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The study of sediment‐riverflow interactions during discrete hydrological events is vital for enhancing our understanding of the hydrological cycle. Hysteresis analysis, relying on high‐resolution, continuous monitoring of suspended sediment concentration (SSC) and discharge (Q) data, is an effective tool for investigating complex hydrological events. It captures differing sediment dynamic at the same discharge level, which results from the asynchrony between the hydrograph and sediment graph during different phases of the event. However, there has been no comprehensive review systematically addressing the utility and significance of hysteresis analysis in soil and water management. This review synthesizes findings from over 500 global studies, providing a detailed examination of current research. We trace the development and application of hysteresis analysis in hydrology, illustrating its role in classifying and characterizing events, as well as uncovering sediment sources and transport mechanisms. Furthermore, hysteresis analysis has proven effective in identifying critical hydrological events, offering valuable insights for targeted watershed management. Our spatiotemporal analysis of global hysteresis research shows that over 70% of studies are located in semi‐arid and Mediterranean climate zones, with an increasing focus on alpine and tropical regions due to climate change. This review also highlights critical limitations, including the scarcity of high‐resolution data, inconsistent use of quantitative indices, and limited integration of hysteresis patterns into predictive hydrological approaches. Future research should focus on developing region‐specific hydrological models that incorporate hysteresis dynamics, along with standardizing methodologies to apply hysteresis analysis across diverse climatic and geomorphic settings. Key Points We review hysteresis methods and identify global hotspots for analyzing suspended sediment dynamic during discrete hydrological events Combining qualitative and quantitative hysteresis methods effectively classify events and reveal sediment sources and transport processes Hysteresis methods face challenges in accuracy and applicability under the growing complexity of future extreme events

A novel wide-neck classifier (WNC) was designed to address the problem that thickeners cannot achieve classification prior to flocculation in a single unit. Using the computational fluid dynamics-discrete phase method and PIV experimental method, the reliability of the model was validated. We studied the motion characteristics of different particles within the novelty-designed WNC. The primary forces acting on coal slime particles in the composite force field were gravity, drag force, pressure gradient force, and virtual mass force. Drag force dominated the classification and sedimentation processes. In contrast, gravity, pressure gradient, and virtual mass forces promoted downward sedimentation but hindered upward overflow. The classification of slime particles in WNC was divided into initial classification after tangential feeding and centrifugal classification in a cone. Both simulation and experimental results demonstrate that, under consistent feed conditions, mineral density significantly affected the distribution of particles at the classification underflow and classification overflow. Among the three minerals, kaolinite has the highest classification effect, followed by quartz, while coal has the lowest classification effect.

Rock identification plays a fundamental role in geological work, particularly in resource reservoir characterization, stratigraphic division, engineering stability assessment, and hazard prevention. However, traditional manual identification approaches exhibit low efficiency and limited ability to capture dynamic and fine-grained features. To address these challenges, this study employs image recognition and object detection techniques to classify igneous, sedimentary, and metamorphic rocks. We propose an improved You Only Look Once version 11 (YOLO11)-based model by integrating the Efficient Visual State Space (EVSS) module, which enhances the extraction of key rock characteristics-such as texture and fractures-by modeling long-range spatial dependencies and overcoming the locality limitations of conventional convolutional networks. The proposed method is evaluated against three mainstream deep learning models. Experimental results show that the EVSS-enhanced YOLO11 achieves the highest classification accuracy of 92%, outperforming the Vision Transformer (ViT, 85%), ResNet (74%), and the standard YOLO11 (87%). In object detection tasks, the EVSS-integrated YOLO11 also demonstrates superior performance, achieving a mean average precision at 50% intersection-over-union (mAP50) of 91.8% compared to 87.7% for the original YOLO11. By combining efficient visual feature modeling with multi-scale detection capability, this study confirms the effectiveness and robustness of the EVSS-YOLO11 framework for rock image identification, providing strong technical support for intelligent geological analysis.

This study presents the identification and classification of eight distinct petrophysical rock types in the GCA-1 well of the Chirag field using a comprehensive petrophysical integration workflow. The Petrophysical Integration Process Model (PIPM) was applied through two independent yet complementary approaches: (1) the Winland method, supported by capillary pressure data for pore throat radius characterization, and (2) a statistical clustering methodology, including elements from Heubeck and other prior works. Both approaches yielded consistent rock type classifications and leveraged permeability as the primary distinguishing parameter, given its variation across five orders of magnitude and its dominant control on fluid flow behavior. Pittman’s R20 methodology, which defines the pore throat radius at the 20th percentile mercury saturation, demonstrated the strongest correlation between pore throat size and permeability, making it the most effective tool for rock typing in this dataset. A reasonably strong correlation between porosity and permeability was also observed, providing additional confidence in the classification results. The identified rock types show a general correspondence with depositional lithofacies as defined by Reynolds and Nummedal; however, rock types often transcend individual lithofacies boundaries. This emphasizes the need for integrated petrophysical approaches that go beyond sedimentological classification alone. The integration of multiple datasets and methodologies provides a robust foundation for reservoir characterization and flow unit delineation within a geologically complex and tectonically active setting. The workflow and findings from this study deliver valuable insights for field development planning, reservoir modeling, and future petrophysical evaluations in similar depositional systems.

The discrimination of seafloor substrate type is an extremely important part of seafloor science, and the substrate information is of great significance for the development of marine science and the protection of the marine environment. Current sonar equipment can efficiently generate seafloor images and present seafloor information visually, so the seafloor substrate classification technology based on sonar images has become a hot research topic. Convolutional neural network, as one of the most important classification algorithms in seabed substrate sonar image classification, has excellent performance in most cases. However, the size of the convolutional kernel of convolutional neural network limits the global feature extraction ability, and the ability to discriminate global features in sonar images is weak. In addition, seabed substrate sonar images have labelled data acquisition difficulty and high cost, and acoustic seabed substrate classification in practice generally belongs to small sample classification scenarios. Aiming at the above problems, this thesis selects Swin Transformer, which has strong global feature extraction ability, as the classifier, and uses MoCo self-supervised learning to pre-train the unlabeled data in order to achieve better results.

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.

The accurate classification of seabed sediment and benthic covers in shallow-water environments remains a key challenge for marine activities and oceanographic research. However, coastal areas of shallow waters are influenced by complex dynamic environments, making it difficult to obtain authentic sediment and benthic-cover samples. Therefore, to address the problem of few-shot classification of seabed sediment and benthic covers, a few-shot classification algorithm of seabed sediment and benthic covers based on the fusion model of airborne LiDAR bathymetry (ALB) and multispectral images is proposed in this article. Based on the extracted features, a scale-invariant feature transform-progressive sample consensus (SIFT-PROSAC) algorithm and perspective transform model were constructed to achieve feature fusion. Then, multi-modal feature selection is realized using a formal concept analysis-Relief-F (FCA-Relief-F) algorithm. Finally, a graph attention network-prototype network (GAT-PN) model was established to classify five types of sediment and benthic cover (coral reef, stone, sand, vegetation, and coastal zone). To validate the effectiveness of the proposed method, experimental data from actual measurements at Ganquan Island in the Xisha Islands of China were used. Compared to other classical classifiers, the GAT-PN algorithm achieves a higher classification accuracy, with an overall accuracy (OA) and Kappa coefficient of 97.50% and 0.97, respectively. The findings of this study provide effective technical support for marine engineering and related fields.

Flocculation sedimentation state observation could be used to facilitate interaction visualization in laboratories and establish theories for industrial manufacture. Herein, kaolinite was obtained from different sources and labeled as K1 and K2. Those characteristic characterizations indicated that silica sand was difficult to remove from refined commercial kaolinite with different sources. Some biosafe and removable surfactants have been used to evaluate the flocculation sedimentation state of kaolinite-surfactant slurry for a long time, indicating those surfactants could accelerate the settling velocities within a certain range, which could be attributed to sufficient surfactant-kaolinite interactions, but decelerate the settling velocities with the excess surfactant addition, which could be attributed to the surfactant-water colloidal characteristic. Then, the flocculation sedimentation mechanism was illustrated in detail based on the kaolinite-surfactants interaction, indicating that the excellent flocculation sedimentation state was beneficial to silica removal with sedimentation method, but the difficult flocculation sedimentation state was beneficial to size classification with differential centrifugation.

The suspended sediment load (SSL) is one of the major hydrological processes affecting the sustainability of river planning and management. Moreover, sediments have a significant impact on dam operation and reservoir capacity. To this end, reliable and applicable models are required to compute and classify the SSL in rivers. The application of machine learning models has become common to solve complex problems such as SSL modeling. The present research investigated the ability of several models to classify the SSL data. This investigation aims to explore a new version of machine learning classifiers for SSL classification at Johor River, Malaysia. Extreme gradient boosting, random forest, support vector machine, multi-layer perceptron and k-nearest neighbors classifiers have been used to classify the SSL data. The sediment values are divided into multiple discrete ranges, where each range can be considered as one category or class. This study illustrates two different scenarios related to the number of categories, which are five and 10 categories, with two time scales, daily and weekly. The performance of the proposed models was evaluated by several statistical indicators. Overall, the proposed models achieved excellent classification of the SSL data under various scenarios.

National mapping programs (e.g., INFOMAR and MAREANO) and global efforts (Seabed 2030) acquire large volumes of multibeam echosounder data to map large areas of the seafloor. Developing an objective, automated and repeatable approach to extract meaningful information from such vast quantities of data is now essential. Many automated or semi-automated approaches have been defined to achieve this goal. However, such efforts have resulted in classification schemes that are isolated or bespoke, and therefore it is necessary to form a standardised classification method. Sediment wave fields are the ideal platform for this as they maintain consistent morphologies across various spatial scales and influence the distribution of biological assemblages. Here, we apply an object-based image analysis (OBIA) workflow to multibeam bathymetry to compare the accuracy of four classifiers (two multilayer perceptrons, support vector machine, and voting ensemble) in identifying seabed sediment waves across three separate study sites. The classifiers are trained on high-spatial-resolution (0.5 m) multibeam bathymetric data from Cork Harbour, Ireland and are then applied to lower-spatial-resolution EMODnet data (25 m) from the Hemptons Turbot Bank SAC and offshore of County Wexford, Ireland. A stratified 10-fold cross-validation was enacted to assess overfitting to the sample data. Samples were taken from the lower-resolution sites and examined separately to determine the efficacy of classification. Results showed that the voting ensemble classifier achieved the most consistent accuracy scores across the high-resolution and low-resolution sites. This is the first object-based image analysis classification of bathymetric data able to cope with significant disparity in spatial resolution. Applications for this approach include benthic current speed assessments, a geomorphological classification framework for benthic biota, and a baseline for monitoring of marine protected areas.