【作者】 王伟伟；

【导师】 戴永寿；

【作者基本信息】 中国石油大学 ， 信号与信息处理， 2008， 硕士

【摘要】 准确的地震子波提取是地震资料反褶积处理、地震波阻抗反演和正演的基础,是地震勘探中的重要一环。由于在保持信号相位信息的同时可以压制任意加性高斯噪声,高阶统计量被广泛用于混合相位的地震子波提取。在地震子波为非最小相位、噪声为加性高斯噪声的假设下,可通过构建地震记录四阶累积量与地震子波参数模型四阶矩的拟合目标函数来提取地震子波。为验证参数模型解的唯一性,本文从地震子波模型的三谱出发,对基于四阶累积量的地震子波提取方式进行了理论分析。公式推导结果表明,除在幅度和时延上有一定的线性偏移外,基于四阶统计量的模型参数提取方法所提取的子波为地震记录中地震子波的准确参数模型,所得的地震子波波形符合地震反褶积处理的要求。对上述子波估计模型的求解最终可归结为对一多维多峰值目标函数的非线性优化问题,对该目标函数所用优化算法须兼有对参数向量整体的全局随机搜索能力和对单个参数的深度搜索能力,传统的优化算法很难求得其最优解。由于结合了有向和随机两种搜索方式,遗传算法在对搜索空间进行深度搜索和广度搜索之间维持了较好的平衡性,但是其易“早熟”以及局部搜索能力较差的特点严重制约了其寻优性能。为此,本文并从多样性的测度与维持、基本算子的设置与选择以及小邻域搜索等几个方面对基于实数编码的遗传算法进行了分析和改进,最终提出一种基于多样性双重维持的遗传算法。仿真结果表明:该算法能够有效克服“早熟”收敛,具有强大的全局搜索能力。为克服算法变异方向的随机性,保证变异向优良的方向进行转化,本文从混沌序列的分布特性、混沌映射的遍历性及其计算效率等角度出发,对Logistic映射、Tent映射以及Arnold映射等进行了仿真上的比较分析,最终将基于Arnold映射的混沌优化算法融入遗传算法,提出一种适于高维多峰函数寻优的新型混沌遗传算法,以进行精确的子波估计。高维测试函数仿真实验表明,该算法能够迅速收敛到全局最优解,在搜索效率、搜索精度均较单一算法有了很大提高,并且具有良好的稳定性。其在基于MA和ARMA模型地震子波描述下地震子波拟合提取实验中的应用结果表明:该算法能够迅速有效地提取精确的地震子波,具有良好的适用性。更多还原

【Abstract】 Accurate seismic wavelet estimation is the base of deconvolution processing, inverse and forward models of seismic wave impedance, and it is significant in seismic exploration fields. Because the higher order statistic retains the phase information of the signal and is able to eliminate the Gaussian noise, it is studied extensively in the field of seismic wavelet estimation.In this thesis, on the assumption that seismic wavelet was mixed-phase and the noise was Gaussian, the wavelet estimation objective function was constructed via the fourth-order cumulants of seismic data matching the fourth-order moments of parametric wavelet model. In order to validate the uniqueness of the model solution, the wavelet estimation methods based on fourth-order cumulants were analyzed and studied according to the wavelet three-order spectral. Theoretical analysis demonstrated that this approach could estimate the wavelet model parameters precisely with some linear migration of the amplitude or the phase of the seismic wavelet, and the accuracy of the seismic wavelet estimated through this approach meets the requirement of seismic signal processing.The solution of the objective function leads to a multidimensional and multimodal non-linear optimization problem. The minimizing process of the objective function should able to search the parameter vectors globally, and the deep-searching ability is also the necessary characteristic of the optimization algorithm, this makes it difficult to get the global optimal via the general algorithm. The genetic algorithm perfectly balances the global searching capacity and the local search capability, but often gets premature in the late searching process. To overcome this flaw and get the optimal solution more effectively and stably, this paper did some reasearch on the measurement and maintenance of the population, the setting of the basic operaters, and the small neighborhood seaeching respectively. Then the improved Genetic Algorithm, which could overcome the premature and search the global optimal effectively, is proposed in this paper, simulation alse demonstrated this characteristic of this algorithm.Chaos optimization algorithm is also absorbed for further improvement of the mutation ability of this algorithm. The characteristics of Logistic mapping, Tent mapping and Arnold mapping are discussed in the view of the egodicity and the computational efficiency of the chaotic mapping or the distribution of the chaotic sequence. Then the Arnold mapping is absorbed in the Genetic Algorithm to extract the wavelet. The high dimensional function simulation demonstrated that this algorithm could improve the searching ability and get the global optimal effectively and stably. The application of seismic data processing results demonstrated that the novel chaos genetic algorithm is able to estimate the accuracy seismic wavelet fastly and effectively.更多还原