An Improved Generative Adversarial Network for Micro-Resistivity Imaging Logging Restoration
Researchers have developed an improved Generative Adversarial Network (GAN) specifically for restoring partially missing micro-resistivity imaging logs. The proposed method incorporates a Feature Pyramid Network (FPN) as the generative backbone, enhanced with depth-separable convolutional residual blocks and Inception modules to better capture pixel and semantic information across multiple scales. Experimental results show an average structural similarity measure of 0.903 on test data, outperforming other methods by approximately 0.3 and improving semantic structure coherence and texture details for subsequent interpretation. AI
IMPACT Enhances image restoration techniques for specialized geological logging, potentially improving data interpretation accuracy.