An Improved Generative Adversarial Network for Micro-Resistivity Imaging Logging Restoration

2026-06-08

AI Summary

This study addresses the challenge of localized missing data in micro-resistivity imaging logs by proposing an enhanced generative adversarial network (GAN)-based inpainting method. The generator is built upon a fully convolutional network architecture, integrating depthwise separable convolutional residual blocks, Inception modules, multi-scale feature extraction, and a spatial-channel attention mechanism to effectively capture both global structure and fine details. The discriminator employs a dual-branch design—combining global and local pathways—to jointly optimize semantic consistency and textural fidelity. Experimental results demonstrate that the proposed approach achieves an average structural similarity index (SSIM) of 0.903 on the test set, representing an improvement of approximately 0.3 over existing methods, while simultaneously preserving high-fidelity textures and significantly enhancing the semantic coherence of the reconstructed 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 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.

Problem

• micro-resistivity imaging logging
• image restoration
• missing data
• generative adversarial network

Innovation

• Generative Adversarial Network
• Micro-resistivity Imaging Logging
• Multi-scale Feature Extraction
• Spatial Attention
• Depth-separable Convolution