Search Results (79)

Efficient and timely data collection in Underwater Acoustic Sensor Networks (UASNs) for Internet of Underwater Things (IoUT) applications remains a significant challenge due to the inherent limitations of the underwater environment. This paper presents a Value of Information (VoI)-based trajectory planning framework for a single Autonomous Underwater Vehicle (AUV) operating in coordination with an Unmanned Surface Vehicle (USV) to collect data from multiple Cluster Heads (CHs) deployed across an uneven seafloor. The proposed approach employs a VoI model that captures both the importance and timeliness of sensed data, guiding the AUV to collect and deliver critical information before its value significantly degrades. A forward Dynamic Programming (DP) algorithm is used to jointly optimize the AUV’s trajectory and the USV’s start and end positions, with the objective of maximizing the total residual VoI upon mission completion. The trajectory design incorporates the AUV’s kinematic constraints into travel time estimation, enabling accurate VoI evaluation throughout the mission. Simulation results show that the proposed strategy consistently outperforms conventional baselines in terms of residual VoI and overall system efficiency. These findings highlight the advantages of VoI-aware planning and AUV–USV collaboration for effective data collection in challenging underwater environments. Full article

Marine litter threatens ocean ecosystems, and nautical tourism, as a source of litter, contributes significantly. This paper presents a qualitative and quantitative study of seafloor litter in the Bay of Selehovica in the northern Adriatic Sea. The bay is accessible only by sea and is attractive to nautical tourism vessels. The survey was conducted using a remotely operated vehicle across 22,100 m² of seafloor, before and after the tourist season (summer) in 2024. The analysis shows a 25.90% increase in litter items after one season. The predominant litter category is plastic, followed by glass, metal, rubber, and textiles. The abundance of marine litter increased from 1.3 to 1.7 items per 100 m² in the post-season, reflecting a measurable rise in litter density. Due to non-normal data distribution (Shapiro–Wilk test, p < 0.001), the Wilcoxon Signed-Rank Test was used, revealing a statistically significant increase in marine litter (W = 0, p < 0.001) with a large effect size (Cohen’s d = 0.89). A strong positive correlation between the pre- and post-season values was observed (Spearman’s r = 0.96, p < 0.001), suggesting that areas with higher initial litter levels tend to accumulate more over time. The results point to the necessity of targeted management strategies to reduce the pressure of nautical tourism on marine ecosystems and to protect the marine environment. Full article

Seafloor communities along the eastern Svalbard coast remain poorly studied. To address this gap, we sampled benthic organisms on the soft sediments of Storfjord in 2017 and 2019, a large fjord predominantly influenced by cold Arctic waters, to study the local fauna and identify the key environmental drivers shaping community structure. In total, 314 taxa were recorded, with an increase in abundance (from 3923 to 8977 ind. m⁻², mean 6090 ind. m⁻²) and a decline in biomass (ranging from 265 to 104 g m⁻², mean 188 g m⁻²) toward the outer part of the fjord. However, no clear spatial trends were observed for alpha diversity (approximately 100 species per 0.3 m²) or the Shannon index (mean 3 per station). The primary factors influencing benthic abundance were the duration of the ice-free period (IFP) and the degree of siltation (DS), both of which are proxies for trophic conditions. The prevailing taxa displayed a high tolerance to temperature fluctuations and seasonal variability in nutrient inputs. Benthic biomass showed a negative relationship with IFP, DS, and water depth, but it was positively correlated with the proportion of fine-grained sediment. The Yoldia hyperborea community (mean abundance: 3700 ind. m⁻², mean biomass: 227 g m⁻²) was associated with Arctic waters characterized by higher inorganic suspension loads. In contrast, areas with reduced or weaker sedimentation were dominated by the communities of Maldane sarsi (6212 ind m⁻², 226 g m⁻²) and Maldane sarsi + Nemertini g.sp. (5568 ind m⁻², 165 g m⁻²). The Spiochaetopterus typicus community (7824 ind m⁻², 139 g m⁻²) was observed in areas under moderate influence of Atlantic waters, characterized by low sedimentation rates and increased fresh detritus flux. Full article

Autonomous Underwater Vehicle (AUV) navigation relies on bottom-tracking velocity from Doppler Velocity Log (DVL) for positioning through dead-reckoning or aiding Strapdown Inertial Navigation System (SINS). In mid-water environments, the distance between the AUV and the seafloor exceeds the detection range of DVL, causing failure of bottom-tracking and leaving only water-relative velocity available. This makes unknown ocean currents a significant error source that leads to substantial cumulative positioning errors. This paper proposes a method for mid-water ocean current estimation using multibeam bathymetric survey data. First, the method models the regional unknown current field using radius basis functions (RBFs) and establishes an AUV dead-reckoning model incorporating the current field. The RBF model inherently satisfies ocean current incompressibility. Subsequently, by dividing the multibeam bathymetric point cloud data surveyed by the AUV into submaps and performing a terrain-matching algorithm, relative position observations among different AUV positions can be constructed. These observations are then utilized to estimate the RBF parameters of the current field within the navigation model. Numerical simulations and experiments based on real-world bathymetric and ocean current data demonstrate that the proposed method can effectively capture the complex spatial variations in ocean currents, contributing to the accurate reconstruction of the mid-water current field and significant improvement in positioning accuracy. Full article

Guyots are a special type of seamount with a flat top and are widely distributed in the global ocean. In this paper, a geomorphologic classification method for guyots based on multibeam bathymetry data is proposed. By studying typical guyots, namely, the Jiaxie Guyots, the Caiwei Guyots, and the DD Guyot in the Western Pacific Ocean, in this study, a multilevel classification system was established, integrating elevation, slope, and bathymetric position index (BPI). The method successfully classified seafloor geomorphology into nine types: summit platform, extremely steep slope, steep slope, gentle slope, very gentle slope, gully on the slope, seafloor plain, local crest, and local depression. Significant differences in the area distribution, depth characteristics, and slope extent of different geomorphologic units in the guyots were revealed by quantitative analysis. The flexibility and accuracy of the method were demonstrated through depth profile validation and method comparison validation. This classification system provides a new cognitive framework for defining the boundaries of seamounts, as well as for the study of the genesis mechanisms of the gullies on the slopes, local crests, and local depressions formed by volcanic activity and other actions. 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

Global Navigation Satellite System–Acoustic ranging (GNSS-A) technology can achieve centimeter-level seafloor positioning. However, the horizontal gradient of ocean sound speed limits the seafloor positioning accuracy of GNSS-A. This paper evaluates the impact of ocean sound speed horizontal gradients on GNSS-A seafloor positioning utilizing Bayesian estimation. Publicly available GNSS-A datasets from 2012 to 2021 were processed using strategies with and without estimating sound speed horizontal gradients. The comparison of results demonstrates that the ocean sound speed horizontal gradient has a significant impact on horizontal positioning but a smaller impact on vertical positioning. The mean root mean square (RMS) of horizontal positioning differences for both strategies is 0.12 m, with a maximum of 0.19 m. The mean RMS of vertical positioning differences is 0.014 m, with a maximum of 0.021 m. The mean RMS of station velocity differences is 0.004 m/a and 0.008 m/a in the east and the north components, respectively, with a maximum RMS of 0.01 m/a in the horizontal component. The vertical station velocity differences for both strategies are relatively small, with a mean RMS of 0.002 m/a and a maximum RMS of 0.003 m/a. The mean RMS difference in sound speed correction for both strategies is 0.01 m/s. The sound speed horizontal gradient is larger in the shallow portion than in the deep portion. In the shallow portion, the mean RMS is 0.052 m/s/km and 0.072 m/s/km in the east and north component, respectively. In the deep portion, the mean RMS is 0.023 m/s/km and 0.024 m/s/km in the east and north components, respectively. The sound speed horizontal gradient varies significantly at different locations due to the marine environment discrepancies, which require refined GNSS-A processing to improve seafloor positioning accuracy. Full article

Reducing carbon emissions and increasing carbon sinks have become the core issues of the international community. Although coastal blue carbon ecosystems (such as mangroves, seagrass beds, coastal salt marshes and large algae) account for less than 0.5% of the seafloor area, they contain more than 50% of marine carbon reserves, occupying an important position in the global carbon cycle. However, with the rapid development of the economy and the continuous expansion of human activities, coastal wetlands have suffered serious damage, and their carbon sequestration capacity has been greatly limited. Ecological restoration has emerged as a key measure to reverse this trend. Through a series of measures, including restoring the hydrological conditions of damaged wetlands, cultivating suitable plant species, effectively managing invasive species and rebuilding habitats, ecological restoration is committed to restoring the ecological functions of wetlands and increasing their ecological service value. Therefore, this paper first reviews the research status and influencing factors of coastal wetland carbon sinks, discusses the objectives, types and measures of various coastal wetland ecological restoration projects, analyzes the impact of these ecological restoration projects on wetland carbon sink function, and proposes suggestions for incorporating carbon sink enhancement into wetland ecological restoration. Full article

Global Navigation Satellite System/Acoustic (GNSS/A) underwater positioning technology is attracting more and more attention as an important technology for building the marine Positioning, Navigation, and Timing (PNT) system. The random error of the tracking point coordinate is also an important error source that affects the accuracy of GNSS/A underwater positioning. When considering its effect on the mathematical model of GNSS/A underwater positioning, the Total Least-Squares (TLS) estimator can be used to obtain the optimal position estimate of the seafloor transponder, with weak consistency and asymptotic unbiasedness. However, the tracking point coordinates and acoustic ranging observations are inevitably contaminated by outliers because of human mistakes, failure of malfunctioning instruments, and unfavorable environmental conditions. A robust alternative needs to be introduced to suppress the adverse effect of outliers. The conventional Robust TLS (RTLS) strategy is to adopt the selection weight iteration method based on each single prediction residual. Please note that the validity of robust estimation depends on a good agreement between residuals and true errors. Unlike the Least-Squares (LS) estimation, the TLS estimation is unsuitable for residual prediction. In this contribution, we propose an effective RTLS_Eqn estimator based on “total residuals” or “equation residuals” for GNSS/A underwater positioning. This proposed robust alternative holds its robustness in both observation and structure spaces. To evaluate the statistical performance of the proposed RTLS estimator for GNSS/A underwater positioning, Monte Carlo simulation experiments are performed with different depth and error configurations under the emulational marine environment. Several statistical indicators and the average iteration time are calculated for data analysis. The experimental results show that the Root Mean Square Error (RMSE) values of the RTLS_Eqn estimator are averagely improved by 12.22% and 10.27%, compared to the existing RTLS estimation method in a shallow sea of 150 m and a deep sea of 3000 m for abnormal error situations, respectively. The proposed RTLS estimator is superior to the existing RTLS estimation method for GNSS/A underwater positioning. Full article

The Crabster CR200 is an underwater walking robot inspired by crabs and lobsters, designed for precise seabed inspection and manipulation. It maintains stability and position on the seafloor, even in strong currents, by adjusting its posture through six legs, each with four degrees of freedom. The key advantage of the CR200 lies in its ability to resist drifting in strong currents by adapting its posture to maintain its position on the seafloor. However, information is still lacking on which specific posture generates the maximum downforce to ensure optimal stability in the presence of currents and the seabed. This study aims to determine the fluid forces acting on the CR200 in various postures using Computational Fluid Dynamics (CFD) and identify the posture that generates the maximum downforce. The posture is defined by two parameters: angle of attack and seafloor clearance, represented by the combination of the robot’s pitch angle and distance to the seabed. By varying these parameters, we identified the posture that produces the greatest downforce. Through a series of analyses, we identified two main fluid dynamic principles affecting the downforce on a robot close to the seabed. First, an optimal pitch angle exists that generates the maximum downward lift on the robot’s body. Secondly, there is an ideal distance from the seabed that produces maximum suction on the bottom surface, thereby creating a strong Venturi effect. Based on these principles, we determined the optimal robot posture to achieve maximum downforce in strong current conditions. The optimal underwater robot posture identified in this study could be applied to similar robots operating on the seafloor. Furthermore, the methodology adopted in this study for determining the optimal posture can serve as a reference for establishing operational postures for similar underwater robots. Full article

The complex topography of deep sea presents numerous challenges for the accurate exploration of sub-bottom profiles. These include real-time tracking of seafloor reflectors, acquisition and storage of deep-sea long-term reflection data, and splicing of successive profiles. Based on the actual survey data of deep sea, we have developed automatic positioning and noise suppression algorithms, namely the double-difference threshold of proximity points. Furthermore, we have created automatic algorithms, namely content expansion and group data moving, based on extremum in seafloor’s depth. These have been designed to automatically suppress the random noise and effectively splice the sub-bottom profile data in deep water. The aforementioned processing techniques facilitate the enhancement of the quality of deep-water sub-bottom profile data, thereby enabling the provision of a comprehensive and successively long profile for interpretation in the context of deep-water sub-bottom profile data. Full article

In this work, we introduce the first pipeline that combines a refraction-aware structure from motion (SfM) method with a deep learning model specifically designed for airborne bathymetry. We accurately estimate the 3D positions of the submerged points by integrating refraction geometry within the SfM optimization problem. This way, no refraction correction as post-processing is required. Experiments with simulated data that approach real-world capturing conditions demonstrate that SfM with refraction correction is extremely accurate, with submillimeter errors. We integrate our refraction-aware SfM within a deep learning framework that also takes into account radiometrical information, developing a combined spectral and geometry-based approach, with further improvements in accuracy and robustness to different seafloor types, both textured and textureless. We conducted experiments with real-world data at two locations in the southern Mediterranean Sea, with varying seafloor types, which demonstrate the benefits of refraction correction for the deep learning framework. We made our refraction-aware SfM open source, providing researchers in airborne bathymetry with a practical tool to apply SfM in shallow water areas. Full article

Powell Basin is an ocean basin formed as a result of the Scotia Sea evolution. The existing tectonic models propose a variety of starting and ending ages for the spreading of the basin based on seafloor magnetic anomalies. Here, we use recent magnetic field data obtained from eight magnetic profiles in Powell Basin to provide insights into the oceanic spreading evolution. The differences found between the number of anomalies on both sides of the axis and the asymmetry in the spreading rates suggest different opening models for different parts of the basin. We propose a spreading model starting in the late Eocene (38.08 Ma) and ending in the early Miocene (21.8 Ma) for the northern part of Powell Basin. For the southern part, the opening started in the late Eocene (38.08 Ma) and ended in the middle Paleogene (25.2 Ma). The magnetic data have been combined with gravity and sediment thickness data to better constrain the age models. The gravity and sediment thickness information allow us to more accurately locate the position of the extinct spreading axis. Geothermal heat flow measurements are used to understand the relationship between the low amplitudes of the magnetic anomalies and the heat beneath them. Our proposed oceanic spreading models suggest that the initial incursions of the Pacific mantle outflow into the Powell Basin occurred in the Oligocene, and the initial incursions of oceanic currents from the Weddell Sea occurred in the Eocene. Full article

The Police Robot for Inspection and Mapping of Underwater Evidence (PRIME) is an uncrewed surface vehicle (USV) currently being developed for underwater search and recovery teams to assist in crime scene investigation. The USV maps underwater scenes using sidescan sonar (SSS). Test exercises use a clothed mannequin lying on the seafloor as a target object to evaluate system performance. A robust, automated method for detecting human body-shaped objects is required to maximise operational functionality. The use of a convolutional neural network (CNN) for automatic target recognition (ATR) is proposed. SSS image data acquired from four different locations during previous missions were used to build a dataset consisting of two classes, i.e., a binary classification problem. The target object class consisted of 166 196 × 196 pixel image snippets of the underwater mannequin, whereas the non-target class consisted of 13,054 examples. Due to the large class imbalance in the dataset, CNN models were trained with six different imbalance ratios. Two different pre-trained models (ResNet-50 and Xception) were compared, and trained via transfer learning. This paper presents results from the CNNs and details the training methods used. Larger datasets are shown to improve CNN performance despite class imbalance, achieving average F1 scores of 97% in image classification. Average F1 scores for target vs background classification with unseen data are only 47% but the end result is enhanced by combining multiple weak classification results in an ensemble average. The combined output, represented as a georeferenced heatmap, accurately indicates the target object location with a high detection confidence and one false positive of low confidence. The CNN approach shows improved object detection performance when compared to the currently used ATR method. Full article

Autonomous Underwater Vehicles (AUVs) equipped with Side-Scan Sonar (SSS) play a critical role in seabed mapping, where precise navigation data are essential for mosaicking sonar images to delineate the seafloor’s topography and feature locations. However, the accuracy of AUV navigation, based on Strapdown Inertial Navigation System (SINS)/Doppler Velocity Log (DVL) systems, tends to degrade over long-term mapping, which compromises the quality of sonar image mosaics. This study addresses the challenge by introducing a post-processing navigation method for AUV SSS surveys, utilizing Factor Graph Optimization (FGO). Specifically, the method utilizes an improved Fourier-based image registration algorithm to generate more robust relative position measurements. Then, through the integration of these measurements with data from SINS, DVL, and surface Global Navigation Satellite System (GNSS) within the FGO framework, the approach notably enhances the accuracy of the complete trajectory for AUV missions. Finally, the proposed method has been validated through both the simulation and AUV marine experiments. Full article