Search Results (276)

Recent advances in satellite measurements make satellites suitable candidates for monitoring the ocean environment, especially in the case of offshore wave resources. In this context, the present work aims to evaluate the accuracy of the wave dataset provided by the European Space Agency’s Sea State Climate Change Initiative (or CCI-SS) project, in order to establish its viability to be used for renewable energy applications in general and those associated with some European locations in particular. Seventeen years of ERA5 data (2002–2018) are also considered for comparison with the satellite measurements. The first step is to derive the wave periods corresponding to the significant wave heights provided by the satellite from the ERA5 data by establishing a quadratic relationship between the significant wave height (Hs) and the wave period (Te). As a next step, the local wave conditions are expressed in terms of wave power distribution, also considering the performance of three wave energy generators with nominal power ranging from 250 to 3619 kW. By comparing with ERA5 data, it was observed that the CCI-SS dataset generally overestimates the wave energy for the sites located in the oceanic environment, also indicating much higher values for the converters with a rated power that does not exceed 1000 kW. Full article

This study investigated the mechanical response and instability precursors of highly porous coral blocks from the South China Sea under complex stress paths through conventional triaxial compression tests, two types of triaxial unloading tests, and synchronous acoustic emission (AE) monitoring. The effects of confining pressure, unloading path, and unloading stage on strength, deformation, dilatancy, and failure behavior were examined. The coefficients of variation of dry density, saturated density, porosity, and P-wave velocity were 5.07%, 3.56%, 3.72%, and 5.77%, respectively, indicating relatively limited variability in the measured physical properties, although the influence of specimen heterogeneity cannot be fully excluded. Within the 0–2 MPa confining-pressure range, peak strength increased from 8.81 to 16.85 MPa, whereas axial strain at peak strength changed from 0.33% at 0 MPa to 0.63% at 1 MPa and then decreased to 0.40% at 2 MPa, indicating strong strength sensitivity but a nonmonotonic deformation response. During unloading, all specimens exhibited a transition from compaction to dilatancy. At unloading rates of 0.2 and 0.5 MPa/min, the absolute value of the volumetric strain evolution slope was higher under the increasing-axial-pressure unloading path than under the constant-axial-pressure unloading path, indicating that the path-related difference in dilatancy appears more pronounced under the present test conditions. AE activity increased progressively near peak stress during conventional compression, whereas unloading-induced AE events concentrated near macroscopic failure. Lateral strain anomalies generally preceded AE bursts, suggesting that lateral deformation appears to provide a more sensitive early-warning indicator under the present test conditions. Full article

Microseisms, as the most energetic component of the Earth’s background noise field, represent a forefront area of research where precise location of their sources is paramount. This study systematically investigates the spatiotemporal characteristics of 50 mHz Rayleigh wave microseisms using the dense Shandong array deployed in eastern China, through beamforming and a multi-array combined analysis. The results reveal that the incident direction of the Rayleigh waves exhibits distinct temporal and seasonal variations, primarily originating from four back-azimuth sectors. To further constrain the source regions, we integrate background noise data from the Alaska array and the Venezuela array (supplemented by the Indonesia array). The multi-array product back-projection, by cross-constraining back-azimuths from geographically separated arrays, mitigates the inherent ambiguity of single-array analyses and enables robust global source localization. This approach not only improves the reliability of source attribution but also demonstrates the potential of using microseismic noise as a passive tool for monitoring ocean wave activity and investigating solid-Earth structure. The combined analysis identifies four microseism source regions (M1–M4): the Bering Sea–Gulf of Alaska–Aleutian Islands, the central South Pacific, the southwestern Indian Ocean off southern Africa, and the northeastern North Atlantic–Northern Europe. These source regions fundamentally correspond to areas of elevated significant wave height, confirming the coupled ocean–solid Earth excitation mechanism. These findings provide a methodological basis for future applications of multi-array microseismic monitoring in ocean-climate studies and seismic imaging. Full article

Employing the WaveMIMO methodology, the present numerical study evaluates a submerged horizontal plate (SHP) device under the incidence of representative regular and realistic irregular waves associated with the sea state off the coast of Rio Grande, Brazil. The dual functionality of the SHP device is investigated, considering its operation as a breakwater (BW) and as a wave energy converter (WEC). The main focus of this study is to investigate the effects of numerical beach (NB) positioning on the hydrodynamic response of the SHP. The governing equations for mass, momentum, and volume fraction are solved using the finite volume method (FVM), while the water–air interaction is modeled through the volume of fluid (VOF) approach. The analysis assessed the influence of SHP length (L_p) using five different values. For the tested Rio Grande sea state, SHP geometry, two-dimensional numerical model, and adopted hydrodynamic indicators, the results show that the exclusive use of representative regular waves was not sufficient to reproduce the hydrodynamic trends obtained under realistic irregular waves. The SHP demonstrates its highest BW performance in reducing the significant wave height at 3L_p for representative regular waves and realistic irregular waves. As a WEC, it achieves its highest axial velocity at 3L_p for representative regular waves and 1.5L_p and 2L_p for realistic irregular waves. The performance of the SHP as BW-WEC is the highest at 3L_p for regular waves and 2.5L_p for realistic irregular waves. In contrast to previous work, in which the NB was kept at a fixed position, the present study indicates that the downstream computational-domain configuration, including the relative positioning between the SHP and the NB, is an important factor affecting the monitored hydrodynamic response and should be carefully defined in CFD wave-flume simulations. Full article

Accurate multi-horizon Significant Wave Height (SWH) forecasting is vital for offshore safety and efficiency. Beyond scheduling maintenance windows, reliable lead-time predictions provide critical early warnings to protect personnel and high-value assets from hazardous high-wave conditions. However, the non-stationary and multi-scale nature of sea states poses challenges for consistent long-term accuracy. To address this challenge, we propose a robust three-stage framework for decomposition, feature selection, and multi-horizon forecasting. Specifically, Optimal Variational Mode Decomposition (OVMD) is adopted to construct multiscale and multi-view representations of nonlinear SWH sequences, while a Triangulated Maximally Filtered Graph (TMFG) constructs a sparse dependency network to select informative and non-redundant predictors from decomposed components and environmental variables. A hybrid prediction model then combines a Temporal Convolutional Network (TCN) for local multi-scale patterns with a Bidirectional Gated Recurrent Unit (BiGRU) for long-range dependencies. Experiments on real-world buoy observations show that the proposed approach improves accuracy and robustness over commonly used statistical and deep-learning baselines across short-, medium-, and long-term horizons. Ablation studies confirm that integrating modal decomposition with sparse feature selection enhances model robustness, offering reliable decision support for offshore window planning and high-wave condition monitoring. Full article

The intertidal beach profile provides a fundamental representation of beach morphology and serves as a key indicator of shoreline morphodynamics. To enable frequent and accurate mapping of intertidal beach profiles, this study proposes an automated reconstruction framework that integrates single-pixel image columns with a stacked bidirectional long short-term memory (Bi-LSTM) network. Time-exposure imagery, commonly referred to as Timex imagery, acquired from a shore-based video monitoring station at Xisha Bay, China, is used as the primary data source, while wave records obtained from a wave buoy are incorporated to assign elevations to the detected waterline breakpoints, thereby enabling automatic beach profile reconstruction. The stacked Bi-LSTM network is trained for land–sea segmentation and waterline breakpoint localization. achieving the best performance among the tested methods, with precision, recall, accuracy, and F1 score values of 0.951, 0.894, 0.978, and 0.903, respectively, and a mean breakpoint localization error of 2.23 pixels. Breakpoint elevations were then estimated using a local slope–wave setup attribution model. Validation against field-measured topographic data from four fixed profiles and three survey periods showed good agreement between the reconstructed and measured profiles, with a period-based root mean square error (RMSE) of 0.212 ± 0.080 m. When all validation points were combined, the reconstructed elevations showed strong agreement with the measured elevations, with a coefficient of determination (R²) of 0.988 and an overall RMSE of 0.24 m. The profile comparisons further showed that the reconstructed profiles generally captured the overall profile shape and cross-shore morphological pattern of the measured profiles, although reconstruction accuracy varied among the four fixed profiles. These differences demonstrate that camera viewing angle, field-of-view position, camera-to-profile distance, and image quality are important factors influencing video-derived beach profile reconstruction. These results indicate that the proposed method can directly reconstruct fixed intertidal beach profiles from shore-based Timex imagery without generating a digital elevation model of the entire intertidal zone. It provides a practical tool for high-frequency monitoring of intertidal profile morphology and supports the quantitative analysis of beach erosion–accretion dynamics. Full article

This study investigates the distribution and concentration of microplastics (MPs) across the littoral profile of a beach, from dune base to offshore sector, including an estuarine channel and Sabellaria alveolata bioconstructions. The research was conducted at Pino di Lenne beach (Taranto, Ionian Sea), a wave-dominated, microtidal littoral system representing a unique natural laboratory with minimal anthropogenic pressure. An eco-friendly extraction protocol was used, combining methods that were already known in the literature. Olive oil proved highly effective in isolating a wide range of MP densities from sediment samples. Statistical analysis identified key accumulation zones, with the highest mean concentrations found in the submerged sandbar (2435 MPs/kg), Sabellaria bioconstructions (2324 MPs/kg), and the base of the dune (2065 MPs/kg). Fibres were the predominant morphology across all sub-environments. Distribution is interpreted as controlled by hydrodynamic processes and biological activity. The submerged beach drives MP transport, with the sandbar and shoreface acting as dynamic sinks. Sabellaria bioconstructions function as biological trap, actively incorporating MPs into their tubular structures. The dune base acts as a sink for wind-blown and storm-deposited plastics. These sub-environments function as critical littoral traps for MPs, essential for developing targeted monitoring and remediation strategies in similar coastal systems. Full article

Unmanned Aerial Vehicle (UAV) remote sensing images provide high-resolution and flexible monitoring data for oil spill detection. To address the high computational cost and low accuracy of traditional models, this study proposes an improved model, YOLOv8m-CGSE. The model replaces standard convolution with Group Shuffle Convolution (GSConv), substitutes the C2f module with SENetV2, and introduces a light-weight Cross-scale Context Fusion Module (CCFM) to enhance multi-scale feature representation while maintaining a lightweight structure. Mosaic augmentation was applied to the marine oil spill dataset, improving mAP50 and mAP50–95 to 85.4% and 62.0%, respectively. Based on YOLOv8m, the proposed YOLOv8m-CGSE achieved mAP50 and mAP50–95 of 91.2% and 73.3%, respectively, improving accuracy while reducing parameters by 16.1% and computational cost by 12.6%. Furthermore, a supplementary vulnerability test on highly deceptive oil-free sea surfaces demonstrated that the proposed model actively suppresses complex background clutter (e.g., ship wakes and wave anomalies), effectively reducing false positive detections from 21 (baseline) to 15. The results demonstrate that the proposed model effectively balances high precision, robustness against visual lookalikes and computational efficiency for real-time marine oil spill monitoring. Full article

A synthetic aperture radar (SAR) provides vital global observations of ocean waves. However, the quasi-linear inversion algorithm routinely used for Sentinel-1 Level-2 Ocean Swell Wave (OSW) products suffers from inherent nonlinear imaging limitations. These include severe distortions and the inability to resolve wind-sea components under a strong azimuth cut-off effect. To address these challenges, this paper proposes a novel prior-knowledge-integrated machine learning framework to reconstruct complete and accurate directional wave spectra from Sentinel-1A SAR wave mode data. First, an extreme gradient boosting model is trained to accurately estimate wind-sea heights, which are then used to construct a theoretical JONSWAP prior spectrum. Subsequently, a U-Net architecture seamlessly integrates this physical prior knowledge with the official OSW swell spectra baseline. Independent validation demonstrates that the proposed framework significantly increases the spectral similarity against ERA5 reanalysis compared to the standard OSW. Furthermore, the derived parameters of total significant wave height, mean wave period, and mean wave direction exhibit remarkable improvements, with root mean square errors of 0.4026 m, 0.4342 s and 20.42°, respectively. The enhancement of SAR inferred two-dimensional wave spectra is also examined and discussed by three typical case studies. It is indicated that integrating physical wave knowledge with machine learning robustly mitigates the non-linear limitations of SAR imaging, providing highly reliable directional wave spectra for global ocean monitoring and forecasting. Full article

Field measurements of ship motions at berth are often sparse, heterogeneous, and collected across multiple vessels and locations, limiting the applicability of conventional response-modelling approaches. This study presents a statistical framework for analysing sea-state-conditioned motion responses using long-term monitoring data with incomplete overlap between degrees of freedom (DoF). Each DoF is analysed independently and conditioned on significant wave height ($H_s$) and peak wave period ($T_p$), with directional values retained across the full angular range (0–360°) and examined separately. A two-stage quality-control procedure combining plausibility checks and robust regression removes inconsistent response–sea-state pairs while preserving dominant behaviour. Motion response envelopes are derived by binning observations in sea-state space and computing median and upper-percentile statistics. To quantify sampling uncertainty, bootstrap resampling provides 95% confidence intervals for envelopes and derived metrics, ensuring robust comparative conclusions. Results show systematic growth in motion variability with increasing $H_s$, with surge exhibiting the strongest translational sensitivity and roll the largest amplification. Synthetic sea surfaces generated using a spectral random-phase approach reproduce prescribed sea-state characteristics, supporting physical interpretation. The study contributes a data-driven framework for heterogeneous berth datasets, robust quality control, uncertainty-aware response envelopes, and statistically consistent synthetic seas, aligning field-based monitoring with practical port operability assessment. Full article

The resolution and accuracy of airborne/spaceborne SAR are continuously improving, making it an effective means for observing ocean dynamic processes and detecting marine targets. In contrast, utilizing its unique orbital angular momentum (OAM) mode, vortex radar does not require temporal accumulation to achieve azimuthal resolution, making it particularly suitable for observing moving sea surfaces. This capability enables stable and continuous monitoring of dynamic ocean scenes. This paper proposes a vortex radar imaging method based on three-dimensional sea surface scattering characteristics: first, a three-dimensional wind-driven sea surface geometric model is established based on the Elfouhaily sea spectrum, and its scattering characteristics under different incident angles, wind speeds, and wind directions are analyzed using the semi-deterministic facet-based two-scale method; then, two-dimensional range-azimuth imaging is achieved through coordinate transformation, echo modeling, pulse compression, and fast Fourier transform (FFT) in OAM mode domain, with the correctness of the imaging algorithm verified through multiple point target imaging results. Finally, simulation results of two-dimensional sea surface vortex imaging under different incident angles are presented, and the influence of wind speed and direction on sea surface vortex imaging is analyzed. The study shows that the vortex imaging system can effectively reflect wave fluctuations and wind direction characteristics, demonstrating the feasibility and potential of vortex radar imaging in oceanographic applications. Full article

Oil slicks pose a severe threat to marine ecosystems, making accurate and real-time detection increasingly urgent. Marine X-band radar has become an essential tool for oil slick monitoring due to its high temporal resolution and its ability to sensitively capture the damping of capillary waves on the sea surface caused by oil films. Building upon this, an unsupervised and lightweight SLIC-KMeans-GJO detection framework is proposed. The method first generates superpixels by using Simple Linear Iterative Clustering (SLIC) and then applies K-means clustering to extract region of interest (ROI). An improved Golden Jackal Optimizer (GJO) is adaptively initialized based on the grayscale distribution and information entropy. To enhance optimization performance, Lévy flight and stochastic perturbation mechanisms are incorporated to improve global exploration and local convergence precision. Experimental results demonstrate that the proposed method significantly outperforms conventional thresholding approaches and other intelligent optimization-based segmentation algorithms in terms of noise suppression, target identification accuracy, and discrimination precision for oil slick targets. It effectively mitigates over-segmentation and false detections while preserving fine edge details and the true spatial extent of oil slicks. The proposed framework offers a novel and practical solution for real-time oil slick monitoring, holding strong potential for operational maritime emergency response. Full article

Wind-driven surface processes are a major source of underwater ambient sound and are therefore an important component of coastal soundscapes. Yet their frequency-dependent expression in shallow nearshore reef environments remains insufficiently characterized from field observations. This study investigates low-to-mid-frequency (20–1000 Hz) ambient acoustic variability at Faro’s natural reef (southern Portugal) using short-term passive acoustic monitoring combined with concurrent sea state measurements. The results show evidence of a relationship between frequency-dependent acoustic response and wind-driven surface processes. At frequencies of 20–100 Hz, ambient sound levels exhibit a weak relationship with wind-driven surface conditions, with elevated variability under low agitation. This is attributed to persistent background anthropogenic noise, particularly vessel traffic. In contrast, above 100 Hz, the ambient sound level increases consistently with wind-driven agitation, indicating that wind-driven surface processes dominate ambient sound in the 100–1000 Hz frequency range. Transient high-energy peaks increase in frequency and intensity with surface agitation, consistent with breaking-wave events, even though elevated background sound levels persist after peak removal. These findings demonstrate that wind-related ambient sound variability at Faro’s natural reef is robustly expressed above approximately 100 Hz. This highlights the importance of frequency-dependent interpretation in passive acoustic monitoring as a necessary baseline for assessing the nearshore reef environment’s influence on ambient sound levels and acoustic propagation under variable sea state conditions. Full article

Satellite-derived shorelines (SDSs) are increasingly used to monitor beach morphology worldwide, yet their application remains poorly validated in microtidal environments strongly influenced by atmospheric forcing. In this study, the performance of CoastSat and CoastSat.slope using nine years of in situ beach profiles from six sandy beaches in Buenos Aires (Argentina) was evaluated. The analysis compares alternative sea level forcings—including global tidal predictions (FES2022), a regional barotropic model with meteorological forcing (MSAS), and wave setup from reanalysis products—and evaluates the effect of using locally trained classifiers on shoreline detection. The results show that locally trained classifiers markedly reduced RMSE values, from 9–21 m with the default classifier to 7–12 m with the locally trained one, while the MSAS model consistently outperforms FES2022 for sea level corrections across all sites. CoastSat.slope provided effective slope estimates for tidal corrections but tended to overestimate values relative to field data. Sensitivity tests confirmed that overestimation has a smaller impact on water level correction than underestimation, explaining why validation metrics improved when using CS.slope-derived slopes. These findings translate into actionable guidelines: (i) prioritize regional sea level models when nontidal variability is large; (ii) apply wave setup corrections cautiously in microtidal coasts; and (iii) use locally trained classifiers in heterogeneous or urbanized beaches. Overall, this study demonstrates that with appropriate parameterization, CoastSat is a reliable tool for shoreline monitoring in atmospherically forced, microtidal coasts, and its methodological insights are transferable to other low-energy, data-scarce regions worldwide. Full article

Predicting the motion of ships and floating structures is essential for ensuring economical and environmentally friendly operations in the ocean. In this study, we propose a hybrid encoder–decoder Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture to predict motions of a moored Floating Production Storage and Offloading (FPSO) vessel under varying sea conditions. The model integrates a CNN for spatial wave-field feature extraction and an LSTM encoder–decoder to capture temporal dependencies in vessel motion. Synthetic datasets were generated using mid-fidelity dynamics simulations of a coupled FPSO–mooring–riser system subjected to wave excitations. Five sea states ranging from calm to severe were considered to evaluate the model’s robustness. A key preprocessing step involved determining the optimal spatial domain for wave field input, and a wave field size of 600 m × 600 m was identified as the most cost-effective configuration while maintaining accuracy. The model was validated using the Root Mean Square Error (RMSE) or relative RMSE (RRMSE). Despite low RRMSE values in low sea states, predictions were noisier due to high-frequency, low-amplitude responses. In contrast, higher sea states yielded more stable predictions despite higher RRMSE values. The proposed method offers high-resolution motion forecasting capability, which can enhance operational safety and energy efficiency of offshore platforms, particularly when integrated with stereo camera-based wave monitoring systems. Full article