Ferromanganese (Fe-Mn) crusts are potential marine deposits for many high-tech metals and are exciting proxies for recording the oceanic paleoenvironment. During their growth, phosphatization generally occurs, causing the remobilization and reorganization of the elements and minerals in Fe-Mn crusts. Rare earth elements plus yttrium (REY), well-known critical metals for many new and emerging technologies, as well as valuable geological proxies, are the important critical metals in Fe-Mn crusts. The REY occurrence is closely influenced by the phosphatization processes, which still remain discursive. In this study, the textures, structures, and REY geochemistry of the growth of an Fe-Mn crust sample (MP2D32A) from the Line Islands archipelago were analyzed using multiple microanalysis methods. The analyzed Fe-Mn crust is mainly characterized by the presence of laminated and concentric colloforms. Massive fine particles and some veins of carbonate-rich fluorapatite (CFA) were observed in the old part of MP2D32A, demonstrating that this sample underwent phosphatization. The phosphatized and non-phosphatized layers, as well as the CFA veins, display distinctly different PAAS-normalized REY patterns. Higher REY contents in the phosphatized layer than those in the non-phosphatized layer suggest the positive role of phosphatization in REY enrichment. Moreover, the phosphatized layer contains higher REY contents than the CFA, implying that the REY enrichment in the phosphatized layer is not only influenced by CFA and Fe-Mn (oxyhydr)oxides but also other factors, such as the probable PO43− complexation induced by Fe oxyhydroxides. The synergistical sorption of REY(III) and HPO42− ions on Fe oxyhydroxides should facilitate REY enrichment during the phosphatization processes. These fundamental results provide novel insights into the influence of phosphatization in REY geochemical behaviors in the Fe-Mn crust.

Clear underwater images can help researchers detect cold seeps, gas hydrates, and biological resources. However, the quality of these images suffers from nonuniform lighting, a limited range of visibility, and unwanted signals. CycleGAN has been broadly studied in regard to underwater image enhancement, but it is difficult to apply the model for the further detection of Haima cold seeps in the South China Sea because the model can be difficult to train if the dataset used is not appropriate. In this article, we devise a new method of building a dataset using MSRCR and choose the best images based on the widely used UIQM scheme to build the dataset. The experimental results show that a good CycleGAN could be trained with the dataset using the proposed method. The model has good potential for applications in detecting the Haima cold seeps and can be applied to other cold seeps, such as the cold seeps in the North Sea. We conclude that the method used for building the dataset can be applied to train CycleGAN when enhancing images from cold seeps.