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<p>To effectively address issues of poor efficiency and high subjectivity in manual rock core integrity assessment, a deep semantic segmentation-based intelligent prediction algorithm named DSS-RCI is proposed. In DSS-RCI, a feature extraction network based on position-aware circular convolution is first designed to capture intricate rock core details and global contextual information, significantly enhancing the network’s target localization capability and resistance to environmental interference. Subsequently, a multi-level feature enhancement network based on a feature refinement fusion network and spatial context-aware module is built to achieve refined processing and effective information enhancement of features at different levels, eliminating interference from redundant features and improving the network’s contextual feature extraction capabilities. After that, DySample, a dynamic up-sampler feature decoding module, is used to decode the enhanced feature layer to output the segmentation image of the rock core block. Finally, Rock Quality Designation (RQD) of the rock core is automatically calculated based on the segmentation results, and the rock core integrity grade is predicted. The experimental results show that the mAP and mIoU of DSS-RCI are 93.12% and 95.63%, respectively, which are superior to four commonly used segmentation algorithms: DeepLabv3 + , Unet, Segformer, and Swin-Unet. In addition, the average error between the RQD prediction results of DSS-RCI and manual measurements is only 2.89%, and the prediction accuracy for rock core integrity grades reaches 95.7%, which can achieve high-precision and intelligent prediction of rock core integrity.</p>

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An Intelligent Prediction Method for Rock Core Integrity Based on Deep Semantic Segmentation

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Zhaoxia Hu,
  • Xin Zhou,
  • Lei Yu

摘要

To effectively address issues of poor efficiency and high subjectivity in manual rock core integrity assessment, a deep semantic segmentation-based intelligent prediction algorithm named DSS-RCI is proposed. In DSS-RCI, a feature extraction network based on position-aware circular convolution is first designed to capture intricate rock core details and global contextual information, significantly enhancing the network’s target localization capability and resistance to environmental interference. Subsequently, a multi-level feature enhancement network based on a feature refinement fusion network and spatial context-aware module is built to achieve refined processing and effective information enhancement of features at different levels, eliminating interference from redundant features and improving the network’s contextual feature extraction capabilities. After that, DySample, a dynamic up-sampler feature decoding module, is used to decode the enhanced feature layer to output the segmentation image of the rock core block. Finally, Rock Quality Designation (RQD) of the rock core is automatically calculated based on the segmentation results, and the rock core integrity grade is predicted. The experimental results show that the mAP and mIoU of DSS-RCI are 93.12% and 95.63%, respectively, which are superior to four commonly used segmentation algorithms: DeepLabv3 + , Unet, Segformer, and Swin-Unet. In addition, the average error between the RQD prediction results of DSS-RCI and manual measurements is only 2.89%, and the prediction accuracy for rock core integrity grades reaches 95.7%, which can achieve high-precision and intelligent prediction of rock core integrity.