Conventional predictive deconvolution assumes that the reflection coefficients of the earth conform to an uncorrelated white noise sequence. The Wiener-Hopf (WH) equation is constructed to solve the filter and elimina...Conventional predictive deconvolution assumes that the reflection coefficients of the earth conform to an uncorrelated white noise sequence. The Wiener-Hopf (WH) equation is constructed to solve the filter and eliminate the correlated components of the seismic records, attenuate multiples, and improve seismic resolution. However, in practice, the primary refl ectivity series of fi eld data rarely satisfy the white noise sequence assumption, with the result that the correlated components of the primary reflectivity series are also eliminated by traditional deconvolution. This results in signal distortion. To solve this problem, we have proposed an improved method for deconvolution. First, we estimated the wavelet correlation from seismic records using the spectrum-modeling method. Second, this wavelet autocorrelation was used to construct a new autocorrelation function which contains the correlated components caused by the existence of multiples and avoids the correlated components of the primary reflectivity series. Finally, the new autocorrelation function was brought into the WH equation, and the predictive fi lter operator was calculated for deconvolution. In this paper, we have applied this new method to simulated and field data processing, and we have compared its performance with that of traditional predictive deconvolution. Our results show that the new method can adapt to non-white refl ectivity series without changing the statistical characteristics of the primary reflection coefficient series. Compared with traditional predictive deconvolution, the new method reduces processing noise and improves fidelity, all while maintaining the ability to attenuate multiples and enhance seismic resolution.展开更多
基金supported by Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents(No.2017RCJJ034)
文摘Conventional predictive deconvolution assumes that the reflection coefficients of the earth conform to an uncorrelated white noise sequence. The Wiener-Hopf (WH) equation is constructed to solve the filter and eliminate the correlated components of the seismic records, attenuate multiples, and improve seismic resolution. However, in practice, the primary refl ectivity series of fi eld data rarely satisfy the white noise sequence assumption, with the result that the correlated components of the primary reflectivity series are also eliminated by traditional deconvolution. This results in signal distortion. To solve this problem, we have proposed an improved method for deconvolution. First, we estimated the wavelet correlation from seismic records using the spectrum-modeling method. Second, this wavelet autocorrelation was used to construct a new autocorrelation function which contains the correlated components caused by the existence of multiples and avoids the correlated components of the primary reflectivity series. Finally, the new autocorrelation function was brought into the WH equation, and the predictive fi lter operator was calculated for deconvolution. In this paper, we have applied this new method to simulated and field data processing, and we have compared its performance with that of traditional predictive deconvolution. Our results show that the new method can adapt to non-white refl ectivity series without changing the statistical characteristics of the primary reflection coefficient series. Compared with traditional predictive deconvolution, the new method reduces processing noise and improves fidelity, all while maintaining the ability to attenuate multiples and enhance seismic resolution.
文摘基于协方差驱动随机子空间识别(covariance-driven stochastic subspace identification,SSI-COV)方法的模态参数识别具有强鲁棒性、高精度的优势,在结构工作模态分析中应用广泛。为保证模态参数识别的准确性,新近提出的基于随机子空间(stochastic subspace identification,SSI)的模态参数不确定性量化方法,可有效估计模态参数的方差,但由于其计算各中间变量时,需显式表示出Jacobian矩阵,导致矩阵运算维度高、计算效率低。为此,提出一种基于SSI-COV的模态参数不确定度高效计算方法。首先,计算振动响应相关函数的方差,通过奇异值分解(singular value decomposition,SVD),选取合适的奇异值截断阶数,由每阶奇异向量组装出多组Hankel矩阵的扰动。其次,根据一阶矩阵摄动理论,隐式计算SSI-COV算法各中间变量的一阶扰动,最终,由多组模态参数的扰动叠加计算出方差。最后,以桁架结构模型为例,采用所提方法辨识结构模态参数并计算模态参数方差,分析了Hankel矩阵维度及相关函数奇异值截断阶数对辨识结果的影响,结果表明计算得到的模态参数方差与蒙特卡洛仿真(Monte Carlo simulation,MCS)结果非常接近,且模态参数不确定度可作为剔除虚假模态的有效依据。
