Folk's textual classification scheme which is widely used for sediment study operates with the proportions of gravel, sand, silt and clay fractions conventionally. However, dealing with data from ...different sources usually needs to face missing values that may make the classification difficult. To solve this problem and discover other methods of analyzing the scheme, with samples of offshore seabed sediment, a two-stage model was established to predict a sample's class using the XGBoost algorithm as well as the grain size parameters as input features. The final model was evaluated with quantitative performance measures of recall, precision and F1 score, and by comparing sediment texture maps using the predicted and the actual data. The results show that the model performs well on extraction of sediment samples without gravel fraction, and prediction of classes that have independent characteristics of grain size parameters or samples not near the boundaries of classes in the ternary diagram. The predicted sediment texture is close to the actual and could be reliable due to errors with little impact on further applications. It is demonstrated that the model could be an auxiliary or alternative approach to offshore sediment texture mapping, as well as supplementary to the analysis of sedimentary environment.
•Apply XGBoost Algorithm to study the Folk's textual classification scheme.•Evaluate the uncertainty of prediction using confusion matrix.•Boundaries between sediment classes on the diagram are where the errors occur.•Error regions may indicate to complicated areas of seabed transport.•Classes of sand, sandy silt, silty sand and silt predicted by the model are reliable.
This paper gives an overview of the research results obtained in 10 years after the accident at Fukushima Daiichi Nuclear Power Plant (FDNPP), focusing on the distribution and dynamics of
137
Cs, ...which is one of major accident-derived radionuclides. Immediately after the FDNPP accident, 8-21 PBq of
137
Cs was transported to the ocean mainly due to direct discharge to the ocean, and deposition in the ocean via the atmosphere.
137
Cs in seawater traveled eastward on the surface of the North Pacific Ocean along the Kuroshio Extension over a period of several years. Some of
137
Cs is also recirculated to the western margin of the subtropical Pacific via the intermediate layer. The concentration of radiocesium in marine organisms also increased immediately after the accident, and then decreased over time. The concentration of radiocesium in brackish and demersal fishes decreased at a slower rate than the concentration of radiocesium in surface-dwelling fish. The amount of
137
Cs accumulated on the seafloor is only about 1% (0.2PBq) of the amount carried to the ocean, but it remains in the sediments in the coastal area for a long period of time and gradually migrates to the seawater and ecosystems near the seafloor.
Recognition of seabed sediment is one of the critical foundations of marine exploitation. This paper proposes a probabilistic neural network (PNN) based method to improve the identification accuracy ...of seabed sediment from side-scan sonar imagery. The feature set of side-scan images consists of two types of features, namely textural features and color features. In this study, partial eigenvalues of the gray co-occurrence matrix are selected as the textural feature, and the color features are represented by color moments. Combining textural features with color features, we get the input matrix, which is then fed into PNN for classification. PNN calculates the distance between the sample eigenvector to be predicted and the training sample eigenvector, then accumulates the probability belonging to a certain category. Finally, PNN outputs the forecasted class of samples eigenvector with the largest posterior probability. It is the first time that PNN has been used in seabed sediment classification from side-scan sonar imagery. Compared with the traditional clustering methods, PNN improved the accuracy of the classification and attained a highest accuracy of 92.2%.
•The first report about the probabilistic neural network method being applied in seabed sediment recognition.•Probabilistic neural network produces much better results than the widely used clustering methods.•The composition of textural and color features makes full use of the characteristics of sonar imagery.
We use flume experiments and numerical modeling to examine the penetration depth of internal solitary waves (ISWs) on partially saturated porous sandy silt and clayey silt seabed. The results of the ...experiment and model showed that the instantaneous excess pore water pressure in both the sandy silt and clayey silt seabed followed the same trend of decreasing with the seabed depth. In general, the excess pore water pressure generated by the sandy silt was bigger than that by clayey silt at the same depth. The ISW-induced excess pore water pressure greatly influenced the surface seabed and showed a linear relationship. The penetration depth was approximately one order of magnitude smaller than the half-wavelength of the ISWs, which might be larger than the penetration depth induced by surface waves. Our study results are helpful for understanding the damage that ISWs inflict upon the seabed and for informing future field experiments designed to directly measure the interaction between ISWs and seabed sediments.
High-precision habitat mapping can contribute to the identification and quantification of the human footprint on the seafloor. As a representative of seafloor habitats, seabed sediment classification ...is crucial for marine geological research, marine environment monitoring, marine engineering construction, and seabed biotic and abiotic resource assessment. Multibeam echo-sounding systems (MBES) have become the most popular tool in terms of acoustic equipment for seabed sediment classification. However, sonar images tend to consist of obvious noise and stripe interference. Furthermore, the low efficiency and high cost of seafloor field sampling leads to limited field samples. The factors above restrict high accuracy classification by a single classifier. To further investigate the classification techniques for seabed sediments, we developed a decision fusion algorithm based on voting strategies and fuzzy membership rules to integrate the merits of deep learning and shallow learning methods. First, in order to overcome the influence of obvious noise and the lack of training samples, we employed an effective deep learning framework, namely random patches network (RPNet), for classification. Then, to alleviate the over-smoothness and misclassifications of RPNet, the misclassified pixels with a lower fuzzy membership degree were rectified by other shallow learning classifiers, using the proposed decision fusion algorithm. The effectiveness of the proposed method was tested in two areas of Europe. The results show that RPNet outperforms other traditional classification methods, and the decision fusion framework further improves the accuracy compared with the results of a single classifier. Our experiments predict a promising prospect for efficiently mapping seafloor habitats through deep learning and multi-classifier combinations, even with few field samples.
Microplastic (MP) pollution in coastal areas has received increasing attention recently. However, studies focused on MP pollution in rural coastal areas remain limited compared to those in ...metropolitan coastal areas. This study observed MP particles accumulated on the seafloor of the Otsuchi Bay, a small ria bay located on the Pacific coast, Sanriku, Japan. The MP concentrations in the sediment ranged from 2.6 ± 0.3 to 13.6 ± 9.8 pcs g−1 dry weight (DW) and 2.6 ± 1.4 to 5.1 ± 1.2 pcs g−1 DW in March and September 2021, respectively. No significant difference in MP concentrations was detected between March and September. The MP concentration in the Otsuchi Bay was lower than that observed in other highly populated coastal areas but was relatively high considering the population size of the catchment area. MP particles smaller than 1000μm were the most prevalent, accounting for 96.3% of all MP samples. MP size at the bay head was smaller than that at the central bay for high-density MPs; however, the relationship was reversed for low-density MPs. Analysis of the MP distribution pattern using a two-dimensional numerical model suggests that the horizontal distribution of MPs in the Otsuchi Bay depends on the size and density of MP particles. It is also strongly influenced by both the tidal oscillating currents characteristic to the bay and vertical terminal velocity of MP particles. Sedimented MP distributions in a bay with a small catchment population with limited MP sources shed light on our understanding of MP transport dynamics.
Synthesized gas hydrate in lab was mixed with kaoline to form core samples, so as to simulate the hydrate-bearing sedimentary layers in the seabed. Under conditions of different confining pressure, ...triaxial compressive tests were conducted on hydrate-bearing sediments with different volume content of kaoline. The results show that: (1) The failure strength of gas hydrate-bearing sediments increases with the confining pressure at a low-pressure stage. The strength tends to decline gently with further increases of confining pressure. (2) The modulus of elasticity
E
0 keeps unchanged under various confining pressures. But the secant modulus
E
50 presents a great dependency on the confining pressure. Secant modulus increases to the peak and then decreases with the increase of confining pressure; (3) The internal friction angles of gas hydrate-bearing sediments are not sensitive to the volume ratio of kaoline, but its cohesion depends on the volume ratio of kaoline. The sediment strength increases with the increase of kaoline content.
Seabed sediment classification is of great significance in acoustic remote sensing. To accurately classify seabed sediments, big data are needed to train the classifier. However, acquiring seabed ...sediment information is expensive and time-consuming, which makes it crucial to design a well-performing classifier using small-sample seabed sediment data. To avoid data shortage, a self-attention generative adversarial network (SAGAN) was trained for data augmentation in this study. SAGAN consists of a generator, which generates data similar to the real image, and a discriminator, which distinguishes whether the image is real or generated. Furthermore, a new classifier for seabed sediment based on self-attention densely connected convolutional network (SADenseNet) is proposed to improve the classification accuracy of seabed sediment. The SADenseNet was trained using augmented images to improve the classification performance. The self-attention mechanism can scan the global image to obtain global features of the sediment image and is able to highlight key regions, improving the efficiency and accuracy of visual information processing. The proposed SADenseNet trained with the augmented dataset had the best performance, with classification accuracies of 92.31%, 95.72%, 97.85%, and 95.28% for rock, sand, mud, and overall, respectively, with a kappa coefficient of 0.934. The twelve classifiers trained with the augmented dataset improved the classification accuracy by 2.25%, 5.12%, 0.97%, and 2.64% for rock, sand, mud, and overall, respectively, and the kappa coefficient by 0.041 compared to the original dataset. In this study, SAGAN can enrich the features of the data, which makes the trained classification networks have better generalization. Compared with the state-of-the-art classifiers, the proposed SADenseNet has better classification performance.
In this study, seabed sediment was collected from 26 stations located within 160 km from the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) during the 2 years which followed the FDNPP accident of ...March 2011 and the concentrations of 129I and 137Cs were measured. By comparing the distribution of these two radionuclides with respect to their different geochemical behaviors in the environment, the transport of accident-derived radionuclides near the seafloor is discussed. The concentration of 129I in seabed sediment recovered from offshore Fukushima in 2011 ranged between 0.02 and 0.45 mBq kg−1, with 129I/137Cs activity ratios of (1.9 ± 0.5) × 10−6 Bq Bq−1. The initial deposition of 129I to the seafloor in the study area was 0.36 ± 0.13 GBq, and the general distribution of sedimentary 129I was established within 6 months after the accident. Although iodine is a biophilic element, the accident-derived 129I negligibly affects the benthic ecosystem. Until October 2013, a slight increase in activity of 129I in the surface sediment along the shelf-edge region (bottom depth: 200–400 m) was observed, despite that such a trend was not observed for 137Cs. The preferential increase of the 129I concentrations in the shelf-edge sediments was presumed to be affected by the re-deposition in the shelf-edge sediments of 129I desorbed from the contaminated coastal sediment. The results obtained from this study indicate that 129I/137Cs in marine particles is a useful indicator for tracking the secondary transport of accident-derived materials, particularly biophilic radionuclides, from the coast to offshore areas.
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•Concentration of 129I in seabed sediment off Fukushima is reported for the first time.•Deposition of the FDNPP accident-derived 129I to the seafloor was 0.36 ± 0.13 GBq•Until October 2013, 129I activity in sediment increased in the shelf-edge region.•Remobilization of 129I near the seafloor likely affected the sequential accumulation.•The accident-derived 129I is considered to negligibly affect the benthic ecosystem.
Seabed sedimentary bedforms (SSBs) are strong indicators of current flow (direction and velocity) and can be mapped in high resolution using multibeam echosounders. Many approaches have been designed ...to automate the classification of such SSBs imaged in multibeam echosounder data. However, these classification systems only apply a geomorphological contextualisation to the data without making direct assertions on the velocities of benthic currents that form these SSBs. Here, we apply an object-based image analysis (OBIA) workflow to derive a geomorphological classification of SSBs in the Moira Mounds area of the Belgica Mound Province, NE Atlantic through k-means clustering. Cold-water coral reefs as sessile filter-feeders benefit from strong currents are often found in close association with sediment wave fields. This OBIA provided the framework to derive SSB wavelength and wave height, these SSB attributes were used as predictor variables for a multiple linear regression to estimate current velocities. Results show a bimodal distribution of current flow directions and current speed. Furthermore, a 5 k-means classification of the SSB geomorphology exhibited an imprinting of current flow consistency which altered throughout the study site due to the interaction of regional, local, and micro scale topographic steering forces. This study is proof-of-concept for an assessment tool applied to vulnerable marine ecosystems but has wider applications for applied seabed appraisals and can inform management and monitoring practice across a variety of spatial and temporal scales. Deriving spatial patterns of hydrodynamic processes from widely available multibeam echosounder maps is pertinent to many avenues of research including scour predictions for offshore structures such as wind turbines, sediment transport modelling, benthic fisheries, e.g., scallops, cable route and pipeline risk assessment and habitat mapping.
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