Deep blue artificial intelligence for knowledge discovery of the intermediate ocean
Oceans at a depth ranging from ~100 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 a...
Main Authors: | Ge Chen, Baoxiang Huang, Jie Yang, Milena Radenkovic, Linyao Ge, Chuanchuan Cao, Xiaoyan Chen, Linghui Xia, Guiyan Han, Ying Ma |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2023-01-01
|
Series: | Frontiers in Marine Science |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2022.1034188/full |
Similar Items
-
Global Oceanic Eddy Identification: A Deep Learning Method From Argo Profiles and Altimetry Data
by: Xiaoyan Chen, et al.
Published: (2021-05-01) -
An Efficient Oceanic Eddy Identification Method With XBT Data Using Transformer
by: Hongfeng Zhang, et al.
Published: (2022-01-01) -
Submesoscale oceanic eddy detection in SAR images using context and edge association network
by: Linghui Xia, et al.
Published: (2022-12-01) -
WildFishNet: Open Set Wild Fish Recognition Deep Neural Network With Fusion Activation Pattern
by: Xiaoya Zhang, et al.
Published: (2023-01-01) -
A neural network approach to the estimation of in-water attenuation to absorption ratios from PACE mission measurements
by: Jacopo Agagliate, et al.
Published: (2023-05-01)