关键字：EXAMPLE

摘要：In geophysical exploration, logging images are frequently incomplete due to the mismatch between the size of the logging instruments and that of the boreholes, which significantly impacts geological analysis. Existing methods, which rely on standard algorithms or unsupervised learning techniques, tend to be computationally intensive and time-consuming. In addition, they are difficult to inpaint regions with high-angle fractures or finegrained textures. To address these challenges, we propose a deep learning approach for inpainting stratigraphic features. Our method utilizes pseudo-labeled training datasets to alleviate the issue of limited training labels, thereby reducing both computational cost and processing time. We introduce a Fusion-Perspective- Enhancement Module (FPEM) designed to accurately infer missing regions based on contextual guidance, thus enhancing the inpainting process for high-angle fractures. Furthermore, we present a novel discriminator known as SM-Unet, which improves fine-grained textures by adjusting the weight assigned to various regions through soft labeling during training. Our approach achieves a Peak Signal-to-Noise Ratio (PSNR) of 25.35 and a Structural Similarity Index (SSIM) of 0.901 on the logging image dataset. This performance surpasses that of state-of-the-art methods - particularly in managing high-angle fractures and fine-grained textures - while requiring less computational effort.

卷号：196

期号：

是否译文：否