Unsupervised Seismic Data Denoising Using Diffusion Denoising Model

Seismic data denoising is a crucial and challenging task for high-quality seismic exploration. Recent advancements in deep learning methods have demonstrated promising results in seismic denoising. However, the acquisition of ground truth data required for training remains unavailable, especially in field tests. We propose an unsupervised deep denoiser called the iterative diffusion denoising model (IDDM) based on a diffusion model to remove random noise. We present the diffusion process of IDDM according to the seismic noise model and the two-stage reverse process to iteratively train a deep restorer with the data pair created solely from the observed data. Hence, the IDDM is learned to approximate the reverse diffusion process of the seismic data, which leads to the effective seismic signal recovery and robustness to the variant noise level and complex distribution of the field seismic data. Moreover, the invariant features between adjacent states are introduced to the generative denoising model by the signal preserving module, enabling IDDM to gradually recover the effective seismic signals in high fidelity while thoroughly suppressing noise using only noisy data. The proposed approach shows excellent denoised results in synthetic and field data tests at low signal-to-noise ratios (SNRs), demonstrating its potential for practical applications in seismic data processing.