A Sparse NMF-SU for Seismic Random Noise Attenuation

A novel method based on nonnegative matrix factorization (NMF) spectral unmixing is proposed for land seismic additive random noise attenuation. In the method, the noisy seismic signal is first decomposed into a collection of intrinsic mode functions (IMFs) instead of being directly processed. These IMFs can be considered as a new set of observations. In the short-time Fourier transform (STFT) spectrum of each IMF, the degree of mixing from the effective signal and the random noise is considerably reduced. Then, a sparse NMF is used to unmix the STFT spectrum of each IMF. We get the subsignals out of the separated subspectrums by the inverse STFT. Finally, the desired signal is reconstructed from the subsignals by
$K$
-means clustering algorithm. Experimental results of both synthetic and real seismic records show that the proposed method can significantly suppress random noise and preserve the effective signal components. Comparisons with time–frequency peak filtering and
$f{-}x$
filtering further verify its better performance.