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.

²³⁰Th/U dating can provide high-precision age constraints on Quaternary hydrothermal sulfides. However, low content of U and Th often involves extraction chemistry for the separation and enrichment of U and Th, but these chemical processes are very complex. We developed a simplified procedure consisting of total sample dissolution and single-column extraction chemistry, which can reduce the time and improve the accuracy of the dating. Concentrated HCl-HF followed by HNO₃ was added to ensure complete dissolution. A single column filled with 0.4 mL of AG 1-X8 anion resin was used, then 8 M HNO₃, 8 M HCl and 0.1 M HNO₃ were used to elute most of the matrix metals, Th and U. This process provided more than 95% recoveries for U and Th, and negligible blanks. Meanwhile, Pb and Bi interferences were tested and showed no effect on the U and Th isotope ratio. The ²³⁰Th/²³⁸U activity of the Geological Survey of Japan geochemical reference material JZn-1 in secular equilibrium was determined and showed a radioactive equilibrium (1.00 ± 0.01, n = 5, all errors 2σ) and an in-house standard QS-1 was consistent to 0.0078 ± 0.0001 (n = 8, ±2σ) with an average age of 705 ± 10 yrs BP (n = 8, ±2σ). The technique greatly shortens the sample preparation time and allows more concise and effective analysis of U-Th isotopes. It is ideally suited for the high-precision ²³⁰Th/U dating of Quaternary submarine hydrothermal sulfides and sulfides from other settings.

Seamount cobalt-rich crusts are rich in cobalt resources and are sought after worldwide. Among different affecting parameters, crust thickness is the most important in evaluating cobalt-rich crust resources in seamounts. Generally, there are two challenges to crust thickness evaluation: firstly, due to high operating costs, most geological stations for seamount exploration have sparse sampling distributions so there are insufficient data to estimate the crust thickness distribution; secondly, a single evaluation method has advantages and disadvantages, and it is not feasible to benefit from the advantages only. These methods cannot simultaneously make full use of the sampling data in local areas, providing a more appropriate evaluation of the whole area. As a result, the estimated results cannot fully reflect the thickness distribution. Based on the thickness data of the station survey and topographic data, geostatistical units are divided, and a comprehensive crust thickness assessment scheme is established on the ArcGIS platform. To this end, the adjacent area method is applied to calculate the crust thickness within the influence range of the station. Combined with the station buffer radius and Thiessen polygon method, the crust thickness within 1.5 km of the survey station was estimated. Then the “slope–distance” Kriging interpolation method was used to calculate the crust thickness in the study area, and the crust thickness in the optimal effective radius area was given to compensate for the missing part in the first step. Finally, the geological blocks were divided using the topographic classification method, and the crust thickness of the remaining unassigned regions was estimated using the mathematical expectation method. The proposed method was applied to evaluate the Il’ichev Guyot’s crust thickness and reasonable results were achieved. It was found that the thickness estimation of the area near the station is consistent with the measured values. Since finer topographic data are used in the calculation, the thickness estimation result is more detailed. In this regard, a simple and effective calculation method was established on the ArcMap platform. The mathematical expectation estimation method of the crust thickness, based on the topographic and geomorphological classification from the perspective of the mineralization mechanism, compensates for the drawbacks of the first two methods originating from the lack of data points. The results show that the proposed method is an appropriate scheme to evaluate seamount crust thickness without comprehensive investigation.

Marine sediments enriched with rare earth elements (REEs) serve as a significant reservoir, particularly for heavy REEs. Conventional lab-based REE exploration restricts rapid and large-scale assessment, whereas hyperspectral imaging provides a promising approach for quantitative evaluation. This study evaluates the capacity of hyperspectral data for the quantitative determination of REEs in marine sediments. A total of 53 samples from various locations were analyzed to determine their chemical composition and spectral characteristics within the 380–1000 nm range under natural light. The influence of surface conditions on spectral integrity was evaluated, and multiple preprocessing and spectral feature extraction methods were applied to enhance data reliability. This study proposes a novel approach, termed Feature Importance within Pearson Correlation Coefficient-Based High-Correlation Spectral Range (PCCR-FI), designed for the identification of characteristic spectral bands associated with REEs. Machine learning models were subsequently constructed to estimate REE concentrations, and the following key findings were observed: (a) technical adjustments effectively addressed variations in sediment surface conditions, ensuring data reliability. (b) The PCCR-FI technique identified characteristic REEs spectral bands, enhancing processing efficiency and prediction accuracy. (c) The integration of the reciprocal logarithmic first derivative (TLOG-FD) technique with a multilayer perceptron (MLP) model, termed TLOG-FD-MLP, efficiently captured critical spectral features, resulting in improved prediction accuracy. For light REEs, the model achieved coefficient of determination (R²) values exceeding 0.60 and relative performance deviation (RPD) values exceeding 1.60, with some elements demonstrating R² values as high as 0.81 with RPD values surpassing 2.00. Furthermore, several heavy REEs exhibited moderate prediction performance, with R² values consistently exceeding 0.60. When considering the total REE content, an R² of 0.73 and an RPD of 1.97 were achieved. These findings demonstrate the use of hyperspectral imaging as a viable tool for quantitative evaluation of REE concentrations in marine sediments, providing valuable guidance for resource mapping and the exploration of seafloor polymetallic deposits.

Hydrothermal activity on the modern seafloor varies depending on the tectonic setting. In particular, the neovolcanic zones (NVZs) along mid-ocean ridges, where magmatism is intense, generally host high-temperature hydrothermal activities. These high-temperature hydrothermal activities on the NVZs can promote the development of many polymetallic sulfide deposits. Currently, many high-temperature hydrothermal activities and sulfide accumulations have been discovered on the NVZs of major mid-ocean ridges worldwide, but relatively few have been found in the Southern Mid-Atlantic Ridge (SMAR), which limits our understanding of the hydrothermal mineralization characteristics on the NVZs of SMAR. Fortunately, in 2015, a new hydrothermal field—Tongguan—developed on the NVZ of the SMAR was discovered. In this study, we conducted mineralogical and sulfur isotope studies on hydrothermal chimney and massive sulfide samples collected from the Tongguan field. We revealed the mineral composition and growth sequence in the chimney structures and sulfides and discovered two different chimney growth patterns featuring rhythmic banding and opal-filled structures. Additionally, sulfur isotopes suggest the presence of mixing between seawater within the oceanic crust and the upwelling hydrothermal fluid in this hydrothermal field. Our investigation revealed small-scale fluid heterogeneities during the submarine hydrothermal mineralization process, which is due to fluctuations in fluid temperatures and mineral deposition within individual vent frameworks. This work provides a reference for further understanding and comprehension of hydrothermal mineralization on the NVZs of SMAR.

The mineral composition and chemistry of a Co-rich ferromanganese crust from Skornyakova Guyot in the Magellan Seamount Cluster is investigated by X-ray diffraction and ICP-OES to scrutinize the effects of phosphatization on the depletion of Co. The section of this crust is divided into four layers (layers I-1, I-2, II and III) from substrate to surface based on texture. The average Co concentrations of older layers I-1 and I-2 are 0.41% and 0.30%, respectively, whereas the average Co concentrations of younger layers II and III are 0.53% and 0.29%. The variation ranges of SiO₂, Al₂O₃, CaO, P₂O_5, and Fe₂O₃ along the section are 1.5%–22.9%, 0.4%–6.9%, 2.7%–24.6%, 0.83%–15.26%, and 10.7%–28.4%, respectively, which are capable of diluting the Co concentrations significantly. The todorokite detectable by XRD does not occur in layers I-1 and I-2, which suggests that most of the depletion of Co in phosphatized crust cannot be attributed solely to the mineral transition from Fe-vernadite to todorokite during phosphatization, and other controls, including the dilution from detrital minerals and carbonate fluorapatite, need to be involved.