Rapid acquisition of critical failure characteristics following a landslide is essential for effective emergency response and risk mitigation. However, in complex terrain, conventional field investigations are often restricted by inaccessibility, adverse weather conditions, and potential safety hazards. These constraints impede the rapid and comprehensive disaster assessment, thereby compromising the efficiency of emergency decision-making. Taking the 2025 Junlian landslide in Sichuan, China, as a case study, this study combines time-series interferometric synthetic aperture radar (InSAR) and terrestrial laser scanning (TLS) to establish a rapid, non-contact framework for landslide emergency investigation and failure mechanism analysis. Deformation evolution was reconstructed via time-series InSAR analysis combined with transfer entropy (TE) causality detection, facilitating the extraction of characteristic deformation patterns. Concurrently, high-resolution three-dimensional (3D) terrain models were generated utilizing TLS and unmanned aerial vehicle (UAV) data. This allowed for the automatic identification of discontinuities to reveal their spatial distribution and elucidate their control over the failure mechanism. The investigation identified four dominant joint sets. Two conjugate joint sets dissect the rock mass into wedge-shaped blocks, constituting the structural prerequisite for wedge toppling failure. Extreme rainfall in early 2025 significantly degraded the shear strength of discontinuities and weak interlayers, ultimately triggering slope failure. The results demonstrate that the proposed framework overcomes the limitations of traditional methods in timeliness and safety, providing an efficient, precise, and non-contact solution for post-landslide emergency investigation and mechanism analysis.

Deformation monitoring is essential for the early warning of landslide disasters. Constructing a three-dimensional (3D) deformation field can effectively reveal the overall deformation characteristics of a landslide, thereby enhancing monitoring and early warning performance. Using limited discrete monitoring data obtained from experiments, this study proposed a novel method for reconstructing the 3D deformation field of landslides. An improved iterative closest point algorithm was applied to process multi-temporal point cloud data acquired from terrestrial laser scanning in segments, yielding the surface deformation field of the slope. The potential slip depth was initially estimated using a balanced cross-section algorithm based on surface deformation. Subsequently, a physics-informed deep learning model was employed to integrate limited surface and subsurface deformation data, enabling the reconstruction of the 3D deformation field. Compared to traditional numerical simulation methods and geological interpolation techniques, the proposed approach leverages field-measured data for modeling, offering higher accuracy and a simpler workflow, which facilitates effective real-time monitoring of landslide deformation fields. The reconstructed 3D deformation field aids in identifying potential rupture and slip surfaces, predicting deformation at unmonitored locations, and providing clearer and more intuitive observational outcomes.

In large-scale underground caverns within jointed rock masses, the presence of multiple joints profoundly alters the overall strength and deformation characteristics of the rock, significantly impacting the stability of the surrounding rock. The discrete-based methods often suffer from low modelling and computational efficiency when applied to large-scale projects, while conventional continuum-based methods fail to accurately capture the governing influence of joints on the mechanical response of rock masses. To address these limitations, this study develops a three-dimensional Ubiquitous Multiple Joint Model (UMJM) that incorporates the mechanical behavior of multiple joint sets within a continuum framework. And the distance from the stress state point to the corresponding yield surface was used to determine the sequential yielding of multiple joint sets. Its accuracy and efficiency in strength prediction and deformation characterization is validated through uniaxial compression tests and circular tunnel excavation simulations. Subsequently, the UMJM is applied to the stability analysis of the left-bank underground caverns at the Baihetan Hydropower Station. The results demonstrate that the model accurately captures the large deformation behavior of surrounding rock under the interaction of joints and faults, with the computed deformations closely aligning with field monitoring data. Moreover, it reveals the controlling influence of joints on the evolution of plastic zones and stress perturbations. The UMJM enhances the realism of rock mass response simulations in jointed rock engineering without compromising computational efficiency, making it well-suited for large-scale underground engineering applications.

Comprehending the dynamics and flow resistance of granular-fluid systems is essential for mitigating natural hazards. This study presents a series of laboratory experiments that systematically investigate how physical attributes and topographical conditions affect granular-fluid flow behavior. The results demonstrate that the velocity, shear rate, and basal stress of granular-fluid flows are strongly influenced by the composition and slope. The flow dynamics are primarily governed by solid inertial stress and liquid viscous stress. Solid inertial stress reduces the apparent friction coefficient, leading to higher flow velocity, while liquid viscous stress exerts the opposite effect. An inertial-viscous model incorporating the effects of solid inertial and liquid viscous stresses is proposed to accurately capture the flow resistance in granular-fluid flow. This model helps interpret the contributions of these stresses to flow resistance and their influences on flow dynamics. Moreover, analysis of small particle size compositions indicates that special refinements are required to accurately depict the flows of nearly viscoplastic materials. Accordingly, a more comprehensive model that accounts for solid particle contact friction is thus proposed, providing new insights into internal stress transitions and flow regime variations relevant to geological hazards such as debris flows.

Complex topographic and climatic conditions in mountainous canyon regions often introduce severe decorrelation noise and atmospheric delay errors in interferometric synthetic aperture radar (InSAR) monitoring, thereby restricting the accuracy of landslide identification and deformation inversion. To address this challenge, we propose a deep learning based multi-source noise suppression framework that integrates a U-Net architecture with residual connections to construct an end-to-end supervised network. The core of the technique is that training samples were generated from high-quality unwrapped phases and real noise images, enabling the model to automatically recognize and correct complex interference signals. A typical reservoir area in southwestern China was selected to build a training dataset containing multiple temporal baselines and diverse noise types. After training, the model was applied to real unwrapped InSAR phases. It reduced the phase standard deviation to 15% of the original value and lowered the phase-topography correlation to nearly zero. The method effectively suppresses noise induced by low coherence and atmospheric delays and maintains stable performance under complex noise conditions. The corrected phases greatly enhanced the clarity of landslide deformation boundaries and reduced background noise in SBAS-InSAR time series analysis. As a result, the derived deformation estimates became smoother and more reliable, showing strong consistency with UAV LiDAR and field observations. The model also demonstrates strong generalization capability in untrained areas, indicating its potential for large-scale InSAR deformation monitoring in complex mountainous terrains. Overall, the proposed approach offers an efficient and scalable solution for enhancing InSAR deformation interpretation accuracy in mountainous canyon regions.

Monitoring steep slopes in mountainous canyon areas has always been a challenging problem, especially during the construction of large hydropower projects. Effective monitoring is crucial for construction safety and operational security. However, under complex terrain conditions, existing monitoring methods have significant limitations and cannot comprehensively and accurately cover steep slopes. To address the above challenges, this study proposes a multi-temporal UAV-based photogrammetric offset tracking (POT) monitoring method assisted by terrestrial laser scanning (TLS), which is primarily applicable to rocky and texture-rich steep slopes. This method utilizes TLS point cloud data to provide supplementary ground control points (TLS-GCPs) for UAV image modeling, effectively overcoming the difficulty of deploying conventional RTK ground control points (RTK-GCPs) on high and steep slopes, thereby significantly improving the accuracy of UAV-based Structure-from-Motion (SfM) models. In a case study at a hydropower station, we employed TLS-assisted UAV modeling to produce high-precision UAV images. Using POT technology, we successfully identified signs of slope deformation between January 2024 and December 2024. Comparative experiments with traditional algorithms demonstrated that in areas where RTK-GCPs cannot be deployed, this method greatly enhances UAV modeling accuracy, fully meeting the monitoring requirements for steep slopes in complex terrains.

Rapid and accurate identification of potential landslides is essential for disaster prevention and mitigation. Interferometric Synthetic Aperture Radar (InSAR) provides an effective tool for detecting slow-moving landslide deformation. However, in mountainous areas with dense vegetation and strong atmospheric delays, InSAR noise and deformation can be highly intermingled in both amplitude and spatial morphology. Traditional image-segmentation models tend to rely primarily on visual similarity rather than physically plausible temporal evolution, which limits their ability to achieve accurate automated recognition when transferred to complex, noise-dominated environments. To address these challenges, this paper proposes a Fusion Spatial–Temporal Network (FuSTNet). FuSTNet captures evolution patterns from landslide deformation time series, while combining deformation-rate maps and digital elevation model (DEM) data to encode slope geometry constraints. In addition, a geospatial continuity constraint is introduced to incorporate the prior that landslide deformation generally exhibits local spatial continuity, thereby reducing false detections caused by isolated noise. Experimental results show that FuSTNet achieves 93.70% mIoU, 96.69% recall, and 96.75% F1-score on the test set. Its detection results are highly consistent with expert interpretation and further identify potential landslides with low-amplitude deformation that may be overlooked during manual mapping. The proposed framework shifts the emphasis from image segmentation to dynamics-aware discrimination, improving the mechanism-level transparency and geological consistency of the predictions and providing a reliable approach for InSAR-based landslide-deformation recognition in mountainous areas affected by strong noise.

Cadmium (Cd) isotope signatures in natural soil organic matter and fractionation during Cd partitioning among soil organic and mineral components remain unclear, yet they are critical for tracing Cd fate in soils. This study focused on particulate organic matter (POM), a labile fraction of organic matter with significant Cd enrichment, and POM (2000250 and 25053 μm), mineral (2000250 and 25053 μm) and organo-mineral (< 53 μm) fractions were physically separated from six contaminated soils for Cd concentration and isotope analyses. Cadmium concentrations in the POM fractions were 0.8451.1 fold higher than those in the mineral fractions. Carboxylic groups drove Cd enrichment in POM whereas iron (Fe) oxides dominated Cd sequestration in the mineral fractions, with POM systematically enriched in heavy Cd isotopes relative to the mineral fractions (Δ114/110CdPOM-mineral = 0.090.50‰). The observed Cd isotope fractionation from mineral to POM here was different from that for humic acid (HA) complexation preferring light Cd isotopes (Δ114/110CdHA-solution = -0.15 ± 0.01‰). Cadmium-carboxyl complexation appeared to be a major mechanism controlling Cd isotope signatures in POM and POM with more hydroxylic groups was likely to enrich heavier Cd isotopes. The enrichment of Fe oxides likely contributed to the lighter Cd isotope compositions in the coarser mineral fractions. Cadmium isotope fractionation between the POM and mineral fractions is in accord with an equilibrium-like isotope fractionation pattern, indicating reversible Cd exchange between the mineral and POM fractions via soil solutions. This study provides the first systematic assessment of Cd isotope fractionation associated with the distribution of Cd in different soil pools. As such, it advances mechanistic understanding of Cd interaction with the solid organic and mineral phases of soils.

Rapid expansion infrastructure on Qinghai-Tibet Plateau (QTP) demands optimal design of structural and non-structural engineering measures to mitigate geohazards while insufficient historical records hinder this effort. Dendrogeomorphology, which uses tree rings to reconstruct past hazards and inform engineering practices, remains underutilized and lacks clear operational areas on the QTP. To better exert its role in basic data collection and serve key projects, this study first assesses potential regions for dendrogeomorphology on the QTP. By overlapping coniferous forest distributions with socioeconomic, disaster, and environmental indicators, using spatial overlay and PCA, it identifies the southeastern QTP as an undeveloped but ideal region due to accessible sampling and its abundant conifers, with spruce (40.7%), fir (30.7%), and hemlock (9.2%) as key species. High-potential municipalities include Gannan, Diqing, and Nyingchi; basins of the Yarlung Zangbo, Yangtze, and Yellow Rivers; and key nature reserves such as Yarlung Zangbo Grand Canyon, Miyaluo, Gongga Mountain, Taining Yuke, Bita Sea, Baima Snow Mountain, and Ruoergai. Motuo and Kangding are optimal for studying post-earthquake disasters, while Qamdo, Gannan, and Shangri-La show strong potential for calibrating historical records. Then, we selected 12 high-potential zones on the southeast margin of the QTP for verification, sampling and analysis, which illustrates the reliability of the model. Integrating Space-Sky-Ground and tree-ring technologies will enable researchers and engineers to reconstruct century-scale disaster histories, providing evidence-based support on the QTP. This study offers a transferable approach in engineering geology to obtain alternative data through tree rings and identify potential sites in mountainous regions with scarce data.

Debris avalanches characterized by rapid granular flow pose significant hazards, yet the joint influence of particle size distribution, moisture content, and compound topography on their dynamics remains poorly constrained. To address this, we performed multivariable flume experiments simulating the transformation of landslides into granular flows under varying gradations and moisture levels (0-4 %). Our results reveal a robust linear scaling law between deposit runout and width, establishing a stable geometric proportion governing planform spreading. Kinematic analysis demonstrates that moisture content shortens total motion duration primarily by accelerating the deposition stage, whereas mixed gradations exhibit regime-shifting behaviors due to particle segregation. Crucially, we introduce a planform spreading-angle framework that unifies these observations into a single back-calculated coefficient (eta). This parameter quantifies lateral spreading capacity, increasing monotonically with particle size while remaining weakly sensitive to moisture for single-size groups. Furthermore, a volumetric-equivalent scale analysis indicates that moisture drives a three-dimensional redistribution of the deposit mass rather than uniform scaling. These findings offer a simplified, physically based approach to predict the runout and spreading of non-cohesive debris avalanches under complex terrain conditions.

As a critical underground structure, the seismic response of a subway station is not only governed by seismic motions but is also influenced by uncertainties in site conditions. This study employs the probability density evolution method (PDEM) to systematically investigate the seismic fragility of a three-story, three-span subway station, explicitly considering the uncertainty of site shear-wave velocity (Vs). Sobol sampling is first used to generate the composite samples that simultaneously incorporate the uncertainties of seismic motion and Vs. Based on these samples, a soil-structure interaction finite element model is established to provide training data. A one-dimensional convolutional neural network (1D-CNN) is introduced to achieve rapid prediction of interstory drift ratio time histories. Subsequently, PDEM is applied to compute the time-varying probability density distribution of structural responses. Based on this, the seismic fragility curves and time-dependent fragility surfaces were developed to assess the damage probabilities of the subway station. It was found that Vs uncertainty significantly increases the dispersion of structural responses, particularly at higher seismic intensities, and that neglecting it may overestimate damage probabilities. Compared with the lognormal model, PDEM provides a more accurate characterization of probabilistic response features and reveals the temporal evolution of structural damage.

Combined contamination of soils with thallium (Tl) and cadmium (Cd) is widespread. Despite comparable accumulation of Tl and Cd by rice plants, the mechanisms underlying significantly lower Tl concentrations than Cd in grains remain unclear. Here, pot experiments, rhizotron observations and field sampling were combined to investigate the fate of Tl/Cd across the soil-rice system. Sequential extraction shows that flooding decreased Tl/Cd mobility in bulk soil, with Cd enriched in the oxidizable fraction and Tl in the residual fraction. In the rhizosphere, planar optode and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) reveals that flooding induced iron-plaque formation, restricting rice Tl/Cd uptake compared with continuous drainage. Tissue- and microscale-level (LA-ICP-MS) analyses show that Tl transfer from node-to-leaf was 3.00-8.85 times that of Cd which exhibited greater upward translocation. In brown rice, 66.0-86.0% of Cd transported to grains was allocated to the endosperm. In contrast, Tl was preferentially enriched in the embryo, which represented only 1.78-2.66% of the biomass but contained 42.9-60.8% of Tl, potentially associated with high K demand and K-related transport pathways. Thus, contrasting shoot translocation and brown rice partitioning patterns of Tl/Cd explain differential grain concentrations, offering mechanistic insights for safe rice production.

Red-bed soil is a widely existing type of soil, and its thermal conductivity represents a fundamental indicator of thermodynamic behavior and plays a vital role in numerous civil engineering applications. In this study, a predictive model is developed to estimate the thermal conductivity of red-bed soil. The model begins with the derivation of an expression for the equivalent thermal conductivity of a three-phase porous medium based on Wiener's boundary theory. Following this, a series of experiments are performed, including specific heat capacity tests on three remolded soil samples and thermal conductivity tests carried out under two water contents (0% and 10%). In these tests, three different porosity and saturation conditions are adjusted through compaction cycles. Based on the experimental results, numerical simulations are then carried out using a point heat source to explore how thermal conductivity varies with changes in porosity and saturation levels. Finally, a predictive model is constructed based on these findings. The results indicate that this predictive model provides good predictions of thermal conductivity under various scenarios and achieves satisfactory application effects.

Toppling failure is a fundamental mode of instability in rock slopes and occurs predominantly in reservoir bank anti-dip bedded rock masses. Reservoir impoundment changes seepage conditions and weakens slopes, whereas discontinuity non-persistence introduces uncertainty and complicates the identification of coupled toppling-sliding mechanisms. To address this, a probabilistic framework using the Goodman-Bray limit equilibrium method is developed. Equivalent strength parameters are introduced to unify the strength contrast between unsaturated and saturated segments along a common basal surface. Basal discontinuity connectivity is modeled as a random variable, and a Monte Carlo simulation is used to derive failure mode probabilities and a probability-weighted factor of safety. The framework is applied to the Huangcaoping anti-dip slope in the Dagangshan reservoir area at a normal water level of 1130 m. The most probable scenario has a probability of 0.116, involving sliding at 1120-1420 m and toppling at 1420-1550 m, with a probability-weighted mean factor of safety of 0.978. Predicted failure characteristics and deformation intervals are consistent with engineering observations, confirming the method's effectiveness. This integration enables the simultaneous characterization of stability levels and the evolution mechanism. The approach provides mechanism-explicit mode likelihoods and a robust stability metric to support hazard assessment, monitoring placement, and reinforcement design.

The high-speed movement characteristics of landslides represent a core scientific challenge in the fields of geotechnical engineering and geological hazards. Their essence lies in uncovering the physical mechanism underlying the reduction in shear strength during the sliding process. Existing thermal-hydro-mechanical (THM) coupling models often overlook the influence of hydrodynamic pressure on the dynamics of reservoir landslides, leading to discrepancies between simulation results and real-world scenarios. To address this gap, this study develops a theoretical framework for THM-coupled landslide dynamics that incorporates hydrodynamic pressure, grounded in the principles of mass conservation, energy conservation, and momentum. The framework employs the Crank-Nicholson finite difference scheme for discretizing the governing equations and utilizes the Thomas algorithm to solve the resulting tridiagonal matrix system, enabling dynamic simulation of temperature distribution in the shear zone, pore water pressure, and landslide kinematics. Furthermore, the effectiveness of the proposed model is validated through engineering case studies of the 2017 Xinmo landslide and the Yanshangou landslide in the Baihetan Reservoir, while the mechanism driving the high-speed movement of these landslides is analyzed in depth. Simulation results reveal two distinct softening mechanisms during landslide instability: frictional softening and THM coupling softening. These mechanisms exhibit phase-dependent dominance and synergistic effects throughout the sliding process. In conclusion, the proposed model provides reliable theoretical support for predicting the landslide dynamics in reservoir areas.

Highlights What are the main findings? A block-wise ICP approach is proposed to directly retrieve 3D displacement vectors from multi-temporal TLS point clouds. Compared with M3C2, it produces a more continuous displacement field and clearer deformation boundaries, which were validated using a tower target and a seasonal vegetation change scene. What are the implications of the main findings? The method improves interpretable deformation mapping under occlusion, heterogeneous point density, and vegetation disturbances, which are common in field landslide monitoring. It supports practical boundary delineation and target-based displacement verification and can be extended via adaptive multi-scale blocking and uncertainty quantification.Highlights What are the main findings? A block-wise ICP approach is proposed to directly retrieve 3D displacement vectors from multi-temporal TLS point clouds. Compared with M3C2, it produces a more continuous displacement field and clearer deformation boundaries, which were validated using a tower target and a seasonal vegetation change scene. What are the implications of the main findings? The method improves interpretable deformation mapping under occlusion, heterogeneous point density, and vegetation disturbances, which are common in field landslide monitoring. It supports practical boundary delineation and target-based displacement verification and can be extended via adaptive multi-scale blocking and uncertainty quantification.Abstract Terrestrial laser scanning (TLS) provides dense point clouds for landslide monitoring, yet occlusion, heterogeneous point density, and seasonal vegetation introduce noise and unstable deformation boundaries in multi-temporal change detection. To overcome the limitations of the multiscale model-to-model cloud comparison (M3C2) method under dominant downslope tangential motion and vegetation disturbance, we propose a block-wise ICP method to retrieve 3D displacement vectors. The scene is partitioned into local sub-blocks; rigid registration is performed within each sub-block, and the estimated translation is assigned to the sub-block center. A two-stage matching and quality control procedure removes under-constrained sub-blocks, enabling the direct retrieval of 3D displacement vectors and interpretable boundaries. Applied to the Longxigou landslide in Wenchuan using RIEGL VZ-2000i surveys on 1 November 2023 and 23 May 2024, the proposed method produces a more continuous displacement field and clearer boundaries than M3C2. For a tower target, manual measurements indicate a displacement of 0.41-0.63 m; our estimates are within 0.33-0.40 m, whereas M3C2 mostly falls between -0.25 and 0.25 m. In a seasonal vegetation change scene, we detect a canopy envelope expansion of approximately 0.20-0.40 m, while M3C2 shows scattered canopy responses that hinder boundary interpretation. A sensitivity analysis indicates a block-scale trade-off between boundary stability and peak preservation, motivating adaptive multi-scale blocking and uncertainty quantification.

Excavating spillways is a common measure for mitigating landslide dam risks. While proven effective, the mechanisms through which spillway morphology influences breach dynamics are not fully quantified. This study employs numerical simulations to analyze how various morphological parameters (position, slope, longitudinal profile, cross-section) influence breach processes. The results reveal that a spillway confines the overflow to a predefined channel, preventing random erosion and reducing peak discharge by 13.7 to 25.1

Landslide dams often undergo seepage due to poor particle gradation and loose structure, yet most existing studies focus solely on overtopping-induced breaching mechanisms, neglecting the potential influence of pre-breaching seepage. Seepage may alter the dam’s erodibility, structural stability, and material composition, thereby affecting the overtopping breaching process. Through flume experiments, this study investigates the breaching mechanisms of cohesionless landslide dams with different gradations within the same particle size range under coupled seepage-overtopping conditions. The results demonstrate that pre-breaching seepage significantly impacts breaching dynamics. Within a specific particle size range, compared to pure overtopping, seepage reduces downstream slope stability, increases material erodibility, shortens breaching duration, amplifies peak discharge, and advances the timing of peak flow. As the median particle size (D50) increases, the amplification effect of seepage on peak discharge initially increases then decreases, the advancement of peak flow timing diminishes, and the breach erosion rate declines. When D50 is sufficiently large, seepage has negligible effects on breach development. For smaller D50, seepage markedly accelerates breach widening and deepening. Furthermore, coupled seepage-overtopping extends the downstream deposition area and exacerbates channel erosion due to differences in sediment sorting. These findings highlight the critical role of seepage in landslide dam breaching, providing a scientific basis for hazard prevention and mitigation.

To predict adsorption profiles in more complex scenarios of high gradient magnetic separation (HGMS), we further propose two restrictive criteria (RC), including RC1) 90 degrees >= alpha >= 0 degrees, 60 degrees >= theta >= 0 degrees; and RC2) F-m/F-o > (beta/180 degrees)(Fm/Fo). Among them, V is a vector whose direction is determined by solving the Laplace equation with no physical meaning and an additional infinite element domain, alpha is the angle between F-m and V, theta is the angle between F and V, and beta is the angle between F-m and F-o (F-o = F-d + G). In particular, F-m is the modulus of F-m (i.e. F-m = |F-m|), and F-o is the modulus of F-o (i.e. F-o = |F-o|). Recent experiments have shown that the final profile upstream of the matrix is not fan-shaped when the direction of the background magnetic field is longitudinal, but rather a peach shaped profile with a slight addition of a pointed cap. RC we proposed here can fully reproduce this phenomenon. In addition, RC can not only actively respond to the attenuation phenomenon of the adsorption profile with increasing release velocity u(0) of PBF, but also to the saddle buildup effect caused by non slip boundary layers of the matrix support plate in three-dimensional HGMS.