Hyperspectral Technology: A Method Framework for the Estimation of Metal Content in Cobalt-Rich Crusts

Abstract

Cobalt-rich ferromanganese crusts are an important deep-sea mineral resource, and the ore grade is a key indicator for evaluating their resource potential. Conventional ore-grade assessment relies on representative samples and extensive laboratory analyses, posing significant challenges due to limited sampling opportunities, high operational costs, and the pronounced structural heterogeneity of the crusts. To enable rapid estimation, the study combined uncalibrated hyperspectral radiance data acquired under natural daylight (without conversion to reflectance) with high-resolution electron probe micro-analysis (EPMA) measurements from the MED69A sample collected from the Magellan Seamounts to construct a sample-scale spectral–chemical dataset. This dataset was used to systematically compare spectral preprocessing methods, feature band selection strategies, and machine learning models. Based on the overall evaluation, the CR-CARS-MLP framework was selected as the optimal approach for spectral feature extraction, informative band selection, and metal concentration estimation under the current acquisition conditions. For cobalt (Co), the proposed framework achieved a coefficient of determination (R2) of 0.9646, a root mean square error (RMSE) of 0.0404, a residual predictive deviation (RPD) of 5.3720, and a mean absolute error (MAE) of 0.0216, demonstrating satisfactory internal test performance on the available EPMA–hyperspectral dataset. The results indicate that radiance data acquired under natural illumination at a close range (15.5 cm) retain statistically informative value for estimating element concentrations in cobalt-rich ferromanganese crusts (the data were not converted to reflectance values). The proposed framework therefore provides an effective approach for rapid, non-destructive estimation of metal elements in cobalt-rich ferromanganese crusts. Further, it facilitates investigation of metal enrichment patterns and grade variations associated with crust growth layers, providing a valuable reference for ore-grade estimation and resource assessment. The real-time application potential and robustness of the proposed framework across different instrument platforms and illumination conditions require further validation.