摘要
Radon transform is to use the speed difference between primary wave and multiple wave to focus the difference on different"points"or"lines"in Radon domain,so as to suppress multiple wave.However,the limited migration aperture,discrete sampling,and AVO characteristics of seismic data all will weaken the focusing characteristics of Radon transform.In addition,the traditional Radon transform does not take into account the AVO characteristics of seismic data,and uses L1 Norm,the approximate form of L0 Norm,to improve the focusing characteristics of Radon domain,which requires a lot of computation.In this paper,we combine orthogonal polynomials with the parabolic Radon transform(PRT)and find that the AVO characteristics of seismic data can be fitted with orthogonal polynomial coefficients.This allows the problem to be transformed into the frequency domain by Fourier transform and introduces a new variable,lambda,combining frequency and curvature.Through overall sampling of lambda,the PRT operator only needs to be calculated once for each frequency,yielding higher computational efficiency.The sparse solution of PRT under the constraints of the smoothed L0 Norm(SL0)obtained by the steepest descent method and the gradient projection principle.Synthetic and real examples are given to demonstrate that the proposed method has This method has advantages in improving the Radon focusing characteristics than does the PRT based on L1 norm.
传统的Radon变换是利用一次波与多次波的速度差异将其聚焦在Radon域内的不同"点"或"直线"上。然而有限的"偏移孔径"、"离散采样"和"地震数据的AVO特性"会使Radon变换的聚焦变差,降低了该方法的分辨率,影响多次波压制精度。此外传统Radon变换没有考虑地震数据的AVO特性,使用L_0范数的近似形式L1范数来提高Radon域的聚焦特性,计算量极大。本文就上述问题,本文将SL_0范数和正交多项式同时引入λ-f域Radon变换,提出基于SL_0范数的高阶高分辨率λ-f域Radon变换。在考虑地震数据的AVO特性基础上,将正交多项式和Radon变换相结合。首先推导出时间域高阶Radon正反变换形式,随后使用傅里叶变换将其变换到频率域,再引入变量,将频率和曲率相结合,对其进行整体采样,具备了较快的计算效率,最后使用平滑高斯函数替代L_1范数来近似L_0范数(SL_0范数),通过最速下降和梯度投影原理求得Radon变换在SL_0范数下的稀疏解。理论模型和实际资料试算表明,同基于L_1范数的Radon变换比较,本文方法既在Radon域内具有更好的聚焦特性,同时在有效压制多次波(NMO)后存在剩余时差)的同时能更好地保存一次波的AVO特性。
基金
funded by the National Natural Science Foundation of China(No.41774133)
major national science and technology projects(No.2016ZX05024-003 and 2016ZX05026-002-002)
the talent introduction project of China University of Petroleum(East China)(No.20180041)
