Wavelet Extraction Based on Complex Cepstrum of High-Order Statistics
详细信息
- 责任者：Meng Dajiang (College of Geoexploration Science and Technology ; Jilin University ; Changchun 130026 ; China) ; Wang Deli
- 刊名：Geophysical and Geochemical Exploration
- 出版年：2013
- 卷期：37(3)
- 关键词：地震勘探 ; seismic exploration ; 地震子波 ; seismic wavelets ; 信噪比 ; signal-to-noise ratio
- 页码：p.494-499,511
- 图表：illus., 21 refs.
- 分类号：1600
- issnNo：ISSN1000-8918
- isbnNo：CN11-1906/P
摘要
Wavelet deconvolution is one of the main methods to improve the resolution of seismic data,and the key is the accuracy of the wavelet.Conventional methods usually presume that the wavelet is the minimum phase,but this may not be correct.To solve this problem,the authors utilized the property of the higher order statistic quantity that it includes phase information of the system and suppresses Gaussian noise to study the wavelet extraction method based on cepstrum and,as a result,gave the principles of extracted wavelet based on complex cepstrum,bispectrum and trispectra.In addition the authors constructed a smooth window function to improve the accuracy of wavelet.The method does not need to make any assumption about wavelet’s phase,and it can extract wavelet of any phase.Tackling the mixed wavelet,the authors made theoretical experiments to verify the effectiveness of the method,and the results of actual data processing showed the effectiveness of the method.
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