Search Results (142)

Based on the new high-resolution multibeam bathymetry data collected by the “Dongfanghong 3” vessel in 2023 in the Parece Vela Basin (PVB) and previous magnetic anomaly data, we systematically analyze the seafloor topographical changes of abyssal hills and oceanic core complexes (OCCs) in the “Chaotic Terrain” region, and the revised seafloor spreading model is constructed in the PVB. Using detailed analysis of the seafloor topography, we identify typical geomorphological features associated with seafloor spreading, such as regularly aligned abyssal hills and OCCs in the PVB. The direction variations of seafloor spreading in the PVB are closely related to mid-ocean ridge rotation and propagation. The formation of OCCs in the “Chaotic Terrain” can be explained by links to the continuous and persistent activity of detachment faults and dynamic adjustments controlled by variations of deep magma supply in the different segments in the PVB. We use 2D discrete Fourier image analysis of the seafloor topography to calculate the aspect ratio (AR) values of abyssal hills in the western part of the PVB. The AR value variations reveal a distinct imbalance in magma supply across various regions during the basin spreading process. Compared to the “Chaotic Terrain” area, the region with abyssal hills indicates a higher magma supply and greater linearity on seafloor topography. AR values fluctuated between 2.1 and 1.7 of abyssal hills in the western segment, while in the “Chaotic Terrain”, they dropped to 1.3 due to the lower magma supply. After the formation of the OCC-1, AR values increased to 1.9 in the eastern segment, and this shows the increase in magma supply. Based on changes in seafloor topography and variations in magma supply across different segments of the PVB, we propose that the seafloor spreading process in the magnetic anomaly linear strip 9-6A of the PVB mainly underwent four formation stages: ridge rotation, rift propagation, magma-poor supply, and the maturation period of OCCs. Full article

As fundamental geophysical information, the high-precision detection of shallow water bathymetry is critical data support for the utilization of island resources and coral reef protection delimitation. In recent years, the combination of active and passive remote sensing technologies has led to a revolutionary breakthrough in satellite-derived bathymetry (SDB). Optical SDB extracts bathymetry by quantifying light–water–bottom interactions. Therefore, the apparent differences in the reflectance of different bottom types in specific wavelength bands are a core component of SDB. In this study, refined classification was performed for complex seafloor sediment and geomorphic features in coral reef habitats. A multi-model synergistic SDB fusion approach constrained by coral reef habitat classification based on the deep learning framework Mamba was constructed. The dual error of the global single model was suppressed by exploiting sediment and geomorphic partitions, as well as the accuracy complementarity of different models. Based on multispectral remote sensing imagery Sentinel-2 and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) active spaceborne lidar bathymetry data, wide-range and high-accuracy coral reef habitat classification results and bathymetry information were obtained for the Yuya Shoal (0–23 m) and Niihau Island (0–40 m). The results showed that the overall Mean Absolute Errors (MAEs) in the two study areas were 0.2 m and 0.5 m and the Mean Absolute Percentage Errors (MAPEs) were 9.77% and 6.47%, respectively. And R² reached 0.98 in both areas. The estimated error of the SDB fusion strategy based on coral reef habitat classification was reduced by more than 90% compared with classical SDB models and a single machine learning method, thereby improving the capability of SDB in complex geomorphic ocean areas. Full article

The Earth’s degassing is an important factor in evaluating global carbon budget estimates and understanding the carbon cycle. As a result, numerous studies have focused on this topic. However, current estimates predominantly focus on subaerial CO₂ emissions and CO₂ deep submarine emissions, particularly along mid-ocean ridges (MORs), whereas very few and only spatially limited estimates of shallow submarine CO₂ emissions have been reported, despite being widespread features of the seafloor. This study reports the results of measuring the dissolved CO₂ concentrations in shallow submarine environments along the coast of Vulcano Island (Aeolian Islands, Italy). For the areas exhibiting the highest concentrations, we calculated the amount of diffuse degassing by computing the sea–air CO₂ flux. The results revealed extremely high dissolved CO₂ concentrations, reaching up to 24 vol.% in areas with visible hydrothermal activity, including one location far from the island’s main crater. Notably, elevated CO₂ levels were also detected in areas with minimal or no apparent hydrothermal discharge, indicating the occurrence of diffuse degassing processes in these areas. In addition, the calculated diffuse degassing flux was comparable in magnitude to the CO₂ flux directly emitted into the atmosphere from the island’s main bubbling pools. Full article

Seafloor topography super-resolution reconstruction is critical for marine resource exploration, geological monitoring, and navigation safety. However, sparse acoustic data frequently result in the loss of high-frequency details, and traditional deep learning models exhibit limitations in uncertainty quantification, impeding their practical application. To address these challenges, this study systematically investigates the combined effects of various regularization strategies and uncertainty quantification modules. It proposes a hybrid dropout model that jointly optimizes high-precision reconstruction and uncertainty estimation. The model integrates residual blocks, squeeze-and-excitation (SE) modules, and a multi-scale feature extraction network while employing Monte Carlo Dropout (MC-Dropout) alongside heteroscedastic noise modeling to dynamically gate the uncertainty quantification process. By adaptively modulating the regularization strength based on feature activations, the model preserves high-frequency information and accurately estimates predictive uncertainty. The experimental results demonstrate significant improvements in the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Peak Signal-to-Noise Ratio (PSNR). Compared to conventional dropout architectures, the proposed method achieves a PSNR increase of 46.5% to 60.5% in test regions with a marked reduction in artifacts. Overall, the synergistic effect of employed regularization strategies and uncertainty quantification modules substantially enhances detail recovery and robustness in complex seafloor topography reconstruction, offering valuable theoretical insights and practical guidance for further optimization of deep learning models in challenging applications. Full article

The deep sea is gradually being exploited, yet research on the stability of the deep seabed is scarce. In this study, the seafloor stability of the Jiaxie Seamount Group in the western Pacific Ocean was assessed using the weight-of-evidence (WoE) method based on seafloor topographic data. Slope failure features were identified by analyzing multibeam bathymetric data, revealing 21 failure zones and multiple debris accumulation areas. Topographic factors, such as water depth, slope, slope direction, planar curvature, profile curvature, and ruggedness, were selected as assessment indicators. These indicators were reclassified as evidence factors, and a WoE model was constructed to assess the failure probability in the study area. A stability zoning map indicated that over 93% of the area had high stability. In comparison, areas with low and very low stability comprised less than 4%, mainly located on steep ridges and rugged slopes. The model’s performance was validated through an ROC curve, yielding an AUC value of 0.929, indicating a high predictive capability. This study presents a statistical framework for assessing the stability of deep-sea floors and provides theoretical support for upcoming seabed mining and deep-sea engineering endeavors, despite limitations due to data constraints and dependence on visually interpreted slope failure zones. Full article

The northwestern slope of the Dongdao Platform in the Xisha Sea exhibits a complex geomorphological structure. Utilizing high-resolution multibeam bathymetric data and 2D seismic profiles, this study systematically reconstructs the slope morphology and its evolutionary processes. The study area displays a distinct threefold zonation: the upper slope (160–700 m water depth) has a steep gradient of 15°–25°, characterized by deeply incised V-shaped channels and slump deposits, primarily shaped by gravity-driven erosion; the middle slope (700–1200 m water depth) features a gentler gradient of 10°–15°, where channels stabilize, adopting U-shaped cross-sections with the development of lateral accretion deposits; the lower slope (1200–1500 m water depth) exhibits a milder gradient of 5°–10°, dominated by a mixture of fine-grained carbonate sediments and hemipelagic mud–marine sediments originating partly from the open ocean and partly from the nearby continental margin. The slope extends from 160 m to 1500 m water depth, hosting the C1–C4 channel system. Seismic facies analysis reveals mass-transport deposits, channel-fill facies, and facies modified by bottom currents—currents near the seafloor that redistribute sediments laterally—highlighting the interplay between fluid activity and gravity-driven processes. The slope evolution follows a four-stage model: (1) the pockmark formation stage, where overpressured gas migrates vertically through chimneys, inducing localized sediment instability and forming discrete pockmarks; (2) the initial channel development stage, during which gravity flows exploit the pockmark chains as preferential erosional pathways, establishing nascent incised channels; (3) the channel expansion and maturation stage, marked by intensified erosion from high-density debris flows, resulting in a stepped longitudinal profile, while bottom-current reworking enhances lateral sediment differentiation; (4) the stable transport stage, wherein the channels fully integrate with the Sansha Canyon, forming a well-connected “platform-to-canyon” sediment transport system. Full article

The weathering of seafloor hydrothermal sulfides is facilitated by microbial activities, yet the specific mechanisms of different sulfide types are not well understood. Previous studies have primarily been carried out under laboratory conditions, making it difficult to accurately replicate the complex in situ conditions of deep-sea hydrothermal fields. Herein, we deployed two well-characterized pyrite (Py)-dominated and chalcopyrite (Ccp)-dominated sulfide slices, which were placed 300 m from an active venting site in the Wocan-1 hydrothermal field (Carlsberg Ridge, Northwest Indian Ocean) for an 18-month in situ incubation experiment. Microscopic observations and organic matter analyses were conducted on the recovered sulfide slices to investigate the microbial weathering features of different sulfide types. Our results demonstrated that the weathering of the Py-dominated sulfide sample was primarily mediated by extracellular polymeric substances (EPSs) through indirect interactions, whereas the Ccp-dominated sulfide sample exhibited both direct microbial dissolution, resulting in the formation of distinct dissolution pits, and indirect EPS-mediated interactions. Four distinct phases of microbe–sulfide interactions were identified: approach, adsorption, stable attachment, and extensive colonization. Furthermore, the weathering products and biomineralization structures differed significantly between the two sulfide types, reflecting their different microbial colonization processes. Our study confirms that microorganisms are crucial in seafloor sulfide weathering. These findings advance our understanding of microbial-driven processes in sulfide mineral transformations and their role in marine ecosystems. Our findings are also valuable for future research on biogeochemical cycles and for developing bioremediation strategies for deep-sea mining. Full article

The acoustic signals of the drill pipe of a seafloor drill present weak features under noise interference such as marine environmental noise and the mechanical vibration of the seafloor drill. Accurately extracting the features of the weak acoustic signals of a drill pipe under a strong background noise is an effective means of realizing wireless acoustic communication for a seafloor drill. However, the existing short-time Fourier transform and wavelet transform methods have the defects of fixed window length, wavelet basis function, and decomposition layers, which lead to the inability to accurately extract the weak acoustic signal features of a drill pipe. To overcome these challenges, this study investigates the application of S-transform (ST) in the weak acoustic signal feature extraction of a seafloor drill pipe based on its fundamental principles. Firstly, a time-frequency analysis of the drill pipe’s acoustic signal using ST is conducted, which yields the distribution of the signal across the time and frequency axes. Secondly, singular value decomposition (SVD) is applied to mitigate the noise within the time-frequency matrix. Finally, the noise-reduced time-frequency matrix is analyzed to extract the subtle features of the acoustic wave present within the signal. In order to more accurately assess the differences between the different time-frequency analysis methods in the extraction of weak acoustic wave signals, short-time Fourier transform, wavelet transform, and ST are used to extract the weak acoustic wave characteristics of the drill pipe, respectively. The results show that the ST-based method can effectively improve the accuracy of weak acoustic wave signal feature extraction and provide strong support for reliable transmission of cone penetration test data from the seafloor drill. Full article

The recognition and segmentation of seafloor topography play a crucial role in marine science research and engineering applications. However, traditional methods for seafloor topography recognition and segmentation face several issues, such as poor capability in analyzing complex terrains and limited generalization ability. To address these challenges, this study introduces the SG-MKD dataset (Submarine Geomorphology Dataset—Seamounts, Sea Knolls, Submarine Depressions) and proposes YOLO-SG (You Only Look Once—Submarine Geomorphology), an algorithm for seafloor topographic unit recognition and segmentation that leverages a lightweight upsampling operator and attention mechanisms. The SG-MKD dataset provides instance segmentation annotations for three types of seafloor topographic units—seamounts, sea knolls, and submarine depressions—across a total of 419 images. YOLO-SG is an optimized version of the YOLOv8l-Segment model, incorporating a convolutional block attention module in the backbone network to enhance feature extraction. Additionally, it integrates a lightweight, general upsampling operator to create a new feature fusion network, thereby improving the model’s ability to fuse and represent features. Experimental results demonstrate that YOLO-SG significantly outperforms the original YOLOv8l-Segment, with a 14.7% increase in mean average precision. Furthermore, inference experiments conducted across various research areas highlight the model’s strong generalization capability. Full article

Gera Gulf, a relatively small embayment on the island of Lesvos, serves as a representative example of a semi-enclosed, shallow marine system in Greece. Previous studies revealed that the gulf seafloor is occupied by numerous small reefs that are evenly distributed. Recently, seismic surveys together with gravity coring have shown numerous relict reefs within a fine-grained matrix, hosted at different stratigraphic levels above the inferred Holocene/Pleistocene boundary and locally extending up to the present seabed. The reefs are primarily engineered by the bivalve Ostrea edulis, with additional colonization by other marine organisms such as the coral Cladocora caespitosa. Key features identified in the seismic profiles include the widespread distribution of buried reef structures, erosional surfaces and unconformities also related to a paleolake, extensive fluid concentrations, and a major fault system paralleling the northeastern coast. Seismic record analysis and sediment dating suggest that the flooding of Gera Gulf began approximately 7500 BP, with O. edulis colonizing the seabed shortly thereafter. Buried reef structures were identified within the transgressive and highstand system tracts, characterized by varying sedimentation rates. These variations reflect changing environmental conditions, probably linked to specific climatic events during the Holocene epoch, which contributed to the evolution and shaping of the oyster reef terrain. Given the limited studies on recent or buried oyster reefs in similar environments, this study provides critical insights into the Holocene evolution of oyster reef terrains and their response to climate changes. Full article

Detecting bright point scatterers plays an important role in assessing the quality of many sonar, radar, and medical ultrasound imaging systems, especially for characterizing the resolution. Traditionally, prominent scatterers, also known as coherent scatterers, are usually detected by employing thresholding techniques alongside statistical measures in the detection processing chain. However, these methods can perform poorly in detecting point-like scatterers in relatively high levels of speckle background and can distort the structure of the scatterer when visualized. This paper introduces a fast image-processing method to visually identify and detect point scatterers in synthetic aperture imagery using the bright feature transform (BFT). The BFT is analytic, computationally inexpensive, and requires no thresholding or parameter tuning. We derive this method by analyzing an ideal point scatterer’s response with respect to pixel intensity and contrast around neighboring pixels and non-adjacent pixels. We show that this method preserves the general structure and the width of the bright scatterer while performing tone mapping, which can then be used for downstream image characterization and analysis. We then modify the BFT to present a difference of trigonometric functions to mitigate speckle scatterers and other random noise sources found in the imagery. We evaluate the performance of our methods on simulated and real synthetic aperture sonar and radar images, and show qualitative results on how the methods perform tone mapping on reconstructed input imagery in such a way to highlight the bright scatterer, which is insensitive to seafloor textures and high speckle noise levels. Full article

Seafloor topography data are fundamental for marine resource development, oceanographic research, and maritime rights protection. However, approximately 75% of the ocean remains unsurveyed for bathymetry. Sole reliance on shipborne measurements is insufficient for constructing a global bathymetric model within a short timeframe; consequently, satellite altimetry-based inversion techniques are essential for filling data gaps. Recent advancements have improved the variety and quality of satellite altimetry gravity data. To leverage the complementary advantages of multiparameter gravity data, we propose a 1D convolutional neural network based on a convolutional attention module, termed the Attention Channel 1D Convolutional Network (AC1D). Results of a case study of the Mariana Trench indicated that the AC1D grid predictions exhibited improved agreement with single-beam depth checkpoints, with standard deviation reductions of 6.32%, 20.79%, and 36.77% and root mean square error reductions of 7.11%, 22.82%, and 50.99% compared with those of parallel linked backpropagation, the gravity–geological method, and a convolutional neural network, respectively. The AC1D grid demonstrated enhanced stability in multibeam bathymetric validation metrics and exhibited better consistency with multibeam bathymetry data and the GEBCO2023 grid. Power spectral density analysis revealed that AC1D effectively captured rich topographic signals when predicting terrain features with wavelengths below 6.33 km. Full article

Hydrothermal activity on the modern seafloor varies depending on the tectonic setting. In particular, the neovolcanic zones (NVZs) along mid-ocean ridges, where magmatism is intense, generally host high-temperature hydrothermal activities. These high-temperature hydrothermal activities on the NVZs can promote the development of many polymetallic sulfide deposits. Currently, many high-temperature hydrothermal activities and sulfide accumulations have been discovered on the NVZs of major mid-ocean ridges worldwide, but relatively few have been found in the Southern Mid-Atlantic Ridge (SMAR), which limits our understanding of the hydrothermal mineralization characteristics on the NVZs of SMAR. Fortunately, in 2015, a new hydrothermal field—Tongguan—developed on the NVZ of the SMAR was discovered. In this study, we conducted mineralogical and sulfur isotope studies on hydrothermal chimney and massive sulfide samples collected from the Tongguan field. We revealed the mineral composition and growth sequence in the chimney structures and sulfides and discovered two different chimney growth patterns featuring rhythmic banding and opal-filled structures. Additionally, sulfur isotopes suggest the presence of mixing between seawater within the oceanic crust and the upwelling hydrothermal fluid in this hydrothermal field. Our investigation revealed small-scale fluid heterogeneities during the submarine hydrothermal mineralization process, which is due to fluctuations in fluid temperatures and mineral deposition within individual vent frameworks. This work provides a reference for further understanding and comprehension of hydrothermal mineralization on the NVZs of SMAR. Full article

Side-scan sonar and multi-beam echo sounder (MBES) are the most widely used underwater surveying tools in marine mapping today. The MBES offers high accuracy in depth measurement but is limited by low imaging resolution due to beam density constraints. Conversely, side-scan sonar provides high-resolution backscatter intensity images but lacks precise positional information and often suffers from distortions. Thus, MBES and side-scan images complement each other in depth accuracy and imaging resolution. To obtain high-quality seafloor topography images in practice, matching between MBES and side-scan images is necessary. However, due to the significant differences in content and resolution between MBES depth images and side-scan backscatter images, they represent a typical example of heterogeneous images, making feature matching difficult with traditional image matching methods. To address this issue, this paper proposes a feature matching network based on the LoFTR algorithm, utilizing the intermediate layers of the ResNet-50 network to extract shared features between the two types of images. By leveraging self-attention and cross-attention mechanisms, the features of the MBES and side-scan images are combined, and a similarity matrix of the two modalities is calculated to achieve mutual matching. Experimental results show that, compared to traditional methods, the proposed model exhibits greater robustness to noise interference and effectively reduces noise. It also overcomes challenges, such as large nonlinear differences, significant geometric distortions, and high matching difficulty between the MBES and side-scan images, significantly improving the optimized image matching results. The matching error RMSE has been reduced to within six pixels, enabling the accurate matching of multi-beam and side-scan images. Full article

Marine cobalt-rich crusts, extensively used in industries such as aerospace, automotive, and electronics, are crucial mineral resources located on the ocean floor. To effectively exploit these valuable resources, underwater imaging is essential for real-time detection and distribution mapping in mining areas. However, the presence of suspended particles in the seabed mining environment severely degrades image quality due to light scattering and absorption, hindering the effective identification of the target objects. Traditional image processing techniques—including spatial and frequency domain methods—are ineffective in addressing the interference caused by suspended particles and offer only limited enhancement effects. This paper proposes a novel underwater image restoration method that combines polarization imaging and homomorphic filtering. By exploiting the differences in polarization characteristics between suspended particles and target objects, polarization imaging is used to separate backscattered light from the target signal, enhancing the clarity of the cobalt crust images. Homomorphic filtering is then applied to improve the intensity distribution and contrast of the orthogonal polarization images. To optimize the parameters, a genetic algorithm is used with image quality evaluation indices as the fitness function. The proposed method was compared with traditional image processing techniques and classical polarization imaging methods. Experimental results demonstrate that the proposed approach more effectively suppresses backscattered light, enhancing the clarity of target object features. With significant improvements in image quality confirmed by several no-reference quality metrics, the method shows promise as a solution for high-quality underwater imaging in turbid environments, particularly for deep-sea mining of cobalt-rich crusts. Full article