基于VAEAC的成像测井图像复原方法研究  

Research on Imaging Logging Image Restoration Method Based on VAEAC

作　　者：曹茂俊[1] 张鹏杰 CAO Mao-jun;ZHANG Peng-jie(School of Computer and Information Technology,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

出　　处：《计算机技术与发展》2025年第3期18-25,共8页Computer Technology and Development

基　　金：黑龙江省建设项目(YJSKCSZ_202309);黑龙江省高等教育教学改革项目(SJGY20220253);中石油科技术开发项目(2021DJ4001)。

摘　　要：成像测井图像能清晰展示地下的地质结构和特征,助力地质分析和资源勘探。但由于电成像测井仪器的设计限制,电成像测井图像上会出现空白条带,这严重影响了图像的完整性和可用性。现有的图像复原方法在处理空白条带的填充问题时效果不佳。为此,提出一种基于改进任意条件变分自编码器(Variational Autoencoder with Arbitrary Conditioning,VAEAC)的成像测井图像复原方法。该方法在解码器部分引入卷积块注意力机制,以增强模型在通道和空间两个维度上对特征图重要性的自适应学习能力。此外,采用转置卷积替代传统的上采样技术,提高了上采样过程中对细节信息的捕捉能力。实验结果表明,测试集中五组具有不同缺失区域的成像测井图像的平均结构相似性度量为0.94,与其他同类方法比较提升了0.25左右。改进后的VAEAC模型在处理电成像测井图像复原任务时表现更为出色,不仅有效复原了成像测井图像的纹理特征,还保留了其语义结构,为后续的成像测井图像解释提供了更为准确的图像信息。Imaging logging images can clearly show the geological structure and characteristics of the underground,which helps geological analysis and resource exploration.However,due to the design limitations of electrical imaging logging instruments,blank strips will appear on electrical imaging logging images,which seriously affects the integrity and usability of the image.Existing image restoration methods are not effective in dealing with the problem of filling blank strips.To this end,an imaging logging image restoration method based on improved Variational Autoencoder with Arbitrary Conditioning(VAEAC)is proposed.This method introduces a convolutional block attention module in the decoder part to enhance the model's adaptive learning ability of the importance of feature maps in both channel and spatial dimensions.In addition,transposed convolution is used instead of traditional upsampling technology to improve the ability to capture detailed information during upsampling.Experimental results show that the average structural similarity measure of five groups of imaging logging images with different missing areas in the test set is 0.94,which is about 0.25 higher than that of other similar methods.The improved VAEAC model performs better in processing electrical imaging logging image restoration tasks.It not only effectively restores the texture features of the imaging logging images,but also retains their semantic structure,providing more accurate image information for subsequent imaging logging image interpretation.

关 键 词：电成像测井 图像复原 VAEAC 转置卷积 卷积块注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]