Abstract: Construction of offshore wind power platforms requires the use of geological engineering data from the seabed for geological parameter prediction and modeling. Traditional methods for predicting geological parameters include Bayesian and Kriging interpolation. With continuous advancements in computational power, machine learning has demonstrated outstanding performance in prediction tasks. This study introduces support vector regression, random forests, and neural network algorithms into the field of geological parameter prediction. Using 2D seismic data and CPT data, we predict geotechnical parameters for the Yinggehai Basin in the South China Sea. Finally, error histograms and validation scatter plots are used to compare the results of the three methods. It is found that neural network predictions exhibit the best overall performance, support vector regression results are relatively simple, and random forest predictions show abrupt changes in the horizontal direction due to truncation. These three methods provide new approaches for predicting submarine geological and geotechnical parameters.

Key words: CPT, Random Forest, Support Vector Regression, Neural Networks, Geotechnical Parameter Prediction