Application of GNMF wavelet spectral unmixing in seismic noise suppression

Seismic noise suppression and signal-to-noise ratio (SNR) improvement is an important task in the process of seismic signal processing. With the development of seismic exploration in depth and complexity, the requirement of the quality of seismic data is becoming higher. The complexity of acquisition environment makes seismic data mixed with a lot of noise, which makes the effective signal difficult to identify. It directly affects the follow-up data processing and interpretation process. Wavelet denoising is a common and mature method, which is used in seismic exploration. However, the selection of threshold function is always a troubling problem, which hindering its performance. Although many improved methods have been proposed, they still had some shortcomings, respectively. The proposed method applies the popular graph nonnegative matrix factorization (GNMF) spectral separation theory to seismic random noise suppression. In the constraint function of GNMF, an additional item is added to the conventional constraint function of NMF, which plays an important role in the performance and accuracy improvement of GNMF for the wavelet coefficients spectrums unmixing. It makes full use of the wavelet decomposition characteristics that embodiment the details of the signals in time domain and frequency domain. The novel method first uses GNMF to separate the wavelet coefficient spectrums into some sub-spectrums, and then reconstructs the corresponding sub-signals from these sub-spectrums through inverse transform. Then, it classifies the sub signals into signal class and noise class by K-mean clustering algorithm. The sum of the sub-signals in signal class is the effective signal and the sum of the sub-signals in noise class is the separated noise. The novel method effectively improves the accuracy and precision of separation signal and noise in the spectral space. Meanwhile, it avoids the worse noise suppression or serious energy loss problems, which is caused by threshold selection. The experimental results on synthetic records and actual seismic data both show the effectiveness and advantages of the proposed algorithm in the aspect of noise suppression and effective composition maintain. Firstly, based on wavelet decomposition, GNMF algorithm is used to decompose the signal components and the noise components. Then, the sub signals are decomposed into signal and noise by using K means clustering algorithm. A new method avoids the problem of noise suppression caused by the threshold selection and the loss of the effective components. Meanwhile, the effectiveness of the method in improving the accuracy and precision of the signal and noise separation is verified. Compared with the results of wavelet de-noising, the proposed method is more effective and has great advantages in noise suppression and effective maintenance. In order to solve the problem of threshold selection in wavelet denoising method, the wavelet method is applied to the optimization problem of wavelet coefficient threshold in time domain and frequency domain. A novel method based upon GNMF is proposed. The novel method resolves' the problem of the threshold selection in the traditional wavelet de-noising method and it can suppress the noise and protects the effective signal components effectively. The simulation and real data results both show that the novel wavelet denoising method has obvious advantages over the traditional wavelet de-noising method.