Optimized gradient boosting models and reliability analysis for rock stiffness C13

The Extreme gradient boosting algorithm XGBoost has been confirmed to be an accurate method for predicting rock stiffnesses and anisotropic parameters from basic input features such as rock porosity, density, vertical compression stress, pore pressure and burial depth (Nguyen-Sy, T., To, Q.D., Vu, M.N., Nguyen, T.D. and Nguyen, T.T., 2020. Study the elastic properties and the anisotropy of rocks using different machine learning methods. Geophysical Prospecting, 68(8), 2557–2578). This study has the following contributions: reducing the R2-error score (that is, 1-R2) by 35 %, RMSE by 21 % and MAE by 16 % comparing to the previous study by considering an advanced CatXG hybrid boosting model in combination with the optimizer Optuna for predicting $C_{13}$ (the most difficult stiffness to accurately predict); 2-conduct a reliability analysis for the predicted stiffness $C_{13}$ with respect to the randomness of the input features. We also discuss the use of $C_{11}$ or $C_{33}$ as additional input features for accurately predicting $C_{13}$ as well as the prediction of the related anisotropic parameter $\delta$. This significant improvement of predicted stiffness $C_{13}$ is extremely important because it encourages petrophysical engineers to use machine learning for predicting the elastic stiffnesses of rocks.