and phases of urban construction and land reclamation. These observations suggest a transition in dominant subsidence controls over time. The results provide a long-term observational perspective on subsidence evolution in Tianjin and offer a geospatial basis for land-use planning and infrastructure risk assessment in coastal cities.

The evolution of open-pit coal mining over several decades has encompassed two distinct stages: resource exploitation and ecological restoration. Each phase has led to significant changes in surface coverage and elevation, accompanied by distinct patterns of surface deformation. Reconstructing deformation characteristics across both mining and restoration periods, analyzing the underlying mechanisms, and validating results are crucial for understanding the surface evolution. This study examines post-restoration deformation characteristics using archived Synthetic Aperture Radar (SAR) data and Intermittent Small Baseline Subset-Interferometric Synthetic Aperture Radar (ISBAS-InSAR) techniques. Non-negative Matrix Factorization (NMF) is then applied to decompose deformation signals and analyze the two-stage deformation mechanisms. Lastly, validation is performed through comparisons with differential Digital Elevation Models (DEM) and optical imagery acquired before and during the open-pit mining phase. The Emma mining area in Spain was selected as the study site. Sentinel-1 SAR imagery from 2017 to 2024 reveals vertical deformation rates of up to 21.6 cm/year. The two components derived from NMF correspond to long-term subsidence due to historical mining and short-term deformation resulting from slope restoration. The first NMF component is validated using differential DEM (2009-2020), revealing a positive correlation between deformation magnitude and backfill thickness. The second component is supported by optical imagery from 2015 to 2016, indicating that large-scale slope modification during ecological restoration contributed to short-term deformation. This study presents a quantitative framework for analyzing mining-induced deformation and assessing the effects of ecological restoration on terrain stability.

The slip-surface geometry of a landslide is crucial for stability analysis and early warning, making its accurate and detailed characterization essential for hazard assessment and mitigation. However, traditional methods for obtaining slip-surface geometry are resource-intensive, requiring significant manpower and materials. Some studies have used the three-dimensional (3D) deformation from remote sensing data to infer detailed slip-surface geometry of landslides using the mass conservation method. However, this method usually requires prior data to calibrate the model parameters, and its underlying assumptions may not be valid for all landslide scenarios. In this study, we designed an Extended Vector Inclination Method (EVIM) to determine detailed landslide slip-surface geometry and established a framework for deriving landslide thickness based on 3D deformation derived from SAR and optical remote sensing data. The experiments based on actual events (Hooskanaden, Shangxintian, and Daopo landslide) and simulations demonstrate that EVIM can reliably estimate landslide thickness in a manner consistent with in-situ measurements. Furthermore, simulations indicate that the magnitude of deformation and the error in the constrained 3D deformation affect the accuracy of our method. We conclude that in cases where the mass conservation method is difficult to apply (e.g., landslides lacking prior information), the EVIM may serve as an alternative method. The proposed framework enables rapid slip-surface inversion, making it well-suited for regional-scale landslide stability assessments.

We propose an orbital angular momentum (OAM) quantum holography scheme based on multi-mode Bessel-Gaussian (MBG) beams. Entangled photon pairs are generated through spontaneous parametric down-conversion (SPDC) process, and the axis prism parameters and topological charges of the idler photons are used for encoding to construct Bessel-Gaussian quantum selective holograms; then, the corresponding mode parameters carried by the signal photons are used for correlated decoding and information reconstruction. Theoretical analysis and numerical simulation results show that this scheme can effectively realize OAM quantum holography based on Bessel-Gaussian modes encoding. Compared with traditional single OAM encoding methods, our scheme introduce an additional mode degrees of freedom, which can enhance multiplexing dimension and encoding capacity; at the same time, relying on the non-classical correlation characteristics of entangled photons, quantum holography has a potential advantages in noise-resistance performance.

High-precision three-dimensional geometric feature documentation is crucial for monitoring the structural health of the Great Wall, yet existing methods struggle to balance efficiency with geometric fidelity when processing massive, complexly point clouds. To address this, we propose a bioinspired stereoscopic feature extraction framework that mimics raptor visual perception. Validated on the Ming Great Wall, including the Jiayuguan Pass in Gansu Province and the Guangwu section in Shanxi Province, China. This method condenses complex morphologies into sparse yet recognizable feature sets, significantly enhancing representation efficiency while minimizing storage and rendering costs. These high-fidelity features facilitate downstream tasks such as contour extraction and solid modeling and offer diagnostic insights into heritage pathology by capturing geometric anomalies associated with surface erosion and structural instability. By harmonizing large-scale processing with microscale detail recognition, this work provides a scalable, automated solution for refined surveying and digital preservation of extensive linear cultural heritage sites.

We numerically investigate the long-time intra-cavity-field dynamics of a periodically pump- modulated pair of coherently coupled Kerr micro-ring resonators. Along a detuning scanning, fixed-phase stroboscopic trajectories change from recurrent regular state to states with positive maximum-Lyapunov growth. Long observation windows near the transition, where finite-time estimates converge slowly. The low- and high-detuning states both retain multi-mode Kerr-comb spectra, while their cycle-to-cycle field organization changes much more strongly. Spatiotemporal intensities, adjacent-cycle profiles, and full-field recurrence measures show a loss of one-period recurrence together with changes in the inter-resonator phase relation, coherent exchange, and dispersive intensity flow. We linearize the coupled equations around the long-time field and examine the growth of an infinitesimal perturbation. The resulting tangent-energy balance shows that the loss contribution changes little along the scanning, whereas the Kerr contribution increases and exceeds the loss magnitude in the high-detuning states with positive Lyapunov growth. A separate dimensional correspondence relates the normalized working range to Si3N4 micro-ring. These results provide a field-resolved description of the long-time dynamical change and its associated perturbation growth.

Synthetic Aperture Radar Interferometry (InSAR) is a remote sensing technique used to measure deformation over large areas. In many cases, deformation occurs locally, while numerous monitoring points remain stable. Unlike traditional spatially constrained or single-reference InSAR approaches, this letter proposes a multi-reference-point InSAR method. The proposed method employs stable points as spatial constraints or references to improve the reliability of InSAR deformation measurements. First, point targets exhibiting stationary time series are identified through hypothesis testing. These points contain no deformation information, and their fluctuations represent spatially correlated errors, such as atmospheric delay. Next, constrained least squares is applied to estimate deformation within Delaunay triangular networks, using stable points as spatial constraints or references. Finally, the reliability of this method is demonstrated through experimental simulations and the analysis of 231 Sentinel-1A SAR scenes covering Xiamen Island, China.

The AlN micro-ring provides an ideal platform for the study of soliton-comb spectra. The reflective Pancharatnam-Berry (PB) metasurface implements a programmable geometric-phase mapping from polarization to orbital angular momentum (OAM). The challenge is to match the frequency-domain multiplicity of micro-comb teeth with the spatial-mode multiplicity of OAM channels in a complete channel-space description. We use a tensor-product space of frequency, polarization (spin), spatial ports, and OAM: an on-chip demultiplexer (DE-MUX; WDM) stage performs frequency channelization, while polarization optics provides per-tooth projection between linear and circular bases. Scalar diffraction and annular Fourier decomposition are used to evaluate tooth-resolved intensity/phase profiles and OAM purity/crosstalk. Our results clarify end-to-end information pathways across all channel dimensions. The vortex soliton micro-combs hold significant implications for high-dimensional preci-sion spectroscopy of optical vortices, high-capacity communication links, high-dimensional parallel sensing, and multi-channel coherent information processing. (c) 2026 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Global assessments of landslide impact on critical communication infrastructure have become urgent because of rising occurrences related to human activities and climate change. The landslide and glacial slide susceptibility along the Karakoram Highway poses a significant threat to the infrastructure ecosystem, local communities, and the critical China–Pakistan Economic Corridor. This research paper utilized the Small Baseline Subset InSAR technique to monitor the deformation patterns over the past 5 years, yielding high-resolution insights into the terrain instability in this geologically active region. The SBAS time series results reveal that the substantial cumulative deformation in our study area ranges from 203 mm to −486 mm, with annual deformation rates spanning from 62 mm/year to −104 mm/year. Notably, the deformation that occurred is mainly concentrated in the northern section of our study area. The slope’s aspect is responsible for the maximum deformed material flow towards the Karakoram Highway via steep slopes, lost glacial formations, and the climate variations that cause the instability of the terrain. The given pattern suggests that the northern area of the Karakoram Highway is exposed to a greater risk from the combined influence of glacial slides, landslides, and climatic shifts, which call for the increased monitoring of the Karakoram Highway. The SBAS-InSAR method is first-rate in deformation monitoring, and it provides a scientific basis for developing real-time landslide monitoring systems. The line of sight limitations and the complexity and imprecision of weather-induced signal degradation should be balanced through additional data sources, such as field surveys to conduct large slide and glacial slide susceptibility evaluations. These research results support proactive hazard mitigation and infrastructure planning along the China–Pakistan Economic Corridor by incorporating SBAS-InSAR monitoring into the original planning. The country’s trade policymakers and national level engineers can enhance transport resilience, efficiently manage the landslide and glacial slide risks, and guarantee safer infrastructure along this strategic trade route.

The frequency and intensity of glacial lake outburst floods (GLOFs) are increasing with rapid glacier retreat under a warming climate, yet the processes linking triggers and downstream responses remain poorly understood. Here, we investigate the 2013 GLOF at Ranzerio lake in southeastern Tibet using multi-source remote sensing, field observations, and hydrodynamic modeling. A glacier tongue collapse, with an estimated volume of 3.8 × 106 m3, was identified as the primary trigger of the moraine dam breach. Flood routing simulations with the HEC-RAS 2D model reproduced a peak discharge of 7930 ± 18 m3/s about 25 ± 5 min after the outburst, capturing flood propagation and geomorphic impacts downstream. The results reveal the multistage process chain of the outburst and highlight the importance of monitoring lake evolution, glacier movement, terrain change, and meteorological conditions for early warning and risk management in glacierized mountain regions.

Landslides pose a significant hazard to lives and property worldwide. Understanding the triggering factors of landslides provides essential information for hazard mitigation. While much research has focused on the effects of precipitation, underground mining, water level changes, and earthquakes on landslides, there remains a gap in understanding the impact of long-term and subtle tectonic interseismic motion, particularly over large-scale areas. Interferometric Synthetic Aperture Radar (InSAR) is widely used in landslide research, effectively detecting wide-area landslides and monitoring high-risk individual landslides. Additionally, it provides insights into the triggering factors and failure mechanisms of landslides. This study focuses on the Chuandian block area in southeastern Tibet, China, an area characterized by active tectonic motion.First, we proposed an automated method for detecting landslides from wide-area InSAR deformation rates, utilizing density clustering and minimum boundary extraction. Using this method, potential landslides were successfully detected in the Chuandian block. The relationship between landslide distribution and the shallow coupling and creep of faults in the Chuandian block was then comprehensively analyzed based on the results of wide-area landslide distribution and interseismic deformation. Specifically, three-dimensional deformation along the Ganzi-Yushu and Xianshuihe faults was monitored using multi-orbit Sentinel-1 SAR and GNSS observations. An elastic dislocation model was also applied to invert shallow creep along these faults. Finally, the development patterns of landslides under the combined influence of internal and external dynamics were summarized. In high-creep areas along the faults, long-term and subtle interseismic motion of the shallow surface led to significant fissure development and structural deterioration in rock and soil, creating internal conditions conducive to landslide formation. External dynamics, including river erosion, precipitation, freezing, and thawing, further accelerated landslide development. Our findings underscore the importance of understanding the relationship between interseismic motion and landslides to enhance knowledge of how tectonic processes influence landslide formation and to support improved hazard mitigation strategies.

Surface heights often change over tens meters in certain scenarios, such as mountain excavation and city construction (MECC). It is crucial to estimate the time and magnitude of surface height changes and the deformation time series with the interferometric synthetic aperture radar (InSAR) technique. For the height change regions, ground points become temporarily coherent scatterers, i.e., they are coherent before and after the height changes, but they become incoherent across the height change event. In this case, the traditional Small Baseline Subset (SBAS) InSAR method cannot estimate the digital elevation model (DEM) errors and deformation with high accuracy, as height changes will introduce time-variable signals, which can be misinterpreted as ground deformation. Therefore, this article proposes an improved SBAS method to estimate the time and magnitude of surface height changes and deformation time series. As the DEM errors have different linear relationships with the perpendicular baseline before and after the surface height changes, the time point of the height changes can be detected by minimizing the sum of the squares of the residuals. The simulated and real experimental results show that the improved method can accurately acquire the time and magnitude of surface height changes, which can accurately remove the DEM error from different temporal subsets, and contribute to the highly accurate estimation of deformation time series in the height change regions.