Search

The compositions of metalliferous sediments associated with hydrothermal vents can provide key geochemical data for locating seafloor sulfides. In this study, we present the geochemistry of seabed sediments from the Xunmei hydrothermal field (HF) in the South Mid-Atlantic Ridge (SMAR). The results indicate that the sediments are mainly composed of pelagic material (biogenic calcium components), basaltic debris, iron-manganese oxides, and hydrothermal components. The sediments are significantly enriched in Cu, Zn, Fe, and Co deriving from hydrothermal fluids, as well as Mn, V, Mo, U, and P, which are primarily scavenged from seawater. The northeastern Xunmei has the highest concentrations of Cu and Zn, while the northeastern, northern, and southern regions are characterized by great inputs of Fe. Manganese and Mo are mainly enriched in the western and southern parts and show a strong positive correlation, indicating that Mo is mainly scavenged by Mn oxides. Uranium, P, and Fe exhibit strong positive correlations, suggesting that they coprecipitate with Fe from hydrothermal plumes. Vanadium and Co are introduced into sediments in different ways: V is scavenged and coprecipitated by hydrothermal plumes, and Co is derived from sulfide debris. Based on the contents of Cu and Zn and Cu/Fe (0.159), Zn/Fe (0.158), and Fe/Mn (1440) ratios, it can be inferred that a high-temperature hydrothermal vent existed in northeastern Xunmei. In combination with the distribution patterns of the above elements, the hydrothermal vents in the southern part ceased erupting after a short period of activity. In addition, the high Mn anomaly and the high U/Fe ratios at the boundaries of the investigated area indicate the presence of a relatively oxidized environment in southwestern Xunmei. [ABSTRACT FROM AUTHOR]

Seabed sediment composition is an important component of benthic habitat and there are many approaches for producing maps that convey sediment information to marine managers. Random Forest is a popular statistical method for thematic seabed sediment mapping using both categorical and quantitative supervised modelling approaches. This study compares the performance and qualities of these Random Forest approaches to predict the distribution of fine-grained sediments from grab samples as one component of a multi-model map of sediment classes in Frobisher Bay, Nunavut, Canada. The second component predicts the presence of coarse substrates from underwater video. Spatial and non-spatial cross-validations were conducted to evaluate the performance of categorical and quantitative Random Forest models and maps were compared to determine differences in predictions. While both approaches seemed highly accurate, the non-spatial cross-validation suggested greater accuracy using the categorical approach. Using a spatial cross-validation, there was little difference between approaches—both showed poor extrapolative performance. Spatial cross-validation methods also suggested evidence of overfitting in the coarse sediment model caused by the spatial dependence of transect samples. The quantitative modelling approach was able to predict rare and unsampled sediment classes but the flexibility of probabilistic predictions from the categorical approach allowed for tuning to maximize extrapolative performance. Results demonstrate that the apparent accuracies of these models failed to convey important differences between map predictions and that spatially explicit evaluation strategies may be necessary for evaluating extrapolative performance. Differentiating extrapolative from interpolative prediction can aid in selecting appropriate modelling methods. [ABSTRACT FROM AUTHOR]