Genetic Classification of Deep-sea Polymetallic Nodules Based on Machine Learning
Manganese nodules are widely distributed across deep-sea plains and have significant commercial mining potential due to their vast reserves.Based on the geochemical data of 1 128 iron and manganese nodule samples and 8 geological and marine elements,the genetic classification of nodule was discussed by using random forest machine learning method.Firstly,based on Mn,Fe,Cu,Co,Ni,Mn/Fe and Fe/Co geochemical data,1 128 samples were classified by Gaussian mixture model clustering method and used as training data.Secondly,a prediction model was established based on the geological and marine characteristics such as seabed deposi-tion rate,dissolved oxygen at the bottom of seawater,and biological primary productivity on the surface of seawater,and the nodules were divided into hydroforming,diagenetic and mixed types.The results show that the classification accuracy of the model for hydro-forming and diagenetic nodules is 91％and 66％respectively,while the classification accuracy of the mixed type is only 23％.The ge-netic classification of 4 119 ferromanganese nodules in the world by this model shows that hydroforming nodules account for 71.8％,mixed type 21.8％and diagenetic type 6.2％.Hydroforming nodules are widely distributed in the oceans,while diagenetic and mixed nodules are concentrated in the mid-latitudes,such as the Clarion-Clipaton fault zone in the eastern Pacific Ocean and the Peru Basin in the Southeast Pacific Ocean.Sediment development,seafloor biomass and oxygen content in these areas significantly affect nodule distribution.Although classification methods based on geochemical data are more reliable,studies have shown that the use of geological and marine elements and machine learning methods can also be effective.
marine mineral resourcesiron-manganese nodulesgenetic classificationspatial distributionmachine learning
万方数据
维普
