Dissolved oxygen concentration is one of the important indexes to measure seawater quality. In order to grasp the change of seawater quality in time and reduce the risk and loss of seawater pollution, it is very important to establish the prediction mechanism of marine water quality parameters. Therefore, this paper proposes a prediction model of dissolved oxygen concentration in seawater based on temporal and spatial information fusion of buoy Networks and Generative Adversarial Networks (GAN), which aims to integrate topological information of buoy networks in the monitoring area and realize multi-feature fusion of buoy sensors. The model uses the Graph Attention Mechanism (GAT) to mine the influence of different nearest neighbor points on the target node and calculate the weights of the adjacent nodes, so as to capture the spatio-temporal characteristics of the buoy data. The two-head attention mechanism and the two-time-scale Update Rule (TTUR) were used to optimize the GAN prediction network and the network training process, improve the training speed balance of the generated adversarial network, and improve the fitting effect of the generator network. The mean squared error, root mean squared error, mean absolute error and R-Square are used as evaluation indexes to compare the model prediction performance. The results show that the evaluation indexes of the proposed model are superior to other models, and can effectively mine the spatial information of multiple buoys. It overcomes the shortcomings of traditional methods in the prediction of dissolved oxygen concentration in seawater, such as low accuracy, inability to flexibly use historical spatial data, poor training stability and slow speed, and can provide important technical support for marine water quality monitoring and prediction.

The Trachurus murphyi is affected by the environment, and the environment itself changes with time, with short-term seasonal changes and long-term regime shifts. Based on the data on jack mackerel stock and the environment from 1970 to 2017, this paper analyzes the relationship between the environment and jack mackerel stock from month to year using integral regression, regime shifts analysis, and all-subsets regression analysis. The month-on-month analysis results show that the influence of Sea Surface Temperature (SST) on stock biomass changes most obviously with time. Chilean jack mackerel is more dependent on SST in spawning and overwintering seasons. Followed by the Pacific Decadal Oscillation (PDO), the effects of Sea Surface Salinity (SSS) and Oceanic Niño Index (ONI) vary less in different months. The impact of Sea Surface Height (SSH) hardly changes from month to month. The annual analysis revealed four distinct regime shifts in Chilean mackerel resources over a long-time scale, with each regime characterized by unique dominant factor combinations. Notably, with the escalation of global climate change in recent years, a broader array of environmental factors has potentially influenced fishery resources, leading to significant changes in the patterns of environmental impact on fisheries.

Short-term precipitation nowcasting is a critical task in both meteorology and hydrology. However, current deep learning methods often yield ambiguous prediction results and exhibit significant cumulative errors. To address the limitations associated with these predictive methods, particularly the challenges of cumulative error and lack of clarity in prediction sequences, we propose a novel approach based on a Multi-scale Attention Encoding-Dynamic Decoding Network (MAEDDN) for short-term precipitation nowcasting. This method leverages the learning of spatiotemporal features from input data to accurately predict future precipitation scenarios. To obtain richer feature information from the input sequences, the encoding process employs convolutional blocks with spatial and channel attention for encoding. And a multi-scale fusion module is introduced to address the challenge of capturing both small-scale and large-scale information in precipitation distribution simultaneously. To enhance the clarity of the predicted sequences, the model needs to better understand the precipitation process. Therefore, in the decoding process, a dynamic decoding network is proposed in response to the generation and dissipation processes accompanying short-term precipitation. This network flexibly filters the decoding process by learning the intensity distribution and change trends of past input data. Experiments are conducted by using the precipitation data from the open-source SEVIR dataset, and comparisons are made with the best methods reported so far. The experimental results reveal that: (1) MAEDDN enhances the forecasting capability in areas with high-intensity precipitation, and (2) The clarity of the predicted image sequences by MAEDDN is significantly better than that of other models. The constructed multi-scale attention encoding captures the complex relationships in meteorological data more effectively, while the dynamic decoding adapts the decoding process based on different scenarios, resulting in more accurate prediction outcomes.

The streamline construction and placement of the marine flow field is of great significance for recognizing and understanding the marine flow field. In the process of streamline drawing, the selection of integration step is very important, which can directly affect the effect of streamline placement. The fixed step size algorithm is often not used because it cannot adapt to the changing curvature. The previous adaptive step size streamline algorithm has the problems of low degree of freedom and poor multi-scale applicability. In view of the above problems, this paper introduces information entropy into the step size calculation for the first time, and proposes an adaptive step size algorithm of marine streamline controlled by information entropy. Firstly, the entropy field is obtained by calculating the information entropy of the flow field, and then the flow field is divided into high entropy region and low entropy region according to the entropy value, and each integration point is given a new step size, so that the flow field can adaptively adjust the step size according to the intensity of change, that is, the step size of the high entropy region (the region with sharp change) is smaller, and the step size of the low entropy region (the region with gentle change) is larger. The experimental results show that the proposed algorithm can significantly increase the number of integration points and streamlines in the rapidly changing region, better draw the details of the streamline at the feature, and reduce the number of integration points and streamlines in the unimportant region without affecting the placement effect to improve the computational efficiency. Compared with the previous adaptive step size algorithm, the proposed algorithm significantly improves the degree of freedom of step size adjustment and the scale applicability, and can be applied to different scales of marine flow field.

The buried hill oil and gas reservoirs have become an important exploration field in China’s marine basins. The northwestern area of Shaleitian area of Bohai Bay Basin is a typical carbonate buried hill zone. Due to the lack of research on the reservoir control effect of multiple stage fractures and their related karstification, the oil and gas exploration of carbonate buried hills is restricted. This paper conducts a detailed analysis of the development characteristics of the fracture-cave system in carbonate buried hill reservoirs in the northwestern Shaleitian Uplift, and studies the reservoir control effects of fractures and karst. The results indicate that the lower Paleozoic carbonate buried hills in the northwestern area of Shaleitian Uplift belong to fracture related karst reservoirs. The reservoir space includes dissolution pores, structural fractures, and expansion pores along the structural fractures. High quality reservoirs have lithological selectivity, and fractures and dissolution pores developed in microlite crystalline dolomite and fine crystalline dolomite are better. The reservoir mainly develops three sets of fractures, with E−W and NE oriented shear fractures mainly related to two tectonic compressions during the Indosinian and Late Yanshanian. The third set of NW oriented tensile fractures is related to the intracratonic movement during the Himalayan orogeny, and compression is the main mechanism for forming high-density fractures. The later stage of extension is a necessary condition for the relaxation of fractures to form reservoir spaces. The Lower Paleozoic carbonate buried hill reservoirs have undergone three stages of karstification, which are karstification in the steady Caledonian tectonic background, karstification in the Indosinian compressive background, and fault block-horst karstification in the Yanshanian-Himalayan extensional background. In summary, the carbonate buried hill reservoirs in the northwestern Shaleitian Uplift are formed by multiple stages and multiple types of tectonic-karst processes, and the analysis of the differences in the degree of recombination in different structural parts is an important factor in understanding the reservoir formation mechanism.

The prediction of El Niño-Southern Oscillation is one of the hot issues in climate change research. This paper combines swin-transformer model with spatio-temporal fusion attention mechanism, and uses CMIP6 multi-model simulation historical data from 1850 to 2014, SODA assimilated data from 1871 to 1979 and GODAS assimilated data from 1980 to 2023 to construct El Niño-Southern Oscillation prediction model—ENSO-STformer. The model was fully trained on CMIP6 and SODA datasets and evaluated on GODAS data. The results show that the average skill of this model in predicting the Niño3.4 index at 11-month lead times exceeds those of CanCM4, CCSM3, and GFDLaer04 by 5.1%, 21.6%, and 12.4% respectively. Meanwhile, the Niño3.4 index related skills of the proposed model are significantly better than other deep learning models in the medium and long term. Effective ENSO forecasts can be made for up to 24 months, and the 2015−2016 El Niño event simulation shows strong ability to cope with spring forecast obstacles.

Rapid changes in the Arctic environment significantly impact the characteristics of water masses in the Arctic Ocean, potentially affecting the ocean’s physical and biogeochemical processes. This study utilizes the latest MOSAiC observation data (from October 2019 to August 2020) and high-resolution reanalysis data (GLORYS12V1) to analyze the variations in temperature and salinity of water masses across the Eurasian Basin along the MOSAiC drift trajectory, and to explore the influence of the Atlantic inflow on these variations. The results show that: (1) Both temperature and salinity within the upper 100 m layer along the drift trajectory exhibit an overall pattern of initially increasing and then decreasing from the Amundsen Basin to the Nansen Basin. The spatial variation in salinity is greatest within the 0−20 m layer, with highly saline surface water (S >34) present in Nansen Basin. In contrast, the variation in temperature is greatest at the 100 m layer, with the depth of 0℃ isothermal less than 100 m in parts of the Nansen Basin. Although GLORYS12V1 simulates the higher temperature in the upper Nansen Basin, it reasonably captures the main features of horizontal and vertical variations in temperature and salinity along the drift trajectory. (2) The warm and saline Atlantic water generally flows anticlockwise in the Eurasian Basin, with its depth gradually deepening during transport, which predominantly determines the overall variations in temperature and salinity in intermedia and upper layers in the Eurasian Basin. The high salinity of surface water in the Nansen Basin is due to the drift trajectory involved into the regions influenced by deep winter convection in northern Svalbard. Strong wind events play a limited role in the distributional differences of temperature and salinity along the drift trajectory. (3) In the western Nansen Basin, the GLORYS12V1 reanalysis exhibits a northward deviation in the simulated horizontal extent of Atlantic Water, which results in an over estimation of temperature compared to in-situ observations. To improve the accuracy of the GLORYS12V1 simulated results, refining the setting of Atlantic inflow flux at the open boundary is suggested.

In engineering practice, the Morison equation is commonly used to calculate wave loads on slender structures. Traditionally, the Morison equation for wave force calculation is often simplified, assuming the pile as a rigid body and neglecting the elastic deformation of the pile. By employing the Radial Basis Function (RBF), a mesh-free method, this study simultaneously solves the Morison equation, which considers pile elastic deformation, and the dynamic balance equation. This approach obtains the wave force and dynamic response of a single pile under wave load, and compares the results with those from standard methods and previous literature to validate its accuracy. Applying this method to actual engineering cases reveals the dynamic response of the working platform under the most unfavorable conditions. The RBF method is computationally straightforward and easy to master, making it suitable for practical engineering applications and providing a new direction for the calculation of offshore structures in the future.

Sediment transport is a fundamental issue in the study of coastal and estuarine environments, holding significant scientific importance and practical value for the evolution of estuarine geomorphology, ecological environment, and engineering construction. This paper takes the estuary of the Moyang River as an example, based on the sea current, wave and suspended sediment concentration data measured by ship and bottom tripod, analyzes the alongshore and cross-shore transport trends of suspended sediment on the fixed cross-section of the Moyang River estuary, and calculates the sediment transport flux. It explores the sediment transport mechanisms and patterns in wave-tidal estuaries, with the main findings including: (1) During the flood season at the river mouth, the sediment transport is mainly controlled by the runoff, with the sediment transport flux increasing as the flow flux increases. The alongshore and cross-shore sediment transport reaches the maximum value during the neap tide with the largest flow, which are 111.9 g/(m²·s) and 269.5 g/(m²·s) respectively. At the mouth bar in the flood season, the sediment transport is jointly controlled by waves and tides. The alongshore sediment transport is consistently westward along the coast during both spring and neap tides, while the cross-shore sediment transport is dominated by the ebb tide during the spring tide with an offshore transport of 4.0 g/(m²·s), and by waves during the neap tide with an onshore transport of 19.0 g/(m²·s). (2) During the dry season, the mouth bar is primarily influenced by tidal currents and wave action. Sediment transport along the vertical shore predominantly occurs due to falling tidal currents moving seaward, while coastal transport is governed by wave energy, resulting in an eastward movement under the influence of wave-generated coastal currents. On the eastern side of the mouth bar during this season, tidal currents and waves also play a significant role; vertical shore transport is mainly driven by rising tides during spring tide periods before transitioning to offshore transport as tidal forces diminish. Coastal transport remains affected by wave-induced coastal currents and continues its eastward trajectory. (3) During the flood season observation period, the offshore transport at the river mouth is significant, and the flow direction of each water layer is consistent vertically. During the neap tide, there is a differentiation in the flow direction of the water layers, with the surface layer transporting offshore and the bottom layer onshore. At the mouth bar, the flow direction of each water layer is relatively consistent vertically during both spring and neap tides. Still, after tidal averaging, the spring tide shows offshore transport in all water layers, while the neap tide shows onshore transport in all water layers. During the neap tide, the influence of waves is evident, with the onshore transport ratio reaching 79%. (4) Under the influence of runoff and ebb current, the mouth of Moyang River estuary mainly carries sediment to the sea. The most significant factors affecting sediment transport at the mouth bar are the seaward tidal currents and the alongshore sediment movements driven by waves.

Sandy and mixed beach-bar, which has good exploration potential, are widely developed in the upper fourth member of Shahejie Formation(Es₄^U) to the lower third member of Shahejie Formation (Es₃^L) of the Laizhou Bay Sag in the Bohai Bay Basin. At present, the sedimentary characteristics, genesis mechanism and evolution model of the beach-bar are still poorly understood, which seriously restricts the exploration and prediction of this type of sedimentation. Therefore, this study makes comprehensive use of drilling, logging, and seismic data to finely recover the micro-paleogeomorphology of the study area, clarifies the controlling role of geomorphology of the multi-stage gentle slope on the development of the beach-bar in the study area, sums up the depositional characteristics and the main controlling factors of the sand bodies of the beach-bar at different locations, and constructs the depositional model. The results show that: (1) sandy beach-bar are mainly developed in the front flanks of the braided river delta of the Es₄^U, which are mainly controlled by the windward geomorphic features of the first-stage gentle slope, strong sediment supply and paleo-wind direction. (2) The thick-layered mixed beach-bar are mainly developed in the windward zone of the secondary gentle slope of the Es₄^U, which is jointly influenced by paleogeomorphology, medium sediment supply and strong coastal currents. (3) The thin-layered mixed beach-bar are developed in the windward zone of the first-stage gentle slope of the Es₃^L, which is influenced by the combination of paleogeomorphology, weak sediment supply, paleo-wind direction and littoral current. By dividing the multi-stage gentle slope geomorphology, the establishment of the depositional model can help to predict the distribution of the sand body of the beach-bar and provide a reference for the exploration of the sand body of the beach-bar in the Bohai Bay Basin.