Prediction of the shear wave speed of seafloor sediments in the northern South China Sea based on an XGBoost algorithm - Meng, Wenjing; Meng, Xiangmei; Wang, Jingqiang; Li, Guanbao; Liu, Baohua; Kan, Guangming; Lu, Junjie; Zhao, Lihong; Zhi, Pengyao (Frontiers in Marine Science, 2024)
Based on data of the shear wave speed and physical properties of the shallow sediment samples collected in the Northwest South China Sea, the hyperparameters 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 porosity (n), water content (w), density (ρ) and average grain size (MZ), and their Pearson correlation coefficient 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, which has a very high goodness of fit (R 2 ) of the validation data is 0.914, the very low mean absolute error (MAE) and mean absolute percentage error (MAPE) of the model prediction are 3.366 m/s and 9.90%, respectively; (3) Compared with Grain-Shearing (GS) model, single and dual parameter regression equation prediction models, the XGBoost prediction model for the shear wave speed of seafloor sediments has higher fitting goodness and lower prediction error. The result demonstrates that the XGBoost prediction model for the shear wave speed of seafloor sediments has good applicability in the prediction of sediment speed for the study area.