An Improved Generative Adversarial Network for Micro-Resistivity Imaging Logging Restoration
Quick Answer
This paper presents an enhanced GAN-based method for restoring micro-resistivity imaging log images, achieving an average structural similarity measure of 0.903, which is a 0.3 improvement over existing techniques.
Quick Take
This paper presents an enhanced GAN-based method for restoring micro-resistivity imaging log images, achieving an average structural similarity measure of 0.903, which is a 0.3 improvement over existing techniques. The approach integrates FCN, depth-separable convolutions, and multi-scale feature extraction to enhance semantic coherence and texture details, thereby facilitating better interpretation of logging images.
Key Points
- Utilizes FCN and depth-separable convolutional residual blocks for effective pixel retention.
- Incorporates an Inception module to enhance multi-scale perception while reducing parameters.
- Achieves a structural similarity measure of 0.903 in image restoration tests.
- Combines global and local discriminative networks for improved coherence in restored images.
- Provides a novel deep learning approach for micro-resistivity imaging log interpretation.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 10200v1 Announce Type: new 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.
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