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Artificial intelligence in oceanography has demonstrated a great potential with the explosive growth of ocean observation data and numerical model products. This article first reviews the history of ocean big data development, and then introduces in detail the current status of artificial intelligence in oceanography applications including identifying ocean phenomenon, forecasting ocean variables and phenomenon, estimating dynamic parameters, correcting forecast errors, and solving dynamic equations. Specifically, this article elaborates the research on the intelligent identification of ocean eddies, internal waves and sea ice, the intelligent prediction of sea surface temperatures, El Ni?o-Southern Oscillation, storm surges, waves and currents, the intelligent estimation of ocean turbulence parameterization for numerical models, and the intelligent correction of waves and current forecast errors. In addition, it discusses the recent progress of applying physical mechanism fusion and Fourier neural operator for solving ocean dynamic equations. This article is based on the current status of artificial intelligence in oceanography and aims to provide a comprehensive demonstration of the advantages and potential of applying artificial intelligence methods in the field of oceanography. With the two emerging research hotspots: digital twin oceans and artificial intelligence large models, the future development direction of artificial intelligence provides enlightenment and reference for interested scientists and researchers.

Based on the mesoscale atmospheric model WRF and the regional ocean model ROMS, a two-way coupled WRF-ROMS air-sea model was constructed to simulate the super typhoon Mangkhut in 2018. The results showed that the simulation results of the coupled air-sea model were better than those of the only atmospheric or ocean model, and the error of the typhoon track obtained from the coupled model was within 60 km, which was in good agreement with the best track. Compared with the observation results, the simulation results of wind speed and sea level pressure in the coupled model were better than others model. Based on the simulation results of the coupled air-sea model, the spatial and temporal distribution of the wind field, pressure field, sea surface flow field, and storm surge under the super typhoon Mangkhut were further analyzed. The results showed that: (1) In terms of spatial distribution, after the typhoon entered the South China Sea, the radius of the seven-level wind circle was larger behind the right side of the typhoon; the cyclonic flow field showed a significant Ekman effect with the typhoon wind field, and the flow direction was 45° from the wind direction. The wind field, pressure field, wind-generated flow field and water gain distribution all had obvious asymmetry, and the typhoon intensity, flow velocity and water gain were greater on the right side of the typhoon path than on the left side. (2) In terms of time distribution, the distribution of the wind field and the pressure field were similar and synchronized with the typhoon center, while the wind-driven flow field and storm surge were three hours behind the typhoon track.

Waves are one of the most important phenomena in the ocean. The accurate and quick updated wave forecasting is of crucial significance for ensuring marine activities safety. The development of wave forecast is presented, including the traditional statistical wave forecasting methods, numerical wave prediction models, and the rapidly developing artificial intelligence (AI) wave forecasting methods. Currently, AI wave forecast models have been demonstrated unique advantages in terms of computational efficiency and adaptive forecasting accuracy, and they are gradually being applied in practical wave forecasting operations, transitioning from the research stage. However, they also have limitations, including limited forecasting elements, underestimation of extreme wave conditions, and weak forecasting generalization ability. Based on the characteristics of AI wave prediction, key scientific and technological issues that need to be addressed in current AI wave forecasting are proposed. These include efficient utilization of observational data, incorporation of prior physical knowledge, and enhancement of AI model safety and generalization ability.

Due to its advantages of strong adaptability and high safety,the jacket platform has been widely used in offshore oil and gas development. As more and more jacket platforms will reach or exceed the design life, the platform decommissioning has become one of the important problems in the field of offshore oil and gas development. This paper systematically introduces domestic and international regulations on jacket platform decommissioning, focuses on the decommissioning constraints, application fields, similarities and differences between different international conventions, and summarizes the key points of regulations and requirements on jacket platform decommissioning among China, the United Kingdom, Norway and the United States, and analyzes four typical decommissioning projects worldwide. In view of this, the different decommissioning schemes, and scope of application of jacket platform are summarized and analyzed, as well as the operational difficulties and implementation details of the three key process technologies of dismantling, cutting and transportation during decommissioning. The review can provide a theoretical basis for optimizing the decommissioning plan and key technical methods of the offshore jacket platforms, and has important engineering significance for marine environmental protection, navigation and fishery production.

Submarine earthquake is one of the most major factors causing deep-water international submarine cables damage. Understanding the process of submarine cables damage and the mechanism of submarine cables damage caused by turbidity currents after earthquake are of great significance to the security maintenance of international submarine communications. Combined with the lastest research result of global seabed topography and using professional international submarine cables engineering software Makaiplan, the process of plenty of submarine cables damage after Grand Banks Earthquake and Hengchun Earthquake were studied, then the relationship between the pattern of submarine cable damage and the developing process of turbidity currents after earthquake was found, and the mechanism of submarine cables damage caused by turbidity currents after earthquake was summarized. Study result shows that submarine cables break points are located intentively in submarine canyons and trenches. The movement speed of turbidity currents in submarine canyon and submarine trench, which caused submarine cable damage, can reach several ten kilometers to several hundred kilometres per hour. Terrestrial rivers and continental shelf undersea river channels provide materials transportation for the development of turbidity currents. Submarine canyons and trenchs are the pathes of turbidity currents movement then damage plenty of submarine cables. The turbidity currents that developed from upper continental slope in passive continental margin after earthquake can damage submarine cables laid on continental slope, continental rise and abyssal plain. This kind of turbidity currents achieves maximum speed on continental slope, then self-accelerate on abyssal plain. Multiple turbidity currents can develop at different positions of continental slope at the same time in active continental margin, then strike submarine cables which laid on canyons and trenches for multiple times. This kind of turbidity currents achieves maximum speed and self-accelerates in submarine trenches. There are several earthquake-resistance measures: submarine cable routes trying to avoid crossing submarine canyons and trenches which connected to terrestrial rivers or continental shelf channels; using shallow water type submarine cable which has outer armor protection when crossing inevitably; laying submarine cables suspended slightly on the bottom of canyons or trenches with Uraduct protection on them; changing the cross-section shape of submarine cable.

Deep-sea REY-rich sediments that are rich in lanthanides and yttrium (REY) extensively distributed in regions such as the Western Pacific, Eastern Pacific, Southeastern Pacific, and the Indian Ocean. This study analyzed the mineralogical and geochemical characteristics of deep-sea REY-rich sediments from two sites in the Clarion-Clipperton Fracture Zone (CCFZ) of the Eastern Pacific. Additionally, geochemical data on elements from 92 deep-sea REY-rich sediment sites across the Pacific were collected. Based on geochemical characteristics in conjunction with mineral composition, the Pacific deep-sea REY-rich sediments are categorized into three types: Al-rich, Fe-rich, and Ba-rich. The Al-rich type, prevalent in the Western Pacific region, primarily consists of zeolite clay, with an average whole-rock Al₂O₃ content reaching up to 14.9%. The Fe-rich type, found near the Eastern Pacific Rise in the Southeastern and Northeastern Pacific, exhibits a high average TFe₂O₃ content of 18.8%. Some samples within this type show a significant positive Eu anomaly, indicating that hydrothermal activity may contribute to the enrichment of REY and associated carrier minerals. The Ba-rich type, mainly located in the CCFZ of the Eastern Pacific, consists predominantly of (siliceous) clay, with an average Ba content of approximately 8 092×10^-6. The elevated Ba levels suggest that the area of sediment formation may have experienced high primary productivity. This environmental condition likely resulted in extensive biogenic apatite deposition, which coupled with strong bottom currents in the CCFZ since the Oligocene, enhanced the accumulation of apatite, thereby promoting the enrichment of rare earth elements.

Using the data of high resolution satellite sea surface temperature (SST) from January 1, 1990 to December 31, 2020, the spatial characteristics of marine heatwaves (MHWs) in the South China Sea were identified with a deep-first-search algorithm, and the characteristics of marine heatwaves at different spatial scales were further investigated. The results indicated that the small-scale marine heatwave events in the South China Sea (Type I MHWs, area<1.8×10⁴ km²) occurred the most frequently, accounting for 94.20% of the total marine heatwave occurrences. Large-scale marine heatwaves with areas exceeding 1.2×10⁵ km² (Type III MHWs) occurred only 74 times during the 31-year period, with the largest event recorded in 2015. Further analysis revealed significant differences in the spatial distribution of intensity, duration, and frequency of marine heatwaves for different spatial scales. Compared to Type I MHWs, Type II MHWs (1.8×10⁴~1.2×10⁵ km²) exhibited a noticeable increase in the average coverage area with an intensity exceeding 1.5 ℃. Statistical analysis showed that the intensity, duration, and cumulative intensity of South China Sea MHWs increased with the spatial scale of the MHWs. The intensity of Type III MHWs was 1.4 times that of Type I MHWs and 1.2 times that of Type II MHWs. In addition, the response of South China Sea MHWs areas to the El Ni?o-Southern Oscillation (ENSO) was also investigated. The results showed a significant increase in the areas of Type I to III MHWs during El Ni?o periods, with a lag of 6 to 7 months. The duration of Type III MHWs during El Ni?o was longer by 2 days compared to La Ni?a periods. This study explored the fundamental characteristics of South China Sea MHWs areas and further analyzed the commonalities and differences of MHWs at different spatial scales, providing new insights into the characteristics and mechanisms of the formation and dissipation of South China Sea MHWs.

The type of sea ice is one of the important attributes of polar sea ice, and the physical properties of multi-year ice are significantly different from those of first-year ice. Therefore, the identifying the types of sea ice is of great significance to the research of polar climate change and the navigation security of ships in ice-covered regions. Satellite remote sensing is an effective method to obtain multi-temporal and large-scale sea ice type information. Based on three deep learning models (ResNet, Vision Transformer, Swin Transformer) and Sentinel-1 satellite dual-polarization synthetic aperture radar (SAR) images, this paper studies the classification method for sea ice in the regions of the Northwest and Northeast Passage in the Arctic. The results showed that the sea ice classification effect of 8×8 pixel slice dataset was better than that of other size slice datasets. Offset processing false color images could effectively reduce the influence of noise on sea ice classification. Among the three deep learning models, the Swin Transformer model had the highest classification accuracy, with the overall accuracy and Kappa coefficient above 98%. Comparing the multi-year ice concentration, it was found that the results of the three models deviate less than 10% from the AMSR2 data.

By interpolation of high and low tide data and application of NS_TIDE model, the tidal characteristics of the tidal reach of Xijiang River (Makou-Dahengqin) were analyzed. Compared with cubic spline interpolation and linear interpolation, it is found that Hermite interpolation is the best method to simulate the hourly tide level. The verification results of tide level show that the overall error of NS_TIDE model is low, and the outliers mainly come from the influence of typhoon and flood. The mean water level and the amplitude of tidal component in the tidal reach of Xijiang River are different in wet and dry season. The influence of runoff in the upper reaches is greater than that of the tides, and the opposite is true in the lower reaches. With the increase of runoff and tidal difference, the mean water level of the upper reaches increases, and the influence on the amplitude and the phase of tidal component is different in different section, which is related to the spatial location and the frequency of the tidal component.

Ocean eddies are prevalent oceanic phenomenon that play a crucial role in the global transportation of oceanic materials and energy. Although traditional methods for detecting ocean eddies are widely used, they suffer from significant drawbacks such as excessive reliance on expert-set thresholds, continuous manual intervention, large detection errors, low efficiency, and poor global applicability, making it difficult to adapt to the complex and variable marine environment. Currently, the rapid development of artificial intelligence (AI) presents a promising solution for the intelligent detection of ocean eddies. AI can automatically and rapidly extract deep features from images, effectively address the challenges posed by the high similarity in oceanic phenomenon features and significant geometric variability. This paper provides an overview of AI-based oceanic eddy identification methods based on different deep learning methods, focuses on coder-decoder structure, fully convolutional neural network, multi-scale context method and attention mechanism, and aims to provide valuable insights and references for future ocean eddy research.

Mesoscale eddies widely exist in the ocean and affect the sound propagation. Using AVISO altimeter and Argo buoy data from 2000 to 2018, the multi-year average three-dimensional structure of mesoscale eddies in the Kuroshio and Oyashio extension regions in the Northwest Pacific Ocean was constructed by synthesis method, and the structural characteristics of temperature anomalies, salt anomalies and sound velocity were analyzed. The sound propagation in eddies is simulated by using Bellhop ray acoustic model. The results show that : (1) Under the background of the cold eddy, the temperature anomaly is negative, the salinity anomaly is negative in the upper layer and positive in the lower layer, and the sound velocity contour rises. Under the background of warm eddy, the temperature anomaly is positive, the salinity anomaly is positive in the upper layer and negative in the lower layer, and the sound velocity contour is sinking. (2) The cold eddies cause the convergence region to shift towards the sound source direction and the width of convergence zone to decrease; the warm eddies cause the convergence zone to move away from the sound source and increase its width. The convergence area in the Kuroshio extension region is wider than that in the Oyashio extension region, and is further away from the sound source. (3) The cold eddies make the convergence zone turning depth shallower, while the warm eddies make the convergence zone turning depth deeper. In the Kuroshio extension region, the inversion depth is shallower with the increase of longitude,but in the Oyashio extension region, the inversion depth is deeper with the increase of longitude.

The intertidal zone is a key area connecting terrestrial ecosystems and marine ecosystems, among which muddy tidal flat is an important and easily overlooked CO₂ collection habitat, and the macrobenthos play a central role in the input, transport and preservation of carbon. Macrobenthos community and living organic carbon pools of muddy tidal flat were analyzed in Aiwan Bay, eastern coast of Zhejiang Province in summer. The average abundance of macrobenthos was 105.2 ±37.2 ind/m², and the average biomass was 46.9 ±6.4 g/m². The major taxa components within the habitat were crustaceans and mollusks, and the ecosystem health status was excellent. The organic carbon contents of macrobenthos at Aiwan Bay from highest to lowest were other animals including fish and nemertinea (40.95%), polychaetas (22.98%), crustaceans (17.24%), echinoderms (15.90%), mollusks (10.76%), and estimated the macrobenthos carbon pool was 163.90 Mg, of which crustaceans have the largest contribution rate, accounting for 59.80%. The exploration of macrobenthos community structure and living organic carbon pools size in muddy tidal flat can provide scientific suggestion for constructing the blue carbon survey system and supply fundamental data to further quantify the overall carbon pool size in coastal habitats.

Based on the data of satellite altimetry and six tide gauge stations along the coast of China, linear regression function was used to estimate the absolute sea level rise rate in the coastal areas of China from 1993 to 2020, which was 4.17±1.32 mm/a, and the relative sea level rise rate was 4.47±0.90 mm/a. Taking the atmospheric data, ocean data and climate modal index from 1958 to 2020 as prediction factors, a variety of neural network models such as long short-term memory neural network model (LSTM model), recurrent neural network model (RNN model), gated recurrent unit neural network model (GRU model) and support vector machine regression model (SVR model) were established to predict the trend of relative sea level changes around the six tide gauge stations along the coast of China. The model evaluation results show that the average correlation coefficient and root mean square error of the observed value and the predicted value obtained by the LSTM model that simultaneously introduces atmospheric and ocean variables and climate modal index variables are 0.866 and 19.279 mm, respectively, which performs the best among the four models, and therefore the LSTM model can be used as a new method for predicting relative sea level changes.

Combined with the Holland wind fields and the ERA5 wind fields, the mixed wind fields was set up by introducing a weight coefficient varying with the radius of wind speed, and a typhoon wave model in Zhejiang sea area was established using MIKE21 SW. Then, the Holland, the ERA5 and the mixed wind fields were used as the input wind fields to simulate the wind speed and the significant wave height during No.1918 typhoon Mitag, respectively. The verification shows that the simulated results obtained using the Holland wind fields and the ERA5 wind fields cannot agree accurately with the observed data, while the mixed wind fields proposed in this study can improve the simulation accuracy. In order to study whether the above conclusion is universal in Zhejiang sea area, five typical typhoons that have the most serious impact on Zhejiang sea area in the recent 5 years were selected for typhoon wave numerical simulations and the error statistical analysis. The results indicate the wind speed around the typhoon center is relatively good using the Holland wind fields and the average relative errors of the maximum wind speed are 8.62%-10.19%, but the average relative errors of the wind speed below 10 m/s is relatively bigger, reaching 29.76%-44.29%. However, the wind speed around the typhoon center using the ERA5 wind fields is smaller than the observed data, and the average relative errors of the maximum wind speed are 17.64%-25.77%, but the average relative errors of wind speed below 10 m/s are smaller than that using the Holland wind fields, which are 19.64%-32.00%. During the five typhoon processes, the average values of the average relative errors of the significant wave height driven by Holland, the ERA5 and the mixed wind fields are 29.92%, 25.62% and 22.82%, respectively. Correspondingly, the average root mean square errors are 0.46 m, 0.42 m and 0.39 m and the consistency indexes are 0.94, 0.95 and 0.96. The above results shows that the mixed wind fields proposed in this study is universal in Zhejiang sea area and can improve the simulation accuracy of typhoon waves.

Accurate monitoring and assessment of the temperature rise intensity and spatio-temporal distribution of thermal discharge from power plants are of great significance for ensuring safe operations of the power plants and protecting the ecological environment of surrounding marine areas. Based on thermal infrared remote sensing observation technology, information such as the spatial distribution, temperature rise intensity, and temporal variation of thermal discharge from coastal power plants can be obtained, making it an important means for monitoring thermal discharge from these plants. The current research status and progress in monitoring thermal discharge from coastal power plants using thermal infrared remote sensing technology are systematically reviewed, including remote sensing data sources, water temperature retrieval, thermal discharge background temperature information extraction, spatio-temporal statistics of thermal discharge, and analysis of influencing factors. Furthermore, the paper proposes future directions for remote sensing research on thermal discharge from coastal power plants.

Coastal erosion leads to land loss and seriously threatens people’s life and property safety. It is great significant to identify coastal erosion vulnerability for disaster prevention and mitigation. The evaluation index system was constructed from three aspects: coastal dynamics, coastal morphology and social economy. Using the DSAS model and remote sensing data, the coast was discretized into equally spaced units based on section method, the weight and grade of the evaluation index were determined based on the entropy weight method, the coastal erosion vulnerability in the study area was calculated, and the spatial differentiation and influencing factors of coastal erosion vulnerability were identified by geographic detector. The results showed that the proportions of coastal erosion vulnerability for extremely high vulnerability, high vulnerability, medium vulnerability, low vulnerability and extremely low vulnerability in central coast of Jiangsu were 5.60%, 15.80%, 30.93%, 24.21%, and 23.46%, respectively, that showed a decreasing trend from north to south. The extremely vulnerable areas of coastal erosion were mainly located in the coastal area between the Zhongshan Estuary and the Sheyang Estuary. The spatial differentiation of coastal erosion vulnerability in central Jiangsu was the result of the synergistic effect of multiple factors such as coastal dynamics, coastal morphology, and economic and social activities. Among them, tidal slope, land cover, average tidal range, and coastline change rate were the dominant factors for the spatial differentiation of coastal erosion vulnerability.

Acoustic propagation in horizontally varying double duct waveguides under ice cover was investigated for the phenomenon of double duct waveguides in some Arctic seas. The ice reflection coefficients on the rough undersurface were derived and determined by the perturbation method, and the acoustic propagation characteristics of the horizontally varying double duct waveguide in the measured sea area were computed and analyzed by combining with the Bellhop ray model, and the influences of the depth of the sound source, the angle of incidence of the sound source and the frequency of the sound source on the acoustic propagation in the horizontally varying double duct waveguide were also investigated. The results show that in the Arctic, acoustic propagation in the deep-sea sound duct is mostly confined to the upper and lower edges of the deep-sea sound duct; the acoustic propagation loss is smaller when the sound source is at the same depth as the horizontally varying deep-sea sound duct axis, and the horizontally varying sound velocity profile has a lower acoustic propagation loss compared to the horizontally unchanged one when the sound source is located outside of the boundary of the deep-sea sound duct; the angle of incidence has a smaller effect on the acoustic propagation in the double duct waveguide; as the frequency of the sound source increases, the acoustic propagation loss in the surface duct increases, but the effect on the deep-sea duct is not obvious, and the horizontally varying sound velocity profile is more favorable for acoustic propagation at the same frequency.

Deep-sea sediments and polymetallic nodules are rich habitats for microorganisms. Exploring their community structure and functionality is crucial for understanding microbial genetic resources and their role in mineral formation. Current research on the bacterial diversity and structure within the nodules and surrounding sediments is limited, especially regarding microbial contributions to nodule formation. Using full-length 16S rRNA sequencing, we analyzed the bacterial composition of various nodule types and surrounding sediments in the Pacific Ocean. Scanning electron microscopy and energy dispersive spectroscopy revealed bacterial-like microsphere structures and metal element distribution on their surfaces. The bacterial community composition varied among different nodules and sediments, with Proteobacteria and Bacteroidetes dominating. Functional groups like Shewanella and Colwellia, known for metal oxidation-reduction and biofilm formation, may contribute to nodule formation. These microsphere structures promoted metal aggregation, potentially serving as mineral precipitation sites. This study enhanced our understanding of microbial functions and mineral interactions, crucial for insights into deep-sea biogeochemical cycles and microbial mineralization.

Submarine cable and pipeline hold multifaceted strategic significance for national security, socio-economic development, and marine ecological environmental protection. This paper systematically reviews the evolution of China’s management policies for submarine cable and pipeline, traces the development trajectory of both the international legal framework and the domestic policy system. It analyzes the main existing issues such as administrative approval, protection management, spatial regulation, and national security. The study argues that with increasingly frequent marine development activities, lagging management mechanisms, and a complex and volatile international geopolitical landscape, submarine cable and pipeline face challenges including intensified competition for spatial resources and heightened security risks. The article proposes optimizing management policies across four dimensions: institutional restructuring, systematic protection, spatial governance, and security safeguarding. This approach aims to promote the construction of a forward-looking, efficient, and resilient governance system for submarine cable and pipeline. Such a system is essential to safeguard the implementation of China’s maritime strategy and ensure sustainable development.

Seafloor topography is of considerable value in commercial, engineering, military and scientific research. Currently, gravity field data, such as free air gravity anomalies and vertical gravity gradients, are commonly used to inverse seafloor topography. However, due to the strong multi-resolution of the existing methods to inverse seafloor topography, it is still impossible to obtain accurate high-precision seafloor topography. A new data-knowledge-driven method was proposed to reconstruct accurate seafloor topography, which combines the gravity-density method with random forests. This method was applied to the South China Sea and compared with the gravity-density, random forest, and existing SIO models. The inversion results show that the data-knowledge-driven method provides better inversion performance, followed by the random forest and gravity-density methods, and the SIO model is the worst. The mean absolute error, mean relative error and root mean square error of the data-knowledge-driven are reduced by 21%, 25% and 7%, respectively, compared to those of the gravity-density method, while they are also reduced by 20%, 20% and 20%, respectively, compared to those of the random forest. In addition, the data-knowledge-driven model has a high degree of agreement with the shipboard bathymetry data, with approximately 72% of its differences distributed within ±10 m, which is higher than that of the other three models. The results demonstrate the feasibility and effectiveness of the data-knowledge-driven approach in seafloor topography inversion, which helps to accelerate the mapping of high-precision seafloor topography.