Robust Seismic data denoising via self-supervised deep learning

Seismic data denoising is a critical component of seismic data processing, yet effectively removing erratic noise, characterized by its non-Gaussian distribution and high amplitude, remains a substantial challenge for conventional methods and deep learning (DL) algorithms. Supervised learning frameworks typically outperform others, but they require pairs of noisy datasets alongside corresponding clean ground truth, which is impractical for real-world seismic datasets. On the other hand, unsupervised learning methods, which do not rely on ground truth during training, often fall short in performance when compared to their supervised or traditional denoising counterparts. Moreover, current unsupervised deep learning methods fail to address the specific challenges posed by erratic seismic noise adequately. This paper introduces a novel zero-shot unsupervised DL framework designed specifically to mitigate random and erratic noise, with a particular emphasis on blending noise. Drawing inspiration from Noise2Noise and data augmentation principles, we present a robust self-supervised denoising network named ““Robust Noiser2Noiser.”.” Our approach eliminates the need for paired noisy and clean datasets as required by supervised methods or paired noisy datasets as in Noise2Noise (N2N). Instead, our framework relies solely on the original noisy seismic dataset. Our methodology generates two independent re-corrupted datasets from the original noisy dataset, using one as the input and the other as the training target. Subsequently, we employ a deep-learning-based denoiser, DnCNN, for training purposes. To address various types of random and erratic noise, the original noisy dataset is re-corrupted with the same noise type. Detailed explanations for generating training input and target data for blended data are provided in the paper. We apply our proposed network to both synthetic and real marine data examples, demonstrating significantly improved noise attenuation performance compared to traditional denoising methods and state-of-the-art unsupervised learning methods.