Journal of Marine Science and Engineering

The 2004 Indian Ocean earthquake and tsunami significantly impacted the coastal shoreline of the Andaman and Nicobar Islands, causing widespread destruction of infrastructure and ecological damage. This study aims to analyze the short- and long-term shoreline changes in South Andaman, focusing on 2004–2005 (pre- and post-tsunami) and 1990–2023 (to assess periodic changes). Using remote sensing techniques and geospatial tools such as the Digital Shoreline Analysis System (DSAS), shoreline change rates were calculated in four zones, revealing the extent of the tsunami’s impact. During the pre- and post-tsunami periods, the maximum coastal erosion rate was −410.55 m/year, while the maximum accretion was 359.07 m/year in zone A, the island’s east side. For the 1990–2023 period, the most significant coastal shoreline erosion rate was also recorded in zone A, which was recorded at −2.3 m/year. After analyzing the result, it can be seen that the tsunami severely affected the island’s east side. To validate the coastal shoreline measurements, the root mean square error (RMSE) of Landsat-7 and Google Earth was 18.53 m, enabling comparisons of the accuracy of different models on the same dataset. The results demonstrate the extensive impact of the 2004 Indian Ocean Tsunami on South Andaman’s coastal shoreline and the value of analyzing shoreline changes to understand the short- and long-term consequences of such events on coastal ecosystems. This information can inform conservation efforts, management strategies, and disaster response plans to mitigate future damage and allocate resources more efficiently. By better understanding the impact of tsunamis on coastal shorelines, emergency responders, government agencies, and conservationists can develop more effective strategies to protect these fragile ecosystems and the communities that rely on them. Full article

The decommissioning of manmade structures in the marine environment causes large volumes of greenhouse gas (GHG) emissions to be released. Current GHG emissions calculation methods for decommissioning offshore oil and gas industry infrastructure leave large sources of GHG emissions unaccounted for. The results presented here show that these consequential decommissioning GHG emissions are underreported by 50%. Until now, no study has looked at the cumulative impact of decommissioning, but this study shows that globally offshore oil and gas infrastructure decommissioning has produced 25 MtCO₂e to date, around 0.5% of annual global GHG emissions. Importantly, this study also shows that due to the growth of the offshore wind industry, increasing numbers of manmade structures will be emplaced in the marine environment, and GHG emissions from decommissioning will increase 200-fold to 5 GtCO₂e by 2067. Crucially, this growth of GHG emissions is not compatible with the Paris Agreement, and new decommissioning methods will be required to meet this challenge. Full article

Reconstructing changes in deep/bottom-water redox conditions are critical for understanding the role of the deep ocean in global carbon and metals cycling; nevertheless, the quantitative relationships between redox proxies and abyssal dissolved oxygen are poorly investigated. In this work, we studied the rock magnetic properties of surficial sediments in the western Pacific to investigate their relationship with regional redox conditions. Our results reveal a consistent sedimentary magnetic mineral assemblage in the western Pacific, dominated by pseudo-single-domain magnetite (Fe₃O₄), while the ratio of detrital and biogenic magnetite particles in different sites varies substantially. Detailed analyses identified two major magnetic-coercivity components, with modal coercivity values of 13.1 ± 1.6 mT and 54.7 ± 5.3 mT, respectively. All the magnetic parameters we measured, including both concentration-dependent and grainsize-dependent parameters, and the magnetic coercivities, are generally correlated to sedimentary redox conditions; however, the coercivities obtained by mathematical unmixing exhibit a stronger linkage, explaining about a quarter of variance of redox changes. Our findings confirm the potential of magnetic properties for tracing abyssal redox changes in the western Pacific, while the observed magnetic-redox relationships are complex and need further investigation. Full article

The article discusses various aspects of the interaction of vortices with the barotropic flow. Vortex interaction with a flow results in rotation variants, nutational oscillations, and unlimited stretching of its core. The vortex remains in a localized formation, with the semi-axes of the ellipse experiencing fluctuations near an average value in the first two cases. In the third case, the vortex is significantly elongated, and its shape in the horizontal plane changes as follows: one axis of the ellipse increases, and the other decreases. In this case, the vortex, when viewed from above, stretches into a thread, while remaining ellipsoidal. These vortex formations are called filaments. The latter arise from initially almost circular vortices in the horizontal plane and represent structures with non-zero vorticity elongated in one direction. Here, we aim to study the energy transformation of a vortex during its evolution process, mainly due to changes in its shape by stretching. The energy evolution of a mesoscale vortex located in the Norwegian Sea is analyzed using GLORYS12V1 ocean reanalysis data to verify the theoretical conclusions. During the evolution, the vortex is found to transform from a round shape and becomes elongated, and after three weeks its longitudinal scale becomes 4 times larger than the transverse one. During the transformation of a vortex, the kinetic energy and available potential energy decrease respectively by 3 times and 1.7 times. Concurrently, the total energy of the vortex is found to decrease by 2.3 times. We argue that the stretching of vortices results in a loss of energy as well as its redistribution from mesoscale to submesoscale. The lost part of the energy returns to the flow and results in the occurrence of the reverse energy cascade phenomenon. Full article

A critical step in the visual navigation of unmanned surface vehicles (USVs) is horizon line detection, which can be used to adjust the altitude as well as for obstacle avoidance in complex environments. In this paper, a real-time and accurate detection method for the horizon line is proposed. Our approach first differentiates the complexity of navigational scenes using the angular second moment (ASM) parameters in the grey level co-occurrence matrix (GLCM). Then, the region of interest (ROI) is initially extracted using minimal human interaction for the complex navigation scenes, while subsequent frames are dynamically acquired using automatic feature point matching. The matched ROI can be maximally removed from the complex background, and the Zernike-moment-based edges are extracted from the obtained ROI. Finally, complete sea horizon information is obtained through a linear fitting of the lower edge points to the edge information. Through various experiments carried out on a classical dataset, our own datasets, and that of another previously published paper, we illustrate the significance and accuracy of this technique for various complex environments. The results show that the performance has potential applications for the autonomous navigation and control of USVs. Full article

Biofouling is a significant means for introducing non-indigenous marine species internationally, which can alter habitats and disturb marine ecosystems. This study estimated the flux of ships’ wetted surface area (WSA) to Korea in 2020 to assess the risks of biological invasion via biofouling on ships’ hulls. The annual total WSA flux entering Korea was estimated to be 418.26 km², with short-stay vessels (<3 weeks) contributing to 99.7% of the total WSA flux. Busan and Ulsan ports were identified as the main sources of high-risk flux, with container ships being a major vector in Busan and tankers in Ulsan. Gwangyang port had the third-highest total WSA flux, with nearly half of the flux driven from coastwise voyages, making it particularly vulnerable to the spread of hull fouling organisms. These findings could help enhance the management and inspection of hull fouling organisms in Korea. Full article

It is well-known that coastal low-level jets (CLLJs) play an essential role in transporting heat, water vapor and pollutants. However, the CLLJ characteristics in the Pearl River Estuary have not been deeply revealed due to the lack of long-term observations. Based on the long-term observations from a wind lidar, we analyze the primary climatic characteristics of the CLLJs in the Pearl River Estuary and investigate their relationships with large-scale and local-scale synoptic systems. The results show that the CLLJs mainly appear during the flood season, with the most occurrence in May. The CLLJ occurrence during the flood season is mainly influenced by the large-scale north–south pressure gradient driven by the western Pacific subtropical high and terrestrial low-pressure systems. The occurrence of the CLLJs exhibits a distinct diurnal cycle with two different peaks in different seasons. One peak appears at nighttime, mainly during non-flood seasons. The other appears in the afternoon, mainly during the flood season. In the non-flood seasons, under the influence of cold air, the inertial oscillations triggered by the land–sea thermal contrast lead to CLLJ onset at nighttime in the Pearl River Estuary. During the flood season, the strong near-surface pressure gradient contributes to CLLJ onset in the afternoon, while the topography (blocking and passing) is more conducive to the occurrence of the CLLJs in the Pearl River Estuary. These findings reveal the formation mechanisms of the CLLJs over the Pearl River Estuary, thus providing a basis for further understanding the precipitation in the Pearl River Estuary and the occurrence of the CLLJs in other coastal areas with complex mountain ranges. Full article

Performing object tracking tasks and efficiently perceiving the underwater environment in real time for underwater vehicles is a challenging task due to the complex nature of the underwater environment. A hybrid excitation model based lightweight Siamese network is proposed to solve the mismatch between underwater objects with limited characteristics and complex deep learning models. The lightweight neural network is applied to the residual network in the Siamese network to reduce the computational complexity and cost of the model while constructing a deeper network. In addition, to deal with the changeable complex underwater environment and consider the timing of video tracking, the global excitation model (HE module) is introduced. The model adopts the excitation methods of space, channel, and motion to improve the accuracy of the algorithm. Based on the designed underwater vehicle, the underwater target tracking and target grabbing experiments are carried out, and the experimental results show that the proposed tracking algorithm has a high tracking success rate. Full article

With the increasing installed capacity of wind turbines, ensuring the safe operation of wind turbines is of great significance. However, the failure of wind turbines is still a severe problem, especially as blade damage can cause serious harm. To detect blade damage in time and prevent the accumulation of microdamage of blades evolving into severe injury, a damage dataset based on GH Bladed simulation of blade damage is proposed. Then, based on the wavelet packet analysis theory method, the MATLAB software can automatically analyze and extract the energy characteristics of the signal to identify the damage. Finally, the GH Bladed simulation software and MATLAB software are combined for fault diagnosis analysis. The results show that the proposed method based on GH Bladed to simulate blade damage and wavelet packet analysis can extract damage characteristics and identify single-unit damage, multiple-unit damage, and different degrees of damage. This method can quickly and effectively judge the damage to wind turbine blades; it provides a basis for further research on wind turbine blade damage fault diagnosis. Full article

Blade icing often occurs on wind turbines in cold climates. Blade icing has many adverse effects on wind turbines, and the loss of output power is one of the most important effects. With the increasing emphasis on clean energy around the world, the design and production of wind turbines tend to be large-scale. So this paper selected the 15 MW wind turbine provided by NREL (American Renewable Energy Laboratory) to study the influence of blade icing on output power. In this paper, a multi-program coupled analysis method named CFD-WTIC-ILM (CFD: Computational fluid dynamics; WTIC: Wind Turbine Integrated Calculation; ILM: Ice loss method) was proposed to analyze the whole machine wind turbine. Firstly, Fensap-ice was used to simulate the icing of the wind turbine blades, and then the icing results were input into WTIC for the integrated calculation and analysis of the wind turbine. Then, the WTIC calculation results were used to simulate SCADA (supervisory control and data acquisition) data and input into ILM to calculate the power loss. Finally, this paper analyzed the comprehensive influence of icing on output power. The calculation results show that the ice mainly accumulates on the windward side of the blade. Icing has a great influence on the aerodynamic characteristics of the airfoil, leading to a significant decrease in the power curve. The rated wind speed is pushed from 10.59 m/s to 13 m/s. The power loss of the wind turbine in the wind speed optimization stage is as high as 37.48%, and the annual power loss rate caused by icing can reach at least 22%. Full article

The complex underwater environment and light scattering effect lead to severe degradation problems in underwater images, such as color distortion, noise interference, and loss of details. However, the degradation problems of underwater images bring a significant challenge to underwater applications. To address the color distortion, noise interference, and loss of detail problems in underwater images, we propose a triple-branch dense block-based generative adversarial network (TDGAN) for the quality enhancement of underwater images. A residual triple-branch dense block is designed in the generator, which improves performance and feature extraction efficiency and retains more image details. A dual-branch discriminator network is also developed, which helps to capture more high-frequency information and guides the generator to use more global content and detailed features. Experimental results show that TDGAN is more competitive than many advanced methods from the perspective of visual perception and quantitative metrics. Many application tests illustrate that TDGAN can significantly improve the accuracy of underwater target detection, and it is also applicable in image segmentation and saliency detection. Full article

When a ship sailing on the sea encounters flooding events, quickly predicting the flooding time of the compartments in the damaged area is beneficial to making evacuation decisions and reducing losses. At present, decision-makers obtain flooding data through various sensors arranged on board to predict the time of compartment flooding. These data help with the calculation of the flooding time in emergency situations. This paper proposes a new approach to obtaining the compartment flooding time. Specifically in damage scenarios, based on Convolutional Neural Network and Recurrent Neural Network (CNN-RNN), using a composite neural network framework estimates the time when the compartment’s flooding water reaches the target height. The input of the neural network is the flooding images of the damaged compartment. Transfer learning is utilized in the paper. The ResNet18 model in Pytorch is used to extract the spatial information from the flooding images. The Long Short-Term Memory (LSTM) model is then applied to predict when the compartment flooding water reaches the target height. Experimental results show that, for the damaged compartment, the flooding time predicted by the neural network is 85% accurate while the others’ accuracy is more than 91%. Intuitively, when it comes to the actual flooding event, the composite neural network’s average prediction error for compartment flooding time is approximately 1 min. To summarize, these results suggest that the composite neural network proposed above can provide flooding information to assist decision-makers in emergency situations. Full article

To improve navigation safety in maritime environments, a key step is to reduce the influence of human factors on the risk assessment of ship collisions by automating the decision-making process as much as possible. This paper optimizes a dynamic elliptical ship domain based on Automatic Identification System (AIS) data, combines the relative motion between ships in different encounter situations and the level of ship intrusion in the domain, and proposes a ship intrusion collision risk (SICR) model. The simulation results show that the optimized ship domain meets the visualization requirements, and the intrusion model has good collision risk perception ability, which can be used as the evaluation standard of ship collision risk: when the SICR is 0.5–0.6, the ship can establish a collaborative collision avoidance decision-making relationship with other ships, and the action ship can take effective collision avoidance action at the best time when the SICR is between 0.3 and 0.5. The SICR model can give navigators a more accurate and rapid perception of navigation risks, enabling timely maneuvering decisions, and improving navigation safety. Full article

Autonomous underwater vehicles (AUVs) are an important equipment for ocean investigation. Actuator fault diagnosis is essential to ensure the sailing safety of AUVs. However, the lack of failure data for training due to unknown ocean environments and unpredictable failure occurrences is challenging for fault diagnosis. In this paper, a meta-self-attention multi-scale convolution neural network (MSAMS–CNN) is proposed for the actuator fault diagnosis of AUVs. Specifically, a two-dimensional spectrogram of the vibration signals obtained by a vibration sensor is used as the neural network’s inputs. The diagnostic model is fitted by executing a subtask-based gradient optimization procedure to generate more general degradation knowledge. A self-attentive multi-scale feature extraction approach is used to utilize both global and local features for learning important parameters autonomously. In addition, a meta-learning method is utilized to train the diagnostic model without a large amount of labeled data, which enhances the generalization ability and allows for cross-task training. Experimental studies with real AUV data collected by vibration sensors are conducted to validate the effectiveness of the MSAMS–CNN. The results show that the proposed method can diagnose the rudder and thruster faults of AUVs in the cases of few-shot diagnosis. Full article

To estimate the atmospheric deposition flux of ²¹⁰Pb in the equatorial western Indian Ocean, we determined the dissolved (<0.45 μm) and particulate ²¹⁰Pb (>0.45 μm) in the water column. In addition, we calculated the atmosphere-derived dissolvable Pb in seawater using the budget of ²¹⁰Pb. The dissolved ²¹⁰Pb and total ²¹⁰Pb were higher in the surface layer and, overall, showed a decreasing distribution with depth. In particular, radioactive ²¹⁰Pb activities in the surface-to-upper layer (<1000 m depth) were 1.5 to 2 times higher than those reported in the 1970s (in nearby regions), suggesting that there has been additional ²¹⁰Pb input in recent years. Based on the mass balance of the total ²¹⁰Pb budget in the water column, we estimated the atmospheric deposition flux of ²¹⁰Pb and the residence time of Pb for the first time in this region. The atmospheric deposition flux of ²¹⁰Pb was estimated to be 0.1–0.5 dpm cm⁻² yr⁻¹, and these values agreed with the general global estimations for the major oceans (0.1–0.7 dpm cm⁻² yr⁻¹). Considering the residence time of ²¹⁰Pb (29–41 years) in the water column (estimated from the ²¹⁰Pb inventory and ²³⁴Th-based Pb scavenging rate), the atmospheric input of seawater-dissolvable Pb was quantified to be 0.08–0.1 nmol cm⁻² yr⁻¹, which is about eight times higher than the estimated input in the early 1990s in the region. Therefore, these results imply that radioactive ²¹⁰Pb could be a useful tracer for quantifying Pb flux in seawater. Full article

The observation and detection of the subsea environment urgently require large-scale and long-term observation platforms. The design and development of subsea AUVs involve three key points: the subsea-adapted main body structure, agile motion performance that adapts to complex underwater environments, and underwater acoustic communication and positioning technology. This paper discusses the development and evolution of subsea AUVs before proposing solutions to underwater acoustic communication and positioning navigation schemes. It also studies key technologies for the agile motion of subsea AUVs and finally gives an example of a solution for implementing underwater AUVs, i.e., the disk-shaped autonomous underwater helicopter (AUH). This paper will provide guidance for the design of subsea AUVs and the development of corresponding observation and detection technologies. Full article

The long-span double-deck truss girder bridge has become a recommend structural form because of its good performance on traffic capacity. However, the vortex-induced vibration (VIV) characteristics for double-deck truss girders are more complicated and there is a lack of related research. In this research, wind tunnel tests were utilized to investigate the VIV characteristics of a large-span double-deck truss girder bridge. Meanwhile, the VIV suppression effect of the aerodynamic mitigation measures was measured. Furthermore, the VIV suppression mechanism was studied from the perspective of vortex shedding characteristics. The results indicated that the double-deck truss girder had a significant VIV when the wind attack angles were +3° and +5°. The aerodynamic mitigation measures had an influence on the VIV response of the double-deck truss girder. The upper chord fairing and lower chord inverted L-shaped deflector plate played a crucial role in suppressing VIV. Numerical analysis indicated that vortex shedding above the upper deck or in the wake region may dominate vertical VIV, while vortex shedding in the wake region of the lower deck may dominate torsional VIV. The upper chord fairing and lower chord inverted L-shaped deflector plate disrupted the original vortex shedding pattern in both regions, thereby suppressing VIV. This research can provide a foundation for bridge design and vibration suppression measures for large-span double-deck truss girder bridges. Full article

Several inputs of metal contamination can affect port seawater, such as industries and sludges. Despite the urge of developing new techniques to face this problem, most of the studies focus on traditional methods of remediation. Bioremediation by fungi represents an innovative and sustainable tool to efficiently remove metals from seawaters. The study’s aim is to develop a new green technology using fungi (myco-barriers) to maintain a good standard for water quality in port areas. A large commercial port (Port of Genoa) and a small marina (Port of Cavo) in Italy were chosen as pilot sites. Myco-barriers were realised by inoculating sterile straw and sawdust with mycelium of macro- and microfungi. After the incubation, myco-barriers were placed in the ports and sampled after 15 and 30 days to verify metal bioaccumulation. Myco-barriers with macrofungi showed the tendency to bioaccumulate more efficiently after 15 treatment days (Zn 7.0 mg kg⁻¹, Cu 6.5 mg kg⁻¹, Pb 1.2 mg kg⁻¹), while myco-barriers with microfungi showed higher bioaccumulation after 30 days (Ni 0.6 mg kg⁻¹, Pb 0.6 mg kg⁻¹, Cu 5 mg kg⁻¹). Results showed that myco-barriers have metal bioaccumulation potential and can represent a significant alternative to traditional techniques of remediation (chemical–physical). Full article

Underwater target detection is the foundation and guarantee for the autonomous operation of underwater vehicles and is one of the key technologies in marine exploration. Due to the complex and special underwater environment, the detection effect is poor, and the detection precision is not high. In this paper, YOLOv5 (You Only Look Once v5) is used as the overall structural framework of the target detection algorithm, and improvement is made on the basis of its detection precision in the underwater environment. Specifically, an attention mechanism (Channel and Spatial Fusion Attention, CSFA) that fuses the channel attention and spatial attention is proposed and added to the YOLOv5 network framework, enabling the network to focus on both the prominent features of the detected object and the spatial information of the detected object. The proposed method was tested on the underwater target detection dataset provided by the China Underwater Robot Professional Competition. The experimental detection precision (P) reached 85%, the recall (R) reached 82.2%, and the mean average precision (mAP) reached 87.5%. The effectiveness of the proposed method was verified, and its underwater target detection performance was better than that of ordinary models. Full article