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.

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.