An Improved Generative Adversarial Network for Micro-Resistivity Imaging Logging Restoration
arXiv:2606.10200v1 Announce Type: cross 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
The continuous advancements in AI, particularly generative adversarial networks (GANs), allow for increasingly sophisticated solutions to complex data restoration problems across various industries.
Improved data restoration techniques for micro-resistivity imaging logging can lead to more accurate subsurface analysis, impacting resource exploration and infrastructure integrity.
This specific GAN application demonstrates a method to restore partially missing imaging logging data, potentially reducing operational costs and improving decision-making for geological and engineering applications.
- · Oil & Gas Industry
- · Mining Industry
- · Geophysical Service Providers
- · AI Software Developers
More reliable geological data for resource estimation and exploration becomes available.
Reduced need for expensive and time-consuming re-logging operations, improving efficiency.
Enhanced AI capabilities could be adapted for image restoration in other critical infrastructure monitoring contexts.
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Read at arXiv cs.LG