GHM-LIKE ORTHOGONAL MULTIWAVELET TRANSFORM AND ITS APPLICATION TO DE-NOISING OF SEISMIC DATA
Chen Xiang-peng · Seismology and Geology · 2005
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.