Ground-Truth-Free 3D Seismic Denoising Based on Diffusion Models: Achieving Effective Constraints Through Embedded Self-Supervised Noise Modeling

Three-dimensional (3D) seismic data, essential for revealing subsurface structures and exploring oil and gas resources, are often contaminated by noise with an unknown prior distribution. Existing denoising research faces great challenges due to the scarcity of ground truth and the difficulty in obtaining prior knowledge of noise distributions. Moreover, few algorithms are specifically designed to leverage the unique spatial structural information inherent in 3D seismic data, leading to inefficient utilization of this valuable information during denoising. To address these issues, we propose Self-Supervised Seismic Denoising using the Denoising Diffusion Probabilistic Model (SSDn-DDPM), an algorithm specifically tailored for 3D seismic data that utilizes diffusion generative models for self-supervised blind denoising. The algorithm begins with self-supervised modeling of seismic noise to estimate its distribution. Subsequently, spatial structural information of 3D seismic data is leveraged to improve the accuracy of noise distribution estimation. Furthermore, the algorithm integrates the noise distribution estimation network into the diffusion model to further guide and refine the sampling process, thereby optimizing computational complexity and improving detail representation. Finally, it performs self-supervised 3D seismic noise suppression using the diffusion probabilistic model. In the experimental section, we comprehensively compare the proposed algorithm with six different types of seismic denoising methods. Various comparative experiments demonstrate that the proposed algorithm achieves exceptional denoising performance on 3D seismic data, even without ground truth or any prior knowledge about the noise distribution.