3-D Water Temperature Distribution Observation via Coastal Acoustic Tomography Sensing Network

Water temperature is a critically important parameter for characterizing aquatic ecosystem, particularly in hydrodynamic processes produced by human activities or climate effects in shallow water regions (oceanic pasture, tidal currents, upwelling currents, etc.). The variation of water temperature is a slight and slow process, as technology advances, high-precision observation of the spatial and temporal variation of water temperature on 3-D scale is crucial to environmental research and predictions. To address this issue, 3-D water temperature field is reconstructed by coastal acoustic tomography technology and inversion water temperature variations are obtained. A multiacoustic station experiment was conducted in a reservoir in Changsha, China, from March 4, 2022 to March 5, 2022. Two grid-averaged 3-D inversion methods (Lagrangian-least-squares method and improved-Landweber iteration method) with multistation underwater sensing network are processed to reveal water temperature fluctuations. The rotated empirical orthogonal function is used to further analyze and evaluate the sensitivity of temperature variations in the individual spatial grids. In this article, water temperature distribution and dynamic variation process in the whole spatial structure are obtained and discussed using 3-D sound transmission information, and the feasibility of the methods is verified by comparing them with temperature depth sensor array observation results. This study provides high-precision ideas and methods for 3-D water temperature activities of underwater acoustic tomography network in shallow environments.