Sheng Chen

Accurate yet computationally efficient prediction of deep-sea hydrothermal plume dispersion is needed for autonomous vent detection and localization. This study develops a CFD-informed and physically constrained Gaussian plume model for hydrothermal particle dispersion in the Longqi hydrothermal field on the Southwest Indian Ridge. A validated multiphysics CFD model was used to resolve plume bending, the level of neutral buoyancy (LNB), turbulent transport and particle-size-dependent settling under representative near-bottom cross-flow velocities. CFD-derived characteristic heights were introduced as the effective source height and vertical confinement height, and lateral and vertical diffusion coefficients were calibrated by particle swarm optimization. Increasing cross-flow velocity from 0.01 to 0.03 m/s suppresses plume rise, redistributes turbulent mixing downstream, and reduces both the LNB and maximum plume rise height. The proposed height parameterization reproduces the CFD-derived characteristic heights with R^2 > 0.95. Settling analysis shows that the dominant fine-particle fraction can be treated as a passive tracer over hundred-meter to kilometer scales, whereas coarse particles mainly influence the near-vent region. After calibration, the reduced-order model reproduces the CFD concentration field with a fitness residual of 10^-2 to 10^-3 and agrees semi-quantitatively with field turbidity observations, with R^2 > 0.85 in the plume core and average far-field relative error below 15%. The framework converts empirical Gaussian dispersion into a physically interpretable reduced-order model suitable for AUV-based hydrothermal plume tracing.

To overcome the challenges posed by the underwater environment and restore the true colors of marine objects’ surfaces, a novel underwater image illumination estimation model, termed the iterative chaotic improved arithmetic optimization algorithm for deep extreme learning machines (IAOA-DELM), is proposed. In this study, the gray edge framework is utilized to extract color features from underwater images, which are employed as input vectors. To address the issue of unstable prediction results caused by the random selection of parameters in DELM, the arithmetic optimization algorithm (AOA) is integrated, and the search segment mapping method is optimized by using hidden layer biases and input layer weights. Furthermore, an iterative chaotic mapping initialization strategy is incorporated to provide AOA with a better initial search proxy. The IAOA-DELM model computes illumination information based on the input color vectors. Experimental evaluations conducted on actual underwater images demonstrate that the proposed IAOA-DELM illumination correction model achieves an accuracy of 96.07%. When compared to the ORELM, ELM, RVFL, and BP models, the IAOA-DELM model exhibits improvements of 6.96%, 7.54%, 8.00%, and 8.89%, respectively, making it the most effective among the compared illumination correction models.

In this paper, the numerical models are selected to simulate the hydrothermal plume based on the water temperature observation data of the Longqi hydrothermal field in the Southwest Indian Ridge (SWIR). Then, the unsteady Reynolds-averaged Navier–Stokes equations are solved to evaluate the performance of the Realizable k-ε (rke) model and the SST k-ω (sst) model in hydrothermal plume simulation. By comparing the calculated results with the Conductivity Temperature Depth (CTD) observation data and the literature results, the difference in prediction performance between the two models is evaluated. Before the numerical simulation, the optimal mesh parameters are determined by considering the grid independence test. The results show that the relative difference of the maximum plume height calculated by the two models is within 5%. Compared with the CTD 05-2, the rke model calculates the root mean square error of the velocity is 0.5081, which is smaller than that of the sst model. In terms of turbulent viscosity, the rke model is in good agreement with reference value in predicting turbulent viscosity. Therefore, the turbulent viscosity distribution calculated by the rke model is more consistent with the plume development process than that calculated by the sst model. In addition, the two models have the same effect on the prediction of turbulent kinetic energy and plume temperature.

Underwater images have chromatic aberrations under different light sources and complex underwater scenes, which can lead to the wrong choice when using an underwater robot. To solve this problem, this paper proposes an underwater image illumination estimation model, which we call the modified salp swarm algorithm (SSA) extreme learning machine (MSSA-ELM). It uses the Harris hawks optimization algorithm to generate a high-quality SSA population, and uses a multiverse optimizer algorithm to improve the follower position that makes an individual salp carry out global and local searches with a different scope. Then, the improved SSA is used to iteratively optimize the input weights and hidden layer bias of ELM to form a stable MSSA-ELM illumination estimation model. The experimental results of our underwater image illumination estimations and predictions show that the average accuracy of the MSSA-ELM model is 0.9209. Compared to similar models, the MSSA-ELM model has the best accuracy for underwater image illumination estimation. The analysis results show that the MSSA-ELM model also has high stability and is significantly different from other models.

The color of an object appears different from its true color when illuminated with light sources of different hues. To solve this problem, this article proposes a combination algorithm (SCA‐GWO‐LSSVR) based on the sine‐cosine algorithm (SCA) and the gray wolf optimization (GWO) algorithm to optimize the regression prediction model of the least‐squares support vector regression (LSSVR) algorithm. The performance of the traditional LSSVR is significantly affected by the penalty parameter (gamma) and the sig2 kernel function parameter. The proposed method uses the improved GWO algorithm to search the population to find the best LSSVR parameter solution. The proposed algorithm uses the SCA to create multiple random candidate solutions in population initialization to avoid blind initialization of the GWO algorithm. In the process of iterative optimization, the SCA is infiltrated, and its sine‐cosine wave mathematical model is used to quickly identify the best outward or inward position of the gray wolf. Finally, the LSSVR combines the optimal sig2 kernel function parameters and penalty parameters (gamma) to obtain a highly versatile illumination correction model. The experimental results show that the fitting accuracy of the proposed method reaches 86.8%, which is 5% higher than that of the LSSVR algorithm alone.

Non-transform discontinuity (NTD) is one category of tectonic units along slow- and ultraslow-spreading ridges. Some NTD-related hydrothermal fields that may reflect different driving mechanisms have been documented along slow-spreading ridges, but the discrete survey strategy makes it hard to evaluate the incidence of hydrothermal activity. On ultraslow-spreading ridges, fewer NTD-related hydrothermal activities were reported. Factors contributing to the occurrence of hydrothermal activities at NTDs and whether they could be potential targets for hydrothermal exploration are poorly known. Combining turbidity and oxidation reduction potential (ORP) sensors with a near-bottom camera, Chinese Dayang cruises from 2014 to 2018 have conducted systematic towed surveys for hydrothermal activity around a large NTD along the ultraslow-spreading Southwest Indian Ridge (SWIR, 48.1–48.7° E). Five new potential hydrothermal anomaly sites (2 inferred and 3 suspected) of high or low temperature and the previously inferred Sudi hydrothermal field occurred in diverse morphotectonic settings along a 78 km long ridge axis. The calculated vent frequency (Fs, sites/100 km) was ~7.7 over the entire study area, higher than the modified value (Fs ≈ 6.5) between 48 and 52° E of SWIR. Even only for the 54 km long large NTD, three hydrothermal anomaly sites yielded an Fs of ~5.6, which is higher than that of most ridge sections and is comparable to some fast-spreading ridges with high-resolution surveys. This indicates that NTDs along ultraslow-spreading ridges could be promising areas in fertilizing hydrothermal activities. Moreover, the deeply penetrating faults on the rift valley walls and strain-focused areas may contribute to the formation of NTD-related hydrothermal circulations. We suggest that NTDs along ultraslow-spreading ridges may be potential targets for further exploration of hydrothermal activities and seafloor sulfide deposits.

The deep sea is the frontier of materials research in the 21st century. Owing to the particularity of pressure (15–120 MPa), hydrothermal temperature (90–400°C), and explosive fluid (strong H₂S) in the deep‐sea hydrothermal field environment, the research on the corrosion mechanism of service materials in this environment under the coupling action of many harsh factors is almost blank. It has become the bottleneck of equipment and material research and development for China to explore the deep sea. This paper reviews the research progress of corrosion mechanisms of deep‐sea environmental materials at home and abroad, and forecasts the research trend and difficulties in this field, especially in the deep‐sea hydrothermal field. At the same time, it points out the urgency of the construction of harsh environment materials platform and its relevance to the discipline construction of marine college.