Efficient Denoising of Ultrasonic Logging While Drilling Images: Multinoise Diffusion Denoising and Distillation

被引：0

作者：

Zhang, Wei ^{[1
]}

Qu, Qiaofeng ^{[1
]}

Qiu, Ao ^{[2
]}

Li, Zhipeng ^{[1
]}

Liu, Xien ^{[2
]}

Li, Yanjun ^{[1
]}

机构：

[1] University of Electronic Science and Technology of China, School of Automation Engineering, Sichuan, Chengdu

[2] Welltech Research and Design Institute, China Oilfield Services Company, Beijing

来源：

IEEE Transactions on Geoscience and Remote Sensing | 2025年 / 63卷

关键词：

Multiple logging noise processing; multistage progressive refinement network (MSPRN); ultrasonic logging while drilling (ULWD) denoising diffusion method;

D O I：

10.1109/TGRS.2025.3545272

中图分类号：

学科分类号：

摘要：

Ultrasonic logging while drilling (ULWD) often faces challenges due to the complex downhole environment, instrument usage, and inevitable data compression, which significantly degrade the quality of logging images and introduce various noises. These factors impair the accuracy of geological analysis. To address this issue, we propose a novel multinoise ultrasonic logging image denoising diffusion method (MULDDM). This approach simplifies the training process for multiple types of logging noise by incorporating a logging multiple noise factor (LMNF), thereby significantly enhancing ULWD images quality. Additionally, to meet the deployment requirements of edge devices, we design a multistage progressive refinement network (MSPRN) to distill knowledge from MULDDM. This network reduces the model's parameter count by 37.4% while maintaining excellent denoising performance during ULWD. Experimental results show that the MSPRN has a parameter size of just 22.7 M, with the signal-to-noise ratio of the denoised images exceeding 31 dB. The average processing time for a single logging image is approximately 0.1 s, supporting real-time image processing for logging edge equipment. This method effectively eliminates various types of logging noise while preserving crucial geological details, offering reliable data for accurate geological assessment. © 2024 IEEE.

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