成果归属作者：

成秋明

作者：

尹浩文 ;成秋明

单位：

中山大学地球科学与工程学院;OB欧宝娱乐官方平台 地质过程与矿产资源国家重点实验室;OB欧宝娱乐官方平台 教育部深时数字地球前沿科学中心

关键词：

海洋矿产资源;铁锰结核;成因分类;空间分布;机器学习

英文关键词：

marine mineral resources;iron-manganese nodules;genetic classification;spatial distribution;machine learning

摘要：

铁锰结核广泛分布于深海平原，储量巨大，具有商业开采潜力。利用1 128个铁锰结核样本的地球化学数据和8种地质与海洋要素，采用随机森林机器学习方法，探讨结核成因分类。首先，基于Mn、Fe、Cu、Co、Ni、Mn/Fe和Fe/Co地球化学数据使用高斯混合模型聚类方法对1 128个样本进行成因分类，并作为训练数据。其次，基于海底沉积速率、海水底部溶氧量和海水表面生物初级生产力等地质-海洋特征建立预测模型，将结核划分为水成型、成岩型和混合型，结果显示，模型对水成型和成岩型结核的分类精度分别为91%和66%,对混合型的分类精度较低，仅为23%。应用该模型对全球4 119个铁锰结核进行成因分类，结果表明，水成型结核占71.8%,混合型占21.8%,成岩型占6.2%。水成型结核广泛分布于各大洋，而成岩型和混合型则集中在大洋中纬度地区，如东太平洋的克拉里昂-克里帕顿断裂带和东南太平洋的秘鲁海盆等。这些地区的沉积物速率、海底生物量和含氧量显著影响结核分布。尽管基于地球化学数据的分类方法更可靠，研究表明，利用地质和海洋要素及机器学习方法也可有效分类。

英文摘要：

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 deposition 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 hydroforming and diagenetic nodules is 91% and 66% respectively, while the classification accuracy of the mixed type is only 23%. The genetic 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.

基金：

大数据-数学地球科学创新研发团队和极端地质事件团队项目(2021ZT09H399)

语种：

中文

来源：

科学技术与工程,2024(25):10605-10619.

中图分类号：

TP181;P744

出版日期：

2024-09-08

提交日期：

2024-10-12

引用参考：

尹浩文;成秋明. 基于机器学习的深海多金属结核成因分类[J]. 科学技术与工程,2024(25):10605-10619.

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知识共享许可协议-署名