Oceanic internal wave amplitude retrieval from satellite images based on a data-driven transfer learning model

Zhang, Xudong; Wang, Haoyu; Wang, Shuo; Liu, Yanliang; Yu, Weidong; Wang, Jing; Xu, Qing; Li, Xiaofeng

doi:10.1016/j.rse.2022.112940

Cited by 38 publications

(13 citation statements)

References 54 publications

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“…Another scholar used the IW amplitudes simulated by physical oceanographic equipment as auxiliary data. The information between the auxiliary data and the spectral characteristics of remote sensing satellite images (MODIS) is correlated and migrated by observational neural networks to invert the IWs amplitudes (Zhang et al, 2022).…”

Section: Forecast Of Iwsmentioning

confidence: 99%

“…DBAI provides the opportunity to accelerate the recognition process of the intermediate ocean, making up for the deficiency of the existing oceanographic theoretical system. In recent years, DBAI technology, in collaboration with basic ocean theory and ocean big data (Li et al, 2020), has made preliminary achievements in internal wave (IW) inversion, stratification spatio-temporal variation and prediction, eddy identification and trajectory prediction, El Nino and Southern Oscillation prediction and other ocean scientific issues Ham et al (2019); Zhang et al (2022). Nevertheless, it is undeniable that AI-aided remote sensing of the intermediate ocean is still in its infancy, and the challenges lie in the unclear physical mechanism, insufficient profile data, and the low generalization DBAI.…”

Section: Introductionmentioning

confidence: 99%

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Deep blue artificial intelligence for knowledge discovery of the intermediate ocean

et al. 2023

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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 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 ~100 m ocean and vertical stratification within the ~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.

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Section: Forecast Of Iwsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep blue artificial intelligence for knowledge discovery of the intermediate ocean

et al. 2023

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“…Due to differences in temperature and salinity, this phenomenon usually occurs in densely stratified bodies of water (Zheng et al, 2021b). Large amplitudes, long crests, and long propagation distances characterize internal waves (IW) (Zhang et al, 2022). Internal waves occur at all ocean depths and differ from other waves in that they play an essential role in transmitting the energy of mesoscale and large-scale motions (Dong et al, 2022a).…”

Section: Internal Wavesmentioning

confidence: 99%

A review of artificial intelligence in marine science

Song

Pang²,

Hou³

et al. 2023

Front. Earth Sci.

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Utilization and exploitation of marine resources by humans have contributed to the growth of marine research. As technology progresses, artificial intelligence (AI) approaches are progressively being applied to maritime research, complementing traditional marine forecasting models and observation techniques to some degree. This article takes the artificial intelligence algorithmic model as its starting point, references several application trials, and methodically elaborates on the emerging research trend of mixing machine learning and physical modeling concepts. This article discusses the evolution of methodologies for the building of ocean observations, the application of artificial intelligence to remote sensing satellites, smart sensors, and intelligent underwater robots, and the construction of ocean big data. We also cover the method of identifying internal waves (IW), heatwaves, El Niño-Southern Oscillation (ENSO), and sea ice using artificial intelligence algorithms. In addition, we analyze the applications of artificial intelligence models in the prediction of ocean components, including physics-driven numerical models, model-driven statistical models, traditional machine learning models, data-driven deep learning models, and physical models combined with artificial intelligence models. This review shows the growth routes of the application of artificial intelligence in ocean observation, ocean phenomena identification, and ocean elements forecasting, with examples and forecasts of their future development trends from several angles and points of view, by categorizing the various uses of artificial intelligence in the ocean sector.

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“…Vasavi et al [29] applied U-Net for the segmentation and feature extraction of internal wave parameters and used the Korteweg-de Vries (KdV) solver to simulate their velocity and density. Zhang et al [30] established an AI-based ocean wave amplitude inversion model with a transfer learning method using laboratory experiments and satellite in situ observation data. The wave amplitude information was more accurate than that based on the traditional KdV equation, as shown in Figure 2.…”

Section: Internal Wavesmentioning

confidence: 99%

Recent Developments in Artificial Intelligence in Oceanography

Han

Bethel

et al. 2022

Ocean-Land-Atmos Res

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With the availability of petabytes of oceanographic observations and numerical model simulations, artificial intelligence (AI) tools are being increasingly leveraged in a variety of applications. In this paper, these applications are reviewed from the perspectives of identifying, forecasting, and parameterizing ocean phenomena. Specifically, the usage of AI algorithms for the identification of mesoscale eddies, internal waves, oil spills, sea ice, and marine algae are discussed in this paper. Additionally, AI-based forecasting of surface waves, the El Niño Southern Oscillation, and storm surges is discussed. This is followed by a discussion on the usage of these schemes to parameterize oceanic turbulence and atmospheric moist physics. Moreover, physics-informed deep learning and neural networks are discussed within an oceanographic context, and further applications with ocean digital twins and physics-constrained AI algorithms are described. This review is meant to introduce beginners and experts in the marine sciences to AI methodologies and stimulate future research toward the usage of causality-adherent physics-informed neural networks and Fourier neural networks in oceanography.

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Oceanic internal wave amplitude retrieval from satellite images based on a data-driven transfer learning model

Cited by 38 publications

References 54 publications

Deep blue artificial intelligence for knowledge discovery of the intermediate ocean

Deep blue artificial intelligence for knowledge discovery of the intermediate ocean

A review of artificial intelligence in marine science

Recent Developments in Artificial Intelligence in Oceanography

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