Asymmetric learning based deep denoiser for nonstationary desert seismic noise suppression

Abstract

Random noise in desert seismic data has the weak similarity to the effective signals and spatiotemporally variable noise intensity. Mismatch noise intensity degrades the denoising performance of the convolutional neural network in incomplete noise suppression and damaged seismic signals. We propose an asymmetric learning-based nonstationary convolutional denoising neural network (AL-NCDNet) by introducing an asymmetric loss function and the noise level (NL) estimation block. Since the denoised result is robust to the overestimated NL and more sensitive to the underestimated NL, an asymmetric learning framework is adopted by leveraging a weighted loss function for constraining the noise level estimation block. Under the asymmetric learning framework, more penalties are imposed on the underestimated NL than the overestimated ones, alleviating the problem of noise residue caused by underestimated NL. Moreover, the estimated NL is taken as the weight between noise suppression and signal fidelity to guide the main denoising block adaptively suppressing nonstationary noise. The AL-NCDNet is tested on the synthetic, public and field seismic data. The denoised results verify that asymmetric learning is able to improve the accuracy of the estimated NL and the efficiency of the denoising model, thereby allows AL-NCDNet to effectively suppress spatiotemporally variable noise while preserving seismic events.