An improved GAN-based imaging logging image restoration method is presented in this paper for solving the problem of partially missing micro-resistivity imaging logging images. The method uses FCN as the generative network infrastructure and adds a depth-separable convolutional residual block to learn and retain more effective pixel and semantic information; an Inception module is added to increase the multi-scale perceptual field of the network and reduce the number of parameters in the network; and a multi-scale feature extraction module and a spatial attention residual block are added to combine the channel attention. The multi-scale module adds a multi-scale feature extraction module and a spatial attention residual block, which combine the channel attention mechanism and the residual block to achieve multi-scale feature extraction. The global discriminative network and the local discriminative network are designed to gradually improve the content and semantic structure coherence between the restored parts and the whole image by playing off each other and the generative network. According to the experimental results, the average structural similarity measure of the five sets of imaged logging images with different sizes of missing regions in the test set is 0.903, which is an improvement of about 0.3 compared with other similar methods. It is shown that the method in this study can be used for the restoration of micro-resistivity imaging log images with good improvement in semantic structural coherence and texture details, thus providing a new deep learning method to ensure the smooth advancement of the subsequent interpretation of micro-resistivity imaging log images.

翻译：本文提出一种改进的基于生成对抗网络的成像测井图像恢复方法，以解决微电阻率成像测井图像部分缺失的问题。该方法采用全卷积网络作为生成网络基础架构，并引入深度可分离卷积残差块以学习并保留更有效的像素与语义信息；引入Inception模块以增加网络的多尺度感受野并减少网络参数数量；同时添加多尺度特征提取模块与空间注意力残差块，将通道注意力机制与残差块相结合实现多尺度特征提取。全局判别网络与局部判别网络被设计用于通过相互对抗及生成网络逐步提升恢复部分与整幅图像之间的内容与语义结构一致性。实验结果表明，测试集中五组不同缺失区域大小的成像测井图像的平均结构相似性度量值为0.903，较同类方法提升约0.3。研究证实该方法可有效恢复微电阻率成像测井图像，在语义结构一致性与纹理细节方面取得良好改善，从而为保障后续微电阻率成像测井图像解释的顺利推进提供了一种新的深度学习方法。