Application of SNMF-MSS in low-frequency seismic noise suppression in desert areas

Abstract

Noise suppression is an important step in seismic data processing. Various geographical environments make random noise in the acquired seismic records different in characteristics. Random noise in desert areas has non-Gaussian, non-linearity, non-stationary and heterogeneity. What is more, its frequency range is generally below 15 Hz, showing distinct low-frequency characteristics. Therefore, it brings much difficulty to the suppression of desert noise. Most of the existing noise suppression methods perform well on the condition of high-frequency noise, but perform worse for low-frequency noise because the denoising process of some methods depends on the waveform of noisy signal. And in some transformed methods, signal and noise are mixed with each other in the transformed domain. Thus, to resolve this problem, this paper presents a novel multiband spectrum separation (MSS) method based upon sparse nonnegative matrix factorization (SNMF) algorithm. It firstly decomposes noisy signal into some sub-signals of different narrow frequencies bands. Then, signal and noise components of these sub-signals are further separated by spectral unmixing in time-frequency domain. Several groups of experiments were carried out on synthetic records and real seismic data in Tarim oilfield in the northwest of China. Moreover, we compare its denoising effect with time-frequency peak filtering (TFPF), empirical mode decomposing (EMD) and Radon transform methods. The experimental and comparison results demonstrate that the proposed SNMF-MSS method performs better than the other three methods in the aspect of low-frequency suppression and weak signal recovery.