摘要
CT扫描和重构可以提供岩石内部结构的详细视图并对其定量分析,而在此过程中需要对扫描所获得的CT图片中的不同部分进行准确分割。本研究对传统的U-Net、增加残差模块的ResUNet,以及融合了残差模块与迁移学习特性的ResUNet-TL三种深度学习模型进行比较和分析,并借助ImageJ软件中的Weka3D进行图像分割作为对照。通过对比和分析,发现采用预训练的VGG模型以及深度残差网络技术的ResUNet-TL模型在处理复杂的岩石CT图像分割任务上优于其他模型,在准确率和F1得分两种不同的评价指标上都表现出较大的优势。将ResUNet-TL模型应用于二维CT图像中裂隙的识别,随后采用堆叠的方式,将所有被分割的CT图片进行三维重构,获得岩石样本的三维模型并进行定量分析,为岩石科学的研究和应用提供了有效工具。
CT scanning and reconstruction provide detailed insights into the internal structure of rocks and enable their quantitative analysis.A critical step in this process involves accurately segmenting different components in the CT images.This study compares and analyzes three deep learning models:the conventional U-Net,the ResUNet enhanced with residual modules,and the ResUNet-TL,which incorporates both residual modules and transfer learning features.Image segmentation using the Weka3D plugin in ImageJ is employed as a baseline for comparison.The analysis reveals that the ResUNet-TL model,leveraging a pre-trained VGG model and deep residual network techniques,outperforms the other models in segmenting complex rock CT images,demonstrating advantages in both accuracy and F1 scores.The ResUNet-TL model is applied to identify fractures in 2D CT images,which are then stacked and reconstructed into a 3D model of the rock sample for quantitative analysis.This approach provides an effective tool for advancing research and applications in rock science.
作者
朱淳
徐佳俊
孙文斌
何昌迪
王晓
Zhu Chun;Xu Jiajun;Sun Wenbin;He Changdi;Wang Xiao(School of Earth Science and Engineering,Hohai University,Nanjing 211100,P.R.China;College of Energy and Mining Engineering,Shandong University of Science and Technology,Qingdao,Shandong 266590,P.R.China;Department of Mining Engineering,University of Utah,Salt Lake City,Utah 84112,USA)
出处
《地下空间与工程学报》
北大核心
2025年第2期412-419,427,共9页
Chinese Journal of Underground Space and Engineering
基金
国家重点研发计划项目(2022YFC3080100)
国家自然科学基金(52374119,52404090)
江苏省自然科学基金(BK20242059)。
关键词
砂岩
CT扫描
深度学习
三维重构
sandstone
CT scan
deep learning
3D reconstruction
