Diffusion Model for DAS-VSP Data Denoising

Distributed acoustic sensing (DAS) has emerged as a transformational technology for seismic data acquisition. However, noise remains a major impediment, necessitating advanced denoising techniques. This study pioneers the application of diffusion models, a type of generative model, for DAS vertical seismic profile (VSP) data denoising. The diffusion network is trained on a new generated synthetic dataset that accommodates variations in the acquisition parameters. The trained model is applied to suppress noise in synthetic and field DAS-VSP data. The results demonstrate the model's effectiveness in removing various noise types with minimal signal leakage, outperforming conventional methods. This research signifies diffusion models' potential for DAS processing.

Automated summary

This study pioneers the use of diffusion models for denoising DAS-VSP data, demonstrating their effectiveness in removing noise and surpassing conventional methods, and showcasing the potential of diffusion models in DAS processing.