Spatial Modeling of Deep-Sea Ferromanganese Nodules With Limited Data Using Neural Networks

Abstract

Deep-sea ferromanganese nodules found in the Clarion-Clipperton zone (CCZ) in the Pacific ocean are a large potential source of metals such as nickel, cobalt, and manganese. Spatial modeling of these nodules is essential to obtain a better scientific understanding about their formation and distribution, and conduct feasibility studies on their exploitation. However, data on the quantitative and qualitative distribution of nodules in CCZ are sparse and often not divulged, and the accuracy of conventional spatial modeling techniques is limited by this scarcity of data. We present an approach based on artificial neural networks for modeling nodule parameters in the CCZ using the limited data available in the open domain. Our model's predictions are comparable to benchmark predictions from the International Seabed Authority which used a more extensive data set. Moreover, our model can predict small as well as large-scale variations of nodules, which are essential in making evaluations for deep-sea harvesting. We discuss the contribution of each factor in the modeling, and show that small-scale nodule parameter variations can be effectively predicted by incorporating the local topography.