Machine learning: A new approach to intelligent exploration of seafloor mineral resources

doi:10.13745/j.esf.sf.2023.5.90

Abstract

Abstract:

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.

Key words: machine learning, ferromanganese nodule, cobalt-rich crusts, natural gas hydrate, seafloor minerals

CLC Number:

LIU Yang, LI Sanzhong, ZHONG Shihua, GUO Guanghui, LIU Jiaqing, NIU Jinghui, XUE Zimeng, ZHOU Jianping, DONG Hao, SUO Yanhui. Machine learning: A new approach to intelligent exploration of seafloor mineral resources[J]. Earth Science Frontiers, 2024, 31(3): 520-529.

Figures/Tables 5

Fig.1 Machine learning methods and their applications. Adapted from [10].

Fig.2 Schematic representation of supervised machine learning methods (a) Logistic regression; (b) Support vector machine; (c) Random forest; (d) Deep neural network.

Fig.3 Schematic representation of unsupervised machine learning methods (a) K-means clustering; (b) Auto-encoder.

Fig.4 Application of machine learning to seabed non-metallic minerals. Adapted from [31⇓⇓-34].

Fig.5 Application of machine learning in seabed metal minerals. Adapted from [43⇓⇓⇓-47].

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