Sediment Classification Research Articles

Investigating sediment dynamics on a continental shelf mud patch under the influence of a macrotidal estuary: A numerical modeling analysis

Shelf mud patches represent major sinks for fine-grained particles on continental shelves, as well as for carbon and contaminants of continental origin. The West Gironde Mud Patch (WGMP) is an interesting example of such offshore marine systems as it is an active mud deposition area located offshore the Gironde estuarine mouth (France) at depths between 30 and 70 m. It is known to be the trap of fine particles coming from the estuary, but the contribution of this material to the total mass of the depocenter is poorly quantified. In addition, despite the economic and ecological issues at stake, the response of such subtidal sedimentary structure to the combination of tidal currents, waves, and river supply remain poorly understood. Thus, using a realistic 3-D hydrodynamic and mixed (mud/sand) sediment transport model, this study aims at investigating the sediment dynamics of the WGMP under different hydrometeorological conditions. The analysis of the residual fluxes at the estuarine mouth exhibited large discrepancies between the different sediment classes as well as for contrasted hydro-and meteorological conditions induced by different dominant transport mechanisms. During winter, the reinforced density gradients drive strong up-estuary baroclinic circulation at the bottom that dominates the sediment dynamics over the barotropic export of mud particles. The model also reproduced the signature of a subtidal mud accumulation area over the continental shelf around 30–40 m water depth, on the proximal side of the observed WGMP. On average over two years, 26% of the mud mass accumulating on the simulated subtidal mudflat comes from the estuary. The trapping efficiency of this mud patch is negatively correlated with the significant wave height. Moreover, due to the estuarine turbid plume being more concentrated and developed at the surface during high river discharge, the trapping efficiency of the mud body is enhanced compared to lower discharge. This study highlights the sensitivity of mud and sand fluxes to vertical and horizontal residual circulation, and points out the uncertainties associated with the simulation of short-term (i.e., years) fine particle deposits compared to long-term (i.e., centuries) sediment accumulation trends. In addition, these results show the primordial effects of both wave action and riverine sediment supply on the dynamics of such subtidal muddy structures, which raises concern about their fate facing climate change and human activities in the future.

Classification method of seabed sonar image substrate based on ELM-AdaBoost

The results of sediment classification are affected by the measurement environment. The water depth has a significant impact on the sound propagation, and the acoustic characteristics will be different under different water depths. In addition, seabed topography, sediment types and their distribution, acoustic frequency, equipment types and settings, and suspended substances in water will all affect the original sonar data. In order to obtain accurate classification results, a seabed sonar image classification method based on Elm AdaBoost is proposed. The original sound intensity data is compensated for gain and the anomaly is eliminated. After preprocessing the multi beam sonar data, the image texture feature is extracted from the gray level co-occurrence matrix as the feature vector for classification, and the elm classifier is constructed. The elm is enhanced by AdaBoost. The calculated feature is input into the elm AdaBoost network for sediment classification, and the result is output. Simulation results show that the proposed method effectively improves the accuracy of sediment classification, and verifies the feasibility of this method.

Sediment classification in the paleo-oceanic environment based on multi-acoustic reflectance characteristics in the Southern Tianshan Mountains

Sediment classification in the paleo-oceanic environment based on multi-acoustic reflectance characteristics in the Southern Tianshan Mountains

Seabed Geoacoustic Analysis Using Scientific Single Beam Echosounder

Graphical Abstract Highlight Research Geoacoustic quantification. Sediment identification. Sediment classification. Geoacoustic characteristic. Sediment type spatial distribution in Lancang Island. AbstractHydroacoustic technology was able to quantify the seabed substrate and can be estimated accurately and near real time on the acoustic characters of each substrate. The purpose of the research was to identify the geoacoustic characteristics and spatial mapping of the seabed substrate in Lancang Island. Acoustic data was acquired using a Simrad EK-15 Single Beam Echosounder instrument operating at 200 kHz. Sediment samples were taken using an Ekman grab, which will be used to validate the acoustic data. The results of this study indicated that the acoustic backscatter values of the seabed substrate based on the surface backscattering strength value and sediment particle size at fourteen sampling stations are -28.03 decibels to -20.02 decibels divided into 9 sediment type groups, namely medium and very coarse sand mixture; medium sand; medium, fine and coarse sand mixture; medium and fine sand mixture; fine and medium sand mixture; medium and very fine sand mixture; very fine and medium sand mixture; fine and very fine sand mixture; and fine sand. The accuracy level of k-Nearest Neighbour and Random Forest computational used has very good accuracy of 98.21 % and 96.43 % and Naevi Bayes has a lower accuracy of 58.93 %. The identified geoacoustic characteristics included the mean grain size, sound speed, density, acoustic impedance, and reflection coefficient. Faster, more effective, and efficient computational processes with high accuracy make k-Nearest Neighbour and Random Forest models the best alternative to be used as geoacoustic computational models of seafloor substrates.

Effect of particle size distribution of sediments on development of polder soils in Japan

Abstract Purpose Polder soils develop from oceanic and lacustrine sediments covered with seawater, brackish water, and freshwater after artificial drainage. Because there are several concerns regarding the agricultural use of polder soils, soil genesis and properties have been considerably surveyed, mainly focusing on problematic soils developed from fine sediments. Although sediments have a wide range of particle size distributions due to different sedimentary conditions, particle size of parent materials have not been well addressed to understand the soil developmental process. In this study, Japanese polders with different reclamation ages and sedimentary conditions were surveyed to clarify the soil formation process and factors affecting pedogenesis. Materials and methods Soil samples were collected from 15 soil profiles in six Japanese polders under different land use types. Sedimentary conditions of polders were evaluated from particle size distributions using the hydrodynamic classification proposed by Pejrup (The triangular diagram used for classification of estuarine sediments: a new approach. Tide-influenced Sediment Environ Facies, pp 289–300, 1988). The major soil-forming factors of polders were extracted by principal component analysis (PCA) using general soil properties. Results and discussion Brackish lake and inner bay polders were characterized by calm hydrodynamic conditions comprising fine particles. Two polders reclaimed from a shallow inland sea were characterized by violent hydrodynamic conditions. Sandy sediments were also characteristic of immature soils reclaimed from a freshwater lake and an estuarine tidal flat. Soils on polders developed under calm hydrodynamic conditions enabled the accumulation of high total carbon content. The soil-forming process in the brackish bay oxidized pyrite, leading to an acidic soil reaction. Conversely, soils developed from sandy sediments were characterized by low iron content. The PCA extracted two factors explained by particle size and soil reaction relating to acidification and salt leaching. Conclusion Polder soils can be mainly discriminated by their particle size distributions, which are characterized by hydrodynamics under the sedimentary conditions, and the polder soil development is affected by water management in land uses after artificial drainage.

Implementation of a GPU-enhanced multiclass soil erosion model based on the 2D shallow water equations in the software Iber

We present the implementation of a new fully distributed multiclass soil erosion module. The model is based on a 2D finite volume solver (Iber+) for the 2D shallow water equations that computes the overland flow water depths and velocities. From these, the model evaluates the transport of sediment particles due to bed load and suspended load, including rainfall-driven and runoff-driven erosion processes, and using well-established physically-based formulations. The evolution of the mass of sediment particles in the soil layer is computed from a mass conservation equation for each sediment class. The solver is implemented using High Performance Computing techniques that take advantage of the computational capabilities of standard Graphical Processing Units, achieving speed-ups of two orders of magnitude relative to a sequential implementation on the CPU. We show the application and validation of the model at different spatial scales, ranging from laboratory experiments to meso-scale catchments.

Wind and rainfall erosion energy in large sediment generating and coarse sediment class areas of the middle Yellow river

Joint action by strong winds and high-intensive rainfall leads to serious soil erosion problems in large sediment generating and coarse sediment class areas of the middle Yellow River. Investigating the variation of trend and alternating wind and rainfall energy is an important prerequisite for controlling regional sediment transport problems. We calculated the wind and rainfall erosion energies by using the aerodynamic energy and rainfall kinetic energy formulas. The Mann-Kendall (MK) trend test and Theil-Sen approach were used to analyze the spatial-temporal variation and alternation of joint erosion energy during the last 40 years (1979–2018). The results show the following. 1) Wind erosion energy increased and rainfall erosion energy decreased. However, the former is larger than the latter. 2) The dominant erosive energy varies for different seasons. Wind erosive energy dominates in spring, and rainfall erosive energy dominates in summer. 3) There is a regular wind-rainfall-wind alternation of erosive energy. The wind-rainfall energies were alternately distributed in May–July and the rainfall-wind energies were alternately distributed in September–December. Furthermore, the dominant time of rainfall erosive energy decreased when wind erosive energy increased. The results can help decision makers to develop soil erosion control strategies to reduce soil erosion when it occurs in wind and water staggered areas.

Radionuclides in marine sediment

Abstract Most contaminants in the sea originate from land sources. Radionuclides in sea water are transported by sea currents. Marine sediment is a physical trap for pollutants that are introduced to the environment and play an important role in radiological studies. Radionuclides from seawater bound to particulates sink to the seabed. Their resuspension causes the reintroduction of pollutants to the water column. Remobilization and horizontal/vertical transport by various processes may occur. Long-lived radionuclides become buried in sub-surface sediment. Grain size sediment classification and sediment geochemical composition all play a significant role in the development of the radionuclide content of marine sediment. Atmospheric fallout from the Chernobyl and the Fukushima accidents, atmospheric nuclear weapon testing, releases from nuclear industry plants, river runoff, and to a lesser extent directed and submarine groundwater discharges are the major sources of radionuclides in the marine environment.

Arctic puzzle: Pioneering a northern shrimp (Pandalus borealis) habitat model in Disko Bay, West Greenland

Recent advancements in spatial modelling leverage remote sensing data and statistical species-environment relationships to forecast the distribution of a specific species. Our study focuses on Disko Bay in West Greenland, recognized as a significant marine biodiversity hotspot in the region. We conducted comprehensive analyses using multiple datasets spanning from 2010 to 2019, incorporating shrimp and fish surveys, commercial shrimp fishery catches, high-resolution (25 × 25 m) multibeam bathymetry and backscatter data along with a medium-resolution (200 × 200 m) bathymetric model, measured and modelled oceanographic data, and satellite chlorophyll data. Through multivariate regression analysis, we tested the significance of various physical factors (seafloor depth, sediment class, bottom water temperature, bottom water salinity, bottom current velocity, space, and time), biological factors (chlorophyll a, Greenland halibut (Reinhardtius hippoglossoides)), and anthropogenic impact (shrimp fishery; standardized catch per unit effort) on the density of northern shrimp in the area. Our results indicate a significant association between northern shrimp density, seafloor depth, and sediment class, explaining 36 % of the variation in shrimp density. Subsequently, we developed a high-resolution (optimized) spatial linear mixed–effect model to map the distribution of northern shrimp across Disko Bay, representing the first model of its kind developed for an Arctic area. The optimal habitat for northern shrimp is characterized by medium-deep waters (approximately 150–350 m), turbulent conditions, and mixed sediments, predominantly located in the northern and southern regions of Disko Bay. Notably, the northern region hosts a relatively diverse benthic community, with northern shrimp and sponges as the primary contributors of epibenthic biomass. This novel high-resolution model significantly enhances our understanding of the physical drivers and detailed spatial patterns influencing the distribution of northern shrimp in the Arctic.

A newly developed multi-objective evolutionary paradigm for predicting suspended sediment load

The estimation of Suspended Sediment Load (SSL) is challenging due to its complex nature within the field of hydrology. The selection and reduction of input feature dimensions, along with the non-uniformity of the sediment and estimation accuracy, pose challenges when estimating suspended sediment load. By combining support vector regression models with MOGWO, MOPSO, and MOOTLBO, this study aimed to predict suspended sediment load. Hybrid models are constructed with three key objectives: enhancing accuracy, minimizing feature count, and optimizing sediment classification. In order to accomplish this, two scenarios have been formulated. The first scenario prioritizes performance accuracy, whereas the second scenario assigns equal importance to three objectives. The study focuses on the Kosar Dam watershed in southwest Iran. The CHIRPS precipitation product and the GLDAS soil moisture product are considered to be predictors. The extraction of input features is carried out utilizing Principal Component Analysis (PCA). The results from both scenarios indicated that the optimal sediment classification consists of 10 categories, demonstrating superior performance across all three integrated models. Incorporating feature selection enhances the model's performance and decreases the number of features. In the first scenario, 10 features are selected from a set of 30 input feature vectors, while in the second scenario, 14 features are chosen. Consequently, hybrid models prove to be effective in reducing input features, optimizing classification of SSL, and enhancing prediction accuracy. In general, the SVR-MOOTLBO model exhibits superior performance when compared to other models. The performance indices (R, RMSE, MAE, PBIAS, and MD) exhibit variations ranging from 0.02% to 0.75% between the first and second scenarios in SVR-MOOTLBO, while the RPIQ index shows a relatively modest difference of 6.2%. In both scenarios, the SVR parameters are well-tuned, and the search agents of the MOOTLBO algorithm exhibit effective functionality.

Benthic habitat sediments mapping in coral reef area using amalgamation of multi-source and multi-modal remote sensing data

Scientific management of coral reefs is a global research topic due to their latent economic value and ecological significance. This research benefits from optical and acoustic remote sensing technology with high spatial resolution, sufficient coverage, and stable repeatability. However, each sensor with its own set of limitations, such as insufficient penetration ability, limited sensor resolution and range, which often hinder their applications in the precise classification of coral reef habitats. To address these issues, a novel scheme integrating airborne laser bathymetry (ALB), multibeam echo sounder (MBES) and the multispectral image is designed to serve large-scale and high-precision coral reef habitat sediments research. This scheme focuses on valuable attribute mining, inconsistent coverage, feature default, and redundancy according to multi-source data used in engineering demands. After per-system pretreating, numerous properties, including band, waveform, intensity, terrain, and derivative characteristics, are extracted and quantitatively analyzed to be responsible for habitat description. Subsequently, a complete seabed terrain is generated using the LightGBM regression model to establish the conversion relationship between satellite images and depth. Eventually, a new benthic habitat sediments classification method called RF-SAPSO-LightGBM is introduced with a powerful generalization ability. The unique functions of automatic default feature completion and optimal parameter search improve the discrimination ability of complex sediments. The results indicate that the new method outperforms five other approaches in sediment classification accuracy, and the combination of multi-source data provides more extensive coverage and higher accuracy with an overall of 87. 9%. In comparison, the accuracy values for multispectral image and terrain data alone are 74.5% and 54.7% respectively.

Nitrogen loading resulting from major floods and sediment resuspension to a large coastal embayment

Major floods pose a severe threat to coastal receiving environments, negatively impacting environmental health and ecosystem services through direct smothering with sediment and nutrient loading. This study examined the short and long-term impacts of the February 2022 major flood event on mud extent and sediment nitrogen flux in Moreton Bay (the Bay), a large, sub-tropical embayment in Southeast Queensland, Australia. Short-term impacts were assessed three days after the flood peak by sampling surface water at 47 sites in the direction of the predominant circulation pattern. Longer-term impacts were assessed by undertaking an intensive sediment survey of 223 sites and a nutrient flux experiment using sediment core incubations to simulate calm and resuspension conditions for the four key sediment classes. Short-term impacts revealed elevated turbidity levels extended across the Bay but were highest at the Brisbane River mouth, ammonium concentrations varied inversely with surface turbidity, whereas nitrate concentrates closely tracked surface turbidity. The sediment survey confirmed fine sediment deposition across 98 % of the Bay. Porewater within the upper 10 cm contained a standing pool of 280 t of ammonium, with concentrations more than three orders of magnitude higher than overlying surface waters. The nutrient flux experiment revealed an order of magnitude higher sediment ammonium flux rate in the sandy mud sediment class compared to the other sediment classes; and for simulated resuspension conditions compared to calm conditions for sand, muddy sand, and mud sediment classes. Scaling across the whole Bay, we estimated a mean annual sediment flux of 17,700 t/year ammonium, with a range of 13,500 to 21,900 t/year. Delivery of fine sediments by major floods over the last 50 years now impact >98 % of the benthic zone and provide a major loading pathway of available nitrogen to surface waters of Moreton Bay; representing a significant threat to ecosystem health.

Dense Neural Network for Classification of Seafloor Sediment using Backscatter Mosaic Feature

Water transportation plays a vital role in global economic activities, facilitating more than 85% of international trade and serving as a cost-effective and essential means to fulfill the demand for goods and services. Similarly, the Benoa Port, situated in the southern part of Denpasar City, operates in the same manner. By utilizing Multibeam Echo Sounder (MBES) backscatter data, backscatter mosaics can be generated to identify various seafloor sediment types, which consist of rock fragments, minerals, and organic materials. The characteristics of these sediments, such as grain size, density, composition, and others, can be observed. To improve the classification of sediments, the integration of backscatter data and backscatter features, such as ASM (Angular Second Moment), Energy, Contrast, and Correlation, can be employed. Supervised classification models like Dense Neural Network (DNN) can be utilized to accurately determine the types of seafloor sediments. The application of DNN modeling resulted in a training accuracy rate of 88% and a testing accuracy rate of 100%. The accuracy results delineated six distinct sediment types. Notably, sandy silt exhibited the highest distribution, accounting for 49.30%, whereas soft clayey silt registered the lowest distribution at 0.53%, as determined by their respective spatial prevalence.

Seabed Sediment Classification through Multispectral Backscatter Mosaic MBES and Angular Response Analysis

Seabed Sediment Classification through Multispectral Backscatter Mosaic MBES and Angular Response Analysis

Application of Sample Enhancement Method Combining Superpixel Segmentation and Active Learning in MBES Seafloor Sediment Classification

Application of Sample Enhancement Method Combining Superpixel Segmentation and Active Learning in MBES Seafloor Sediment Classification

An Optimized Data and Model Centric Approach for Multi-Class Automated Urine Sediment Classification

An Optimized Data and Model Centric Approach for Multi-Class Automated Urine Sediment Classification

Seabed Sediment Classification using Multi-Frequency MBES Bathymetric and Its Features

Principally, for a few decades, hydrographical surveys have been only to determine the depth of seawater. Sonar measurement tehcnology is the most widely used to conduct hydrographical surveys employing a singlebeam echosounder (SBES) and multibeam echosounder (MBES). In addition to depth information, seafloor sediment distribution maps are essential for port planning and management. In general, the distribution of seafloor sediments is predicted using backscatter data from SBES and MBES at single and multiple frequencies. The bathymetric data generated by the echosounder can be derived into several bathymetric features such as slope, ruggedness, roughness, aspect, bathymetric position index (BPI), and curvature. This study examines the possibility of using bathymetric measurement and bathymetric derivation of multi-frequency MBES to predict the distribution of seafloor sediments, especially in the harbor pond area. The study used a deep neural network (DDN) to classify the distribution of seabed sediments with bathymetric and bathymetric features input, validated with 74 in situ sediment samples (clayey sand, silt, sandy silt, and silty sand). Up to 75% of data sample sediments are used for training and 25% for validation. The classification results by DNN showed 42.6% clayey sand, 7.4% sandy silt, 46.7% silt, and 3.35% silty sand. The overall accuracy (AO) and Kappa classification of seabed sediments with DDN were 59.5% and 0.54 (moderate), respectively.

Surface sediment classification using a deep learning model and unmanned aerial vehicle data of tidal flats

This study proposes a deep learning model, U-Net, to improve surface sediment classification using high-resolution unmanned aerial vehicle (UAV) images. We constructed training datasets with UAV images and corresponding labeling data acquired from three field surveys on the Hwangdo tidal flat. The labeling data indicated the distribution of surface sediment types. We compared the performance of the U-Net model trained in various implementation environments, such as surface sediment criteria, input datasets, and classification models. The U-Net trained with five class criteria—derived from previous classification criteria—yielded valid results (overall accuracy:65.6 %). The most accurate results were acquired from trained U-Net with all input datasets; in particular, the tidal channel density caused a significant increase in accuracy. The accuracy of the U-Net was approximately 20 % higher than that of other classification models. These results demonstrate that surface sediment classification using UAV images and the U-Net model is effective.

Geoacoustic and geophysical data‐driven seafloor sediment classification through machine learning algorithms with property‐centered oversampling techniques

AbstractThis study aims to classify seafloor sediments using physics‐inspired and data‐driven soil models combined with machine learning algorithms and oversampling techniques. The field data used for the input variables include porosity, S‐ and P‐wave velocities and depth. The soil information reported in the original literature and the “six reference sediments” and effective stress‐versus‐depth models proposed by the previous study confirm the sediment type across all of the input variables. We use three machine learning algorithms and four oversampling methods to enhance the performance accuracy and overcome data imbalance in the minority class. The results show that the averaged accuracy of sediment classification with original data corresponds to 0.88 for porosity, 0.61 for S‐wave velocity, and 0.97 for P‐wave velocity. In particular, the enhanced accuracy with oversampled input variables becomes more pronounced when the depth data are considered in a dataset. The class‐oriented grouping method newly proposed in this study appears to be a robust approach to enhancing performance. Surprisingly, model‐based input variables lead to the best performance in all cases. The proposed analyses conducted using machine learning algorithms and oversampling techniques within the physics‐inspired models could be extended to obtain a first‐order assessment of marine sediment properties.

Automated riverbed composition analysis using deep learning on underwater images

Abstract. The sediment of alluvial riverbeds plays a significant role in river systems both in engineering and natural processes. However, the sediment composition can show high spatial and temporal heterogeneity, even on river-reach scale, making it difficult to representatively sample and assess. Conventional sampling methods are inadequate and time-consuming for effectively capturing the variability of bed surface texture in these situations. In this study, we overcome this issue by adopting an image-based deep-learning (DL) algorithm. The algorithm was trained to recognise the main sediment classes in videos that were taken along cross sections underwater in the Danube. A total of 27 riverbed samples were collected and analysed for validation. The introduced DL-based method is fast, i.e. the videos of 300–400 m long sections can be analysed within minutes with continuous spatial sampling distribution (i.e. the whole riverbed along the path is mapped with images in ca. 0.3–1 m2 overlapping windows). The quality of the trained algorithm was evaluated (i) mathematically by dividing the annotated images into test and validation sets and also via (ii) intercomparison with other direct (sieving of physical samples) and indirect sampling methods (wavelet-based image processing of the riverbed images), focusing on the percentages of the detected sediment fractions. For the final evaluation, the sieving analysis of the collected physical samples were considered the ground truth. After correcting for samples affected by bed armouring, comparison of the DL approach with 14 physical samples yielded a mean classification error of 4.5 %. In addition, based upon the visual evaluation of the footage, the spatial trend in the fraction changes was also well captured along the cross sections. Suggestions for performing proper field measurements are also given; furthermore, possibilities for combining the algorithm with other techniques are highlighted, briefly showcasing the multi-purpose nature of underwater videos for hydromorphological assessment.