Estimation of Sound Speed Profiles Using a Random Forest Model with Satellite Surface Observations

Ou, Zhenyi; Qu, Ke; Liu, Chen

doi:10.1155/2022/2653791

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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“…In recent years, Su used the XGBoost model to reconstruct the global underwater thermohaline structure (Su et al, 2019), and Chen used the self-organizing map (SOM) to reconstruct the underwater temperature data in the Kuroshio extension of the Pacific Ocean east of the island of Japan (Chen et al, 2020). Ou used the Random Forest algorithm to reconstruct the underwater sound speed data in the South China Sea (Ou et al, 2022a). Bao et al estimated Pacific subsurface salinity data using a generalized regression neural network FOAGRNN model with a fruit fly optimization algorithm (Bao et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Wide-area sound speed profile estimation based on a pre-classification scheme for sound speed perturbation modes

Liu

Qu²

2023

Front. Mar. Sci.

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IntroductionThe trend of sound speed profile (SSP) inversion is towards wide-area sound speed estimation. However, the traditional inversion method of dividing the latitude and longitude grids has limitations in terms of significantly lower accuracy when samples are lacking. k-means clustering algorithm (K-means) can divide the training class to achieve high accuracy estimation.MethodThis paper proposes a grid-free pre-classification inversion scheme based on empirical orthogonal function (EOF) vectors. The scheme is based on the K-means to classify the samples according to the perturbation mode of the SSP. After classification, the SSP inversion is carried out using the self-organizing map algorithm (SOM). The experimental sea area is selected from the South China Sea, and the inversion results are evaluated using root mean square error (RMSE) as the criterion.ResultThe inversion results show that the inversion error is 2.1 m/s for the pre-classification solution and 2.7 m/s for the solution without pre-classification, a steady improvement of more than 20% in the inversion error. Accuracy is also improved by 2.14 m/s in the depth range where the sound speed perturbance is greatest.DiscussionThis pre-classification scheme has smaller inversion errors and the classification results are reasonable in terms of distribution in time and space. It provides a feasible solution for SSP inversion in sea areas where samples are lacking.

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“…Bao obtained the sea surface salinity (SSS) from both in situ and satellite observations to improve the results of the salinity profile reconstruction [21]. Ou and Chapman estimated SSP using a machine learning method based on SST and SL data [22]. Chen included inverted data from the echo sounder and the depth of the mixed layer in addition to the SST and SL to estimate the SSP [23].…”

Section: Introductionmentioning

confidence: 99%

Estimating Sound Speed Profile by Combining Satellite Data with In Situ Sea Surface Observations

Wang³

et al. 2022

Electronics

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Given that spatiotemporal measurement of the subsurface profile over a wide range are difficult to obtain, surface observations from satellites are often used to estimate the sound speed profile (SSP). This paper proposes a multisource method based on the self-organizing map (SOM) to improve the estimation of the SSP by merging surface observations with satellite data. Surface observations from the Kuroshio Extension Observatory (KEO) were used to supplement satellite observations (anomalies in the measured sea level and sea surface temperature) to this end. Different combinations of the surface parameters were assessed, their errors were analyzed, and differences between the results before and after the multisource parameters were used are discussed. The proposed method significantly increased the accuracy of estimating the SSP when the parameters obtained from in situ measurements were used, with a root mean square error of 2.18 m/s, less than a third of the error obtained when only satellite observations were used. The proposed method provides a new approach to determining an accurate three-dimensional structure of the sound speed when various surface observations are available.

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Estimation of Sound Speed Profiles Using a Random Forest Model with Satellite Surface Observations

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

Wide-area sound speed profile estimation based on a pre-classification scheme for sound speed perturbation modes

Estimating Sound Speed Profile by Combining Satellite Data with In Situ Sea Surface Observations

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