Measurement of water temperature and current in a Reservoir using coastal acoustic tomography

Xu, Pan; Xu, Shengyang; Li, Guangming; Gao, Yixin; Xie, Xinyi; Huang, Haocai

doi:10.1109/oceans47191.2022.9977343

Cited by 1 publication

(1 citation statement)

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“…This method innovatively solves the problem of positioning error in traditional acoustic detection, and it greatly improves the accuracy of water temperature reconstruction in marine and lake environments through the fine calibration of observation data. Mature acoustic tomography techniques have been used to accurately measure water temperature and flow in reservoirs and to map multi-layer flow fields in small-scale shallow water reservoirs [6,7]. Thus, predicted analysis profoundly contributes to a comprehensive understanding of ocean dynamics and plays a vital role in ocean sciences and resources.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Water Temperature Based on Graph Neural Network in a Small-Scale Observation via Coastal Acoustic Tomography

Xu,

Shi

et al. 2024

Remote Sensing

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Coastal acoustic tomography (CAT) is a remote sensing technique that utilizes acoustic methodologies to measure the dynamic characteristics of the ocean in expansive marine domains. This approach leverages the speed of sound propagation to derive vital ocean parameters such as temperature and salinity by inversely estimating the acoustic ray speed during its traversal through the aquatic medium. Concurrently, analyzing the speed of different acoustic waves in their round-trip propagation enables the inverse estimation of dynamic hydrographic features, including flow velocity and directional attributes. An accurate forecasting of inversion answers in CAT rapidly contributes to a comprehensive analysis of the evolving ocean environment and its inherent characteristics. Graph neural network (GNN) is a new network architecture with strong spatial modeling and extraordinary generalization. We proposed a novel method: employing GraphSAGE to predict inversion answers in OAT, using experimental datasets collected at the Huangcai Reservoir for prediction. The results show an average error 0.01% for sound speed prediction and 0.29% for temperature predictions along each station pairwise. This adequately fulfills the real-time and exigent requirements for practical deployment.

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Section: Introductionmentioning

confidence: 99%