Generative Denoising Method for Geological Images with Pseudo-Labeled Non-Matching Datasets

Accurate prediction of oil and gas reservoirs requires precise river morphology. However, geological sedimentary images are often degraded by scattered non-structural noise from data errors or printing, which distorts river structures and complicates reservoir interpretation. To address this challenge, we propose GD-PND, a generative framework that leverages pseudo-labeled non-matching datasets to enable geological denoising via information transfer. We first construct a non-matching dataset by deriving pseudo-noiseless images via automated contour delineation and region filling on geological images of varying morphologies, thereby reducing reliance on manual annotation. The proposed style transfer-based generative model for noiseless images employs cyclic training with dual generators and discriminators to transform geological images into outputs with well-preserved river structures. Within the generator, the excitation networks of global features integrated with multi-attention mechanisms can enhance the representation of overall river morphology, enabling preliminary denoising. Furthermore, we develop an iterative denoising enhancement module that performs comprehensive refinement through recursive multi-step pixel transformations and associated post-processing, operating independently of the model. Extensive visualizations confirm intact river courses, while quantitative evaluations show that GD-PND achieves slight improvements, with the chi-squared mean increasing by up to 466.0 (approximately 1.93%), significantly enhancing computational efficiency and demonstrating its superiority.