Multi-scale DCFF network: a new desert low-frequency noise suppression method

Abstract

Seismic exploration is an essential way for stratigraphic information acquisition and resource exploitation. However, the unique near surface conditions of the desert region in northwest China pose a special problem. On the one hand, compared with Gaussian noise, desert noise concentrates in the low-frequency band, which is seriously overlapped with those of the effective signals; On the other hand, its nonstationary, nonlinear, and non-Gaussian characteristics seriously affect the accuracy of weak signal recovery and increase the difficulty of noise suppression. In the collected field data, effective signals are often submerged by the intense low-frequency noise. Considering that traditional denoising methods have some limitations on this kind of noise, a new multi-scale dense connection feature fusion (MS-DCFF) denoising convolution neural network is presented in this paper. This denoising network can adaptively learn the potential features of effective signals through multi-scale feature fusion techniques and increase the degree of information exchange by utilizing the dense connections between different blocks. Moreover, we construct relatively complete training dataset containing an effective signal dataset and a noise dataset for desert low-frequency noise suppression, thereby boosting the feasibility of the network for desert noise suppression. Both simulation and field data prove that the superior performance of the proposed MS-DCFF in the aspect of low-frequency noise (mainly includes random noise and surface waves) suppression and effective signal recovery, compared with band-pass filtering, f-x filtering, and DnCNNs.