Deep Learning Prior Model for Unsupervised Seismic Data Random Noise Attenuation

Denoising is an indispensable step in seismic data processing. Deep-learning-based seismic data denoising has been recently attracting attentions due to its outstanding performance. In this letter, we investigate the architecture of deep Convolutional Networks (ConvNets) for seismic data denoising. The untrained ConvNets are served as a generative network to a single seismic data profile with Gaussian noise. Starting with random initialized parameters, the generative networks with various handcrafted architectures have different ability to map the seismic data at iterations and can separate the Gaussian noise as residuals. For the purpose of exploring the ability of Gaussian noise separation, the depth, width, and skip connection as the main components of generative network are assembled as various architectures to fit Gaussian noise, clean, and noisy seismic data, respectively. Then, the favorable network architecture with high and low impedance (an ability to hinder data reconstruction) to noise and seismic data is adopted as prior model to seismic data denoising task. Furthermore, a stopping criterion is designed for the data fitting process to obtain the latent clean seismic data automatically. The proposed method does not need data sets for training and it makes use of network architecture as prior. Extensive experiments both on synthetic and field data demonstrate the effectiveness of the selected ConvNet and the advantages are evaluated by comparing the denoising results with f-x multi-channel singular spectrum analysis (MSSA) and state-of-the-art unsupervised neural network (NN)-based method.