An intelligent prediction method for rock core integrity based on deep learning

Abstract

To address the issue of serious inefficiency in the traditional manual evaluation methods of rock core integrity, a deep learning-based algorithm named IDA-RCF (Intelligent detection algorithm for Rock Core Fissure) is proposed in this paper, which realizes the automatic evaluation of rock core integrity in accordance with the fissure identification results. In IDA-RCF, a two-branch feature extraction network is firstly proposed, in which branch one is used to fully extract the complex and variable local detail fissure features by Deformable convolution, and branch two is used to capture the global context information of the rock core images by EfficientViT network based on the self-attention. Then a multi-level feature fusion network is proposed for adaptively fusing local and global features from the same level and the fused feature information from the previous level, thereby capturing more valid information and eliminating redundancies. Then the fused feature layer is decoded by the feature decoder to output the detection results of rock core fissure. Finally, the fissure rate is automatically calculated based on the detection results to predict the degree of rock core integrity. The experimental results show that the accuracy indexes F1, mAP@0.5 and mAP@0.5:0.95 of IDA-RCF are 93.09%, 94.44% and 84.61%, respectively. The relative error between the prediction results and the manual statistical results of the fissure rate is only 4.38%, and the prediction accuracy for the degree of rock core integrity is 93.8%, indicating that the proposed method in this paper is able to accomplish the intelligent evaluation task of rock core integrity with high precision.