The quantitative evaluations of mineral resources and delineation of promising areas in survey regions for future mining have attracted many researchers’ interest. Cobalt-Rich manganese crusts (Mn-crusts), as one of the three significant strategic submarine mineral resources, lack effective and low-cost detection devices for surveying since the challenging distribution requires a high vertical and horizontal resolution. To solve this problem, we have built an engineering prototype parametric acoustic probe named PPPAAP19. With the echo data acquired by the probe, the interpretation of the accurate thickness information and the seabed classification using the deep learning network-based method are realized. We introduce the acoustic dataset of the minerals collected from two sea trials. Firstly, the preprocessing method and data augment strategy used to form the dataset are described. Afterward, the performances of several baseline approaches are assessed on the dataset, and the experimental results show that they all achieve high accuracy for binary classification. We find that the end-to-end approach for binary classification based on a 1D Convolution Neural Network has a comprehensive advantage. Such a demonstration validates the possibility of binary classification for recognizing the ferromanganese crust only in an acoustic manner, which may significantly contribute to the efficiency of the survey.

The assembly and break-up of supercontinents have been hot research topics in international earth sciences because they represent a breakthrough in reconstructing the history of continental evolution and deepening the theory of plate tectonics, which is of indispensable importance to the development of earth sciences. With the continuous enrichment of paleomagnetic, paleontological, chronological, and geochemical data in the last two decades, the evolution of the supercontinent from Columbia to Rodinia has gradually gained unified understanding, and the reconstruction of the major plates within the supercontinent has basically been constrained. In contrast, the reconstruction of microplates, such as South China, Tarim, and Kabul, is controversial and has now become a popular topic and frontier area of supercontinent reconstruction. Hainan lies at the southern tip of South China, and a few Proterozoic units are exposed on the island. At present, Hainan is often taken as a part of the Cathaysia Block. However, due to the lack of exposed Mesoproterozoic igneous and supercrustal rocks in Cathaysia, the reconstruction model of the Cathaysia Block and even the South China Craton based solely on Mesoproterozoic units in Hainan are distinct from those based on units in the Yangtze Block and younger Proterozoic units within the Cathaysia Block, which makes the paleoposition of the South China Craton controversial. In this paper, we provide new detrital zircon U–Pb age data for the Baoban Complex, Hainan, together with the available data to comment on the affinities between Hainan and the Yangtze and Cathaysia Blocks in the Proterozoic, and on this basis, we can reconstruct the South China Craton within the Proterozoic supercontinents.

Ferromanganese nodules, rich in cobalt (Co), nickel (Ni), copper (Cu), manganese (Mn), and rare earth elements (REEs), are important marine mineral resources with the utmost capacity for commercial employment in the future. Recently, the discovery of high abundant Co-rich nodules in the Western Pacific has attracted significant attention. The prediction of nodule abundance is a vital geological problem to be solved in marine mineral resource exploration. Based on the multisource geological data of the study area in the western Pacific Ocean acquired through acoustic, optic and geological sampling, a stochastic probabilistic prediction for nodule abundance was developed via Gaussian process regression (GPR). The results revealed that the predicted abundance of nodules ranged from 0 to 71.2 kg/m2, with an average abundance of 26.3 kg/m2. The high-abundance (~30.0 kg/m2) nodules are mainly distributed in the deep-sea basins around several seamounts, and they may be spatially coupled with the Co-rich crust distributed over seamounts in the targeted study area. Compared to traditional machine learning approaches, such as stepwise linear regression, regression trees and support vector machine, intelligent prediction of nodule abundance by GPR is achieved with improved accuracy. Moreover, with the predicted abundance, the prediction error is obtained simultaneously by GPR. The deep-sea basins between the Magallan and Marcus-Wake seamounts are considered potential areas for further exploration of Co-rich ferromanganese nodules in the western Pacific Ocean.

The optimization of enrichment zones for Co-rich crusts involves multiple factors such as crust thickness, elemental content, topography, slope, and biological distribution, making it a complicated research endeavor. Based on survey data from the contract area, the present study pioneers the integration of environmental factors into enrichment zone selection. It conducts a comprehensive analysis of resource, environment, and mining considerations, alongside optimization methodologies. Quantitative indicators for resource, environment, and mining are spatially correlated and assigned to corresponding grid cells. A weighted scoring method is proposed to compare enrichment zone selection results under different weightings, finally forming the optimal enrichment zone selection scheme. This scheme fully achieves the maximization of resource reserves, the protection of biological communities, and the safeguarding of future mineral development. It also provides technical reference for enrichment zone selection of deep-sea minerals such as polymetallic nodules and hydrothermal sulphides.

Polymetallic nodules constitute a strategically important deep sea mineral resource enriched in nickel, cobalt, copper, and other critical metals. Accurate prediction of their spatial distribution is crucial for resource assessment and sustainable exploitation. However, sparse sampling, high survey costs, and complex nonlinear interactions among environmental factors make this task difficult. To address these challenges, this study applies a quantile regression forest to analyze the data from 233 box-core stations across a 23,000 km² area in the eastern Pacific, integrating bathymetry and backscatter intensity. Cross-validation verifies model performance. The trained model predicts nodule abundance across an adjacent 3000 km² study area and validates it against 12 independent stations that were not involved in the training. This external validation gives a root mean square error of 4.11 kg/m². The quantile regression forest model also estimates uncertainty to quantify prediction reliability. The resulting uncertainty map distinguishes high-confidence zones from areas with elevated uncertainty. In 46% of the study area, normalized uncertainty falls below 0.50, indicating higher reliability. The remaining 54% shows higher uncertainty and requires additional sampling. This method combining independent spatial validation and uncertainty visualization provides a transparent tool for deep sea mineral resource assessment where data are sparse.