Mask-Guided Model for Seismic Data Denoising

Abstract

Low-frequency random noise in the desert seismic data severely obscures the effective information contained in the seismic data due to its similarity to seismic signals. To suppress desert seismic random noise, we propose a novel mask-guided seismic data denoising model (MGDNet) by introducing the signal semantics to guide the deep denoising network. The MGDNet consists of two subnetworks. The first subnetwork utilizes a shallow convolutional network to predict the mask that represents the semantics of the signal contained in the noisy seismic data. The second subnetwork based on the Siamese network combines the global location information of the signal in the mask to learn the difference between low-frequency noise features and signal features so that the following denoising network can estimate the seismic signals. The MGDNet utilizes the signal semantics to guide the denoising process so that the global features associated with the seismic events can be preserved well even obscured by random noise with similarity. The results on the simulated data, public dataset, and field seismic data show that our method achieves significant effects on the suppression of desert random noise and the restoration of the signal structure.