Sound Velocity Predictive Model Based on Physical Properties

Hou, Zhengyu; Wang, J. Q.; Chen, Z.; Yan, Weizheng; Tian, Yuhang

doi:10.1029/2018ea000545

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…Thus, the dimension of the sample data is (226,8). The number of data samples is similar to that used to predict sediment sound speed based on the machine learning algorithm in the following literature (Hou et al, 2019;Hou et al, 2023;Chen et al, 2022Chen et al, , 2023 and can be used to train the machine learning prediction model for predicting shear wave speed of seafloor sediments.…”

Section: Characteristic Parameter Selectionmentioning

confidence: 99%

Prediction of the shear wave speed of seafloor sediments in the northern South China Sea based on an XGBoost algorithm

Meng,

Wang

et al. 2024

Front. Mar. Sci.

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Based on data on the shear wave speed and physical properties of the shallow sediment samples collected in the northwest South China Sea, the hyperparameter selection and contribution of the characteristic factors of the machine learning model for predicting the shear wave speed of seafloor sediments were studied using the eXtreme Gradient Boosting (XGBoost) algorithm. An XGBoost model for predicting the shear wave speed of seafloor sediments was established based on four physical parameters of the sediments: porosity (n), water content (w), density (ρ), and average grain size (MZ). The result reveals that: (1) The shear wave speed has a good correlation with n, w, ρ, and MZ, and their Pearson correlation coefficients are all above 0.75, indicating that they can be used as the suitable characteristic parameters for predicting the shear wave speed based on the XGBoost model; (2) When the number of weak learners (n_estimators) is 115 and the maximum depth of the tree (max_depth) is 6, the XGBoost model has a very high goodness of fit (R2) of the validation data of 0.914, the very low mean absolute error (MAE) and mean absolute percentage error (MAPE) of the predicted shear wave speed are 3.366 m/s and 9.90%, respectively; (3) Compared with grain-shearing (GS) model and single- and dual-parameter regression equation prediction models, the XGBoost model for the shear wave speed of seafloor sediments has higher fitting goodness and lower prediction error.

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Section: Characteristic Parameter Selectionmentioning

confidence: 99%

Prediction of the shear wave speed of seafloor sediments in the northern South China Sea based on an XGBoost algorithm

Meng,

Wang

et al. 2024

Front. Mar. Sci.

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“…Another commonly used method for predicting the velocity of sound is the regression equation, but like the theoretical models, the input parameters of the regression equations are also difficult to obtain, so it is difficult to carry out practical applications. Hou et al first proposed using machine learning methods in models predicting the velocity of sound, and he used the traditional physical parameters to test the random forest algorithm, which has given us a new way to study geo-acoustic properties [16]. In the subsequent research, they proposed that combining multiple parameters of particle size to build a prediction model on the basis of physical parameters could further improve the predictions' accuracy [17].…”

Section: Introductionmentioning

confidence: 99%

A Sound Velocity Prediction Model for Seafloor Sediments Based on Deep Neural Networks

Hou,

Wang,

2023

Remote Sensing

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The acoustic properties of seafloor sediments have always been important parameters in sound field analyses and exploration for marine resources, and the accurate acquisition of the acoustic properties of sediments is one of the difficulties in the study of underwater acoustics. In this study, sediment cores were taken from the northern South China Sea, and the acoustic properties were analyzed. Since traditional methods (such as regression equations or theoretical models) are difficult to apply in practical engineering applications, we applied remote sensing data to sound velocity prediction models for the first time. Based on the influencing mechanism of the acoustic properties of seafloor sediments, the sediments’ source, type and physical properties have a great influence on the acoustic properties. Therefore, we replaced these influencing factors with easily accessible data and remote sensing data, such as parameters of granularity, distance to the nearest coast, decadal average sea surface productivity, water depth, etc., using deep neural networks (DNN) to develop a sound velocity prediction model. Compared with traditional mathematical analyses, the DNN model improved the accuracy of prediction and can be applied to practical engineering applications.

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“…The ML algorithm also can be implemented in the geoacoustic area. Hou et al [16] has established the predictive model of sediment sound speed based on the random forest decision tree (RF) using several physical properties.…”

Section: Introductionmentioning

confidence: 99%

A New Compressional Wave Speed Inversion Method Based on Granularity Parameters

Wang

Hou

et al. 2019

IEEE Access

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How to improve the prediction accuracy of compressional wave speed has always been one of the basic research subjects in geoacoustics study field. Due to the stability of granularity, whether in the laboratory or in the seabed environment, the regression relationship between compressional wave speed and granularity is an important sound speed inversion method. Machine Learning (ML) provides a new solution for more efficient sound speed prediction systems. In this study, two ML algorithm, Random forest (RF) and Support Vector Regression (SVR), combined with nine granularity parameters (mean grain size, median grain size, skewness, kurtosis, sorting coefficient, gravel, sand, silt, and clay content respectively.) to analysis the effect of granularity property on sound speed. As a result, the sound speed-granularity predictive models were established, and the sound speed accuracy obtained based on the predictive models are higher than that of the regression equations, and the RF model has a higher accuracy than the SVR model. Based on the RF predictive model, the feature selection was conducted and the results show that the most influential parameter of granularity is mean grain size. Furthermore, the RF model can also predict the sound speed with high precision in the absence of partial parameters, which can be a useful tool for ocean engineering and seismic inversion.INDEX TERMS Geoacoustic inversion, granularity, machine learning, random forest, sound speed.

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Sound Velocity Predictive Model Based on Physical Properties

Cited by 7 publications

References 28 publications

Prediction of the shear wave speed of seafloor sediments in the northern South China Sea based on an XGBoost algorithm

Prediction of the shear wave speed of seafloor sediments in the northern South China Sea based on an XGBoost algorithm

A Sound Velocity Prediction Model for Seafloor Sediments Based on Deep Neural Networks

A New Compressional Wave Speed Inversion Method Based on Granularity Parameters

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