Several burial assessment surveys using a seabed plough have been carried out along routes on the United Kingdom continental shelf to assess their potential for cable burial. Detailed information on ...sediment thicknesses and characteristics within the top meter of the seabed was collected using a survey tool called the Plough Surveyor. Geophysical surveys along these routes have enabled correlation with the plough data. Results include the range of tensions, tension trace characteristics, and typical penetration, and these have been correlated with seabed types and thickness of sediment.
Abnormally high levels of methane gas in seafloor sediments could pose a major hazard to coastal populations within the next 100 years through their impact on climate change and sea level rise. ...Marine scientists have known for many years that biogenic methane (CH4) is generated in shallow seabed sediments on continental margins, especially in rapidly deposited muddy sediments with high organic matter content. Grassy sediments are found in river deltas, estuaries, and harbors, but also in deeper waters on continental shelves and slopes. Human activities can accelerate natural sea-floor gas generation by increasing the supply of sediments and organic matter from rivers through deforestation and intensive farming, and also by the disposal of human waste at sea. When this extra organic matter becomes buried to about one meter beneath the seabed, biogeochemical processes start to convert it to CH4. The impact of this extra CH4 could be felt within the next 100 years, assuming a one-centimeter-per-year sediment accumulation.
Pub. in EOS, v87 n22, 30 May 2006. The original document contains color images.
Seabed sediment classification using acoustic remote sensing technique is an attractive approach due to its high coverage capabilities and limited costs compared to taking samples of the seafloor. ...This paper focuses on backscatter intensity correction, sonar image quality improvement, and classifier construction, which aims to improve the accuracy of seabed sediment classification. The details are as follows. 1) A series of multibeam echosounder backscatter intensity correction model is constructed, including time-varying gains (TVG), transmission loss, actual area of insonification, source level, transmitting and receiving beam patterns, specular area correction, etc., to obtain accurate intensity values that accurately reflect seabed sediment types. 2) The pulse coupled neural network (PCNN) image enhancement model is established to improve the quality of sonar images, and 40 dimensional features are included to enrich the intensity description. 3) Selecting optimal random forest (SORF) seabed sediment automatic classification models that can select the input feature vectors and optimize the model parameters automatically are established. 4) Taking multibeam backscatter intensity data collected in Jiaozhou Bay as an example, the effectiveness and advantages of SORF are verified by comparing with support vector machine (SVM) and random forest (RF) classifiers.
With the extensive laying of subsea pipe networks, interactions between seabeds and pipelines are attracting increasing attention; nevertheless, the study of buried pipelines impacted by sliding ...seabed sediments with low strength and high sensitivity characteristics has been overlooked. In this paper, a computational fluid dynamics (CFD) model considering the interface shear weakening effect of seabed sediment and a pipeline is proposed to simulate the impact of seabed sediments with shear behavior of non-Newtonian fluids on pipelines buried at different depths in the overlying water. The CFD method is validated using analytical solutions, numerical solutions, and physical model experiments. Various CFD-based cases with different pipeline burial depths and interface contact coefficients are then systematically investigated. The lateral bearing capacity of seabed sediments on pipelines is quantified in the framework of soil mechanics, and the pipeline buried depth and interface contact coefficient significantly affect the lateral bearing capacity. The lateral bearing capacity factor gradually increases and tends to stabilize with increasing pipeline buried depth, reaching a maximum gap of 62%, and the critical buried depth is 2–3 times the pipeline diameter. The lateral bearing capacity factor increases with increasing interface contact coefficient, reaching a maximum gap of 57%. Variations in the velocity, pressure, and shear rate of the seabed sediment are discussed, revealing the physical mechanism leading to changes in bearing capacity factors. Finally, a method to evaluate the lateral bearing capacity factor is established, providing a reference for pipeline design, construction, and safe operation.
•A CFD model considering the interface shear weakening effect of sediment with the shear behavior of non-Newtonian fluids and pipelines is proposed to simulate the impact of seabed sediments on pipelines.•The effects of the pipeline buried depth on the lateral bearing capacity factor are quantified, the critical buried depth is given, and the corresponding physical mechanism is discussed.•The effects of high-sensitivity seabed sediments on the lateral bearing capacity factor are analyzed, revealing the internal mechanism of interface shear weakening of the seabed sediment and pipeline.•A methodology to evaluate the lateral bearing capacity factor is established considering the buried depth of the subsea pipeline and the interface contact coefficient.
Accurate acquisition of information on seabed sediment distributions plays an important role in the construction of basic marine geographic databases. Although a multibeam echo-sounding system (MBES) ...can satisfy large-scale seafloor mapping with high precision and high resolution, the development of a consistent, stable, repeatable and validated seabed sediment classification method based on swath acoustic data is still in its infancy. To achieve accurate prediction and mapping of geographic seabed sediment information, this paper developed a deep learning model based on feature optimization. First, faced with high-dimensional features extracted from multibeam bathymetry and backscatter intensity measurement data, a fuzzy ranking (FR) feature optimization method was proposed. By combining the physical properties of actual sediment samples, the multidimensional features derived from terrain and intensity data are ranked and optimally selected according to the mean square error to eliminate redundant and irrelevant features. Second, the deep belief network (DBN) deep learning method was used to build a supervised seabed sediment classification model. The optimized features and actual sediment samples participate in model training, which further enhances the prediction ability of acoustic data to seabed sediments. Finally, to evaluate the performance of the DBN model, this experiment used large-scale multibeam survey data and ground-truth data (acquired by grabbers, core samplers, dredges, etc.) in the southern Irish Sea to achieve accurate prediction of 10 sediment types (slightly gravelly muddy sand, slightly gravelly sand, gravelly mud, gravelly muddy sand, gravelly sand, muddy sand, muddy sandy gravel, sand, sandy gravel and sandy mud). The experiment results show that by using the optimal feature combination based on FR, the overall classification accuracy and Kappa coefficient reached 86.20% and 0.834, respectively, which are significantly improved compared to the evaluation metrics of other feature selection methods. In addition, compared with the current five typical supervised classification methods (i.e., the random forests, BP neural network, support vector machine, maximum likelihood and decision trees methods), the proposed DBN classification model achieves a better performance, highlighting its application potential in seabed sediment detection and mapping.
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•The new DBN sediment classification model based on fuzzy ranking feature optimization achieves accurate prediction of sediments with 10 types.•The proposed fuzzy ranking method selects acoustic features with close relation to the physical properties of ground-truth sediments.•A newly extracted feature of seafloor terrain morphology can be used as an effective feature to characterize and mapping seabed sediment.•The combination of bathymetry and backscatter intensity features is more effective for characterizing and mapping the seabed sediments.•Deep learning framework based on DBN improves model training speed and stability of supervised seabed sediment classification.
This study investigated small microplastic particle (SMP, < 350 μm) contamination in the surface seabed sediment of the inner part of Tokyo Bay. The SMP concentration during the rainy season (May) ...was 100.3 ± 45.8 pieces g−1 dry weight and higher in the inner side of the bay, whereas that of the dry season (January) was 147.6 ± 19.5 pieces g−1 dry weight. There was no seasonal difference. The main plastic types found in the rainy season were polypropylene (PP), polyethylene (PE), and polyamide (PA). In the dry season (January), the concentration of PP decreased while that of PA increased. The mean SMP diameter did not change by site or season. Our results indicate that the seabed sediments in the inner part of Tokyo Bay are contaminated with relatively high concentrations of SMPs. Furthermore, the deposition rate of SMPs in the inner part of the bay was 12.4 mg cm−2 y−1.
•Small microplastic particles (SMPs) were studied in Tokyo Bay's seabed sediments.•SMP concentration during the rainy season was 100.3 ± 45.8 pcs g−1 DW.•The main plastic types were polypropylene, polyethylene, and polyamide.•SMP deposition rate in the inner part of the bay was an estimated 12.4 mg cm−2 y−1.
Concentrations of
Cs in seawater, seabed sediment, and pore water collected from the area around Fukushima were investigated from 2015 to 2018, and the potential of coastal sediments to supply ...radiocesium to the bottom environment was evaluated. The
Cs concentration in the pore water ranged from 33 to 1934 mBq L
and was 10-40 times higher than that in the overlying water (seawater overlying within 30 cm on the seabed). At most stations, the
Cs concentrations in the overlying water and the pore water were approximately proportional to those in the sediment. The conditional partition coefficient between pore water and sediment was 0.9-14 × 10
L kg
, independent of the year of sampling. These results indicated that an equilibrium of
Cs between pore water and sediment has been established in a relatively short period, and
Cs in the pore water is gradually exported to seawater near the seabed. A simple box model estimation based on these results showed that
Cs in the sediment decreased by about 6% per year by desorption/diffusion of
Cs from the seabed.
ABSTRACTSeabed sediment mapping with acoustical data and ground-truth samples is a growing field in marine science. In recent years, multi-classifier ensemble models have gained prominence for ...classification problems by combining several base classifiers. However, traditional ensemble methods do not consider the confidence scores of base classifiers, leading to suboptimal fusion when there are conflicting predictions. The current study introduces a novel optimization strategy that enhances the ensemble’s accuracy by constructing an ideal ensemble predicted probability matrix based on the fusion of predicted probabilities of the base classifiers, to improve seabed sediment mapping. The proposed approach not only addresses the limitations of traditional ensemble methods but also significantly increases the ensemble’s performance. The proposed approach demonstrates significant accuracy improvements. On the under-sampled dataset, it achieves 73.5% improvement compared to individual classifiers (random forest, decision tree, support vector machine), surpassing their respective accuracies. On the standard dataset, the ensemble model attains an accuracy of 79.1%, surpassing individual classifiers. Employing over-sampling techniques further elevates accuracy to 94.9%, exceeding the individual classifier performances. The proposed method is evaluated on acoustical data obtained from the Irish Sea. The proposed method outperforms base classifiers in terms of accuracy, F1 score, and the Kappa coefficient.
The accident at the Fukushima Daiichi Nuclear Power Plant (FDNPP) caused a radioactive contamination in seabed sediment. The 137Cs supply from rivers could be an important process for the long-term ...behavior of 137Cs in seabed sediment. In this study, a ten-year simulation of the 137Cs behavior in seabed sediment was conducted using an oceanic dispersion model combined with a prediction model of 137Cs behavior in land and river. In the waters north of FDNPP, the simulation results suggested that the 137Cs supply from rivers had a great impact on the concentrations in coastal sediment due to the initial low concentrations in seabed sediment and the large supply of 137Cs from rivers. In the waters near FDNPP and south of FDNPP, the simulation results suggested that the impact of the 137Cs supply on the temporal variation of 137Cs concentration in coastal sediment was relatively small due to the large initial adsorption from seawater. Overall, these results indicated that 137Cs supply from rivers had an impact on the spatiotemporal distribution of 137Cs concentrations in seabed sediment on a decadal time scale and the impact was especially great in the waters north of FDNPP.
Seabed sediment classification has significance for the utilization of marine resources and marine scientific research. Currently, the multibeam echo sounder (MBES) is increasingly becoming the tool ...of choice for large-scale seabed sediment classification. To further explore the technology of seabed sediment classification, this paper proposes a new classification method. In addition to backscatter mosaic, the method also integrates three other different types of features, including texture features of backscatter mosaic, MBES bathymetry features, and backscatter angular response (AR) features, which are given different weights in the classification process. First, geographically weighted regression (GWR) analysis is performed between different types of features and seabed sediment types, and the normalized coefficient of determination (R2) is employed as the weight coefficient for the different types of features. Second, the backscatter mosaic is combined with features from different types to predict the seabed sediment types using a deep neural network (DNN) classifier. Third, the classification residuals of the features from these three different types are acquired through the above classification results. Last, the classification residuals of features from different types are added to the classification results of the backscatter mosaic according to the weights, thereby achieving seabed sediment classification based on MBES multifeatures with different weights. The results show that the overall classification accuracy of the seabed sediments can be significantly improved from 88.98%/85.14% to 93.43% when using the DNN classification model based on MBES multifeatures with different weights compared with the other two models (DNN classification model based on MBES multifeatures with equal weights and DNN classification model based on principal component analysis (PCA) dimensionality reduction). The kappa coefficient can also be significantly improved from approximately 0.85/0.80 to 0.91. Via analysis, the proposed method can reasonably assign the weights of the different features and take advantage of integrating MBES multifeatures for seabed sediment classification. This approach also provides an important reference for future research on seabed sediment classification.
•Integrating three different types of feature is more effective for seabed sediment classification.•The GWR model effectively evaluate the importance of the MBES multifeatures.•The DNN-based seabed classification model achieves prediction of sediments with three types and a shipwreck.
