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

Abstract

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.