An Attention-Assisted AI Model for Real-Time Underwater Sound Speed Estimation Leveraging Remote Sensing Sea Surface Temperature Data

[2502.12817] An Attention-Assisted AI Model for Real-Time Underwater Sound Speed Estimation Leveraging Remote Sensing Sea Surface Temperature Data Skip to main content Search Submit Donate Log in Electrical Engineering and Systems Science > Signal Processing arXiv:2502.12817 (eess) [Submitted on 18 Feb 2025 ( v1 ), last revised 28 Aug 2026 (this version, v4)] Title: An Attention-Assisted AI Model for Real-Time Underwater Sound Speed Estimation Leveraging Remote Sensing Sea Surface Temperature Data Authors: Pengfei Wu , Wei Huang , Yujie Shi , Feng Yin , Hao Zhang View PDF HTML (experimental) Abstract: The estimation of underwater sound velocity distribution serves as a critical basis for facilitating effective underwater communication and precise positioning, given that variations in sound velocity influence the path of signal transmission. Conventional techniques for the direct measurement of sound velocity, as well as methods that involve the inversion of sound velocity utilizing acoustic field data, necessitate on--site data collection. This requirement not only places high demands on device deployment, but also presents challenges in achieving real-time estimation of sound velocity distribution. In order to construct a real-time sound velocity field and eliminate the need for underwater onsite data measurement operations, we propose a self-attention embedded multimodal data fusion convolutional neural network (SA-MDF-CNN) for real-time underwater sound speed profile (SSP) estimation. The proposed model seeks to elucidate the inherent relationship between remote sensing sea surface temperature (SST) data, the primary component characteristics of historical SSPs, and their spatial coordinates. This is achieved by employing CNNs and attention mechanisms to extract local and global correlations from the input data, respectively. The ultimate objective is to facilitate a rapid and precise estimation of sound velocity distribution within a specified task area. The comparative analysis demonstrates that the proposed approach achieves superior performance in terms of both accuracy and stability, exhibiting reduced error rates and enhanced resistance to disturbances when benchmarked against existing advanced techniques. Subjects: Signal Processing (eess.SP) ; Sound (cs.SD) Cite as: arXiv:2502.12817 [eess.SP] (or arXiv:2502.12817v4 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2502.12817 Focus to learn more arXiv-issued DOI via DataCite Journal reference: IEEE Transactions on Neural Networks and Learning Systems, vol. 37, no. 8, pp. 3669-3683, Aug. 2026, Related DOI : https://doi.org/10.1109/TNNLS.2026.3653116 Focus to learn more DOI(s) linking to related resources Submission history From: Wei Huang [ view email ] [v1] Tue, 18 Feb 2025 12:24:26 UTC (15,154 KB) [v2] Wed, 19 Feb 2025 03:24:18 UTC (3,902 KB) [v3] Sun, 2 Mar 2025 09:42:13 UTC (8,507 KB) [v4] Fri, 28 Aug 2026 06:25:30 UTC (19,680 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2025-02 Change to browse by: cs cs.SD eess References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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