Two-stage framework for real-time rock joint identification during drilling of layered rock structures based on multi-parameter machine learning algorithms

Abstract The method of rock joint identification that combines machine learning with drilling parameters is effective in processing large datasets while maintaining high accuracy. However, it predominantly relies on a single parameter and does not fully account for the interactions and relative importance among different parameters. In this study, a novel two-step intelligent recognition method is proposed to classify and identify rock joints by integrating multiple fields, including drilling technology, image processing, and deep learning. Initially, a particle swarm optimization-support vector machine (SVM) model is employed to classify hard and soft layer rock joints using dimensionless processed drilling parameters. For soft layer data, the gram angle field technique is introduced to convert the raw one-dimensional time series data into two-dimensional feature images. Subsequently, a convolutional neural network with residual blocks is constructed and trained on these two-dimensional feature images to classify and recognize different soft-layer rock joint images. A comparative analysis of the classification of soft layer rock joint images is then conducted using the SVM model, validating the effectiveness of the proposed method. The results show that the classification accuracy of this intelligent recognition approach reaches 99%, successfully identifying various joint types in the rock mass. This study establishes a foundation for the intelligent application of rock joint recognition technology and offers new perspectives for rock structure analysis and exploration in complex geological environments.