【摘要】 ARMA模型的最大优点是用较少的参数描述一个精确的子波,超定阶容易造成计算量大、运算速度慢,欠定阶不能满足精确子波描述的要求。针对高阶累积量对特殊切片敏感,且在短时数据下应用效果差的问题,本文采用基于自相关函数的奇异值分解(SVD)法确定AR模型阶数,同时将信息量准则法与高阶累积量法相结合,提出了一种新的MA模型定阶法。数值仿真和实际地震数据处理结果均表明,本文所用方法可有效地压制加性高斯色噪声,信息量准则法可有效提高MA定阶的准确率,在保证子波精度的同时尽可能降低模型阶数,实现运算高效率。更多还原

【Abstract】 The most importantly advantage of ARMA(autoregressive moving average) model is to describe an exact wavelet with fewer parameters.Order over-determination easily leads to large calculation costs while order under-determination cannot meet the wavelet requirements.Higher-order cumulants are sensitive to special slices and it causes poor results with short time data series.This paper focuses on the model order determination.Singular value decomposition(SVD) based on autocorrelation function is exploded to determine the AR model order.Combining the information theoretic criteria method with the cumulant-based method,the author proposes a new MA model order determination method.Numerical simulations and real data processing show that additional Gaussian colored noise is suppressed,and the MA model order determination precision is improved by the information theoretic criteria method.With this approach,the wavelet gets high resolution and the model order number is reduced as much as possible in order to improve the computation efficiency.更多还原