Underwater target detection exhibits extensive applications in marine target exploration and marine environmental monitoring. However, conventional images of underwater targets present challenges including blurred contour information, complex environmental conditions, and pronounced scattering effects. In this work, an underwater target detection method based on YOLOv10 is designed, and the detection performance is compared with the YOLOv5 model. Experimental results demonstrate that the YOLOv10 model has a mAP50 of 85.6% on the URPC 2020 dataset, improving the mAP50 by 1.2% than that of YOLOv5. This model exhibits high detection accuracy and high proceeding speed, which provides a promising support for precise and fast underwater target detection.

Ocean temperature prediction plays crucial roles in the ocean-related fields. The graph neural networks (GNNs) show advantages for modeling complex environmental issues. However, the prior methods typically focus on node features to predict ocean temperature. Note that the edge features are essential, especially for 3-D ocean temperature prediction. In this letter, a GNN with optimized attention mechanisms (GNN-OAMs) model is proposed to predict 3-D ocean temperature. The GNN-OAM introduces a random forest (RF) module to capture the nonstationary temporal dependencies. Especially, the OAMs, via combination of multiple adjacency matrices construct edge features, are presented to capture the dynamic spatial dependencies. The prediction performance of GNN-OAM model is evaluated in the 3-D ocean temperature experimental dataset, and the results show that the GNN-OAM achieves ocean temperature prediction with high accuracy, and the MAEs are 0.146 and 0.26, when predicting temperatures in next one day and five days.

Fiber-optic hydrophones have received extensive research interests due to their advantage in ocean underwater target detection. Here, kernel extreme learning machine (K-ELM) is introduced to source localization in underwater ocean waveguide. As a data-driven machine learning method, K-ELM does not need a priori environment information compared to the conventional method of match field processing. The acoustic source localization is considered as a supervised classification problem, and the normalized sample covariance matrix formed over a number of snapshots is utilized as an input. The K-ELM is trained to classify sample covariance matrices (SCMs) into different depth and range classes with simulation. The source position can be estimated directly from the normalized SCMs with K-ELM. The results show that the K-ELM method achieves satisfactory high accuracy on both range and depth localization. The proposed K-ELM method provides an alternative approach for ocean underwater source localization, especially in the case with less a priori environment information.

Remote sensing of water parameter and flow dynamic progress is of critical importance to water environment observation. Underwater acoustic tomography technology uses multiple sound stations to construct distributed sensing network for large scale measuring, which has big superiority in water parameter observation compared with fix-point measurement. Water parameter variation progress can be monitored with underwater acoustic tomography sensing system. This paper develops a Grid-averaged Trust-Region Method for water parameter detailed profiling with coastal acoustic tomography (CAT) systems. A remote sensing experiment was conducted with 5 Coastal Acoustic Tomography (CAT) systems from February 28th to March 7th, 2022 in the Huangcai Reservoir in Changsha, China. Four CAT systems were deployed in same position with different depth of sound transceivers and another CAT station was set 650m apart. Water temperature and flow current reconstruction results along sound propagate section was analyzed with trust-region method and compared with the temperature data acquired with temperature sensor. The accuracy and feasibility of this method are verified with observation results.

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.

High-precision and spatiotemporal flow field measurement in small-scale waters has a decisive impact on navigation and water resource management.Multi transmission rays and position drift, as two main difficulties in improving the accuracy of small-scale flow field, need to be solved urgently. In this paper, four coastal acoustic tomography (CAT) systems are arranged in Qingyang Lake, Hunan, China on 1, March 2022 for a short-term observation. As the GPS error and shipping caused station drift contaminate the reconstructed flow field, a real-time station position correction method, based on travel time and Temperature, Depth sensors (TD), is applied in our inversion. The inversion range average current (RAC) is in agreement with forward calculation, which proves the reliability of the reconstructed horizontal flow field. Besides, comparisons of net flow and Acoustic Doppler Current Profiler (ADCP) velocity are performed to validate the improvement of the real-time station position correction algorithm. The errors of corrected net flow are less than 3.818 m ² /s, with a minimum of 0.0042 m ² /s. The root-mean-square error (RMSE) between the CAT inversion results and the ADCP velocity is 0.123 m/s after correction, relatively reduced 5.95% than before correction. The smaller the RMSE, the higher the inversion accuracy, so the CAT method with real-time station position correction is feasible for high-precision horizontal flow field reconstruction.