Rare earth elements (REEs) and yttrium (Y), together known as REY, are extremely enriched in deep-sea pelagic sediments, attracting much attention as a promising new REY resource. To understand the influence of hydrothermal processes on the enrichment of REY in deep-sea sediments from the eastern South Pacific Ocean, we conducted detailed lithological, bulk sediment geochemical, and in situ mineral geochemical analyses on gravity core sample S021GC17 from the Yupanqui Basin of eastern South Pacific. The REY-rich muds of S021GC17 are dark-brown to black zeolitic clays with REY contents (ΣREY) ranging from 1057 to 1882 ppm (average 1329 ppm). The REY-rich muds display heavy rare earth elements (HREE) enriched patterns, with obvious depletions in Ce, and positive anomalies of Eu in Post-Archean Australian Shale (PAAS)-normalized REE diagrams. In contrast, the muds of S021GC17 show light rare earth elements (LREE) enriched patterns and positive anomalies of Ce and Eu in the seawater-normalized REE diagrams. Total REY abundances in the core show positive correlations with CaO, P₂O₅, Fe₂O₃, and MnO concentrations. In situ analyses of trace element contents by laser ablation-inductively coupled plasma–mass spectrometry (LA-ICP-MS) demonstrate that bioapatite fossils contain high REY concentrations (998 to 22,497 ppm, average 9123 ppm), indicating that they are the primary carriers of REY. The in situ Nd isotope values of bioapatites are higher than the average values of seawater in Pacific Ocean. Fe–Mn micronodules are divided into hydrogenetic and diagenetic types, which have average REY concentrations of 1586 and 567 ppm, respectively. The high contents of Fe-Mn-Ba-Co-Mo, the positive correlations between ΣREY and Fe-Mn, the ratios of Fe/Ti and Al/(Al + Fe + Mn), and the LREE-enriched patterns in the REY-rich muds, combined with high Nd isotope values shown by bioapatite fossils, strongly indicate that the hydrothermal fluids have played an important role in the formation of the REY-rich sediments in the eastern South Pacific Ocean.

The evaluation of mineral resources on seamounts by geostatistics faces two key challenges. First, the conventional distance/orientation- and the simple distance-based variogram functions used are ineffective at expressing the spatial self-correlation and continuity of cobalt-rich crust thicknesses on seamounts. Second, the sampling stations used for a single seamount are generally very sparsely distributed because of the high survey costs, which results in an insufficient number of information points for variogram fitting. Here, we present an alternative geostatistical method that uses distance/gradient- and distance/relative-depth-based variograms to process data collected from several neighboring seamounts, allowing the variogram fitting. The application example reported for the Magellan seamounts demonstrates the suitability of the method for evaluating the mineral resources of cobalt-rich crusts. The method could be effective also for the analysis of surface data obtained from mountain slopes on land (e.g., soil).

Deep-sea polymetallic nodules, abundant in critical metal elements, are a vital strategic mineral resource. Accordingly, the prompt, accurate, and high-speed acquisition of parameters and distribution data for these nodules is crucial for the effective exploration, evaluation, and identification of valuable deposits. Studies show that one of the primary parameters for assessing polymetallic nodules is the Coverage Rate. For real-time, accurate, and efficient computation of this parameter, this article proposes a streamlined segmentation model named YOLOv7-PMN. This model is particularly designed for analyzing seafloor video data. The model substitutes the YOLOv7 backbone with the lightweight feature extraction framework of MobileNetV3-Small and integrates multi-level Squeeze-and-Excitation attention mechanisms. These changes enhance detection accuracy, speed up inference, and reduce the model’s overall size. The head network utilizes depth-wise separable convolution modules, significantly decreasing the number of model parameters. Compared to the original YOLOv7, the YOLOv7-PMN shows improved detection and segmentation performance for nodules of varying sizes. On the same dataset, the recall rate for nodules increases by 3% over the YOLOv7 model. Model parameters are cut by 61.78%, memory usage by the best weights is reduced by 61.15%, and inference speed for detection and segmentation rises to 65.79 FPS, surpassing the 25 FPS video capture rate. The model demonstrates strong generalization capabilities, lowering the requirements for video data quality and reducing dependency on extensive dataset annotations. In summary, YOLOv7-PMN is highly effective in processing seabed images of polymetallic nodules, which are characterized by varying target scales, complex environments, and diverse features. This model holds significant promise for practical application and broad adoption.

Underwater vision-based detection plays an important role in marine resources exploration, marine ecological protection and other fields. Due to the restricted carrier movement and the clustering effect of some marine organisms, the size of some marine organisms in the underwater image is very small, and the samples in the dataset are very unbalanced, which aggravate the difficulty of vision detection of marine organisms. To solve these problems, this study proposes a marine organism detection framework with a dataset augmentation strategy and Convolutional Neural Networks (CNN)-Vision Transformer (ViT) fusion model. The proposed framework adopts two data augmentation methods, namely, random expansion of small objects and non-overlapping filling of scarce samples, to significantly improve the data quality of the dataset. At the same time, the framework takes YOLOv5 as the baseline model, introduces ViT, deformable convolution and trident block in the feature extraction network, and extracts richer features of marine organisms through multi-scale receptive fields with the help of the fusion of CNN and ViT. The experimental results show that, compared with various one-stage detection models, the mean average precision (mAP) of the proposed framework can be improved by 27%. At the same time, it gives consideration to both performance and real-time, so as to achieve high-precision real-time detection of the marine organisms on the underwater mobile platform.

The deep-sea camera is the most intuitive and effective detection tool for seabed investigation, and the accuracy of camera positioning can ensure its data value. A bundled ultra-short baseline (USBL) positioning system is generally employed to realize the spatial positioning of an underwater camera. The influence of the underwater acoustic environment and other factors cause USBL positioning data to become unstable, leading to abnormalities, or missing data, which creates difficulties for camera positioning. In order to solve the problem, this paper selects the seabed camera data of the “XunMei” mineralization area acquired from the China south Atlantic voyage. Moreover, the USBL positioning data, combined with high-precision terrain, bathymetry, and ship-borne GPS positioning data, were analyzed and mined comprehensively. In order to eliminate the abnormal data, a four-dimensional anomaly culling model of USBL positioning data is established based on the time and space scales through the ArcGIS tool. Then, modeling, simulation, and interpolation prediction are performed for the positioning data after anomaly elimination to achieve the geographic location correction of the hydrothermal sulfide near-bottom camera and its data. This method has achieved good results in practical applications. The corrected water depth profile of the camera survey line is compatible with the high-precision terrain detected at different times in the same area. The characteristics of the corrected video images are compatible with the sample characteristics of the TV grab sampling position. A set of high-quality positioning data (sampling test 5000 points) not participating in the correction model is compared with the corrected USBL data at the same position. The following results are obtained: in the case of a confidence interval of 95%, the correlation coefficient is 1, the significance is 0, there is no significant difference between the corrected data after the simulation and its original positioning data (not participating in the model), and the correction error is below 5 m. This shows that the problem of locating a submarine camera and its data can be solved using the proposed four-dimensional anomaly elimination model established based on the USBL positioning data, high precision terrain, bathymetry and GPS data, and the corresponding cubic polynomial least-squares correction model.

The submarine camera system is one of the most effective detection methods for detecting seabed hydrothermal sulfide. The position fixing of camera equipment is generally achieved by the ultra-short baseline (USBL) positioning systems. The dynamic changes in the marine environment and the loss of acoustic signals lead to many abnormal points in the USBL positioning data. The existing methods cannot perform position fixing correction for USBL positioning data with many scattered abnormal points. This paper selects and focuses on the seabed camera data of the XunMei mineralization area acquired from the China South Atlantic voyage and employs the DTW optimization algorithm to solve this problem. This method is implemented based on Python language and an ArcGIS technical environment, and a total of 97,469 topographic profiles with 10 m spacing are extracted from the topographic data of the object area with 1m accuracy. Moreover, the highest similarity (95.9%) is obtained between the bathymetric profile of the camera survey line and the above topographic profile. The results indicate that the proposed algorithm is fast and effective, and the positioning correction accuracy of the deep-sea near-bottom camera data can reach the meter level (determined by the topographic data accuracy). Moreover, the solution can provide services for accurately position fixing near-bottom exploration equipment and its exploration data. It can also solve the problem of underwater position fixing problem that a GPS cannot solve.