Estimation of sound speed profiles based on remote sensing parameters using a scalable end-to-end tree boosting model

Ou, Zhenyi; Qu, Ke; Shi, Min; Wang, Ya-Fen; Zhou, Jianbo

doi:10.3389/fmars.2022.1051820

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…Huang et al introduced an auto-encoding feature-mapping neural network (AEFMNN) structure, effectively enhancing the robustness of the neural network model in constructing the sound speed field against interference [37]. Ou et al proposed an SSP inversion algorithm based on a comprehensive learning model using random forest (RF), followed by a method reconstructing SSP using the extreme gradient boosting (XGBoost) model [42,43].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Few-Shot Prediction of Ocean Sound Speed Profiles through Hierarchical Long Short-Term Memory Transfer Learning

Lu,

Zhang,

et al. 2024

JMSE

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The distribution of ocean sound speed profiles (SSPs) profoundly influences the design of underwater acoustic communication and positioning systems. Conventional methods for measuring sound speed by instruments entail high time costs, while sound speed inversion methods offer rapid estimation of SSPs. However, these methods heavily rely on sonar observational data and lack the capacity to swiftly estimate SSPs in arbitrary oceanic regions, particularly in scenarios with few-shot data. Precisely estimating non-cooperative maritime SSPs under such conditions poses a significant challenge. To explore temporal distribution patterns of sound speed and achieve precise SSP predictions with limited data, we propose a hierarchical long short-term memory transfer learning (H-LSTM-TL) framework. The core idea involves pre-training the base model on extensive public datasets, transferring the acquired knowledge to task models, and fine-tuning the task model on few-shot data to predict future SSPs. Through H-LSTM-TL, it accelerates model convergence, enhances sensitivity to few-shot input data, alleviates overfitting issues, and notably improves the accuracy of SSP predictions. Experimental results demonstrate that the H-LSTM-TL model exhibits strong generalization capabilities in few-shot data scenarios, effectively reducing overfitting problems and proving its applicability for rapid prediction of SSPs.

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Section: Related Workmentioning

confidence: 99%

Enhancing Few-Shot Prediction of Ocean Sound Speed Profiles through Hierarchical Long Short-Term Memory Transfer Learning

Lu,

Zhang,

et al. 2024

JMSE

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“…Based on the Argo data and the sea surface datasets, the paper [18] considered the eddy kinetic energy in the sEOF-r inversion framework, which proved the concept of global SSP inversion. The paper [19] incorporated XGBoost into the field of sound speed inversion, which further improved the estimation accuracy of SSPs by utilizing satellite remote sensing data. As with the aforementioned investigations, ML-based SSP inversion methods are developing and achieve good results in many aspects, such as feature representations, ML model establishment and SSP-related datasets generation.…”

Section: Introductionmentioning

confidence: 99%

Sound Speed Inversion Based on Multi-Source Ocean Remote Sensing Observations and Machine Learning

Feng,

Tian,

Zhou

et al. 2024

Remote Sensing

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Ocean sound speed is important for underwater acoustic applications, such as communications, navigation and localization, where the assumption of uniformly distributed sound speed profiles (SSPs) is generally used and greatly degrades the performance of underwater acoustic systems. The acquisition of SSPs is necessary for the corrections of the sound ray propagation paths. However, the inversion of SSPs is challenging due to the intricate relations of interrelated physical ocean elements and suffers from the high costs of calculations and hardware deployments. This paper proposes a novel sound speed inversion method based on multi-source ocean remote sensing observations and machine learning, which adapts to large-scale sea regions. Firstly, the datasets of SSPs are generated utilizing the Argo thermohaline profiles and the empirical formulas of the sound speed. Then, the SSPs are analyzed utilizing the empirical orthogonal functions (EOFs) to reduce the dimensions of the feature space as well as the computational load. Considering the nonlinear regression relations of SSPs and the observed datasets, a general framework for sound speed inversion is formulated, which combines the designed machine learning models with the reduced-dimensional feature representations, multi-source ocean remote sensing observations and water temperature data. After being well trained, the proposed machine learning models realize the accurate inversion of the targeted ocean region by inputting the real-time ocean environmental data. The experiments verify the advantages of the proposed method in terms of the accuracy and effectiveness compared with conventional methods.

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“…Machine learning methods not only have advantages over conventional techniques in inferring the temperature profiles, but also in inferring the SSP. Ou used a tree-based algorithm along with parameters of remote sensing to invert the SSP, and reported a 25% improvement in the accuracy of the outcomes (Ou et al, 2022). Furthermore, Li et al successfully inverted the SSP of the South China Sea by using a non-linear approach based on self-organizing maps (Li et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Reconstructing the sound speed profile of South China Sea using remote sensing data and long short-term memory neural networks

Zhao,

Xu,

et al. 2024

Front. Mar. Sci.

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IntroductionSound waves are refracted along the direction of their propagation owing to spatial and temporal fluctuations in the speed of sound in seawater. Errors are compounded when sound speed profiles (SSPs) with low precision are used to detect and locate distant underwater targets because an accurate SSP is critical for the identification of underwater objects based on acoustic data. Only sparse historical spatiotemporal data on the SSP of the South China Sea are available owing to political issues, its complex atmospheric system, and the unique topography of its seabed, because of which frequent oceanic movements at the mesoscale affect the accuracy of inversion of its SSP.MethodIn this study, we propose a method for the inversion of the SSP of the South China Sea based on a long short-term memory model. We use continuous-time data on the SSP of the South China Sea as well as satellite observations of the height and temperature of the sea surface to make use of the long-term and short-term memory-related capacities of the proposed model.ResultIt can achieve highly accurate results while using a small number of samples by virtue of the unique structure of its memory. Compared with the single empirical orthogonal function regression method, the inversion accuracy of this model is improved by 24.5%, and it performed exceptionally well in regions with frequent mesoscale movements.DiscussionThis enables it to effectively address the challenges posed by the sparse sample distribution and the frequent mesoscale movements of the South China Sea.

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Estimation of sound speed profiles based on remote sensing parameters using a scalable end-to-end tree boosting model

Cited by 4 publications

References 30 publications

Enhancing Few-Shot Prediction of Ocean Sound Speed Profiles through Hierarchical Long Short-Term Memory Transfer Learning

Enhancing Few-Shot Prediction of Ocean Sound Speed Profiles through Hierarchical Long Short-Term Memory Transfer Learning

Sound Speed Inversion Based on Multi-Source Ocean Remote Sensing Observations and Machine Learning

Reconstructing the sound speed profile of South China Sea using remote sensing data and long short-term memory neural networks

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