Zero-Shot Denoising for DAS-VSP Data Based on Conditional Diffusion Probabilistic Models

Distributed Acoustic Sensing (DAS) systems offer a promising framework for advanced subsurface imaging and monitoring. Despite the great potential, the complex noise characteristics inherent in seismic data, such as environmental, mechanical, and instrumental disturbances, pose significant challenges to data fidelity and stability. Conventional noise suppression methods cannot be adequately adapted to dynamic seismic environments due to the need to design filters for different a priori conditions. To overcome these limitations, we introduce the conditional diffusion probabilistic model (C-DDPM), which is shown to have strong a priori extraction capabilities and can better cope with seismic signal extraction under different noise combinations. In addition, we incorporate a adaptive FK conditioning approach into the diffusion process, allowing C-DDPM to better learn the data distribution. We also use asymmetric dilated convolution (ADConv) to effectively suppress noise. Our approach is rigorously tested on both synthetic and real-world seismic datasets, demonstrating satisfactory improvements in noise reduction and signal clarity. Comparative analyses with existing classical methods reveal that our framework not only achieves higher PSNR but also reveals previously obscured waveform details, outperforming existing methods in challenging geophysical scenarios.