摘要：Fractures play a critical role in the storage and migration of hydrocarbons within subsurface reservoirs, and their characteristics can be effectively studied through core sample analysis. This study proposes an automated fracture extraction method for full-diameter core Computed Tomography (CT) images based on a deep learning framework. A semantic segmentation network called SCTNet is employed to perform high-precision semantic segmentation, while a sliding window strategy is introduced to address the challenges associated with large-scale image processing during training and inference. The proposed method achieves a mean Intersection over Union (mIoU) of 72.14% and a pixel-level segmentation accuracy of 97% on the test dataset, outperforming traditional thresholding techniques and several state-of-the-art deep learning models. Besides fracture detection, the method enables quantitative characterization of fracture-related parameters, including fracture proportion, dip angle, strike, and aperture. Experimental results indicate that the proposed approach provides a reliable and efficient solution for the interpretation of large-volume CT data. Compared to manual evaluation, the method significantly accelerates the analysis process—reducing time from hours to minutes—and demonstrates strong potential to enhance intelligent workflows for geological core fracture analysis. ? 2025 by the authors.

ISSN号：2227-9717

卷、期、页：卷13期7

发表日期：2025-07-11

影响因子：0.000000

期刊分区（SCI为中科院分区）：四区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：PROCESSES

参与作者：Huang, Ruiqi,Qiao, Dexin,Liu, Xi,Su, Qianxiao,Wang, Wenjie,Bi, Jianzhong,Ren, Yili

第一作者：惠钢

论文类型：期刊论文

论文概要：Huang, Ruiqi,Qiao, Dexin,惠钢,Liu, Xi,Su, Qianxiao,Wang, Wenjie,Bi, Jianzhong,Ren, Yili，A Fracture Extraction Method for Full-Diameter Core CT Images Based on Semantic Segmentation，PROCESSES，2025，卷13期7

论文题目：A Fracture Extraction Method for Full-Diameter Core CT Images Based on Semantic Segmentation