Because a high signal-to-noise ratio (SNR) is beneficial to the subsequent processing procedures, the noise attenuation is important. We propose an adaptive random noise attenuation framework based on convolutional neural networks (CNNs). The framework transforms the target function from effective signal learning to noise learning through residual learning, so as to improve the training efficiency. After sufficient training, the network transfers the learned seismic data features using a large synthetic data set to the testing of complex field data with unknown noise levels and, thus, attenuates the noise in an unsupervised way. Unsupervised noise reduction requires certain representativeness of the training data and a sufficient amount of training data sets. In the network architecture, we introduce residual learning and batch normalization (BN) to reduce the training parameters of the network, thereby shortening the time for feature learning. The activation function with leakage correction function can effectively retain negative information, and its combination with the double convolutional residual block can enhance the generalization ability and feature extraction performance of the network. In the test of synthetic data and complex field data with unknown noise levels, by comparing the noise reduction results of some classic denoising algorithms, the adaptive CNN proposed in this article can more effectively attenuate the noise and reconstruct the seismic waveform.

中文翻译：

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通过改进的残差卷积神经网络进行深度学习地震随机噪声衰减

由于高信噪比（SNR）有利于后续处理过程，因此噪声衰减很重要。我们提出了一种基于卷积神经网络 (CNN) 的自适应随机噪声衰减框架。该框架通过残差学习将目标函数从有效的信号学习转化为噪声学习，从而提高训练效率。经过充分的训练，网络将使用大型合成数据集学习到的地震数据特征转移到具有未知噪声水平的复杂现场数据的测试中，从而以无监督的方式衰减噪声。无监督降噪需要训练数据具有一定的代表性和足够数量的训练数据集。在网络架构中，我们引入残差学习和批量归一化（BN）来减少网络的训练参数，从而缩短特征学习的时间。带有泄漏校正功能的激活函数可以有效保留负信息，其与双卷积残差块的结合可以增强网络的泛化能力和特征提取性能。在噪声水平未知的合成数据和复杂现场数据的测试中，通过比较一些经典去噪算法的降噪结果，本文提出的自适应CNN可以更有效地衰减噪声并重建地震波形。带有泄漏校正功能的激活函数可以有效保留负信息，其与双卷积残差块的结合可以增强网络的泛化能力和特征提取性能。在噪声水平未知的合成数据和复杂现场数据的测试中，通过比较一些经典去噪算法的降噪结果，本文提出的自适应CNN可以更有效地衰减噪声并重建地震波形。带有泄漏校正功能的激活函数可以有效保留负信息，其与双卷积残差块的结合可以增强网络的泛化能力和特征提取性能。在噪声水平未知的合成数据和复杂现场数据的测试中，通过比较一些经典去噪算法的降噪结果，本文提出的自适应CNN可以更有效地衰减噪声并重建地震波形。