Improved GAN-Based Method for Restoring Incomplete Micro-Resistivity Imaging Logs
A preprint proposing an improved generative adversarial network (GAN) method for restoring missing regions in micro-resistivity imaging logging data has been withdrawn from arXiv by its authors. The paper described a system combining fully convolutional networks, depth-separable residual blocks, Inception modules, and dual discriminator networks, reporting a structural similarity score of 0.903 on test data. The withdrawal limits the ability to evaluate or build upon the claimed results until a revised version is submitted.
The preprint, submitted to arXiv on June 8, 2026, proposed a GAN-based pipeline for reconstructing partially missing micro-resistivity imaging logs, which are used in subsurface geological interpretation. The architecture incorporated a fully convolutional network backbone augmented with depth-separable convolutional residual blocks, an Inception module for multi-scale perception, and a spatial attention mechanism combined with channel attention. A dual-discriminator design—one global and one local—was used to improve semantic and textural coherence between restored and original image regions. The authors reported an average structural similarity index (SSIM) of 0.903 across five test sets with varying missing-region sizes, claiming approximately 0.3 improvement over comparable methods. On June 11, 2026, the paper was withdrawn by lead author Souvik Pramanik, citing mistakes in citations and references and an intention to resubmit with improved experiments to a conference venue. No PDF is currently available, making independent verification of the reported results impossible.
What's missing
Because the paper has been withdrawn and no PDF is accessible, it is impossible to assess the validity of the reported SSIM gains, the specific baselines used for comparison, dataset provenance, or whether the ~0.3 SSIM improvement claim is statistically robust. The nature and extent of the citation errors that prompted withdrawal are also undisclosed.
What different sources said
- arXiv cs.LGCenter
An Improved Generative Adversarial Network for Micro-Resistivity Imaging Logging Restoration
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