Obtaining high signal-to-noise ratio (SNR) data is significant for the subsequent processing and interpretation of seismic data. In recent years, the convolutional neural network (CNN) has been widely used in seismic data denoising. However, the existing CNN-based method usually has a single receptive field, making it difficult to effectively extract feature maps at different scales. Therefore, we propose a multiscale residual U-shaped CNN (MRUnet) by combining the multiscale structure, residual structure, and skip connection structure to cope with the random noise of the poststack seismic data. The network can use convolutional kernels of different sizes for feature extraction and transfer these features through more extensive skip connections. We construct a training set using existing seismic data and transfer the trained model to field data for denoising experiments. Experiments on synthetic and field data demonstrate that by training the network, a model that removes the random noise from the poststack seismic data can be obtained and outperforms the existing ones.

中文翻译：

---

用于地震数据去噪的多尺度残差卷积神经网络

获得高信噪比（SNR）数据对于地震数据的后续处理和解释具有重要意义。近年来，卷积神经网络（CNN）在地震数据去噪中得到了广泛的应用。然而，现有的基于CNN的方法通常具有单一的感受野，使得难以有效地提取不同尺度的特征图。因此，我们提出了一种结合多尺度结构、残差结构和跳跃连接结构的多尺度残差U型CNN（MRUnet）来应对叠后地震数据的随机噪声。该网络可以使用不同大小的卷积核进行特征提取，并通过更广泛的跳跃连接传输这些特征。我们使用现有的地震数据构建训练集，并将训练后的模型转移到现场数据进行去噪实验。合成数据和现场数据的实验表明，通过训练网络，可以获得从叠后地震数据中去除随机噪声的模型，并且性能优于现有模型。