Search Results (23)

Frequent marine heatwaves (MHWs) threaten marine ecosystems and economies. To accurately anticipate MHW occurrence probability in the East China Sea (ECS), this work proposes MHW-NET, a deep learning model. The model uses an encoder–bottleneck–decoder dual-branch architecture, ConvLSTM, efficient channel attention (ECA), and a composite loss function with focal Tversky and SSIM losses. The model is trained on data from 1982 to 2011, with an independent test period of 2016–2022. Over lead days 1 to 7, MHW-NET performs relatively well in the Yellow Sea, while exhibiting substantially weaker performance in complex nearshore regions such as Bohai Bay, the Zhejiang coast, and the waters off northern Taiwan. The model accurately characterizes the spatial distribution of MHWs and reproduces the spatial patterns of the two extreme MHW events in 2016 and 2022, with spatial correlations exceeding 0.85. Feature importance analysis indicates that sea surface temperature and 2-m air temperature are the main driving factors of MHWs in the ECS. This study’s ablation experiments show that ConvLSTM and ECA are core modules and noise and refinement are auxiliary. Benchmark comparisons with baseline models further confirm the superiority of MHW-NET, demonstrating its potential for improving MHWs forecasting and early warning in the ECS. Full article

Introduction: Records of rare, deep-water, and non-indigenous fish species are of growing interest in marine biodiversity research because they refine regional inventories, improve taxonomic knowledge, and provide valuable evidence of ecological change. In regions supported by long-term monitoring programs, such findings are especially relevant, as they help detect unusual occurrences and document changes in species composition over time. Objective: The aim of this communication is to present recent records of fish species new to science and new to Spanish waters, together with a non-indigenous species, all from Galician and Cantabrian waters (northern Spain), while emphasizing the importance of scientific surveys and complementary local observations in their detection. Methodology: The material examined was collected during the annual demersal trawl surveys conducted by the Instituto Español de Oceanografía (IEO-CSIC) on the northern Spanish continental shelf (DEMERSALES), as well as during two multidisciplinary surveys carried out on the Galicia Bank in 2010 and 2011. An additional specimen was obtained from a local recreational fisher off Asturias. In all cases, species identification was based on an integrative taxonomic approach combining morphological examination and molecular analyses (COI barcoding). Results: Three species new to science were identified: Gaidropsarus gallaeciae (Gadiformes: Gaidropsaridae), Notacanthus arrontei (Notacanthiformes: Notacanthidae), and Neoscopelus serranoi (Myctophiformes: Neoscopelidae). In addition, Lyconus brachicolus (Gadiformes: Lyconidae) and Lipogenys hyalovelanum (Notacanthiformes: Notacanthidae) were recorded for the first time in Spanish waters. A specimen of Diapterus brevirostris (Perciformes: Gerreidae), native to the tropical and subtropical Pacific coast of America, was also identified off Asturias. Owing to its small size and to the proximity of the commercial port of Gijón, ballast water is considered the most plausible vector for its introduction into the Cantabrian Sea. Conclusions: These records illustrate the value of long-term oceanographic surveys for detecting rare and deep-water fishes and confirm the usefulness of integrative taxonomy for robust species identification. They also highlight the complementary role of fishers and citizen observers in documenting biodiversity change and detecting non-indigenous species in Spanish waters. Full article

The rolling seal is a pivotal sealing technology for marine equipment such as wet-mateable connectors, ensuring operational integrity in deep-sea environments during both static and mating phases. However, its working mechanisms remain inadequately understood, and the effects of sealing parameters and seawater pressure have yet to be systematically studied. To address these issues, a refined model for rolling seals operating in deep-sea pressure-balanced conditions was developed. The model’s accuracy was enhanced by incorporating two key inputs: experimentally measured boundary lubrication friction coefficients (replacing conventional dry friction values) for finite element simulation and torque calculation, and oil pressure under pressure-balanced conditions, derived from shell theory, as a boundary load. Through systematic parametric simulations, the effects of interference fit, rotational speed, and seawater pressure on sealing performance were elucidated. An experimental torque test setup under atmospheric pressure was constructed to validate the numerical model. The results indicate that, while ensuring reliable static sealing, higher rotational speeds and smaller interference fits help reduce rotational torque. Benefiting from the pressure-balanced design, increasing water depth significantly enhances hydrodynamic performance—accounting for over 90% of the total static contact pressure at 1500 m—while leakage shows a decreasing trend. These findings provide theoretical insights for optimizing deep-sea sealing structures. Full article

Accurate segmentation of navigable waters and obstacles is critical for unmanned surface vessel navigation yet remains challenging in real aquatic environments characterized by complex water textures and blurred boundaries. Current models often struggle to simultaneously capture long-range contextual dependencies and fine spatial details, frequently leading to fragmented segmentation results. In order to resolve these issues, we present a novel segmentation model based on the CoAtNet architecture. Our framework employs an enhanced convolutional attention encoder, where a Fused Mobile Inverted Bottleneck Convolution (Fused-MBConv) module refines boundary features while a Convolutional Block Attention Module (CBAM) enhances feature awareness. The model incorporates a Bi-level Former (BiFormer) to enable collaborative modeling of global and local features, complemented by a Multi-scale Attention Aggregation (MSAA) module that effectively captures contextual information across different scales. The decoder, based on U-Net, restores spatial resolution gradually through skip connections and upsampling. In our experiments, the model achieves 95.15% mIoU on a self-collected dataset and 98.48% on the public MaSTr1325 dataset, outperforming DeepLabV3+, SeaFormer, and WaSRNet. These results show the model’s ability to effectively interpret complex aquatic environments for autonomous navigation. Full article

High spatial resolution sea ice concentration (SIC) is crucial for global climate and marine activity. However, retrieving high spatial resolution SIC from passive microwave sensors is challenging due to the trade-off between spatial resolution and atmospheric contamination. Our study develops the Ice-WaterNet framework, a novel superpixel-based deep learning model that integrates Conditional Random Fields (CRF) with a dual-attention U-Net to enhance ice–water classification in Synthetic Aperture Radar (SAR) imagery. The Ice-WaterNet model has been extensively tested on 2735 Sentinel-1 dual-polarized SAR images from 2021 to 2023, covering both winter and summer seasons in the Fram Strait. To tackle the complex surface features during the melt season, wind-roughened open water, and varying ice floe sizes, a superpixel strategy is employed to efficiently reduce classification uncertainty. Uncertain superpixels identified by CRF are iteratively refined using the U-Net attention mechanism. Experimental results demonstrate that Ice-WaterNet achieves significant improvements in classification accuracy, outperforming CRF and U-Net by 3.375% in Intersection over Union (IoU) and 3.09% in F1-score during the melt season, and by 1.96 in IoU and 1.75 in F1-score during the freeze season. The derived high-resolution SIC products, updated every two days, were evaluated against Met Norway ice charts and compared with ASI from AMSR-2 and SSM/I, showing a substantial reduction in misclassification in marginal ice zones, particularly under melting conditions. These findings underscore the potential of Ice-WaterNet in supporting precise sea ice monitoring and climate change research. Full article

The submarine topography in the canyon area of the Qiongdongnan Basin is complex, with severe risks of shallow gas hazards threatening marine engineering safety. To accurately characterize seabed morphology and assess shallow gas risks, this study employed multi-source data fusion technology, integrating 3D seismic data, shipborne multibeam bathymetry data, and high-precision AUV topographic data from key areas to construct a refined seabed terrain inversion model. For the first time, the spatial distribution characteristics of complex geomorphological features such as scarps, mounds, fissures, faults, and mass transport deposits (MTDs) were systematically delineated. Based on attribute analysis of 3D seismic data and geostatistical methods, the enrichment intensity of shallow gas was quantified, its distribution patterns were systematically identified, and risk level evaluations were conducted. The results indicate: (1) multi-source data fusion significantly improved the resolution and accuracy of terrain inversion, revealing intricate geomorphological details in deep-water regions; and (2) seismic attribute analysis effectively delineated shallow gas enrichment zones, clarifying their spatial distribution patterns and risk levels. This study provides critical technical support for deep-water drilling platform site selection, submarine pipeline route optimization, and engineering geohazard prevention, offering significant practical implications for ensuring the safety of deep-water energy development in the South China Sea. Full article

The Niland Moving Mud Spring, located near the southeastern margin of the Salton Sea, represents a rare and evolving geotechnical hazard. Unlike the typically stationary mud pots of the Salton Trough, this spring is a CO₂-driven mud spring that has migrated southwestward since 2016, at times exceeding 3 m per month, posing threats to critical infrastructure including rail lines, highways, and pipelines. Emergency mitigation efforts initiated in 2018, including decompression wells, containment berms, and route realignments, have since slowed and recently almost halted its movement and growth. This study integrates hydrochemical, temperature, stable isotope, and tritium data to propose a refined conceptual model of the Moving Mud Spring’s origin and migration. Temperature data from the Moving Mud Spring (26.5 °C to 28.3 °C) and elevated but non-geothermal total dissolved solids (~18,000 mg/L) suggest a shallow, thermally buffered groundwater source influenced by interaction with saline lacustrine sediments. Stable water isotope data follow an evaporative trajectory consistent with imported Colorado River water, while tritium concentrations (~5 TU) confirm a modern recharge source. These findings rule out deep geothermal or residual floodwater origins from the great “1906 flood”, and instead implicate more recent irrigation seepage or canal leakage as the primary water source. A key external forcing may be the 4.1 m drop in Salton Sea water level between 2003 and 2025, which has modified regional groundwater hydraulic head gradients. This recession likely enhanced lateral groundwater flow from the Moving Mud Spring area, potentially facilitating the migration of upwelling geothermal gases and contributing to spring movement. No faults or structural features reportedly align with the spring’s trajectory, and most major fault systems trend perpendicular to its movement. The hydrologically driven model proposed in this paper, linked to Salton Sea water level decline and correlated with the direction, rate, and timing of the spring’s migration, offers a new empirical explanation for the observed movement of the Niland Moving Mud Spring. Full article

The genus Pasiphaea Savigny, 1816, comprises a diverse assemblage of meso- to bathypelagic shrimps, currently including 69 valid species. Taxonomic investigations have long been hampered by numerous synonymies and by species known only from their original, overly brief descriptions. Here, we address these deficiencies by examining Pasiphaea specimens collected in the Western and Central Pacific during multiple research cruises using mid-water trawls. Integrating traditional morphological characters (carapace carination, pleonal relief, telson end morphology, and pereopodal dentition) with a novel morphometric framework, we standardized terminology and defined a suite of quantitative measurements across the carapace, pleon, telson, and chelae. In order to corroborate species boundaries, we sequenced three mitochondrial gene markers (COI, 16S rDNA, and 12S rDNA) and calculated genetic distances under the Kimura two-parameter model. This combined approach extended or confirmed the geographic ranges of five described taxa, facilitated the formal description of eight new species, and demonstrated the synonymy of P. gracilis Hayashi, 1999, and P. fragilis Hayashi, 1999. Phylogenetic reconstructions further highlighted cases of pseudocryptic speciation in neighboring bathyal habitats. Our results expose substantial lacunae in the Southwest Pacific Pasiphaea fauna and substantiate the necessity for a comprehensive, genus-level revision. This study thereby contributes to both refining species diagnoses and advancing our understanding of deep-sea crustacean biodiversity. Full article

This study examines the upper-ocean response to Typhoon Khanun, which traversed the northern South China Sea in October 2017, by integrating multi-satellite observations with numerical simulations from the Regional Ocean Modeling System (ROMS). For the ROMS simulations, an Arakawa C-grid was adopted with a 4-km horizontal resolution and 40 vertical terrain-following $\sigma$-layers, covering the domain of 105° E to 119° E and 15° N to 23° N. Typhoons significantly influence ocean dynamics, altering sea surface temperature (SST), sea surface salinity (SSS), and ocean currents, thereby modulating air–sea exchange processes and marine ecosystem dynamics. High-resolution satellite datasets, including GHRSSST for SST, SMAP for SSS, GPM IMERG for precipitation, and GLORYS12 for sea surface height, were combined with ROMS simulations configured at a 4-km horizontal resolution with 40 vertical layers to analyze ocean changes from 11 to 18 October 2017. The results show that Typhoon Khanun induced substantial SST cooling, with ROMS simulations indicating a maximum decrease of 1.94 °C and satellite data confirming up to 1.5 °C, primarily on the right side of the storm track due to wind-driven upwelling and vertical mixing. SSS exhibited a complex response: nearshore regions, such as the Beibu Gulf, experienced freshening of up to 0.1 psu driven by intense rainfall, while the right side of the storm track showed a salinity increase of 0.6 psu due to upwelling of saltier deep water. Ocean currents intensified significantly, reaching speeds of 0.5–1 m/s near coastal areas, with pronounced vertical mixing in the upper 70 m driven by Ekman pumping and wave-current interactions. By effectively capturing typhoon-induced oceanic responses, the integration of satellite data and the ROMS model enhances understanding of typhoon–ocean interaction mechanisms, providing a scientific basis for risk assessment and disaster management in typhoon-prone regions. Future research should focus on refining model parameterizations and advancing data assimilation techniques to improve predictions of typhoon–ocean interactions, providing valuable insights for disaster preparedness and environmental management in typhoon-prone regions. Full article

The current standard for sea ice engineering in the Bohai Sea implements a 1/4° grid method, which cannot satisfy the safety of oil and gas activities in the southern Bohai Sea, and therefore more detailed information on ice conditions and a more refined ice zone division are necessary. In the present study, up to 1/12° resolution sea ice characteristic data (period, thickness, concentration, and strength) were obtained based on the NEMO-LIM2 ice–ocean coupling model. On this basis, the design sea ice strength parameters were derived with different return periods from 1 to 100 years. Among the total of 53 grids, the mean ice periods in the southern Bohai Sea from 1951 to 2022 were 2–35 days, the mean ice concentration values were 8.3–64.6%, and the mean ice thicknesses were 2–15 cm. The design uniaxial compressive strengths and shear strengths at almost all grids exceeded 2.00 MPa and 1.00 MPa for return periods over 20 years, respectively. The design flexural strengths for the 100-year return period ranged from 463 to 594 kPa. For the 100-year return period scenario, all grids exhibited design tensile strengths exceeding 200 kPa. Across the southern Bohai Sea, the most severe ice conditions occur in nearshore zones, and the ice conditions display a distinct spatial gradient with Bohai Bay > offshore deep-water areas > Laizhou Bay. The mean ice thickness, concentration, design flexural and tensile strengths derived in this study were lower compared to the ice parameters suggested in the current standard, and design uniaxial compressive and shear strengths derived here were comparable to those suggested in the current standard. The refined grid used here captures more detailed spatial variations in the design strength values of sea ice engineering parameters in the southern Bohai Sea, providing more accurate data support for the anti-ice design of marine structures. Full article

Oceanic fronts delineate the boundaries between distinct water masses within the ocean, typically marked by shifts in weather patterns and the generation of oceanic circulation. These fronts are identified in research on intelligent oceanic front detection primarily by their significant temperature gradients. The refined identification of oceanic fronts is of great significance to maritime material transportation and ecological environment protection. In view of the weak edge nature of oceanic fronts and the misdetection or missed detection of oceanic fronts by some deep learning methods, this paper proposes an oceanic front detection method based on the U-Net model that integrates Edge-Attention-Module and the Feature Pyramid Network Module (FPN-Module). We conduct detailed statistical analysis and change rate calculation of the oceanic front, and batch process to obtain preliminary high-quality annotation data, which improves efficiency and saves time. Then, we perform manual corrections to correct missed detections or false detections to ensure the accuracy of annotations. Approximately 4800 days of daily average sea temperature fusion data from CMEMS (Copernicus Marine Environment Monitoring Service) are used for analysis, and an Encoder-Edge-FPN-Decoder Network (EEFD-Net) structure is established to enhance the model’s accuracy in detecting the edges of oceanic fronts. Experimental results demonstrate that the improved model’s front identification capability is in strong agreement with fronts segmented and annotated using the threshold method, with IoU and weighted Dice scores reaching 98.81% and 95.56%, respectively. The model can accurately locate the position of oceanic fronts, with superior detection of weak fronts compared to other network models, capturing smaller fronts more precisely and exhibiting stronger connectivity. Full article

Controlling groundwater levels is essential for the safe construction of complex or high-rise buildings. In México, dewatering regulations lack detailed references, and piezometric data are limited, making precise groundwater control a challenge. This study aimed to develop a numerical groundwater model by translating a conceptual hydrogeological model into a calibrated MODFLOW simulation using the graphical user interface ModelMuse, developed by the United States Geological Survey (USGS). For the project “Torre Tres Ríos”, field measurements recorded a water-table level of 33 m above sea level (masl) in July, rising to 35.74 masl in October due to rainy season recharge and the influence of the Tamazula River, then decreasing to 35.20 masl in November. The model, calibrated with a mean absolute error of 0.15 m and a standard deviation of 0.174 m, effectively represented steady and transient states. A spatiotemporal analysis based on the calibrated numerical model enabled the evaluation of different dewatering scenarios. Initially, deep wells with a pumping rate of 120 L per second (lps) were required for dewatering; however, a wellpoint system was proposed, showing improved performance with a reduced impact on groundwater flow and the surrounding environment during the critical August–November period. This study highlights the importance of numerical modeling in refining dewatering system designs, ensuring adaptability to fluctuating groundwater conditions. By providing a methodology for optimizing dewatering strategies, it contributes to more efficient and sustainable construction practices in regions with complex hydrogeological conditions. Full article

Artificial upwelling (AU), a geoengineering technique aimed at transporting nutrient-enriched deep-sea water to the sunlit surface layers through artificial systems, is increasingly recognized as a promising approach to enhance oceanic fertility and stimulate primary marine productivity, thereby bolstering the ocean capacity for carbon sequestration. Several air-lifted AU systems have been implemented in countries such as Norway and China. However, research on the optimization of the air injection pipeline network (AIPN)—a critical component of the air-lifted AU system—remains limited. This paper introduces a refined minimum spanning tree algorithm to propose a novel approach for optimizing the AIPN. Furthermore, the bubble-entrained plume loss rate (N_BEP) is developed as a model to assess the efficiency of air-lifted AU systems, which is applied to three case studies involving air-lifted AU systems of varying scales. The findings indicate that the enhanced minimum spanning tree algorithm outperforms the conventional Prim’s algorithm, leading to an average 87% reduction in N_BEP of the optimized AIPN, compared to the AIPN of previous air-lifted AU systems while improving system stability. Consequently, the proposed optimization method for AIPN offers valuable scientific and practical insights for the engineering design of the air-lifted AU systems across diverse scales, offering transformative potential for large-scale carbon sequestration and marine productivity enhancement. Full article

Sea surface wind (SSW) plays a pivotal role in numerous research endeavors pertaining to meteorology and oceanography. SSW fields derived from remote sensing have been widely applied; however, regional and local studies require higher-spatial-resolution SSW fields to identify refined details. Most of the existing studies based on deep learning have constructed mappings from low-resolution inputs to high-resolution downscaled estimates. However, these methods have failed to capture the relationships between multiple variables as revealed by physical processes. Therefore, this paper proposes a spatial downscaling approach for satellite sea surface wind that employs soft-sharing multi-task learning. Sea surface temperature and water vapor are included as auxiliary variables for SSW, considering the close correlation revealed by physical principles and data availability. The spatial downscaling of auxiliary variables is designed as an auxiliary task and integrated into a multi-task learning network with generative adversarial network and dual regression structures. The proposed multi-task downscaling network achieves flexible parameter sharing and information exchange between tasks through a soft-sharing mechanism and bridge modules. Comprehensive experiments were conducted with WindSat SSW products at 0.25° from Remote Sensing Systems. The experimental results validate the outstanding downscaling capability of the proposed methodology with respect to precision in comparison with buoy measurements and reconstruction quality. Full article

This study investigates the spatial and temporal distribution of the artificial radionuclides ¹²⁹I and ²³⁶U in the Western Mediterranean Sea, focusing on their connection to radionuclide sources and circulation dynamics. Taking advantage of unprecedented precision of accelerator mass spectrometry, both tracers were firstly investigated in 2013. Here, we examine tracer observations obtained along four stations (re-)visited during the TAlPro2022 expedition in May 2022. Distributions of both ¹²⁹I and ²³⁶U were related to water masses and clearly linked to local circulation patterns: a tracer-poor surface Atlantic inflow, a thining of the tracer minimum at intermediate depths, and a higher tracer signal in Western Mediterranean Deep Waters due to dense water formation in the Algero-Provençal basin. The comparison to 2013 tracer data indicated recent deep ventilation of the Tyrrhenian Sea, the mixing of deep waters and enhanced stratification in intermediate waters in the Algero-Provençal basin due to a temperature and salinity increase between 2013 and 2022. We estimate an overall ¹²⁹I increase of 20% at all depths between 0 and 500m with respect to 2013, which is not accompanied by ²³⁶U. This suggests either the lateral transport of ¹²⁹I from the Eastern Mediterranean Sea, or an additional source of this tracer. The inventories of ¹²⁹I calculated for each water mass at the four stations point to the deposition of airborne releases from the nuclear reprocessing plants (La Hague and Sellafield) on the surface Mediterranean waters as the more likely explanation for the ¹²⁹I increase. This work demonstrates the great potential of including measurements of anthropogenic radionuclides as tracers of ocean circulation. However, a refinement of the anthropogenic inputs is necessary to improve their use in understanding ventilation changes in the Mediterranean Sea. Full article