Advanced classification of drill core rock type and weathering grade using detection transformer-based artificial intelligence techniques

Abstract

Rock classification plays a crucial role in various fields such as geology, engineering, and environmental studies. Employing deep learning AI (artificial intelligence) methods has a high potential to significantly improve the accuracy and efficiency of this task. The paper delves into the exploration of two cutting-edge AI techniques, namely Mask DINO and Mask R-CNN (convolutional neural network), as means to identify rock weathering grades and rock types. The results demonstrate that Mask DINO, which is a Detection Transformer (DETR), outperforms Mask R-CNN for the aforementioned purposes. Mask DINO achieved f-1 scores of 91% and 86% in weathering grade detection and rock type detection, as opposed to the Mask R-CNN's f-1 scores of 84% and 75%, respectively. These findings underscore the substantial potential of employing DETR algorithms like Mask DINO for automatic classification of both rock type and weathering states. Although the study examines only two AI models, the data processing and other techniques developed in this study may serve as a foundation for future advancements in the field. By incorporating these advanced AI techniques, logging personnel can obtain valuable references to aid their work, ultimately contributing to the advancement of geological and related fields.