Low-frequency noise suppression of seismic signals using a novel framework composed of rank residual constraint and enhanced block matching

W Fan, J Li, Y Li, Y Si, W Yang - IEEE Geoscience and Remote …, 2021 - ieeexplore.ieee.org
W Fan, J Li, Y Li, Y Si, W Yang
IEEE Geoscience and Remote Sensing Letters, 2021ieeexplore.ieee.org
In recent years, methods based on rank reduction, such as nuclear norm minimization
(NNM), have achieved remarkable results in seismic signal processing. These methods are
used to threshold the singular values of the degraded signals, so as to estimate the singular
values of the clean signals directly. Although the effect is obvious, it is easy to produce the
result that the estimated singular values deviate greatly from the actual singular values,
which will lead to the loss of the effective signals. Therefore, we adopt a novel framework …
In recent years, methods based on rank reduction, such as nuclear norm minimization (NNM), have achieved remarkable results in seismic signal processing. These methods are used to threshold the singular values of the degraded signals, so as to estimate the singular values of the clean signals directly. Although the effect is obvious, it is easy to produce the result that the estimated singular values deviate greatly from the actual singular values, which will lead to the loss of the effective signals. Therefore, we adopt a novel framework composed of rank residual constraint model and enhanced block matching to suppress low-frequency noise in seismic signals. In each iteration, we first use the singular value of the degraded signals to estimate a reference singular value, and then obtain the denoised signals by minimizing the residual (difference) between the singular value we want to recover and the reference singular value. In this manner, both the reference and the recovered singular values are updated gradually and jointly in each iteration. In order to make the underlying clean matrix satisfy the low-rank criterion, we use a bandpass filter to enhance the accuracy of block matching before denoising. The experimental results on both simulated and actual seismic records indicate that our method has a better effect on low-frequency noise suppression of seismic data.
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