海底蕴藏着丰富的关键矿产资源,是当前研究的热点,也是未来产业新领域。随着海洋探测技术的不断进步,海底矿产勘探的数据量和数据维数急剧增加,给数据处理与解释带来了巨大困难和挑战。面对海量数据,传统的数据解释与分析方法暴露出许多问题。机器学习以其强大的自学能力,为无法解决或难以解决的问题提供了一系列智能分析决策方案,提高了数据分析的效率,是海底矿产资源智能勘探的新途径。近年来,机器学习在地球科学领域获得了广泛的关注和研究。为此,围绕机器学习技术应用于海底资源勘探技术,本文首先简要介绍了机器学习中经典的模型算法,然后详细阐述了机器学习在海底能源矿产和金属矿产两个方面的应用现状,最后总结了机器学习在海底矿产智能勘探领域的应用前景,指出了现有研究中存在的问题,提出了解决方案和未来的发展方向。

The seafloor is characterized by abundant key mineral resources, which is a hotspot of current research and a new field of industry in the future. With the continuous progress of ocean exploration technology, the volume and dimensions of data from seafloor mineral exploration have increased dramatically, which has brought great difficulties and challenges to data processing and interpretation. In the face of massive data, traditional data interpretation and analysis methods expose many problems. Machine learning, with its strong self-learning ability, provides a series of intelligent analysis and decision-making solutions for unsolvable or difficult problems, improving the efficiency of data analysis. It is a new way for the intelligent exploration of subsea mineral resources. In recent years, machine learning has obtained extensive attention and research in the field of geosciences. Therefore, focusing on the application of machine learning to seafloor resource exploration technology, this paper firstly briefly introduces the classical model algorithms in machine learning; then elaborates on the application status of machine learning in two aspects of seafloor energy resources and metal mineral, and finally summarizes the application prospects of machine learning in the field of intelligent exploration of seafloor minerals, points out the problems in existing research, and proposes solutions and future development directions.