Estimation of the elastic modulus of basaltic rocks using machine learning methods

Abstract

The elastic modulus of basalt is a significant engineering parameter required for many projects. Therefore, a total of 137 datasets of basalts from Digor-Kilittasi, Turkey, were used to predict the elastic modulus of intact rock (E_i) for this study. P wave velocity, S wave velocity, apparent porosity, and dry density parameters were employed as input parameters. In order to predict E_i, seven different models with two or three inputs were constructed, employing four different machine learning methods such as Support Vector Machine (SVM), Gaussian Process Regression (GPR), Ensembles of Tree (ET), and Regression Trees (RT). The performance of datasets, models, and methods was evaluated using the coefficient of determination (R²), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). This study presented and analyzed the performance of four machine learning methods. A ranking approach was employed to determine the best performing method and dataset. Based on these evaluations, all four machine learning techniques effectively estimate the value of E_i. While they can be used as an appropriate choice for estimating the elastic modulus of basaltic rocks, the ET approach appears to be the most successful method. However, the performance of the GPR is the worst according to model assessments. The average R² values for Model 1 through 7 of the ET method for the five test datasets are 0.97, 0.93, 0.89, 0.97, 0.91, 0.99, and 0.99, respectively. The the average R² values for GPR from Models 1 to 7 for the five test datasets are 0.73, 0.55, 0.69, 0.48, 0.47, 0.73, 0.56, respectively. An additional indication that the ET performed better than all the other methods was the Taylor diagram, which made it simple to determine how well the model predictions matched the observations. Furthermore, these findings validate the performance of the machine learning techniques employed in this study as valuable instruments for future investigations into the modeling of complex engineering issues. The results of this study suggest that machine learning algorithms can help reduce the need for high-quality core samples and labor-intensive procedures in predicting the elastic modulus of basaltic rocks, resulting in time and cost savings.