Search Results (1,064)

Tailings storage facilities (TSFs) have experienced numerous global failures, many linked to active deposition on tailings beaches. Understanding these processes is vital for effective management. As deposition alters surface elevation, developing an explainable model to predict the changes can enhance insight into deposition dynamics and support proactive TSF management. This study applies deep learning (DL) to predict surface elevation changes in tailings storage facilities (TSFs) from high-resolution digital elevation models (DEMs) generated from UAV photogrammetry. Three DL architectures, including multilayer perceptron (MLP), fully convolutional network (FCN), and residual network (ResNet), were evaluated across spatial patch sizes of 64 × 64, 128 × 128, and 256 × 256 pixels. The results show that incorporating broader spatial contexts improves predictive accuracy, with ResNet achieving an $R2$ of 0.886 at the 256 × 256 scale, explaining nearly 89% of the variance in observed deposition patterns. To enhance interpretability, SHapley Additive exPlanations (SHAP) were applied, revealing that spatial coordinates and curvature exert the strongest influence, linking deposition patterns to discharge distance and microtopographic variability. By prioritizing predictive performance while providing mechanistic insight, this framework offers a practical and quantitative tool for reliable TSF monitoring and management. Full article

Underreamed anchor bolts, as an emerging anchoring element in geotechnical engineering, operate via a fundamentally distinct load transfer mechanism compared with conventional friction type anchors. The accurate and reliable prediction of their ultimate bearing capacity constitutes a pivotal technological impediment to their broader engineering adoption. Firstly, this paper systematically elucidates the constituent mechanisms of underreamed anchor resistance and their progressive load transfer trajectory. Subsequently, in situ full-scale pull-out experiments are leveraged to decompose the load–displacement response throughout its entire evolution. The multi-stage development law and the underlying mechanisms governing the evolution of anchorage characteristics are thereby elucidated. Based on the experimental dataset, a three-dimensional elasto-plastic numerical model is rigorously established. The model delineates, at high resolution, the failure mechanism of surrounding soil mass and the spatiotemporal evolution of its three-dimensional displacement field. A definitive critical displacement criterion for the attainment of the ultimate bearing capacity of underreamed anchors is established. Consequently, analytical models for the ultimate side frictional stress and end-bearing capacity at the limit state are advanced, effectively circumventing the parametric uncertainties inherent in extant empirical formulations. Ultimately, characteristic parameters of the elasto-plastic branch of the load–displacement curve are extracted. An ultimate bearing capacity prognostic framework, founded on an optimized hyperbolic model, is established. Its superior calibration fidelity to the evolving load–displacement response and its demonstrable engineering applicability are rigorously substantiated. Full article

One widely used method for analyzing vegetation growth from satellite imagery is the Normalized Difference Vegetation Index (NDVI), a key metric for assessing vegetation dynamics. NDVI varies not only spatially but also temporally, which is essential for analyzing vegetation health and growth patterns over time. High-resolution, cloud-free satellite images, particularly from publicly available sources like Sentinel, are ideal for this analysis. However, such images are not always available due to cloud and shadow contamination. To address this limitation, we propose a model that integrates both the temporal and spatial aspects of the data to approximate the missing or contaminated regions. In this method, we separately approximate NDVI using spatial and temporal components of the time-varying satellite data. Spatial approximation near the boundary of the missing data is expected to be more accurate, while temporal approximation becomes more reliable for regions further from the boundary. Therefore, we propose a model that leverages the distance transform to combine these two methods into a single, weighted model, which is more accurate than either method alone. We introduce a new decay function to control this transition. We evaluate our spatiotemporal model for approximating NDVI across 16 farm fields in Western Canada from 2018 to 2023. We empirically determined the best parameters for the decay function and distance-transform-based model. The results show a significant improvement compared to using only spatial or temporal approximations alone (up to a 263% improvement as measured by RMSE relative to the baseline). Furthermore, our model demonstrates a notable improvement compared to simple combination (up to 51% improvement as measured by RMSE) and Spatiotemporal Kriging (up to 28% improvement as measured by RMSE). Finally, we apply our spatiotemporal model in a case study related to improving the specification of the peak green day for numerous fields. Full article

Understanding the fluid storage and production mechanisms in sedimentary rocks is vital for optimising natural gas extraction and subsurface resource management. This study applies high-resolution X-ray computed tomography (≈15 μm) to digitise rock samples from onshore Cyprus, producing digital rock models from DICOM images. The workflow, including digitisation, numerical simulation of natural gas flow, and experimental validation, demonstrates strong agreement between digital and laboratory-measured porosity, confirming the methods’ reliability. Synthetic sand packs generated via particle-based modelling provide further insight into the gas storage mechanisms. A linear porosity–permeability relationship was observed, with porosity increasing from 0 to 35% and permeability from 0 to 3.34 mD. Permeability proved critical for production, as a rise from 1.5 to 3 mD nearly doubled the gas flow rate (14 to 30 fm³/s). Grain morphology also influenced gas storage. Increasing roundness enhanced porosity from 0.30 to 0.41, boosting stored gas volume by 47.6% to 42 fm³. Although based on Cyprus retrieved samples, the methodology is applicable to sedimentary formations elsewhere. The findings have implications for enhanced oil recovery, CO₂ sequestration, hydrogen storage, and groundwater extraction. This work highlights digital rock physics as a scalable technology for investigating transport behaviour in porous media and improving characterisation of complex sedimentary reservoirs. Full article

Bulgaria continues to face serious challenges related to air quality. To mitigate traffic-related air pollution and in line with the European regulations, the Metropolitan Municipal Council of Sofia has adopted and introduced low-emission zones (LEZs) in the city centre. The goal of this study is to address the specific needs of urban planning in the city in support of local decision-making. A bespoke emission inventory was developed for the LEZs in Sofia, and high-resolution numerical simulations (100 m resolution) were carried out to assess the effect of the measures implemented to reduce emissions in the central part of the city. The results show a decrease in nitrogen dioxide concentrations along major roads and intersections, but projected concentrations will still be high. No significant improvement is expected for particulate matter pollution due to the limitations of this study. High-resolution (100 m) emission inventories of domestic heating, minor roads, and bare soil surfaces, the major sources of particulate matter pollution, are not included in this study. An integrated model is needed to analyse and compare different scenarios for the development of the transport system, and the gradual introduction of LEZs must be accompanied by a number of other additional measures and actions. Full article

In this article, the mechanism of relaxation polarization currents occurring at a constant temperature (isothermal process) in crystals with ionic-molecular chemical bonds (CIMBs) in an alternating electric field was investigated. Methods of the quasi-classical kinetic theory of dielectric relaxation, based on solutions of the nonlinear system of Fokker–Planck and Poisson equations (for the blocking electrode model) and perturbation theory (by expanding into an infinite series in powers of a dimensionless small parameter) were used. Generalized nonlinear mathematical expressions for calculating the complex amplitudes of relaxation modes of the volume-charge distribution of the main charge carriers (ions, protons, water molecules, etc.) were obtained. On this basis, formulas for the current density of relaxation polarization (for transient processes in a dielectric) in the k-th approximation of perturbation theory were constructed. The isothermal polarization currents are investigated in detail in the first four approximations (k = 1, 2, 3, 4) of perturbation theory. These expressions will be applied in the future to compare the results of theory and experiment, in analytical studies of the kinetics of isothermal ion-relaxation (in crystals with hydrogen bonds (HBC), proton-relaxation) polarization and in calculating the parameters of relaxers (molecular characteristics of charge carriers and crystal lattice parameters) in a wide range of field parameters (0.1–1000 MV/m) and temperatures (1–1550 K). Asymptotic (far from transient processes) recurrent formulas are constructed for complex amplitudes of relaxation modes and for the polarization current density in an arbitrary approximation k of perturbation theory with a multiplicity r by the polarizing field (a multiple of the fundamental frequency of the field). The high degree of reliability of the theoretical results obtained is justified by the complete agreement of the equations of the mathematical model for transient and stationary processes in the system with a harmonic external disturbance. This work is of a theoretical nature and is focused on the construction and analysis of nonlinear properties of a physical and mathematical model of isothermal ion-relaxation polarization in CIMB crystals under various parameters of electrical and temperature effects. The theoretical foundations for research (construction of equations and working formulas, algorithms, and computer programs for numerical calculations) of nonlinear kinetic phenomena during thermally stimulated relaxation polarization have been laid. This allows, with a higher degree of resolution of measuring instruments, to reveal the physical mechanisms of dielectric relaxation and conductivity and to calculate the parameters of a wide class of relaxators in dielectrics in a wide experimental temperature range (25–550 K). Full article

Flow dynamics were characterized and stable zones in diversions were quantified using physical modeling, in situ experiments, and 3D numerical simulations. ADV (1 cm spatial resolution) and water-level probes (0.01 cm spatial resolution) were used in the physical experiments in a rectangular channel. ADCP (resolution of 50 cm) was employed for in situ validation at a northern China hub. Numerical simulations using ANSYS 2022R2 Fluent software with RNG k-ε and VOF showed little error (<15%) compared to the experiments. The results quantified the diversion zone into four sub-regions: acceleration (length 0.8–1.2 h); stabilization (1.2–3.5 h); diffusion deceleration (3.5–5.0 h); and stagnation (localized eddies, diameter 0.3–0.8 d). The stable zone length was dominantly controlled by the nonlinear coupling of geometric (B_s/B_m, 42%) and hydraulic (Fr, 28%) parameters. Upstream and downstream stable zone empirical models showed high accuracy (R² = 0.83 and 0.76, p < 0.01), with an average relative error <15%. Based on the proposed zoning principles and flow characteristics, measurement facilities in the irrigation area are presented. These tools enhance irrigation diversion design and management for improved water efficiency. Full article

In this study, a numerical model consisting of high-resolution hydrodynamic and Lagrangian particle tracking modules based on the Finite-Volume Coastal Ocean Model framework was established to simulate the hydrodynamic conditions and characteristics of the sedimentation of aquaculture-derived organic matter (AOM) from cage aquaculture in Sansha Bay. The results showed that Sansha Bay was characterized by regular semidiurnal tides and large tidal ranges. Reciprocating currents with main currents directed northward and southward during the rising and falling tides, respectively, predominated the main channels of the bay. Residual feed had larger settling velocities than feces. The maximal dispersion distances of residual feed and feces during the spring tide were 217.1 and 1805.7 m, respectively, three times those during the neap tide (74.2 and 675.6 m, respectively). During the spring tide, the largest dispersion distance of AOM occurred at the rush moment. The AOM movement trajectories were mainly controlled by the main currents. Both the tidal structure and current characteristics affected the AOM sedimentation in Sansha Bay. The sedimentation characteristics of AOM were unrelated to feeding intensity. The results of simulations agreed with the field observations in this study, suggesting that the estimated model had a good accuracy and sensitivity. Full article

This study evaluates the forecast performance of four radiation parameterization schemes—the Rapid Radiative Transfer Model for General Circulation Models (RRTMG), its improved version RRTMG-K, the infrequently applied variant, RRTMG-K_60x, and the neural network emulator, RRTMG-K_NN, within a high-resolution numerical weather prediction (NWP) model. The evaluation uses satellite-derived observations of Outgoing Longwave Radiation (OLR) and Outgoing Shortwave Radiation (OSR) from the Clouds and the Earth’s Radiant Energy System (CERES) over the Korean Peninsula during 2020, including an extreme case study of Typhoon Haishen. Results show that RRTMG-K reduces RMSEs by 4.8% for OLR and 17.5% for OSR relative to RRTMG, primarily due to substantial bias reduction (42.3% for OLR, 60.4% for OSR). The RRTMG-K_NN scheme achieves approximately 60-fold computational speedup while maintaining similar or slightly better accuracy than RRTMG-K; specifically, it reduces OLR errors by 1.2% and OSR errors by 1.6% compared to the infrequently applied RRTMG-K_60x. In contrast, the infrequent application of RRTMG-K (RRTMG-K_60x) slightly increases errors, underscoring the trade-off between computational efficiency and accuracy. These findings demonstrate the value of integrating advanced satellite flux observations and machine learning techniques into the evaluation and optimization of radiation schemes, providing a robust framework for improving cloud–radiation interaction representation in NWP models. Full article

The direct current resistivity method holds advantages such as rapid, efficient, and automatic data acquisition. It is an important geophysical exploration technology for monitoring dynamic changes in subsurface geology. However, this method has such issues as volume effect and non-uniqueness in inversion. To meet the demand for high-resolution direct current resistivity inversion of dynamic geological models characterized by discontinuous changes, this study proposed a cross-gradient constrained time-lapse inversion method, thereby enhancing inversion imaging accuracy. A cross-gradient constraint term between models was incorporated into the objective function of time-lapse inversion to constrain the structural consistency and highlight local resistivity changes. This method avoided excessively smooth imaging as often caused by over-reliance on a reference model in time-lapse inversion, thereby significantly improving both the spatial resolution and quantitative accuracy of direct current resistivity monitoring inversion images. Numerical examples confirmed that the proposed method delivers higher inversion imaging accuracy in identifying dynamic resistivity changes, evidenced by a substantially lower normalized mean-square error (MSE). Furthermore, physical model experiments and a case study confirmed the stability of this method under actual monitoring conditions. The proposed method provides a more precise and effective inversion imaging technique for refined monitoring of dynamic changes in subsurface geologic bodies. Full article

The growing threat of dam-break events, fueled by aging infrastructure and climate change, necessitates comprehensive risk management and mitigation strategies. Experimental studies on partial dam-break flows are pivotal for understanding the complex dynamics of these events, particularly in assessing flood risk and refining predictive models. This review synthesizes current experimental investigations on three-dimensional (3D) partial dam-break flows, with an emphasis on breach dynamics, wave impacts, and the role of urban structures. It highlights the challenges in capturing high-resolution 3D flow characteristics and the advancements in measurement techniques such as particle tracking velocimetry and ultrasonic distance meters. The paper discusses the integration of experimental data with numerical models to validate and improve predictive capabilities, stressing the need for continuous refinement of experimental setups and computational approaches. Gaps in the current literature, including the under-representation of irregular breach geometries and complex terrain, are identified, and future research directions are proposed to address these shortcomings. This work underscores the importance of hybrid measurement techniques and interdisciplinary collaboration to enhance dam-break modeling accuracy and flood risk mitigation. Full article

Extreme weather events, such as typhoons, induce strong wave–current interactions that significantly alter nearshore hydrodynamic conditions, particularly in shallow intertidal zones. This study investigates the influence of wind speed and water depth on wave–current coupling under typhoon conditions in Shenhu Bay, southeastern China—a semi-enclosed bay that hosts multiple ancient forest relics within its intertidal zone. A two-tier numerical modeling framework was developed, comprising a regional-scale hydrodynamic model and a localized high-resolution model centered on a protective structure. Validation data were obtained from in situ field observations. Three structural scenarios were tested: fully intact, bottom-blocked, and damaged. Results indicate that wave-induced radiation stress plays a dominant role in enhancing flow velocities when wind speeds exceed 6 m/s, with wave contributions approaching 100% across all water depths. However, the linear relationship between water depth and wave contribution observed under non-typhoon conditions breaks down under typhoon forcing. A critical depth range was identified, within which wave contribution peaked before declining with further increases in depth—highlighting its potential sensitivity to storm energy. Moreover, structural simulations revealed that bottom-blocked devices, although seemingly more enclosed, may be vulnerable to vertical pressure loading due to insufficient water exchange. In contrast, perforated designs facilitate an internal–external hydrodynamic balance, thereby enhancing protective effect. This study provides both theoretical and practical insights into intertidal structure design and paleo-heritage conservation under extreme hydrodynamic stress. Full article

Since the early 1990s, satellite altimetry has significantly improved our understanding of coastal and estuarine dynamics. The Casamance estuary in Senegal exemplifies a tropical microtidal system with limited instrumentation despite pressing environmental, social, and navigational concerns. This study explores the potential of SWOT satellite data to support the calibration and validation of high-resolution hydrodynamic models. Multi-source dataset of in situ measurements and altimetry observations has been combined with numerical modelling to investigate the hydrodynamics in response to physical drivers. Statistical metrics were used to quantify model performance. Results show that SWOT accurately captures water level variations in the main channel (width 800 m to 5 km), including both tidal and non-tidal contributions, with high correlation (R = 0.90) and low error (RMSE < 0.25 m). Performance decreases in tributaries (R = 0.42, RMSE up to 0.34 m), due to interpolated bathymetry and complex local dynamics. Notably, Delft3D achieves R = 0.877 at Diogué (RMSE = 0.204 m) and R = 0.843 at Carabane (RMSE = 0.225 m). These findings highlight the strategic value of SWOT for improving hydrodynamic modelling in data-scarce estuarine environments. Full article

Synthetic Aperture Radar (SAR) is one of the few instruments capable of providing high-resolution global two-dimensional (2D) measurements of ocean waves. Since 2014 and then 2016, the Sentinel-1A/B satellites, whenever operating in a specific wave mode (WV), have been providing ocean swell spectrum data as Level-2 (L2) OCeaN products (OCN), derived through a quasi-linear inversion process. This WV acquires small SAR images of 20 × 20 km footprints alternating between two sub-beams, WV1 and WV2, with incidence angles of approximately 23° and 36°, respectively, to capture ocean surface dynamics. The SAR imaging process is influenced by various modulations, including hydrodynamic, tilt, and velocity bunching. While hydrodynamic and tilt modulations can be approximated as linear processes, velocity bunching introduces significant distortion due to the satellite’s relative motion with respect to the ocean surface and leads to constructive but also destructive effects on the wave imaging process. Due to the associated azimuth cut-off, the quasi-linear inversion primarily detects ocean swells with, on average, wavelengths longer than 200 m in the SAR azimuth direction, limiting the resolution of smaller-scale wave features in azimuth but reaching 10 m resolution along range. The 2D spectral partitioning technique used in the Sentinel-1 WV OCN product separates different swell systems, known as partitions, based on their frequency, directional, and spectral characteristics. The accuracy of these partitions can be affected by several factors, including non-linear effects, large-scale surface features, and the relative direction of the swell peak to the satellite’s flight path. To address these challenges, this study proposes a novel quality control framework using a machine learning (ML) approach to develop a quality flag (QF) parameter associated with each swell partition provided in the OCN products. By pairing collocated data from Sentinel-1 (S1) and WaveWatch III (WW3) partitions, the QF parameter assigns each SAR-derived swell partition one of five quality levels: “very good,” “good,” “medium,” “low,” or “poor”. This ML-based method enhances the accuracy of wave partitions, especially in cases where non-linear effects or large-scale oceanic features distort the data. The proposed algorithm provides a robust tool for filtering out problematic partitions, improving the overall quality of ocean wave measurements obtained from SAR. Moreover, the variability in the accuracy of swell partitions, depending on the swell direction relative to the satellite’s flight heading, is effectively addressed, enabling more reliable data for oceanographic studies. This work contributes to a better understanding of ocean swell dynamics derived from SAR observations and supports the numerical swell modeling community by aiding in the refinement of models and their integration into operational systems, thereby advancing both theoretical and practical aspects of ocean wave forecasting. Full article

Slope instability in open-pit coal mines threatens safety and infrastructure. Displacement phenomena (cracks, deflection, heaving) signal potential failure. While counterweight backfilling stabilizes slopes, site-specific protocols for heterogeneous settings, such as Indonesia’s Barito Basin (Warukin Formation), lack standardization. This study addresses this gap at PT. Bhumi Rantau Energi’s Mahoni Pit by integrating high-resolution displacement monitoring (Leica Nova TM50), geotechnical analysis (RQD, RMR), and numerical modeling (SLIDE 7.0, RS2 v11). The objectives were to characterize the displacement mechanisms, quantify the counterweight effectiveness, and optimize the geometry. The results show “warning”-level velocities (>10 mm.h⁻¹) across points, with peak displacement (907 mm.day⁻¹ at IPD_MHN_26) driven by pore pressure in weak fill/mud layers (c′: 2–20 kPa; thickness: 71–100 m). Counterweights significantly increased the Factor of Safety (FoS) from critical levels (e.g., 0.960, PF = 74.4%) to stable values (e.g., 1.160, PF = 1.8%), representing 20–35% improvements. RS2 identified fill material as the primary displacement zone (max: 2.10 m). Optimized designs featured phased backfilling (200 k–10 M BCM) with a 50 m width and 11° inclination. Tailored counterweight deployment effectively mitigated the instability in slopes underlain by weak strata. The integrated approach provides a validated framework for optimizing designs in similar sedimentary basins, enhancing safety and reducing costs. Full article