Journal of Marine Science and Engineering

23 pages, 58132 KB

Open AccessArticle

Integrated Rock Physics-Based Interpretation of Time-Lapse Seismic Data for Residual Oil Detection in Offshore Waterflooded Reservoirs

by Haoyuan Li, Xuri Huang, Sheng Yang, Xiaoqing Cui, Yibin Li and Ran Yang

J. Mar. Sci. Eng. 2026, 14(1), 91; https://doi.org/10.3390/jmse14010091 (registering DOI) - 2 Jan 2026

Abstract

Accurate characterization of fluid distribution in offshore waterflooded oilfields has been challenging due to complex heterogeneity and the limitations of traditional interpretation tools, which often cannot integrate multi-scale datasets such as core samples, well logs, and seismic surveys. This study addresses these challenges [...] Read more.

Accurate characterization of fluid distribution in offshore waterflooded oilfields has been challenging due to complex heterogeneity and the limitations of traditional interpretation tools, which often cannot integrate multi-scale datasets such as core samples, well logs, and seismic surveys. This study addresses these challenges by developing an integrated interpretation workflow based on a calibrated rock physical fluid substitution model. The model, constrained by low-frequency laboratory measurements and elastic parameters from well logs, is used to assess the impact of fluid variations on core elastic properties and to ensure physical consistency across core, log, and seismic data scales. Key findings demonstrate that the calibrated model effectively detects impedance changes caused by water injection and accurately identifies remaining oil deposits. When applied to time-lapse seismic interpretation and reservoir numerical simulation, the model proves valuable for guiding infill well placement and optimizing development strategies in mature offshore reservoirs. Additionally, this approach provides a robust framework for integrating multi-source data, thereby enhancing the reliability of reservoir characterization in waterflooded wells. Full article

(This article belongs to the Topic Advanced Technology for Oil and Nature Gas Exploration)

25 pages, 16492 KB

Open AccessArticle

Investigation of Edge Scour and Undermining Process of Conical Structure Around a Monopile

by Jinming Tu, Fan Yang, Chi Yu and Fuming Wang

J. Mar. Sci. Eng. 2026, 14(1), 90; https://doi.org/10.3390/jmse14010090 (registering DOI) - 2 Jan 2026

Abstract

The scour protection performance of the conical structure under different slope angles, α, was investigated through numerical simulations. By solving the Navier–Stokes (N–S) equations, using the Renormalization Group (RNG) k–ε turbulence model and the Meyer-Peter and Müller (MPM) sediment transport formula, the [...] Read more.

The scour protection performance of the conical structure under different slope angles, α, was investigated through numerical simulations. By solving the Navier–Stokes (N–S) equations, using the Renormalization Group (RNG) k–ε turbulence model and the Meyer-Peter and Müller (MPM) sediment transport formula, the scour protection performance, undermining process, and the flow field around the devices were fully analyzed at different slope angles. The findings indicate that the conical scour protection provides effective protection against scour damage. As the slope angle increases, greater scour depth is observed around the structure. A critical slope angle was identified between 30° and 40°, slope angle effects are obvious below the threshold; otherwise, it minimized. Undermining is the main cause of failure of such stiff scour protection, mainly driven by flow contraction and sand sliding. Upstream undermining beneath the structure is more pronounced, while the downstream undermining is largely related to the near-bed flow separation point. The critical undermining point (CUP) is proposed based on the undermining curve to distinguish the undermining state, which is critical in scour protection and structural stability. Full article

(This article belongs to the Special Issue Wave–Structure–Seabed Interaction)

16 pages, 2799 KB

Open AccessArticle

Coupling Effect of the Bottom Type-Depth Configuration on the Sonar Detection Range in Seamount Environments

by Xiaofang Sun, Shisong Zhang, Feiyu Chen and Pingbo Wang

J. Mar. Sci. Eng. 2026, 14(1), 89; https://doi.org/10.3390/jmse14010089 (registering DOI) - 2 Jan 2026

Abstract

Seabed topography exerts a profound influence on underwater acoustic propagation, and the coupling effect between bottom acoustic properties and the source–receiver geometric configuration remains insufficiently quantified, particularly in seamount shielding scenarios. To address this gap, in this study, the BELLHOP ray model was [...] Read more.

Seabed topography exerts a profound influence on underwater acoustic propagation, and the coupling effect between bottom acoustic properties and the source–receiver geometric configuration remains insufficiently quantified, particularly in seamount shielding scenarios. To address this gap, in this study, the BELLHOP ray model was integrated with Earth topography 1 (ETOPO1) topographic data and Hybrid Coordinate Ocean Model (HYCOM) hydrological data for seamounts east of Taiwan. Transmission loss (TL) of 300 Hz sound waves was simulated across four typical bottom types (rock, coarse sand, silt, and clay) under varying source depths (50–1000 m) and receiver depths (50–500 m). The maximum sonar detection range was delineated using an 80 dB TL threshold as the criterion for effective detection. The key findings reveal that the bottom properties are the primary factors that reduce the detection range: the maximum detection range over rock bottom exceeds that over clay by more than 8-fold. Notably, a shallow source–shallow receiver configuration mitigates the acoustic shadow effect induced by seamounts, whereas deep receiver deployment (≥500 m) diminishes the discriminative impact of bottom types on the propagation behavior. Furthermore, a segmented empirical prediction formula was established, which reconciles both the physical mechanisms (e.g., bottom reflection-absorption and seamount shielding) and engineering applicability. This formula provides a robust theoretical basis for evaluating sonar performance in complex seabed topography settings, thereby facilitating optimized underwater detection strategies in seamount-dominated marine environments. Full article

(This article belongs to the Section Ocean Engineering)

► Show Figures

Figure 1

37 pages, 11112 KB

Open AccessArticle

Adaptive Dynamic Prediction-Based Cooperative Interception Control Algorithm for Multi-Type Unmanned Surface Vessels

by Yuan Liu, Bowen Tang, Lingyun Lu, Zhiqing Bai, Guoxing Li, Shikun Geng and Xirui Xu

J. Mar. Sci. Eng. 2026, 14(1), 88; https://doi.org/10.3390/jmse14010088 (registering DOI) - 2 Jan 2026

Abstract

In the dynamic marine environment, the high mobility of intrusion targets, complex interference, and insufficient multi-vessel coordination accuracy pose significant challenges to the cooperative interception mission of multiple unmanned surface vehicles (USVs). This paper proposes an adaptive dynamic prediction-based cooperative interception control algorithm [...] Read more.

In the dynamic marine environment, the high mobility of intrusion targets, complex interference, and insufficient multi-vessel coordination accuracy pose significant challenges to the cooperative interception mission of multiple unmanned surface vehicles (USVs). This paper proposes an adaptive dynamic prediction-based cooperative interception control algorithm and establishes a “mission planning—anti-interference control—phased coordination” system. Specifically, it ensures interception accuracy through threat-level-oriented target assignment and extended Kalman filter multi-step prediction, offsets environmental interference by separating the cooperative encirclement and anti-interference modules using an improved Two-stage architecture, and optimizes the movement of nodes to form a stable blockade through the “target navigation—cooperative encirclement” strategy. Simulation results show that in a 1000 m × 1000 m mission area, the node trajectory deviation is reduced by 40% and the heading angle fluctuation is decreased by 50, compared with the limit cycle encirclement algorithm, the average interception time is shortened by 15% and the average final distance between the intrusion target and the guarded target is increased by 20%, when the target attempts to escape, the relevant collision rates are all below 0.3%. The TFMUSV framework ensures the stable optimization of the algorithm and significantly improves the efficiency and reliability of multi-USV cooperative interception in complex scenarios. This paper provides a highly adaptable technical solution for practical tasks such as maritime security and anti-smuggling. Full article

(This article belongs to the Section Ocean Engineering)

► Show Figures

Figure 1

27 pages, 3615 KB

Open AccessArticle

MF-GCN: Multimodal Information Fusion Using Incremental Graph Convolutional Network for Ship Behavior Anomaly Detection

by Ruixin Ma, Jinhao Zhang, Weizhi Nie, Naiming Ge, Hao Wen and Aoxiang Liu

J. Mar. Sci. Eng. 2026, 14(1), 87; https://doi.org/10.3390/jmse14010087 (registering DOI) - 1 Jan 2026

Abstract

Ship behavior anomaly detection is critical for intelligent perception and early warning in complex inland waterways, where single-source sensing (e.g., AIS-only or vision-only) is often fragile under occlusion, illumination variation, and signal noise. This study proposes MF-GCN, a multimodal (heterogeneous) information fusion framework [...] Read more.

Ship behavior anomaly detection is critical for intelligent perception and early warning in complex inland waterways, where single-source sensing (e.g., AIS-only or vision-only) is often fragile under occlusion, illumination variation, and signal noise. This study proposes MF-GCN, a multimodal (heterogeneous) information fusion framework based on an Incremental Graph Convolutional Network (IGCN) to detect and warn anomalous ship behaviors by jointly modeling AIS, video imagery, LiDAR point clouds, and water level signals. We first extract modality-specific features and enforce temporal–spatial consistency via timestamp and geo-referencing alignment, then construct an evolving graph in which nodes represent multimodal features and edges encode temporal dependency and semantic similarity. MF-GCN integrates a Semantic Clustering-based GCN (S-GCN) to inject historical semantic context and an Attentive Fusion-based GCN (A-GCN) to learn dynamic cross-modal correlations using multi-head attention. Experiments on our constructed real-world datasets demonstrate that MF-GCN achieves accuracies of 93.8%, 93.8%, and 93.3% with F1-scores of 93.6%, 93.6%, and 93.3% for ship deviation warning, bridge-crossing warning, and inter-ship collision warning, respectively, consistently outperforming representative baselines. These results verify the effectiveness of the proposed method for robust multimodal anomaly detection and early warning in inland-waterway scenarios. Full article

(This article belongs to the Special Issue Emerging Computational Methods in Intelligent Marine Vehicles)

34 pages, 4301 KB

Open AccessArticle

Delay-Compensated EKF and Adaptive Delay Threshold Weighting for AUV–MDS Docking

by Han Yan and Shuxue Yan

J. Mar. Sci. Eng. 2026, 14(1), 86; https://doi.org/10.3390/jmse14010086 (registering DOI) - 1 Jan 2026

Abstract

This study tackles real-time state estimation for autonomous underwater vehicle (AUV)–mobile docking station (MDS) cooperation over low-bandwidth, high-latency, jitter-dominated acoustic links, with the goal of turning delayed/out-of-sequence measurements (OOSM) into consistent and informative constraints without sacrificing online operation. We propose an integrated scheme [...] Read more.

This study tackles real-time state estimation for autonomous underwater vehicle (AUV)–mobile docking station (MDS) cooperation over low-bandwidth, high-latency, jitter-dominated acoustic links, with the goal of turning delayed/out-of-sequence measurements (OOSM) into consistent and informative constraints without sacrificing online operation. We propose an integrated scheme centered on a delay-compensated extended Kalman filter (DC-EKF): a ring buffer enables backward updates and forward replay so that OOSM are absorbed strictly at their physical timestamps; a data-driven delay threshold is learned from “effective information gain” combined with normalized estimation error squared (NEES) filtering; and dynamic confidence, derived from innovation statistics, delay, and signal-to-noise ratio (SNR) proxies, scales the measurement noise to adapt fusion weights. Simulations show the learned delay threshold converges to about 6.4 s (final 6.35 s), error spikes are suppressed, and the overall position root-mean-square error (RMSE) is 5.751 m; across the full data stream, 1067 station measurements were accepted and 30 rejected, and the fusion weights shifted smoothly from inertial measurement unit (IMU)-dominant to station-dominant (≈0.16/0.84) over time. On this basis, a cooperative augmented EKF (Co-Aug-EKF) is added as a lightweight upper layer for unified-frame cooperative estimation, further improving relative consistency. The results indicate that the framework reliably maps delayed acoustic measurements into closed-loop useful information, significantly enhancing estimator stability and docking readiness, while remaining practical to deploy and readily extensible. Full article

(This article belongs to the Topic Advanced Technologies and Applications for Unmanned Systems)

36 pages, 2483 KB

Open AccessReview

Machine Learning Applications in Fuel Reforming for Hydrogen Production in Marine Propulsion Systems

by Yexin Chen, Xinyu Liu, Xu Liu, Hao Lu and Ziqin Wang

J. Mar. Sci. Eng. 2026, 14(1), 85; https://doi.org/10.3390/jmse14010085 - 31 Dec 2025

Abstract

In the context of the shipping industry’s transition towards low-carbon solutions, hydrogen energy exhibits substantial application potential in marine propulsion systems. Fuel reforming for hydrogen production represents one of the key technologies for efficient hydrogen production in maritime applications. Nevertheless, this process involves [...] Read more.

In the context of the shipping industry’s transition towards low-carbon solutions, hydrogen energy exhibits substantial application potential in marine propulsion systems. Fuel reforming for hydrogen production represents one of the key technologies for efficient hydrogen production in maritime applications. Nevertheless, this process involves complex multi-scale reaction mechanisms, challenges in catalyst design, and difficulties in system optimization. This paper conducts a comprehensive review of the recent progress in the application of machine learning in fuel reforming hydrogen production technology. In the realm of catalysts, machine learning has expedited the design of efficient catalysts via high-throughput screening, performance prediction, and active site regulation. In reaction modeling, machine learning has facilitated the development of multi-scale kinetic models, enhancing the interpretability and predictive accuracy of reaction pathways. Regarding equipment and system optimization, machine learning has enabled innovations in reactor design, collaborative optimization of process parameters, and intelligent system control. This review aims to provide theoretical foundations and practical guidance for the technological development of ship propulsion systems. Moreover, it explores the future directions for the deep integration of machine learning and hydrogen energy technologies, thereby promoting the low-carbon and intelligent transformation of the shipping industry. Full article

(This article belongs to the Special Issue Advanced Technologies for New (Clean) Energy Ships—2nd Edition)

► Show Figures

Figure 1

42 pages, 12068 KB

Open AccessArticle

Geochemical and Radiometric Assessment of Romanian Black Sea Shelf Waters and Sediments: Implications for Anthropogenic Influence

by Irina Catianis, Mihaela Mureșan, Tatiana Begun, Adrian Teacă, Andra Bucșe, Florina Rădulescu, Florina Macau, Naliana Lupașcu, Daniela Florea, Florentina Fediuc, Sorin Ujeniuc, Radu Seremet, Silvia Ise, Iulian Andreicovici and Ana Bianca Pavel

J. Mar. Sci. Eng. 2026, 14(1), 84; https://doi.org/10.3390/jmse14010084 - 31 Dec 2025

Abstract

The Northwestern Black Sea shelf, strongly influenced by Danube discharge and coastal activities, provides an effective setting for separating lithogenic controls from localized anthropogenic inputs. We applied a multi-proxy geochemical–radiometric approach to Romanian shelf waters and surface sediments. A CTD–Rosette was used to [...] Read more.

The Northwestern Black Sea shelf, strongly influenced by Danube discharge and coastal activities, provides an effective setting for separating lithogenic controls from localized anthropogenic inputs. We applied a multi-proxy geochemical–radiometric approach to Romanian shelf waters and surface sediments. A CTD–Rosette was used to quantify nutrients, chlorophyll-a, TOC, and TN. Dissolved metals and PAHs were measured in seawater, while surface sediments were analyzed for CaCO₃, TOC, trace metals, and γ-emitting radionuclides. Multivariate statistics (PCA/FA) were used to resolve the dominant environmental controls. Summer stratification was characterized by the bottom-layer maxima of PO₄³⁻, SiO₄⁴⁻, and NH₄⁺ and a pronounced subsurface chlorophyll-a maximum at 12–16 m. Surface-water Σ16PAH ranged from 134 to 347 ng L⁻¹ and was dominated by low-molecular-weight compounds, with episodic nearshore enrichment in high-molecular-weight species. In sediments, CaCO₃ ranged from 7.6 to 29.9% and TOC from 0.11 to 0.96%. Trace metals were generally low. Pb and Hg peaked at nearshore station S23, whereas mean Ni (38.88 ppm) slightly exceeded the 35 ppm guideline, consistent with natural Fe/Mn-oxide association. PCA/FA identified a terrigenous axis (Fe-Al-Ti-V-Ni-Cr), a carbonate axis (CaCO₃; Sr where available), and an anthropogenic factor (Pb, Hg, HMW-PAHs). γ-spectrometry provided a compatible radiometric baseline that supports the multi-proxy interpretation. Full article

(This article belongs to the Section Marine Environmental Science)

► Show Figures

Figure 1

22 pages, 3962 KB

Open AccessArticle

Effects of Cross-Linked Structure of Sodium Alginate on Electroosmotic Dewatering and Reinforcement for Coastal Soft Soil

by Guoqiang Wu, Lingwei Zheng, Xunli Zhang, Guanyu Chen, Shangqi Ge, Yuanhong Yu and Xinyu Xie

J. Mar. Sci. Eng. 2026, 14(1), 83; https://doi.org/10.3390/jmse14010083 - 31 Dec 2025

Abstract

The reinforcement of high-water-content, low-permeability soft soils presents a critical challenge in marine and coastal engineering. While electroosmotic dewatering is a promising technique, its widespread application is often hindered by issues such as high energy consumption and limited strength gain. However, the specific [...] Read more.

The reinforcement of high-water-content, low-permeability soft soils presents a critical challenge in marine and coastal engineering. While electroosmotic dewatering is a promising technique, its widespread application is often hindered by issues such as high energy consumption and limited strength gain. However, the specific mechanisms by which marine-derived biopolymers modify soil properties and microstructure to enhance electroosmotic efficiency and significantly improve the post-treatment bearing capacity remain insufficiently understood. To address this gap, this study investigates the use of Sodium Alginate (SA) to enhance the electroosmotic dewatering performance of coastal soft soil. Laboratory experiments were conducted using carbon felt electrodes with varying SA mass fractions (0.0%, 0.2%, 0.5%, and 1.0%). The study integrated macroscopic monitoring with Scanning Electron Microscopy (SEM) to evaluate the electroosmotic efficiency and mechanical property evolution. The results demonstrate that the cross-linked structure of SA gel effectively bridges soil particles and fills inter-granular pores, significantly increasing the liquid limit (from 32.34% to 49.15% at 1.0% SA) and mitigating soil cracking. This microstructural alteration enhanced electrical conductivity and accelerated drainage; the average water content reduction increased from 12.78% (0.0% SA) to 20.86% (1.0% SA). Notably, the 0.5% SA treatment improved the average bearing capacity to approximately 86 kPa (about 7 times that of 0.0% SA) with only a 21% increase in the energy consumption coefficient. This study confirms that utilizing SA for electroosmotic reinforcement effectively modifies soil properties to provide a marine solution for coastal soft soil foundation treatment. Full article

(This article belongs to the Section Coastal Engineering)

38 pages, 3982 KB

Open AccessArticle

Evolving Collective Intelligence for Unmanned Marine Vehicle Swarms: A Federated Meta-Learning Framework for Cross-Fleet Planning and Control

by Yuhan Ye, Hongjun Tian, Yijie Yin, Yuhan Zhou, Yang Xiong, Zi Wang, Yaojiang Liu, Zinan Nie, Zitong Zhang, Yichen Wang and Jingyu Sun

J. Mar. Sci. Eng. 2026, 14(1), 82; https://doi.org/10.3390/jmse14010082 - 31 Dec 2025

Abstract

The development of robust autonomous maritime systems is fundamentally constrained by the “data silo” problem, where valuable operational data from disparate fleets remain isolated due to privacy concerns, severely limiting the scalability of general-purpose navigation intelligence. To address this barrier, we propose a [...] Read more.

The development of robust autonomous maritime systems is fundamentally constrained by the “data silo” problem, where valuable operational data from disparate fleets remain isolated due to privacy concerns, severely limiting the scalability of general-purpose navigation intelligence. To address this barrier, we propose a novel Federated Meta-Transfer Learning (FMTL) framework that enables collaborative evolution of unmanned surface vehicle (USV) swarms while preserving data privacy. Our hierarchical approach orchestrates three synergistic stages: (1) transfer learning pre-trains a universal “Sea-Sense” foundation model on large-scale maritime data to establish fundamental navigation priors; (2) federated learning enables decentralized fleets to collaboratively refine this model through encrypted gradient aggregation, forming a distributed cognitive network; (3) meta-learning allows for rapid personalization to individual vessel dynamics with minimal adaptation trials. Comprehensive simulations across heterogeneous fleet distributions demonstrate that our federated model achieves a 95.4% average success rate across diverse maritime scenarios, significantly outperforming isolated specialist models (63.9–73.1%), while enabling zero-shot performance of 78.5% and few-shot adaptation within 8–12 episodes on unseen tasks. This work establishes a scalable, privacy-preserving paradigm for collective maritime intelligence through swarm-based learning. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles: Perception, Planning, Control and Swarm—2nd Edition)

35 pages, 3389 KB

Open AccessArticle

A Deep Ship Trajectory Clustering Method Based on Feature Embedded Representation Learning

by Yifei Liu, Zhangsong Shi, Bing Fu, Jiankang Ke, Huihui Xu and Xuan Wang

J. Mar. Sci. Eng. 2026, 14(1), 81; https://doi.org/10.3390/jmse14010081 - 31 Dec 2025

Abstract

Trajectory clustering is of great significance for identifying behavioral patterns and vessel types of non-cooperative ships. However, existing trajectory clustering methods suffer from limitations in extracting cross-spatiotemporal scale features and modeling the coupling relationship between positional and motion features, which restricts clustering performance. [...] Read more.

Trajectory clustering is of great significance for identifying behavioral patterns and vessel types of non-cooperative ships. However, existing trajectory clustering methods suffer from limitations in extracting cross-spatiotemporal scale features and modeling the coupling relationship between positional and motion features, which restricts clustering performance. To address this, this study proposes a deep ship trajectory clustering method based on feature embedding representation learning (ERL-DTC). The method designs a Temporal Attention-based Multi-scale feature Aggregation Network (TA-MAN) to achieve dynamic fusion of trajectory features from micro to macro scales. A Dual-feature Self-attention Fusion Encoder (DualSFE) is employed to decouple and jointly represent the spatiotemporal position and motion features of trajectories. A two-stage optimization strategy of “pre-training and joint training” is adopted, combining contrastive loss and clustering loss to jointly constrain the embedding representation learning, ensuring it preserves trajectory similarity relationships while being adapted to the clustering task. Experiments on a public vessel trajectory dataset show that for a four-class task (K = 4), ERL-DTC improves ACC by approximately 14.1% compared to the current best deep clustering method, with NMI and ARI increasing by about 28.9% and 30.2%, respectively. It achieves the highest Silhouette Coefficient (SC) and the lowest Davies-Bouldin Index (DBI), indicating a tighter and more clearly separated cluster structure. Furthermore, its inference efficiency is improved by two orders of magnitude compared to traditional point-matching-based methods, without significantly increasing runtime due to model complexity. Ablation studies and parameter sensitivity analysis further validate the necessity of each module design and the rationality of hyperparameter settings. This research provides an efficient and robust solution for feature learning and clustering of vessel trajectories across spatiotemporal scales. Full article

(This article belongs to the Section Ocean Engineering)

6 pages, 161 KB

Open AccessEditorial

Smart Seaport and Maritime Transport Management

by Lingxiao Wu and Shuaian Wang

J. Mar. Sci. Eng. 2026, 14(1), 80; https://doi.org/10.3390/jmse14010080 - 31 Dec 2025

Abstract

Smart seaport and maritime transport management constitute a prominent and continually evolving field [...] Full article

(This article belongs to the Special Issue Smart Seaport and Maritime Transport Management)

21 pages, 5796 KB

Open AccessArticle

Statistical Grid-Based Analysis of Anthropogenic Film Pollution in Coastal Waters According to SAR Satellite Data Series

by Valery Bondur, Victoria Studenova and Viktor Zamshin

J. Mar. Sci. Eng. 2026, 14(1), 79; https://doi.org/10.3390/jmse14010079 - 31 Dec 2025

Abstract

The problem of adequate quantitative analysis of anthropogenic film pollution of water areas according to synthetic aperture radar (SAR) satellite imagery is addressed here. A quantitative analysis of anthropogenic film pollution (AFP) in the studied coastal water areas of the north sector of [...] Read more.

The problem of adequate quantitative analysis of anthropogenic film pollution of water areas according to synthetic aperture radar (SAR) satellite imagery is addressed here. A quantitative analysis of anthropogenic film pollution (AFP) in the studied coastal water areas of the north sector of the Black Sea and Avacha Gulf has been conducted. The analysis utilized a method that involved the statistical processing of data related to AFP identified within the cells of a regular spatial grid. Time series of Sentinel-1 SAR satellite imagery were used as initial data. Spatiotemporal distributions of the proposed quantitative criterion (e_AFP, ppm) have been calculated and analyzed. This criterion characterizes the intensity of AFP impact within the selected regions of marine waters based on measuring the relative frequency of an AFP event. Among them, the area of the emergency fuel oil spill that occurred in 2024–2025 near the Kerch Strait was investigated (e_AFP values near the wreckage of tankers reached ~13,000 ppm), as well as the area of the emergency oil spill near the Novorossiysk terminal that occurred in 2021 (e_AFP ≤ 6000 ppm). Accidents led to an approximately 3–6-fold increase in e_AFP values against the background level of 0–2000 ppm. The spatiotemporal variability of e_AFP across various water areas and under different conditions has been demonstrated and discussed. Full article

(This article belongs to the Section Marine Pollution)

► Show Figures

Figure 1

17 pages, 19521 KB

Open AccessArticle

Experimental Comparisons of the Wave Attenuation Characteristics Among Different Flexible-Membrane Breakwaters

by Ming-Liang Cai, Jing-Ping Wu, Shao-Min Wang and Xi Yi

J. Mar. Sci. Eng. 2026, 14(1), 78; https://doi.org/10.3390/jmse14010078 - 31 Dec 2025

Abstract

In this paper, physical experiments were conducted to analyze the wave attenuation characteristics of a combinational breakwater in a 2D piston-type wave flume. The proposed breakwater consisted of a box and double flexible membranes, one of which was fixed on both sides of [...] Read more.

In this paper, physical experiments were conducted to analyze the wave attenuation characteristics of a combinational breakwater in a 2D piston-type wave flume. The proposed breakwater consisted of a box and double flexible membranes, one of which was fixed on both sides of the box, while the other was positioned at a specific distance from the box sides. The flexible membranes dropped down naturally and formed a U shape. Optimal configuration parameters of the proposed breakwater were determined separately through a series of comparison experiments among mooring breakwaters in regular waves; the box draft d was determined by a box-type breakwater and the membrane spacings L_u and L_d from the box sides were determined by a U-shaped flexible-membrane breakwater, where the wave attenuation coefficients versus kh and B/λ were provided. Then, using the literature, the present box–membrane breakwater with membrane spacings was compared to two similar flexible-membrane breakwaters. One was the above-mentioned U-shaped flexible-membrane breakwater, the other was a box–membrane breakwater in which the flexible membranes were directly fastened on the box sides. The results indicate that with optimal configuration parameters, the wave attenuation performance of the proposed breakwater had been enhanced due to the increase in both dissipating and reflecting wave energy. Full article

(This article belongs to the Section Ocean Engineering)

► Show Figures

Figure 1

18 pages, 2665 KB

Open AccessArticle

Anti-Disturbance Path Tracking Control for USV Based on Quantum-Inspired Optimization and Dynamic Game Theory

by Xinhao Huang, Yongzheng Li, Biwei Wang, Liting Ding, Zeyu Chen and Jiazheng Liu

J. Mar. Sci. Eng. 2026, 14(1), 77; https://doi.org/10.3390/jmse14010077 - 31 Dec 2025

Abstract

To address the challenge that unmanned surface vehicles (USVs) struggle to effectively balance tracking accuracy, control smoothness, and system energy efficiency under external disturbances, this paper proposes an anti-disturbance path tracking control method integrating quantum-inspired optimization (QIO) and dynamic game theory (GT). The [...] Read more.

To address the challenge that unmanned surface vehicles (USVs) struggle to effectively balance tracking accuracy, control smoothness, and system energy efficiency under external disturbances, this paper proposes an anti-disturbance path tracking control method integrating quantum-inspired optimization (QIO) and dynamic game theory (GT). The proposed control method consists of a two-layer optimization architecture: the upper layer employs dynamic game theory to optimize the guidance process, modeling the optimization of the look-ahead distance (

L d

) and switching radius (

R

) in the LOS guidance algorithm as a non-cooperative game, and achieves adaptive adjustment to path variations and environmental disturbances by solving for the Nash equilibrium. The lower layer, based on a quantum-inspired optimization algorithm, enhances the control process by employing quantum bit probability amplitude encoding for the PID parameter space and utilizing a quantum rotation gate mechanism for efficient global search, thereby achieving online self-tuning of PID parameters under environmental disturbances. Simulation results indicate that, under sea conditions with external disturbances, the proposed method achieves a superior balance among tracking accuracy, control smoothness, and system energy efficiency compared to the traditional fixed-parameter PID-LOS approach, enhancing the comprehensive anti-disturbance robustness of the USV. Full article

(This article belongs to the Section Ocean Engineering)

► Show Figures

Figure 1

35 pages, 2295 KB

Open AccessArticle

On Swarm-Constrained Formation Tracking Control Method for Master–Slave AUVs with Dynamic Transformations

by Yanbin Teng, Dongshi Bian, Lan Wu, Changde Liu and Xiaofeng Kuang

J. Mar. Sci. Eng. 2026, 14(1), 76; https://doi.org/10.3390/jmse14010076 - 30 Dec 2025

Abstract

Aiming at the problem of complex cluster constraints in the process of master slave AUV (Autonomous Underwater Vehicle) formation transformation and tracking in the wave environment, it is proposed to introduce the model predictive control (MPC) method to normalize a variety of complex [...] Read more.

Aiming at the problem of complex cluster constraints in the process of master slave AUV (Autonomous Underwater Vehicle) formation transformation and tracking in the wave environment, it is proposed to introduce the model predictive control (MPC) method to normalize a variety of complex constraints in the process of group formation navigation, unify the kinematic model and dynamic model, and introduce the normalized constraint equation. At the same time, the MPC formation transformation tracking control strategy is designed to achieve AUV formation transformation and tracking control under the unified model, which is verified through simulation tests to provide more effective technical support for AUV group formation. Full article

(This article belongs to the Section Ocean Engineering)

23 pages, 2467 KB

Open AccessArticle

Remaining Useful Life Prediction and Operation Optimization of Offshore Electric Submersible Pump Systems Using a Dual-Stage Attention-Based Recurrent Neural Network

by Xin Lu, Guoqing Han, Bin Liu, Yangnan Shangguan and Xingyuan Liang

J. Mar. Sci. Eng. 2026, 14(1), 75; https://doi.org/10.3390/jmse14010075 - 30 Dec 2025

Abstract

Electric Submersible Pumps (ESPs) serve as the primary artificial lift technology in offshore oilfields and play a crucial role in ensuring stable and efficient marine oil and gas production. However, the harsh offshore operating environment—characterized by high temperature, complex multiphase flow, and frequent [...] Read more.

Electric Submersible Pumps (ESPs) serve as the primary artificial lift technology in offshore oilfields and play a crucial role in ensuring stable and efficient marine oil and gas production. However, the harsh offshore operating environment—characterized by high temperature, complex multiphase flow, and frequent load fluctuations—makes ESPs highly susceptible to accelerated degradation and unexpected failure. To enhance the operational reliability and efficiency of offshore production systems, this study develops a Remaining Useful Life (RUL) prediction method for offshore ESP systems using a Dual-Stage Attention-Based Recurrent Neural Network (DA-RNN). The model integrates an input-attention mechanism to identify degradation-relevant offshore operating variables and a temporal-attention mechanism to capture long-term deterioration patterns in real marine production data. Using field data from a representative offshore oilfield in the Bohai Sea, the proposed method achieves an average prediction error of less than 28 days, demonstrating strong robustness under complex offshore conditions. Beyond prediction, an RUL-driven operation optimization strategy is formulated to guide controllable parameters—such as pump frequency and nozzle size—toward extending ESP lifespan and improving offshore production stability. The results show that combining predictive maintenance with operational optimization provides a practical and data-driven pathway for improving the safety, efficiency, and sustainability of offshore oil and gas development. This work aligns closely with the goals of marine resource development and offers a valuable engineering perspective for advancing offshore oilfield operations. Full article

(This article belongs to the Special Issue Advances in Offshore Oil and Gas Exploration and Development)

► Show Figures

Figure 1

24 pages, 5823 KB

Open AccessArticle

Path Planning of an Underwater Vehicle by CFD Numerical Simulation Combined with a Migration-Based Genetic Algorithm

by Bing Yang, Ligang Yao, Leilei Chen and Weilin Luo

J. Mar. Sci. Eng. 2026, 14(1), 74; https://doi.org/10.3390/jmse14010074 - 30 Dec 2025

Abstract

This paper proposes a physics-informed global path planning framework for underwater vehicles integrating CFD simulation and the genetic algorithm. The CFD simulation models the flow field along the planned path of the underwater vehicle. The current velocity data are incorporated into the following [...] Read more.

This paper proposes a physics-informed global path planning framework for underwater vehicles integrating CFD simulation and the genetic algorithm. The CFD simulation models the flow field along the planned path of the underwater vehicle. The current velocity data are incorporated into the following path planning that is based on an improved genetic algorithm (GA), which uses migration operators to share the information about feasible solutions or paths, improving the fitness of the whole population. In the three steps of the GA procedure, an elite selection strategy is adopted to avoid losing excellent solutions. A segmented crossover strategy is adopted to avoid low-quality crossover. An adaptive mutation strategy is used to enhance the ability to escape a local optimal solution. Using the improved GA, single-target and multi-target underwater path planning are investigated. In multi-target path planning, a combined algorithm is proposed to solve the optimal traversal order of target points and plan a feasible path between target points. The simulation results show that the proposed algorithm has good planning ability for both simple and complex underwater scenarios. Compared with the conventional GA and an improved GA, the number of average iterations decreases by 45.3% and 29.9%, respectively, for 2D multi-target path planning. The number of average inflection points decreases by 50.3% and 44.2%, respectively, for 2D multi-target path planning. Full article

(This article belongs to the Section Ocean Engineering)

► Show Figures

Figure 1

19 pages, 3826 KB

Open AccessReview

A Review of Microplastics Research in the Shipbuilding and Maritime Transport Industry

by Ivana Lučin, Ante Sikirica, Bože Lučin and Marta Alvir

J. Mar. Sci. Eng. 2026, 14(1), 73; https://doi.org/10.3390/jmse14010073 - 30 Dec 2025

Abstract

Microplastics are contaminants of increasing environmental concern, particularly in marine ecosystems where they can be easily ingested by marine organisms, causing adverse health problems in animals and, through trophic transfer, in humans. While numerous studies have examined microplastic pollution in marine environments, most [...] Read more.

Microplastics are contaminants of increasing environmental concern, particularly in marine ecosystems where they can be easily ingested by marine organisms, causing adverse health problems in animals and, through trophic transfer, in humans. While numerous studies have examined microplastic pollution in marine environments, most focus on water, sediment, or biota, thereby only measuring cumulative effects from multiple pollution sources in one area. This review aims to assess existing research on microplastic pollution originating from shipyards and maritime transport activities, with the goal of identifying current knowledge, methodological approaches, and existing research gaps. A review of the scientific literature was conducted, focusing on studies that investigated microplastic pollution associated with shipyards and maritime transport. Priority was given to peer-reviewed publications that included quantitative or qualitative measurements of microplastics. The reviewed literature reveals a limited number of studies explicitly addressing microplastic emissions from shipyards and maritime transport. Available studies employ diverse sampling strategies and analytical methods, making direct comparisons challenging. This review highlights significant gaps in current knowledge regarding microplastic sources and pathways linked to maritime industries. By synthesizing existing data, the paper provides a foundation for future targeted research and supports the development of more effective pollution reduction strategies. Full article

(This article belongs to the Section Marine Pollution)

► Show Figures

Figure 1

Journal Description

Journal of Marine Science and Engineering

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Further Information

Guidelines

MDPI Initiatives

Follow MDPI