【摘要】 在地震子波因果、混合相位的假设下,本文突破突破滑动平均(MA)型假设的地震子波提取技术,提出一种阶数吝啬的自回归滑动平均(ARMA)模型对地震子波进行参数化准确建模的方法.针对地震子波ARMA模型定阶困难,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的要求,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表明,本文所用方法可以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】 Seismic wavelets estimated by the current wavelet extraction technology have low resolution and the computing cost is high.Based on seismic record characteristics,assumptions and boundary constraints,we have analyzed various attributes and characteristics of seismic wavelets,broken through the seismic wavelet extraction technology based on Moving Average (MA) model,and proposed a parsimonious parameters Autoregressive Moving Average (ARMA) model which was used to model the parameterized seismic wavelet accurately,and studied the order determination of seismic wavelet ARMA model mainly.We determined the Average (AR) part order using the SVD method based on autocorrelation firstly,and then proposed a new MA order determination method which introduced information theoretic criteria function into MA order determination based on higher-order cumulant.In the premise of ensuring the accuracy of seismic wavelets,this method reduced the model order as much as possible so as to obtain high-efficiency and the high-precision seismic wavelet model order determination.Theoretical analysis and numerical simulations demonstrated that the computing cost of the SVD method based on autocorrelation is low and the method can be used with moderate noises;and the new method can effectively improve the stability and accuracy of MA model order determination.更多还原