# An Improved GAN-Based Image Restoration Method for Imaging Logging Images
###### Abstract  
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 thesis 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. micro-resistivity imaging logging; generative adversarial networks; depth-separable convolution; residual connectivity; channel attention mechanisms +
Footnote †: journal: Medical  
an image based on the data to restore the true state of the formation, which can clearly characterise the formation lithology, fractures, sediment structure, tectonic dip analysis and in situ stress analysis. Compared with conventional logging, imaging logging has the advantages of directional measurement, intuitive image and high resolution, and can reflect all aspects of the formation structure, lithology, physical properties, oil content and electrical characteristics. Therefore, imaging logging is of great importance in the field of petroleum exploration and development, and has become an indispensable technical tool for logging and evaluation of complex inhomogeneous reservoirs [1]. However, in practice, due to the influence of cable elasticity and the respective hindrance of the downhole instrumentation, as well as the complexity of the borehole environment, the instrumentation cannot carry out measurements at an even speed downhole, making the measurement results distorted or the depth information recorded at the wellhead deviates significantly from the true depth of the instrumentation, resulting in distortions in the measured electrical buckling curve of the pole plate [2]. On the other hand, the imaging log images are often distorted or partially missing due to the collapse of the well wall without recording complete data and individual pole plates without collecting data, which makes the subsequent interpretation work quite difficult. Therefore, in order to provide complete geological information, the imaging log images must be restored. Image restoration is a way of repairing distorted or missing parts of content based on pixel information from intact parts of an image, for which multiple potential solutions may exist, i.e., the missing parts can be filled in using any reasonable hypothesis associated with the surrounding known regions. Moreover, the missing parts can be complex shapes that are missing, further increasing the difficulty of image colouring.