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

被引:0
|
作者
Liu, Yang [1 ]
Li, Sanzhong [1 ]
Zhong, Shihua [1 ]
Guo, Guanghui [1 ]
Liu, Jiaqing [1 ]
Niu, Jinghui [1 ]
Xue, Zimeng [1 ]
Zhou, Jianping [1 ]
Dong, Hao [1 ]
Suo, Yanhui [1 ]
机构
[1] MOE Frontiers Science Center for Deep Ocean Multispheres and Earth System/MOE Key Lab of Submarine Geosciences and Prospecting Techniques, College of Marine Geosciences, Ocean University of China, Qingdao 266100, China Laboratory for Marine Mineral Resourc
关键词
Data Analytics - Data handling - Economic geology - Energy resources - Gas hydrates - Hydration - Iron ores - Machine learning - Mineral exploration - Natural gas deposits;
D O I
10.13745/j.esf.sf.2023.5.90
中图分类号
学科分类号
摘要
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. © 2024 Science Frontiers editorial department. All rights reserved.
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页码:520 / 529
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