LAUSR

Seismic data denoising is an important part of seismic data processing and has attracted much attention in recent years. With the rapid development of neural networks, convolutional neural network (CNN)-based denoising methods have been widely studied and used in seismic data denoising due to their unique convolutional layer and weight sharing characteristics. However, the existing CNN-based seismic data denoising methods mainly use fixed-size convolution kernels in a certain layer, which forces the different kernels to extract features from areas of the same size and fails to extract features from various granularities. To overcome this limitation, we make full use of the local similarity of seismic sections, use different sizes of convolution kernels to parallelly extract features from different granularities, and propose a multigranularity feature fusion CNN (MFFCNN) method to remove random noise from seismic data. This method uses convolution kernels with different sizes to extract features from various granularities and uses feature fusion structures to fuse the extracted features. Experimental results show that the MFFCNN proposed in this article can better deal with details and texture information than the compared methods.