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Random noise suppression of seismic signal using orthogonal multiwavelets

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Article
Multiwavelet is a new development in the wavelet theory and it can offer simultaneously orthogonality, symmetry, and short support. In signal processing, the orthogonality preserves energy, the symmetry avoids signal distortion and the short support reduces the boundary effects. Therefore multiwavelet is very suitable for various signal processing applications, especially denoising. The paper presents multiwavelet principles, transformation procedures, pre-processing methods and proposes a new GHM-like multiwavelet-based denoising method. In seismic data processing, the attenuation of random noise is an important research subject. Conventional temporal or spatial filtering methods often damage the useful signals while suppressing noise, and the single wavelet transforms can cause signal distortion since it fails to offer simultaneously the orthogonality and symmetry. The paper adopts multiwavelet and multiresolution method to remove noise contained in seismic data. Seismic data is first preprocessed to generate a group of vector data, and then approximate and detailed signals of various scales are generated by two-level multiwavelet transformation. Finally detailed signals are processed by soft threshold and denoised seismic data are obtained by reverse multiwavelet transformation. The denoising experiments of synthetic and real data show that multiwavelet transform is effective for noise reduction and can preserve signal features at the same time.
Article
Common-reflection-point (CRP) gather is a bridge that connects seismic data and petrophysical parameters. Pre-stack attributes extraction and pre-stack inversion, both of them are important means of reservoir prediction. Quality of CRP gather usually has great impact on the accuracy of seismic exploration. Therefore, pre-stack CRP gathers noise suppression technology becomes a major research direction. Based on the vector decomposition principle, here we propose a method to suppress noise. This method estimates optimal unit vectors by searching in various directions and then suppresses noise through vector angle smoothing and restriction. Model tests indicate that the proposed method can separate effective signal from noise very well and suppress random noise effectively in single wavenumber case. Application of our method to real data shows that the method can recover effective signal with good amplitude preserved from pre-stack noisy seismic data even in the case of low signal to noise ratio (SNR). © 2015, China University of Geosciences and Springer-Verlag Berlin Heidelberg.
Article
The seislet transform is a wavelet-like transform that analyzes seismic data by following variable slopes of seismic events across different scales. It generalizes the discrete wavelet transform (DWT) in the sense that DWT in the lateral direction is simply the seislet transform with zero slopes. An earlier work used low-order versions of DWT to construct the seislet transform. In this work, we extend this approach to a higher order, using the Cohen-Daubechies-Feauveau 9/7 biorthogonal wavelet transform (the basis for the JPEG2000 compression scheme) as a template. Using synthetic and field-data examples, we demonstrate that the new transform can provide a better compression rate for seismic events than the Fourier transform, DWT, or the low-order seislet transform. Therefore, the high-order seislet transform can be more suitable for data processing tasks such as data regularization and noise attenuation.
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