基于核聚类的砂岩图像孔隙分割方法  

Sandstone pore image segmentation based on kernel clustering

作　　者：王梅[1] 宋晓晖 王治国[3] 韩非 于源泽 WANG Mei;SONG Xiaohui;WANG Zhiguo;HAN Fei;YU Yuanze(College of Computer and Information Technology,Northeast Petroleum University,Daqing 163318,China;Zhoushan Branch of China Mobile Group Zhejiang Co.,Ltd.,Zhoushan 316000,China;Exploration and Development Research Institute of Daqing Oilfield Co.,Ltd.,Daqing 163712,China;Artificial Intelligence Energy Research Institute,Northeast Petroleum University,Daqing 163318,China)

机构地区：[1]东北石油大学计算机与信息技术学院,黑龙江大庆163318 [2]中国移动通信集团浙江有限公司舟山分公司,浙江舟山316000 [3]大庆油田有限责任公司勘探开发研究院,黑龙江大庆163712 [4]东北石油大学人工智能能源研究院,黑龙江大庆163318

出　　处：《石油物探》2024年第5期1051-1060,共10页Geophysical Prospecting For Petroleum

基　　金：国家自然科学基金项目(51774090,62076234);黑龙江省博士后科研启动金资助项目(LBH-Q20080);黑龙江省研究生精品课程建设项目(人工智能及其应用)共同资助。

摘　　要：砂岩孔隙识别是研究孔隙结构的一个重要步骤,采用通用的图像分割算法不易得到理想的图像孔隙分割效果,为此提出了一种使用EfficientNetV2-S模型和核K-Means聚类技术对孔隙进行分割的方法。首先,获得砂岩图像的超像素集合,使用超像素方法预分割输入的致密砂岩图像,构建带标签的孔隙与非孔隙图像库;然后,应用EfficientNetV2-S模型提取砂岩图像的孔隙和非孔隙的语义特征,并结合迁移学习的方法,使用有限的砂岩图像的孔隙和非孔隙样本进行EfficientNetV2-S模型参数学习;最后,设计了一种基于K-Means聚类的区域合并方法——NTK-KCoP方法,根据超像素的语义特征、灰度特征和边缘特征构建目标函数,再由聚类结果合并超像素得到完整的孔隙区域。砂岩CT图像的实验结果验证了所提出的孔隙分割方法的适用性和有效性。Sandstone pore identification is an important step in studying pore structure.It is difficult to achieve ideal results using general image segmentation algorithms.This paper proposes a method for pore segmentation using EfficientNetV2-S and kernel K-Means clustering.First,a superpixel collection of sandstone images is obtained,and the input tight sandstone images are pre-segmented using a superpixel method to construct a library of labeled pore and non-pore images.Then,the EfficientNetV2-S model is applied to extract the semantic features of pores and non-pores in sandstone images.The semantic features are combined with a transfer learning method to learn EfficientNetV2-S model parameters using a limited number of pore and non-pore samples in sandstone images.Finally,a region merging method based on K-Means clustering is designed to construct an objective function by combining the semantic features,grayscale features and edge features of superpixels,which are then merged according to clustering results to obtain a complete pore image.Experiments on sandstone CT images verify the applicability and effectiveness of the pore segmentation method proposed in this paper.

关 键 词：砂岩CT图像 图像分割 超像素 EfficientNetV2-S 核聚类 

分 类 号：P631[天文地球—地质矿产勘探]