Low-frequency noise suppressing of desert seismic data by improved nonlinear autoregressive with external input neural network - Li, Guanghui; Lu, Xuan; Liang, Meiyan; Feng, Zhiqiang (Exploration geophysics (Melbourne), 2022)
Low-frequency signal and noise are the main components of seismic data in Northwest China desert area. To remove noise and preserve data details, we introduce a method of nonlinear autoregressive with external input neural network (NARX) which is based on recent developments in nonlinear time series prediction and improve its performance according to signal detection theory. The input of NARX is a noisy seismic signal, the output is original predicted background noise, and then a threshold is set for the output to eliminate the waveform distortions. The residual error of the input and the output is filtered signal. We test the proposed method on synthetic and field seismic data and compare it with some conventional filtering methods (wavelet denoising method, f-x filter and complex diffusion filtering). The results prove that the proposed method can greatly attenuate low-frequency noise, and preserve data details as completely as possible.