Deep blue artificial intelligence for knowledge discovery of the intermediate ocean

【Author】 Chen, Ge; Huang, Baoxiang; Yang, Jie; Radenkovic, Milena; Ge, Linyao; Cao, Chuanchuan; Chen, Xiaoyan; Xia, Linghui; Han, Guiyan; Ma, Ying

【Source】FRONTIERS IN MARINE SCIENCE

【影响因子】5.247

【Abstract】Oceans at a depth ranging from similar to 100 to similar to 1000-m (defined as the intermediate water here), though poorly understood compared to the sea surface, is a critical layer of the Earth system where many important oceanographic processes take place. Advances in ocean observation and computer technology have allowed ocean science to enter the era of big data (to be precise, big data for the surface layer, small data for the bottom layer, and the intermediate layer sits in between) and greatly promoted our understanding of near-surface ocean phenomena. During the past few decades, however, the intermediate ocean is also undergoing profound changes because of global warming, the research and prediction of which are of intensive concern. Due to the lack of three-dimensional ocean theories and field observations, how to remotely sense the intermediate ocean from space becomes a very attractive but challenging scientific issue. With the rapid development of the next generation of information technology, artificial intelligence (AI) has built a new bridge from data science to marine science (called Deep Blue AI, DBAI), which acts as a powerful weapon to extend the paradigm of modern oceanography in the era of the metaverse. This review first introduces the basic prior knowledge of water movement in the similar to 100 m ocean and vertical stratification within the similar to 1000-m depths as well as the data resources provided by satellite remote sensing, field observation, and model reanalysis for DBAI. Then, three universal DBAI methodologies, namely, associative statistical, physically informed, and mathematically driven neural networks, are elucidated in the context of intermediate ocean remote sensing. Finally, the unique advantages and potentials of DBAI in data mining and knowledge discovery are demonstrated in a top-down way of "surface-to-interior" via several typical examples in physical and biological oceanography.

【Keywords】deep blue artificial intelligence; intermediate ocean; ocean remote sensing; associative statistical neural network; physically informed neural network; mathematically driven neural network

【发表时间】2023 JAN 19

【收录时间】2023-03-04

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