Evaluation of Machine Learning Algorithms for the Classification of Lithology Using Geophysical Logs

Abstract

Summary Classification of subsurface formation lithology from well log data is a significant task in geoscience, petroleum exploration and engineering. Presently, several machine learning algorithms have been implemented for lithology classification to improve the prediction accuracy. However, due to the complex geological conditions, such algorithms are hardly adopted for mineral deposits. In his paper, we evaluated three popular machine learning algorithms, such as the Support Vector Machine, Random Forest and Gradient Boosting Decision Tree. This study used the process of grid search and 10-fold cross-validation to optimize the hyperparameters of each model and avoid overfitting. The performance of each model is evaluated using metrics of accuracy, precision, recall and F1-score of predicted labels of lithology against the true labels. The results show that the Gradient Boosting Decision Tree model has better lithology classification performance, with a precision of 97.74%, recall of 98.67% and F1-score of 98.20% among other models. The interpretation of GBDT model shows that the order of features contributing to the lithology classification is VP >Density >Vs > natural gamma. The study reveals that GBDT model can provide significant information for further exploration targeting of deep mineral deposits.