中南大学学报（自然科学版）2013，Vol.44Issue(2)：687-694,8.
基于混合优化算法的地震数据匹配追踪分解
Seismic data matching pursuit using hybrid optimization algorithm and its application
摘要
Abstract
Morlet wavelet with five parameters, including amplitude, frequency, phase, scale factor and time delay, as atoms in the matching pursuit decomposition was employed. In the processing of established controlled variable, hybrid optimization algorithm was introduced, including particle swarm optimization and BFGS method, so as to in-depend on complex-trace analysis to determine initial value of controlled variable, such as amplitude, frequency, and phase. The scale factor is an important adaptive parameter that controls the width of wavelet in time. After matching pursuit decomposition, removing wavelets with either very small or very large scale value and residual signal can suppress spikes and sinusoid functions, and rand noise effectively from seismic data. For fast matching pursuit algorithm, analytical expressions and the energy of the residual signal were employed which control effectively the iterating times. Synthetic data test and results of practical data application show that using method in the paper has good effect in the aspect of attenuating noise form seismic data, fleetly and accurately implementing time-frequency analysis, and provide an effective means for hydrocarbon detection and reservoir description.关键词
Morlet小波/匹配追踪/粒子群算法/BFGS算法/时频分析/混合优化算法Key words
Morlet wavelet/ matching pursuit decomposition/ particle swarm optimization/ BFGS method/ time-frequency analysis/ hybrid optimization algorithm分类
天文与地球科学引用本文复制引用
蔡涵鹏,贺振华,高刚,黄德济..基于混合优化算法的地震数据匹配追踪分解[J].中南大学学报（自然科学版）,2013,44(2):687-694,8.基金项目
国家自然科学基金资助项目(41174114,41004054) （41174114,41004054）
国家自然科学基金重点资助项目(40839905) （40839905）
