Seismic Random Noise Attenuation via Channel Attention‐Weighted Unsupervised Deep Learning

ABSTRACT Finding a way to effectively suppress random noise in seismic images has great significance. Deep learning techniques have shown great potential in seismic image denoising. Attention has gradually become the most important mechanism in the deep learning field, which can be applied to address many different problems across various domains. We propose an encoder–decoder architecture that uses a channel‐attention mechanism to suppress random noise in seismic data. Our autoencoder structure consists of three parts: an encoder module, a decoder module, and an attention skip connection module, which is a type of unsupervised deep learning model that uses the patching technique. Both encoder and decoder modules use fully connected layers, which allow for comprehensive information integration and feature extraction. The mechanisms of attention are composed of two parts. One is the encoder part. The input is initially entered into one fully connected layer, batch normalization, and nonlinearity in the form of Mish. Then, the output is subjected to a dual‐branch attention block for further extracting deep features. Another part is the skip connection. A channel attention mechanism is introduced to achieve effective information fusion through the connection of the encoder and decoder modules. Comprehensive experiments show that the proposed network significantly outperforms the best‐performing baseline method, improving the signal‐to‐noise ratio by 1.34 dB on three‐dimensional synthetic data, as well as performing well in other synthetic and real seismic denoising tasks based on both quantitative and qualitative evaluations.