Key Takeaways & Executive Findings
- •• Developed a novel sediment sampling system for deep-sea mining plume monitoring with 6000 m operational depth and three-month endurance. • Optimized sampling tube design using rheological tests and coupled Eulerian-Lagrangian simulations to minimize sediment disturbance. • Identified complete tube plugging at 261 mm penetration depth, informing optimal sampling depth parameters. • Validated system performance through deep-sea pressure tests and South China Sea field trials, demonstrating high-fidelity time-series sediment collection and preservation.
Abstract
The spatiotemporal characterization of plume sedimentation and microorganisms is critical for developing plume ecological monitoring model. To address the limitations of traditional methods in obtaining high-quality sediment, a novel sampling system with 6000 m operational capability and three-month endurance was developed. It is equipped with three sediment samplers, a set of formaldehyde preservation solution injection devices. The system is controlled by a low-power, timing-triggered controllers. To investigate low-disturbance rheological mechanisms, gap controlled rheological tests were conducted to optimize the structural design of the sampling and sealing assembly. Stress-controlled shear rheological tests were employed to investigate the mechanisms governing yield stress in sediments under varying temperature conditions and boundary roughness. Additionally, the coupled Eulerian-Lagrangian (CEL) method and sediment rheological constitutive models were employed to simulate tube-soil interaction dynamics and sediment disturbance. The radial heterogeneity of sediment disturbance and friction variation of the sampling tube were revealed. The tube was completely ''plugged'' at a penetration depth of 261 mm, providing critical data support to the penetration depth parameters. The deep-sea pressure test and South China Sea field trials demonstrated the system's capability to collect and preserve quantitative time-series sediment samples with high fidelity.
1. Introduction
With the extensive development of deep-sea mineral resource exploration, the environmental impact of deep-sea mining (DSM) has become a global research hotspot. DSM operations can cause large-scale sediment plumes spreading and redeposition, resulting in material migration, oxygen depletion, acoustic, optical and electromagnetic noise, ultimately causing damage to the seafloor ecosystem [1,2].
Few in-situ investigations of sediment plumes and their redistribution have been carried out to investigate the ecological responses caused by DSM [3]. A benthic disturbance experiment was conducted in the Central Indian Basin, and the redeposition was observed on both sides of the disturbed area after the experiment [4]. A small-scale perturbation experiment was conducted in the Clarion-Clipperton zone (CCZ) of the NE Pacific, an array of optical and acoustic turbidity sensors was used to qualitatively visualize sedimentation at a distance of 100 m from the source [5]. Additionally, the multibeam data and optical image based on autonomous underwater vehicle (AUV) were combined to map the distribution of resettled sediment [6]. Millimeter-scale photogrammetric reconstruction method was applied to quantitatively estimate the thickness of the sediments around the mining lanes area [7]. Underwater wireless sensor networks (UWSN) have been applied to in-situ submarine monitoring, but the coverage range and energy consumption are constrained by environmental conditions [8]. However, the existing mining plume deposition monitoring methods were only focused on basic parameter monitoring (i.e., conductivity, temperature, depth (CTD), turbidity, dissolved oxygen and flow velocity) [9,10], which is still difficult to accurately quantify plume particle size distribution. Thus, in order to obtain high-quality environmental samples and data, the intelligent deep-sea equipment was required to be developed.
Loading authentic research manuscript (Pages 1–5)...
Jiale Wu, Jiawang Chen, Xinghui Tan, Kaichuang Wang, Jianling Zhou, Zhangyong Jin, Congchi Huang, Yuan Lin, Chunsheng Wang, Junyi Yang, Shiquan Lin (2025). A sediment sampling system for monitoring plume redeposition from deep-sea polymetallic nodule mining. SinoTechIntel Verified Research. https://doi.org/10.1016/j.ijmst.2025.08.010
Research & Educational Purpose Only:The translations, structured abstracts, analytical annotations, and data reports provided by SinoTechIntel are intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.
Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoTechIntel claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.
Frequently Asked Questions
What is the operational depth and endurance of the sediment sampling system?
The system is designed for 6000 m operational depth and can endure three months of deployment.
How does the system preserve sediment samples?
It uses a set of formaldehyde preservation solution injection devices to preserve the samples in situ.
What methods were used to optimize the sampling tube design?
Gap-controlled rheological tests and stress-controlled shear rheological tests were conducted, along with coupled Eulerian-Lagrangian (CEL) simulations to analyze tube-soil interaction and sediment disturbance.
What was the critical penetration depth for complete tube plugging?
The tube was completely 'plugged' at a penetration depth of 261 mm, which provides critical data for setting sampling depth parameters.
How was the system validated?
The system underwent deep-sea pressure tests and field trials in the South China Sea, demonstrating its capability to collect and preserve quantitative time-series sediment samples with high fidelity.
Related Technical Papers & Translations
A Novel Approach for Enhanced Brain Tumor Segmentation Using Multimodal MRI and Deep Learning
Brain tumor segmentation from multimodal MRI is crucial for diagnosis and treatment planning. In this study, we propose a novel deep learning framework that integrates structural and functional imaging modalities to improve segmentation accuracy. Our method employs a multi-scale attention mechanism and a hybrid loss function to handle class imbalance and boundary ambiguity. Evaluated on the BraTS benchmark, our approach achieves state-of-the-art performance, with Dice scores of 0.91, 0.87, and 0.84 for whole tumor, core, and enhancing tumor, respectively. Furthermore, we demonstrate the generalizability of our model across different scanners and protocols. Our findings suggest that the proposed method can significantly aid clinical decision-making and surgical planning.
Investigation of coupled acoustic and electrical responses and early warning approaches during re-loading of damaged coal
Initial damage from engineering disturbances in deep coal mining degrades mechanical properties and heightens dynamic-hazard risks, challenging conventional monitoring. This study probes the coupled acoustic-electrical responses of initially damaged coal under reloading and develops a multi-parameter, multi-level dynamic integrated early-warning model. Using a true-triaxial Split Hopkinson Pressure Bar (SHPB) system, we prepared specimens with graded damage by varying static deviatoric stresses and dynamic impacts. Uniaxial compression reloading was conducted with synchronous acoustic emission (AE) and resistivity monitoring. Joint time-domain responses of force, acoustics, and electricity delineated distinct loading stages. Time-frequency features were extracted via Fourier and wavelet transforms; crack architecture was quantified by 3D AE localization and fractal-dimension analysis. Initial damage markedly reduced load-bearing capacity. Resistivity decreased sharply with increasing deviatoric stress, while cumulative AE counts increased strongly. The AE spectrum evolved from bimodal to broadband with low- and high-frequency enhancement. The resistivity spectrum showed progressive bandwidth broadening, energy amplification, and high-frequency advancement. The AE spatial fractal dimension rose significantly during compaction. An integrated warning system combining multiscale entropy fusion, Temporal Convolutional Network (TCN)-Transformer forecasting, recurrence-network analysis, and a Bayesian framework yielded a 28.4 s lead time, offering a theoretical basis and technical pathway for intelligent prevention of dynamic hazards.
Influence of aggregate particle size on fracture behavior and energy evolution of cemented rockfill in the post-peak stage
Cemented rockfill (CRF) combines structural support with sustainable reuse of coal-derived solid waste. This study integrates digital image correlation, acoustic emission monitoring, and finite–discrete element simulations to investigate mechanical behavior, fracture development, and energy evolution of CRF containing 54% aggregate content with three grain-size distributions (5–10, 10–20, and 20–30 mm). Results indicate finer aggregates raise compressive strength and elastic modulus, and increase post-peak softening and residual stiffness. Fracture patterns transition from dominantly unidirectional failure in coarse specimens to pronounced X-shaped conjugate shear in fine specimens, with cracks initiating at boundaries and propagating inward. The proportion of failed joints at comparable strains decreases markedly with finer gradation, reflecting a more homogeneous crack network that enhances post-peak load retention and produces frequent minor stress fluctuations. Energy analyses reveal a coarse > medium > fine ordering in cumulative dissipation; however, finer aggregates delay rapid kinetic and dissipative energy release, promoting slower energy redistribution and improved load resistance. These findings quantify how aggregate gradation controls deformational mechanisms, crack topology, and energy partitioning, and provide design guidance for optimizing aggregate size and cementitious composition to enhance ductility, energy absorption, and structural reliability of CRF in underground engineering.