Deep learning (DL) techniques have recently attracted considerable attention in the field of seismic data denoising. However, most DL-based seismic denoising models require a considerable number of paired noisy–clean samples for training, which limits their application in practice. In this paper, a novel self-supervised DL scheme for noise attenuation in reflection seismic data is proposed based on the Neighbor2Neighbor strategy. The proposed method adopts a U-shaped convolutional network as the main network and incorporates regularization loss during training to improve the stability of network training, thereby constructing an end-to-end self-learning process for seismic data denoising. Specifically, a neighborhood subsampling workflow is built on single noisy seismic data to generate paired noisy images for training. Next, an unsupervised evaluation metric, grounded solely in noisy data, is adopted to quantitatively evaluate the performance of the denoising algorithm without labeled data. The application to synthetic datasets suggests that the proposed method achieves better denoising results than traditional (e.g., f−x deconvolution and wavelet analysis) and self-supervised methods, and rivals a widely used supervised DL model, the denoising convolution neural network. In real application to seismic data denoising without noise-free labels, the proposed method more effectively suppresses migration artifacts and random noise while better preserving the signal integrity compared to other methods. These improvements demonstrate that the proposed method is an independent, novel, and powerful data-driven DL scheme suitable for real seismic data denoising.

深度学习（DL）技术最近在地震数据去噪领域引起了广泛关注。然而，大多数基于深度学习的地震去噪模型需要大量配对的噪声-干净样本进行训练，这限制了它们在实践中的应用。本文提出了一种基于 Neighbor2Neighbor 策略的反射地震数据噪声衰减的新型自监督深度学习方案。该方法采用U型卷积网络作为主网络，并在训练时加入正则化损失，提高网络训练的稳定性，从而构建地震数据去噪的端到端自学习过程。具体来说，邻域子采样工作流程基于单个噪声地震数据构建，以生成用于训练的成对噪声图像。下一个，采用仅基于噪声数据的无监督评估指标来定量评估无标记数据的去噪算法的性能。对合成数据集的应用表明，所提出的方法比传统方法取得了更好的去噪结果（例如，f−x反卷积和小波分析）和自监督方法，可与广泛使用的监督深度学习模型（去噪卷积神经网络）相媲美。在没有无噪声标签的地震数据去噪的实际应用中，与其他方法相比，所提出的方法更有效地抑制偏移伪影和随机噪声，同时更好地保持信号完整性。这些改进表明，所提出的方法是一种独立的、新颖的、强大的数据驱动的深度学习方案，适用于真实地震数据的去噪。