Convolutional sparse coding for noise attenuation in seismic data

Abstract

We propose convolutional sparse coding (CSC) to attenuate noise in seismic data. CSC gives a data-driven set of basis functions whose coefficients form a sparse distribution. The noise attenuation method by CSC can be divided into the training and denoising phases. Seismic data with a relatively high signal-to-noise ratio are chosen for training to get the learned basis functions. Then, we use all (or a subset) of the basis functions to attenuate the random or coherent noise in the seismic data. Numerical experiments on synthetic data show that CSC can learn a set of shifted invariant filters, which can reduce the redundancy of learned filters in the traditional sparse-coding denoising method. CSC achieves good denoising performance when training by the noisy data and better performance when training on a similar but noiseless data set. The numerical results from the field data test indicate that CSC can effectively suppress seismic noise in the complex field data. By excluding filters with coherent noise features, the proposed method can further attenuate the coherent noise and separate ground roll.

Seismic Random Noise Denoising Using Mini-Batch Multivariate Variational Mode Decomposition

“…A variety of methods have been proposed for attenuating or removing random noise in order to enhance the signal-to-noise ratio (SNR) [ 4 – 8 ]. The transform-based methods, such as Fourier transform [ 9 ], wavelet transform [ 10 ], curvelet transform [ 11 ], and seislet transform [ 12 ], assume that the input signal has sparse representation with predetermined base, and under the predetermined base, noise and clean signal can be separated in the transform domain [ 7 , 13 – 15 ].…”