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

以5个参数(幅度、频率、相位移、尺度因子、时移)控制的Morlet小波作为匹配子波原子,在确定控制参数的过程中,提出应用具有全局优化能力的粒子群优化算法与具有局部优化能力的BFGS算法的混合优化算法,能够使得匹配追踪算法不再依赖于复数道分析确定子波原子的振幅、频率和相位的初值。控制子波时间延续长度的尺度因子是一个重要的参数。匹配追踪分解后,消除较小和较大的尺度因子和分解终止时的剩余信号能够有效地压制地震数据噪声。利用局部函数解析表达式和残差信号能量进行有效地控制算法的迭代次数可以提高计算效率。数值试验和实际资料的应用均表明:利用本文方法能够压制地震数据噪声,对地震信号快速地、精确地进行时频谱分析,为烃类检测和储层描述提供有效的手段。

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 aneffective means for hydrocarbon detection and reservoir description.