The numerical dispersion and computational cost are high for conventional Taylor series expansion staggered-grid finite-difference forward modeling owing to the high frequency of the wavelets and the large grid interv...The numerical dispersion and computational cost are high for conventional Taylor series expansion staggered-grid finite-difference forward modeling owing to the high frequency of the wavelets and the large grid intervals. In this study, the cosine-modulated binomial window function (CMBWF)-based staggered-grid finite-difference method is proposed. Two new parameters, the modulated time and modulated range are used in the new window function and by adjusting these two parameters we obtain different characteristics of the main and side lobes of the amplitude response. Numerical dispersion analysis and elastic wavefield forward modeling suggests that the CMBWF method is more precise and less computationally costly than the conventional Taylor series expansion staggered-grid finite-difference method.展开更多
基金supported by the National Major Research Equipment Development Projects(No.ZDYZ2012-1-02-04)the National Natural Science Foundation of China(No.41474106)
文摘The numerical dispersion and computational cost are high for conventional Taylor series expansion staggered-grid finite-difference forward modeling owing to the high frequency of the wavelets and the large grid intervals. In this study, the cosine-modulated binomial window function (CMBWF)-based staggered-grid finite-difference method is proposed. Two new parameters, the modulated time and modulated range are used in the new window function and by adjusting these two parameters we obtain different characteristics of the main and side lobes of the amplitude response. Numerical dispersion analysis and elastic wavefield forward modeling suggests that the CMBWF method is more precise and less computationally costly than the conventional Taylor series expansion staggered-grid finite-difference method.
文摘针对经验模态分解(Empirical Mode Decomposition,EMD)中存在的边界效应及边界发散现象随着筛选层次的增加而增加的问题,提出一种利用延拓与可变余弦窗相结合的改进新方法。首先对信号进行延拓处理,增加一定长度的数据,实现延拓数据与原始信号交界处的光滑过度。其次,根据信号边界的发散程度,在逐层提取各阶本征模函数(Intrinsic Model Function,IMF)之前,在信号两端加上宽度可变的余弦窗函数,使得每一个IMF分量边界发散问题最小化,保证信号有效数据的正确分解,实现EMD边界处理算法的改进。仿真和实例信号分析表明,该方法能较好地抑制EMD边界效应,有效地提取故障信号中的特征信息。